diff --git a/.github/workflows/docs.yml b/.github/workflows/docs.yml
index a69425a5e2da521dad6048bc25bec45be6516fe9..8f38f0409b8641454a645f0bc6a0e090b67d76d7 100644
--- a/.github/workflows/docs.yml
+++ b/.github/workflows/docs.yml
@@ -2,7 +2,7 @@ name: Release Docs
on:
release:
- types: [released]
+ types: [ released ]
jobs:
build-docs:
@@ -14,44 +14,85 @@ jobs:
strategy:
matrix:
python-version:
- - "3.13"
- env:
- POETRY_VERSION: "1.8.4"
+ - "3.11"
steps:
- - uses: actions/checkout@v4
- - name: Cache Poetry
- uses: actions/cache@v4
- with:
- path: ~/.poetry
- key: ${{ runner.os }}-poetry-${{ hashFiles('**/poetry.lock') }}
- restore-keys: |
- ${{ runner.os }}-poetry-
- - name: Install poetry
- run: |
- pipx install poetry==$POETRY_VERSION
- - name: Set up Python ${{ matrix.python-version }}
- uses: actions/setup-python@v4
- with:
- python-version: ${{ matrix.python-version }}
- cache: poetry
- - name: Install dependencies
- run: |
- poetry install --all-extras
- - name: Build docs
- run: |
- poetry run sphinx-build -M html docs/source docs/build
- - name: Authenticate to Google Cloud
- id: auth
- uses: google-github-actions/auth@v2
- with:
- credentials_json: ${{ secrets.GOOGLE_CREDENTIALS }}
- - name: Upload Docs
- run: |
- gcloud storage rsync docs/build/html gs://docs-bucket-production/semantic-router --recursive --delete-unmatched-destination-objects
- # - name: Upload Docs
- # id: upload-directory
- # uses: google-github-actions/upload-cloud-storage@v2
- # with:
- # path: docs/build/html
- # destination: docs-bucket-production/semantic-router
- # parent: false
\ No newline at end of file
+ - uses: actions/checkout@v4
+ - name: Install uv
+ uses: astral-sh/setup-uv@v5
+ with:
+ enable-cache: true
+ cache-dependency-glob: "uv.lock"
+ python-version: ${{ matrix.python-version }}
+ - name: Install Dependencies
+ run: uv sync --extra docs
+ - name: Build docs
+ run: |
+ mkdir -p docs/build
+ uv run pydoc-markdown .pydoc-markdown.yml
+
+ - name: Checkout docs repo
+ uses: actions/checkout@v4
+ with:
+ repository: aurelio-labs/docs
+ path: temp-docs
+ ref: main
+ token: ${{ secrets.PAT }}
+
+ - name: Copy and rename docs
+ run: |
+ ROOT_DIR=$(pwd)
+ rm -rf temp-docs/semantic-router/client-reference
+ mkdir -p temp-docs/semantic-router/client-reference/
+
+ # Find and copy all md files
+ cd docs/build/semantic_router
+ find . -type f -name "*.md" | while read file; do
+ dir=$(dirname "$file")
+ echo "Processing file: $file in directory: $dir"
+ mkdir -p "$ROOT_DIR/temp-docs/semantic-router/client-reference/$dir"
+ cp "$file" "$ROOT_DIR/temp-docs/semantic-router/client-reference/${file%.md}.mdx"
+ done
+
+ - name: Update frontmatter keys
+ run: |
+ find temp-docs/semantic-router/client-reference -type f -name "*.mdx" -exec sed -i 's/sidebar_label:/sidebarTitle:/g' {} +
+
+ - name: Install jq
+ run: sudo apt-get install -y jq
+
+ - name: Update mintlify docs.json navigation
+ run: |
+ # Get files and create grouped structure using jq
+ files=$(cd temp-docs/semantic-router/client-reference && find . -type f -name "*.mdx" | sed 's|^./||' | sed 's|.mdx$||' | sort | jq -R -s '
+ split("\n")[:-1]
+ | map("semantic-router/client-reference/" + .)
+ | reduce .[] as $path (
+ {"root": [], "groups": {}};
+ if ($path | split("/") | length) == 4 then
+ .groups[$path | split("/")[2]] += [$path]
+ else
+ .root += [$path]
+ end
+ )
+ | [.root[], (.groups | to_entries | map({
+ "group": .key,
+ "pages": .value
+ }))[]
+ ]
+ ')
+
+ # Update the docs.json file
+ jq --arg pages "$files" '
+ (.navigation.tabs[] | select(.tab == "Semantic Router") |
+ .groups[] | select(.group == "Client Reference") |
+ .pages) |= ($pages | fromjson)
+ ' temp-docs/docs.json > temp-docs/docs.json.tmp && mv temp-docs/docs.json.tmp temp-docs/docs.json
+
+ - name: Git commit and push
+ working-directory: temp-docs
+ run: |
+ git config --local user.email "admin@aurelio.ai"
+ git config --local user.name "aurelio-bot"
+ git remote set-url origin https://aurelio-bot:${{ secrets.PAT }}@github.com/aurelio-labs/docs.git
+ git add .
+ git diff --quiet && git diff --staged --quiet || (git commit -m "chore: update semantic router docs" && git push)
diff --git a/.github/workflows/lint.yml b/.github/workflows/lint.yml
index ca5a94e9058cedcab1e00554fea84c296a53d9f8..3b2e2013d811466090fde14111f9632b96d0120d 100644
--- a/.github/workflows/lint.yml
+++ b/.github/workflows/lint.yml
@@ -1,12 +1,8 @@
name: lint
on:
- push:
- branches: [main]
pull_request:
-env:
- POETRY_VERSION: "1.8.4"
jobs:
build:
@@ -14,27 +10,18 @@ jobs:
strategy:
matrix:
python-version:
- - "3.13"
+ - "3.13"
steps:
- - uses: actions/checkout@v3
- - name: Cache Poetry
- uses: actions/cache@v4
- with:
- path: ~/.poetry
- key: ${{ runner.os }}-poetry-${{ hashFiles('**/poetry.lock') }}
- restore-keys: |
- ${{ runner.os }}-poetry-
- - name: Install poetry
- run: |
- pipx install poetry==$POETRY_VERSION
- - name: Set up Python ${{ matrix.python-version }}
- uses: actions/setup-python@v4
- with:
- python-version: ${{ matrix.python-version }}
- cache: poetry
- - name: Install dependencies
- run: |
- poetry install
- - name: Analyzing the code with our lint
- run: |
- make lint
+ - uses: actions/checkout@v3
+ - name: Install uv
+ uses: astral-sh/setup-uv@v5
+ with:
+ enable-cache: true
+ cache-dependency-glob: "uv.lock"
+ python-version: ${{ matrix.python-version }}
+ - name: Install Dependencies
+ run: |
+ uv sync --extra dev
+ - name: Run Lint
+ run: |
+ make lint
diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml
index 25fcef711ee7b1234783c9ce9fb4e2b50b441ffe..05c2453f3e2dff53777b98ad95c8cda4ecea967b 100644
--- a/.github/workflows/release.yml
+++ b/.github/workflows/release.yml
@@ -3,40 +3,41 @@ name: Release
on:
push:
tags:
- - '*'
+ - '*'
jobs:
build:
runs-on: ubuntu-latest
steps:
- - uses: actions/checkout@v2
- - name: Set up Python
- uses: actions/setup-python@v2
+ - name: Install uv
+ uses: astral-sh/setup-uv@v5
with:
- python-version: '3.13'
- - name: Install Poetry
- run: |
- curl -sSL https://install.python-poetry.org | python - -y --version 1.5.1
+ enable-cache: true
+ cache-dependency-glob: "uv.lock"
+ python-version: ${{ matrix.python-version }}
- name: Install dependencies
- run: poetry install
+ run: uv sync --extra dev
- name: Build
- run: poetry build
+ run: uv build
publish:
needs: build
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v2
- - name: Set up Python
- uses: actions/setup-python@v2
+ - name: Install uv
+ uses: astral-sh/setup-uv@v5
with:
- python-version: '3.13'
- - name: Install Poetry
- run: |
- curl -sSL https://install.python-poetry.org | python - -y --version 1.8.4
+ enable-cache: true
+ cache-dependency-glob: "uv.lock"
+ python-version: ${{ matrix.python-version }}
+ - name: Install dependencies
+ run: uv sync --extra dev
+ - name: Build
+ run: uv build
- name: Publish to PyPI
run: |
- poetry config repositories.remote https://upload.pypi.org/legacy/
- poetry --no-interaction -v publish --build --repository remote --username "__token__" --password "$PYPI_API_TOKEN"
+ uv config repositories.remote https://upload.pypi.org/legacy/
+ uv --no-interaction -v publish --build --repository remote --username "__token__" --password "$PYPI_API_TOKEN"
env:
PYPI_API_TOKEN: ${{ secrets.PYPI_API_TOKEN }}
diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml
index 944cd476f17e6d6221ec623d3a0d21b8c3326ae7..17e6a6e74c0127df68375c815cb39994efe10114 100644
--- a/.github/workflows/test.yml
+++ b/.github/workflows/test.yml
@@ -3,56 +3,55 @@ name: Test
on:
pull_request:
-env:
- POETRY_VERSION: "1.8.4"
jobs:
build:
runs-on: ubuntu-latest
+ services:
+ pinecone:
+ image: ghcr.io/pinecone-io/pinecone-local:latest
+ env:
+ PORT: 5080
+ PINECONE_HOST: localhost
+ ports:
+ - "5080-6000:5080-6000"
+
strategy:
matrix:
python-version:
- - "3.10"
- - "3.11"
- - "3.12"
- - "3.13"
+ - "3.10"
+ - "3.11"
+ - "3.12"
+ - "3.13"
steps:
- - uses: actions/checkout@v4
- - name: Cache Poetry
- uses: actions/cache@v4
- with:
- path: ~/.poetry
- key: ${{ runner.os }}-poetry-${{ hashFiles('**/poetry.lock') }}
- restore-keys: |
- ${{ runner.os }}-poetry-
- - name: Install poetry
- run: |
- pipx install poetry==$POETRY_VERSION
- - name: Set up Python ${{ matrix.python-version }}
- uses: actions/setup-python@v4
- with:
- python-version: ${{ matrix.python-version }}
- cache: poetry
- - name: Install dependencies
- run: |
- poetry install --all-extras
- - name: Install nltk
- run: |
- pip install nltk
- - name: Download nltk data
- run: |
- python -m nltk.downloader punkt stopwords wordnet punkt_tab
- - name: Pytest All
- env:
- PINECONE_API_KEY: ${{ secrets.PINECONE_API_KEY }}
- OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
- COHERE_API_KEY: ${{ secrets.COHERE_API_KEY }}
- run: |
- make test
- - name: Upload coverage to Codecov
- uses: codecov/codecov-action@v2
- env:
- CODECOV_TOKEN: ${{ secrets.CODECOV_TOKEN }}
- with:
- file: ./coverage.xml
- fail_ci_if_error: false
+ - uses: actions/checkout@v4
+ - name: Install uv
+ uses: astral-sh/setup-uv@v5
+ with:
+ enable-cache: true
+ cache-dependency-glob: "uv.lock"
+ python-version: ${{ matrix.python-version }}
+ - name: Install Dependencies
+ run: uv sync --extra all
+ - name: Install nltk
+ run: uv pip install nltk
+
+ - name: Download nltk data
+ run: |
+ uv run python -m nltk.downloader punkt stopwords wordnet punkt_tab
+
+ - name: Pytest All
+ env:
+ PINECONE_API_KEY: pclocal
+ PINECONE_API_BASE_URL: http://localhost:5080
+ OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
+ COHERE_API_KEY: ${{ secrets.COHERE_API_KEY }}
+ run: |
+ make test
+ - name: Upload coverage to Codecov
+ uses: codecov/codecov-action@v2
+ env:
+ CODECOV_TOKEN: ${{ secrets.CODECOV_TOKEN }}
+ with:
+ file: ./coverage.xml
+ fail_ci_if_error: false
diff --git a/.gitignore b/.gitignore
index 094c61f44a8c7dbc513b20a7b486e6f7d1c48dcc..c0bd2843b6ea8f4610f15e09b091077927afe4c6 100644
--- a/.gitignore
+++ b/.gitignore
@@ -13,6 +13,7 @@ venv/
# local env files
.env*.local
.env
+my.secrets
mac.env
local.mac.env
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 1dfe6d342f478deceb8a02ab83e1cad0569b040d..4bd34b547a651004f60df2b637cabe231db39e64 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -1,77 +1,71 @@
default_language_version:
python: python3.13
repos:
- - repo: meta
- hooks:
- - id: check-hooks-apply
- - id: check-useless-excludes
+- repo: meta
+ hooks:
+ - id: check-hooks-apply
+ - id: check-useless-excludes
- - repo: https://github.com/psf/black
- rev: 23.9.1
- hooks:
- - id: black
+- repo: https://github.com/psf/black
+ rev: 23.9.1
+ hooks:
+ - id: black
- - repo: https://github.com/asottile/blacken-docs
- rev: 1.16.0
- hooks:
- - id: blacken-docs
- additional_dependencies: [black==22.10.0]
+- repo: https://github.com/asottile/blacken-docs
+ rev: 1.16.0
+ hooks:
+ - id: blacken-docs
+ additional_dependencies: [ black==22.10.0 ]
- - repo: https://github.com/alessandrojcm/commitlint-pre-commit-hook
- rev: v9.11.0
- hooks:
- - id: commitlint
- stages: [commit-msg]
- additional_dependencies: ['@commitlint/config-conventional']
+- repo: https://github.com/alessandrojcm/commitlint-pre-commit-hook
+ rev: v9.11.0
+ hooks:
+ - id: commitlint
+ stages: [ commit-msg ]
+ additional_dependencies: [ '@commitlint/config-conventional' ]
- - repo: https://github.com/codespell-project/codespell
- rev: v2.2.4
- hooks:
- - id: codespell
- name: Run codespell to check for common misspellings in files
- language: python
- types: [ text ]
- args: [ "--write-changes", "--ignore-words-list", "asend" ]
- exclude: "poetry.lock"
+- repo: https://github.com/codespell-project/codespell
+ rev: v2.2.4
+ hooks:
+ - id: codespell
+ name: Run codespell to check for common misspellings in files
+ language: python
+ types: [ text ]
+ args: [ "--write-changes", "--ignore-words-list", "asend" ]
+ exclude: "uv.lock"
- - repo: https://github.com/pre-commit/pre-commit-hooks
- rev: v4.4.0
- hooks:
- - id: check-vcs-permalinks
- - id: end-of-file-fixer
- - id: trailing-whitespace
- args: [ --markdown-linebreak-ext=md ]
- - id: debug-statements
- - id: no-commit-to-branch
- - id: check-merge-conflict
- - id: check-toml
- - id: check-yaml
- args: [ '--unsafe' ] # for mkdocs.yml
- - id: detect-private-key
+- repo: https://github.com/pre-commit/pre-commit-hooks
+ rev: v4.4.0
+ hooks:
+ - id: check-vcs-permalinks
+ - id: end-of-file-fixer
+ - id: trailing-whitespace
+ args: [ --markdown-linebreak-ext=md ]
+ - id: debug-statements
+ - id: no-commit-to-branch
+ - id: check-merge-conflict
+ - id: check-toml
+ - id: check-yaml
+ args: [ '--unsafe' ] # for mkdocs.yml
+ - id: detect-private-key
- - repo: https://github.com/commitizen-tools/commitizen
- rev: v3.13.0
- hooks:
- - id: commitizen
- - id: commitizen-branch
- stages:
- - post-commit
- - push
+- repo: https://github.com/commitizen-tools/commitizen
+ rev: v3.13.0
+ hooks:
+ - id: commitizen
+ - id: commitizen-branch
+ stages:
+ - post-commit
+ - push
- - repo: https://github.com/astral-sh/ruff-pre-commit
- rev: v0.0.290
- hooks:
- - id: ruff
- types_or: [python, pyi, jupyter]
+- repo: https://github.com/astral-sh/ruff-pre-commit
+ rev: v0.0.290
+ hooks:
+ - id: ruff
+ types_or: [ python, pyi, jupyter ]
- # - repo: https://github.com/pre-commit/mirrors-mypy
- # rev: v1.8.0
- # hooks:
- # - id: mypy
- # args: [--ignore-missing-imports]
-
- - repo: https://github.com/PyCQA/bandit
- rev: 1.7.6
- hooks:
- - id: bandit
- args: ['-lll']
+- repo: https://github.com/PyCQA/bandit
+ rev: 1.7.6
+ hooks:
+ - id: bandit
+ args: [ '-lll' ]
diff --git a/.pydoc-markdown.yml b/.pydoc-markdown.yml
new file mode 100644
index 0000000000000000000000000000000000000000..3492e2c5e4d3e5c55ba89c8f06de95d40f1b6fce
--- /dev/null
+++ b/.pydoc-markdown.yml
@@ -0,0 +1,20 @@
+loaders:
+ - type: python
+ packages:
+ - semantic_router
+ search_path: ["."]
+
+processors:
+ - type: filter
+ skip_empty_modules: true
+ exclude_private: true
+ exclude_special: true
+ documented_only: true
+ do_not_filter_modules: true
+ - type: smart
+ - type: crossref
+
+renderer:
+ type: docusaurus
+ docs_base_path: docs/build
+ relative_output_path: .
\ No newline at end of file
diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
index 4de28c8242a6f25cfc5f834cd38ddfe298e74214..7713038a582fa8c6bcf5bb1b4f360482c9e33356 100644
--- a/CONTRIBUTING.md
+++ b/CONTRIBUTING.md
@@ -13,7 +13,7 @@ Please follow these guidelines when making a contribution:
- Navigating to /semantic-router.
- Running `make lint` to fix linting issues.
- Running `black .` to fix `black` linting issues.
- - Running `ruff . --fix` to fix `ruff` linting issues (where possible, others may need manual changes).
+ - Running `ruff check . --fix` to fix `ruff` linting issues (where possible, others may need manual changes).
- Running `mypy .` and then fixing any of the issues that are raised.
- Confirming the linters pass using `make lint` again.
- Ensure that, for any new code, new [PyTests are written](https://github.com/aurelio-labs/semantic-router/tree/main/tests/unit). If any code is removed, then ensure that corresponding PyTests are also removed. Finally, ensure that all remaining PyTests pass using `pytest ./tests` (to avoid integration tests you can run `pytest ./tests/unit`.
@@ -27,6 +27,7 @@ While we encourage you to initiate a draft Pull Request early to get feedback on
1. Fork on GitHub:
Go to the [repository's page](https://github.com/aurelio-labs/semantic-router) on GitHub:
Click the "Fork" button in the top-right corner of the page.
+
2. Clone Your Fork:
After forking, you'll be taken to your new fork of the repository on GitHub. Copy the URL of your fork from the address bar or by clicking the "Code" button and copying the URL under "Clone with HTTPS" or "Clone with SSH".
Open your terminal or command prompt.
@@ -34,18 +35,20 @@ While we encourage you to initiate a draft Pull Request early to get feedback on
```
git clone https://github.com/<your-gh-username>/<semantic-router>.git
```
-3. Ensure you have poetry installed: `pip install poetry`.
-4. Then navigate to the cloned folder, create a virtualenv, and install via poetry (which defaults to an editable installation):
+
+3. Ensure you have [`uv` installed](https://docs.astral.sh/uv/getting-started/installation/), for macos and linux use `curl -LsSf https://astral.sh/uv/install.sh | sh`.
+
+4. Then navigate to the cloned folder, create a virtualenv, and install via `uv`:
```
# Move into the cloned folder
cd semantic-router/
# Create a virtual environment
- python3 -m venv venv
+ uv venv --python 3.13
# Activate the environment
- source venv/bin/activate
+ source .venv/bin/activate
- # Install via poetry with all extras relevant to perform unit tests
- poetry install --all-extras
+ # Install via uv with all extras relevant to perform unit tests
+ uv pip install -e .[dev]
```
diff --git a/Makefile b/Makefile
index 30e1f6ad0d44a349017eb3b39aa569226bb03f3c..5e3b17aab58f0fc58e7b41e52c29976f11cdfacb 100644
--- a/Makefile
+++ b/Makefile
@@ -1,22 +1,23 @@
format:
- poetry run black --target-version py313 -l 88 .
- poetry run ruff --select I --fix .
+ uv run black --target-version py312 -l 88 .
+ uv run ruff --select I --fix .
PYTHON_FILES=.
lint: PYTHON_FILES=.
lint_diff: PYTHON_FILES=$(shell git diff --name-only --diff-filter=d main | grep -E '\.py$$')
lint lint_diff:
- poetry run black --target-version py313 -l 88 $(PYTHON_FILES) --check
- poetry run ruff check .
- poetry run mypy $(PYTHON_FILES)
+ uv run black --target-version py312 -l 88 $(PYTHON_FILES) --check
+ uv run ruff check .
+ uv run mypy $(PYTHON_FILES)
test:
- poetry run pytest -vv --cov=semantic_router --cov-report=term-missing --cov-report=xml
+ uv run pytest -vv --cov=semantic_router --cov-report=term-missing --cov-report=xml --exitfirst --maxfail=1
test_functional:
- poetry run pytest -vv -n 20 tests/functional
+ uv run pytest -vv -s --exitfirst --maxfail=1 tests/functional
test_unit:
- poetry run pytest -vv -n 20 tests/unit
+ uv run pytest -vv --exitfirst --maxfail=1 tests/unit
+
test_integration:
- poetry run pytest -vv -n 20 tests/integration
\ No newline at end of file
+ uv run pytest -vv --exitfirst --maxfail=1 tests/integration
diff --git a/coverage.xml b/coverage.xml
index bcccea3f3bcfa3f2adc89fcb5b32308b04522852..797bf7980f1f3c11d1bf1fe2e0326db2beeb5a85 100644
--- a/coverage.xml
+++ b/coverage.xml
@@ -1,148 +1,139 @@
<?xml version="1.0" ?>
-<coverage version="7.4.4" timestamp="1714280906690" lines-valid="2388" lines-covered="978" line-rate="0.4095" branches-covered="0" branches-valid="0" branch-rate="0" complexity="0">
- <!-- Generated by coverage.py: https://coverage.readthedocs.io/en/7.4.4 -->
+<coverage version="7.6.12" timestamp="1739514868967" lines-valid="4124" lines-covered="2081" line-rate="0.5046" branches-covered="0" branches-valid="0" branch-rate="0" complexity="0">
+ <!-- Generated by coverage.py: https://coverage.readthedocs.io/en/7.6.12 -->
<!-- Based on https://raw.githubusercontent.com/cobertura/web/master/htdocs/xml/coverage-04.dtd -->
<sources>
- <source>/Users/jamesbriggs/Documents/projects/aurelio-labs/semantic-router/semantic_router</source>
+ <source>/Users/bogdanbuduroiu/development/aurelio-labs/semantic-router/semantic_router</source>
</sources>
<packages>
- <package name="." line-rate="0.2415" branch-rate="0" complexity="0">
+ <package name="." line-rate="0.7328" branch-rate="0" complexity="0">
<classes>
- <class name="__init__.py" filename="__init__.py" complexity="0" line-rate="0.4" branch-rate="0">
+ <class name="__init__.py" filename="__init__.py" complexity="0" line-rate="1" branch-rate="0">
<methods/>
<lines>
<line number="1" hits="1"/>
<line number="2" hits="1"/>
- <line number="3" hits="0"/>
- <line number="5" hits="0"/>
- <line number="7" hits="0"/>
+ <line number="4" hits="1"/>
+ <line number="6" hits="1"/>
</lines>
</class>
- <class name="hybrid_layer.py" filename="hybrid_layer.py" complexity="0" line-rate="0.1933" branch-rate="0">
+ <class name="linear.py" filename="linear.py" complexity="0" line-rate="1" branch-rate="0">
<methods/>
<lines>
<line number="1" hits="1"/>
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- <class name="logger.py" filename="utils/logger.py" complexity="0" line-rate="1" branch-rate="0">
+ <class name="logger.py" filename="utils/logger.py" complexity="0" line-rate="0.5263" branch-rate="0">
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diff --git a/docs/00-introduction.ipynb b/docs/00-introduction.ipynb
index e96e75b89978a0c9c168e995b8bfb8895a690b21..d464a2d4594332aead5866b8d3d962a8584021b6 100644
--- a/docs/00-introduction.ipynb
+++ b/docs/00-introduction.ipynb
@@ -279,62 +279,6 @@
"sr(\"I'm interested in learning about llama 2\")"
]
},
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "dDZF2eN4f3p4"
- },
- "source": [
- "We can also retrieve multiple routes with its associated score:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "n27I7kmpf3p4",
- "outputId": "2138e077-190b-41b7-a3eb-4fd76e2f59c2"
- },
- "outputs": [
- {
- "data": {
- "text/plain": [
- "[RouteChoice(name='politics', function_call=None, similarity_score=0.8595844842560181),\n",
- " RouteChoice(name='chitchat', function_call=None, similarity_score=0.8356704527362284)]"
- ]
- },
- "execution_count": 9,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "sr.retrieve_multiple_routes(\"Hi! How are you doing in politics??\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "zi4XJ7Amf3p4",
- "outputId": "cf05cd65-d4f4-454a-ef05-77f16f37cc8f"
- },
- "outputs": [
- {
- "data": {
- "text/plain": [
- "[]"
- ]
- },
- "execution_count": 10,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "sr.retrieve_multiple_routes(\"I'm interested in learning about llama 2\")"
- ]
- },
{
"cell_type": "markdown",
"metadata": {},
diff --git a/docs/indexes/pinecone-local.ipynb b/docs/indexes/pinecone-local.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..5cfc54093e1c98c271a01a4c46d02be207dfd53a
--- /dev/null
+++ b/docs/indexes/pinecone-local.ipynb
@@ -0,0 +1,773 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Using PineconeLocalIndex for Routes\n",
+ "\n",
+ "Pinecone Local is an in-memory Pinecone Database emulator available as a Docker image.\n",
+ "\n",
+ "It's useful for running tests using the Pinecone local server. Your data will not leave your system, which is also helpful if you want to do testing locally before committing to a Pinecone account.\n",
+ "\n",
+ "## Limitations\n",
+ "Pinecone Local has the following limitations:\n",
+ "\n",
+ "- Pinecone Local is available only as a Docker image.\n",
+ "- Pinecone Local is an in-memory emulator and is not suitable for production. Records loaded into Pinecone Local do not persist after it is stopped.\n",
+ "- Pinecone Local does not authenticate client requests. API keys are ignored.\n",
+ "- Max number of records per index: 100,000.\n",
+ "\n",
+ "## Getting Started\n",
+ "Make sure [Docker](https://docs.docker.com/get-docker/) is installed and running on your local machine.\n",
+ "\n",
+ "### Download the latest pinecone-local Docker image:\n",
+ "\n",
+ "\n",
+ "Download the latest pinecone-local Docker image:\n",
+ "\n",
+ "```bash\n",
+ "docker pull ghcr.io/pinecone-io/pinecone-local:latest\n",
+ "```\n",
+ "\n",
+ "### Start Pinecone Local:\n",
+ "\n",
+ "```bash\n",
+ "docker run -d \\\n",
+ "--name pinecone-local \\\n",
+ "-e PORT=5080 \\\n",
+ "-e PINECONE_HOST=localhost \\\n",
+ "-p 5080-6000:5080-6000 \\\n",
+ "--platform linux/amd64 \\\n",
+ "ghcr.io/pinecone-io/pinecone-local:latest\n",
+ "```\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "!pip install -qU \"semantic-router[pinecone]==0.0.22\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "c:\\Users\\awesome\\anaconda3\\envs\\py12\\Lib\\site-packages\\tqdm\\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+ " from .autonotebook import tqdm as notebook_tqdm\n"
+ ]
+ }
+ ],
+ "source": [
+ "from semantic_router import Route\n",
+ "\n",
+ "# we could use this as a guide for our chatbot to avoid political conversations\n",
+ "politics = Route(\n",
+ " name=\"politics\",\n",
+ " utterances=[\n",
+ " \"isn't politics the best thing ever\",\n",
+ " \"why don't you tell me about your political opinions\",\n",
+ " \"don't you just love the president\" \"don't you just hate the president\",\n",
+ " \"they're going to destroy this country!\",\n",
+ " \"they will save the country!\",\n",
+ " ],\n",
+ ")\n",
+ "\n",
+ "# this could be used as an indicator to our chatbot to switch to a more\n",
+ "# conversational prompt\n",
+ "chitchat = Route(\n",
+ " name=\"chitchat\",\n",
+ " utterances=[\n",
+ " \"how's the weather today?\",\n",
+ " \"how are things going?\",\n",
+ " \"lovely weather today\",\n",
+ " \"the weather is horrendous\",\n",
+ " \"let's go to the chippy\",\n",
+ " ],\n",
+ ")\n",
+ "\n",
+ "# we place both of our decisions together into single list\n",
+ "routes = [politics, chitchat]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[Route(name='politics', utterances=[\"isn't politics the best thing ever\", \"why don't you tell me about your political opinions\", \"don't you just love the presidentdon't you just hate the president\", \"they're going to destroy this country!\", 'they will save the country!'], description=None, function_schemas=None, llm=None, score_threshold=None, metadata={}),\n",
+ " Route(name='chitchat', utterances=[\"how's the weather today?\", 'how are things going?', 'lovely weather today', 'the weather is horrendous', \"let's go to the chippy\"], description=None, function_schemas=None, llm=None, score_threshold=None, metadata={})]"
+ ]
+ },
+ "execution_count": 2,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "routes"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import os\n",
+ "from getpass import getpass\n",
+ "from semantic_router.encoders import OpenAIEncoder\n",
+ "\n",
+ "# get at platform.openai.com\n",
+ "os.environ[\"OPENAI_API_KEY\"] = os.environ.get(\"OPENAI_API_KEY\") or getpass(\n",
+ " \"Enter OpenAI API key: \"\n",
+ ")\n",
+ "encoder = OpenAIEncoder(\n",
+ " name=\"text-embedding-3-large\", score_threshold=0.5, dimensions=1536\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "For Pinecone Local, you can pass the API key as `pclocal`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "2025-02-03 15:40:01 - semantic_router.utils.logger - WARNING - pinecone.py:169 - __init__() - Default region changed from us-west-2 to us-east-1 in v0.1.0.dev6\n",
+ "2025-02-03 15:40:01 - pinecone_plugin_interface.logging - INFO - discover_namespace_packages.py:12 - discover_subpackages() - Discovering subpackages in _NamespacePath(['c:\\\\Users\\\\awesome\\\\anaconda3\\\\envs\\\\py12\\\\Lib\\\\site-packages\\\\pinecone_plugins'])\n",
+ "2025-02-03 15:40:01 - pinecone_plugin_interface.logging - INFO - discover_plugins.py:9 - discover_plugins() - Looking for plugins in pinecone_plugins.inference\n",
+ "2025-02-03 15:40:01 - pinecone_plugin_interface.logging - INFO - installation.py:10 - install_plugins() - Installing plugin inference into Pinecone\n",
+ "2025-02-03 15:40:01 - semantic_router.utils.logger - ERROR - pinecone.py:233 - _init_index() - index_host exists-pinecone:localhost:5081\n",
+ "2025-02-03 15:40:01 - semantic_router.utils.logger - ERROR - pinecone.py:234 - _init_index() - base_url exists-pinecone:http://localhost:5080\n",
+ "2025-02-03 15:40:01 - semantic_router.utils.logger - ERROR - pinecone.py:251 - _init_index() - index_host exists:http://localhost:5081\n",
+ "2025-02-03 15:40:01 - semantic_router.utils.logger - ERROR - pinecone.py:252 - _init_index() - index exists:<pinecone.data.index.Index object at 0x000002C6C9DC3200>\n"
+ ]
+ }
+ ],
+ "source": [
+ "import os\n",
+ "from semantic_router.index.pinecone import PineconeIndex\n",
+ "\n",
+ "os.environ[\"PINECONE_API_KEY\"] = os.environ.get(\"PINECONE_API_KEY\") or getpass(\n",
+ " \"Enter Pinecone API key: \"\n",
+ ")\n",
+ "\n",
+ "# Pass the pinecone local hosted url as base url\n",
+ "# os.environ[\"PINECONE_API_BASE_URL\"] = \"https://api.pinecone.io\"\n",
+ "os.environ[\"PINECONE_API_BASE_URL\"] = \"http://localhost:5080\"\n",
+ "\n",
+ "index = PineconeIndex(\n",
+ " index_name=\"route-test\", dimensions=1536, base_url=\"http://localhost:5080\"\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "2025-02-03 15:41:01 - semantic_router.utils.logger - ERROR - pinecone.py:233 - _init_index() - index_host exists-pinecone:localhost:5081\n",
+ "2025-02-03 15:41:01 - semantic_router.utils.logger - ERROR - pinecone.py:234 - _init_index() - base_url exists-pinecone:http://localhost:5080\n",
+ "2025-02-03 15:41:01 - semantic_router.utils.logger - ERROR - pinecone.py:251 - _init_index() - index_host exists:http://localhost:5081\n",
+ "2025-02-03 15:41:01 - semantic_router.utils.logger - ERROR - pinecone.py:252 - _init_index() - index exists:<pinecone.data.index.Index object at 0x000002C6CAA8FB90>\n"
+ ]
+ }
+ ],
+ "source": [
+ "from semantic_router.routers import SemanticRouter\n",
+ "\n",
+ "router = SemanticRouter(\n",
+ " encoder=encoder,\n",
+ " routes=routes,\n",
+ " index=index,\n",
+ " auto_sync=\"local\",\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can check our route layer and index information."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "'http://localhost:5081'"
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "index.index_host"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "'http://localhost:5081'"
+ ]
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "router.index.index_host"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'indexes': [{'deletion_protection': 'disabled',\n",
+ " 'dimension': 1536,\n",
+ " 'host': 'localhost:5081',\n",
+ " 'metric': 'dotproduct',\n",
+ " 'name': 'route-test',\n",
+ " 'spec': {'serverless': {'cloud': 'aws', 'region': 'us-east-1'}},\n",
+ " 'status': {'ready': True, 'state': 'Ready'}}]}"
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "router.index.client.list_indexes()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "True"
+ ]
+ },
+ "execution_count": 12,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "router.index.is_ready()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "True"
+ ]
+ },
+ "execution_count": 13,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "router.is_synced()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[' chitchat: how are things going?',\n",
+ " \" chitchat: how's the weather today?\",\n",
+ " \" chitchat: let's go to the chippy\",\n",
+ " ' chitchat: lovely weather today',\n",
+ " ' chitchat: the weather is horrendous',\n",
+ " \" politics: don't you just love the presidentdon't you just hate the president\",\n",
+ " \" politics: isn't politics the best thing ever\",\n",
+ " ' politics: they will save the country!',\n",
+ " \" politics: they're going to destroy this country!\",\n",
+ " \" politics: why don't you tell me about your political opinions\"]"
+ ]
+ },
+ "execution_count": 14,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "router.get_utterance_diff()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "2025-02-03 15:41:08 - httpx - INFO - _client.py:1013 - _send_single_request() - HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
+ "2025-02-03 15:41:08 - semantic_router.utils.logger - ERROR - pinecone.py:575 - query() - retrying query with vector as str\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "RouteChoice(name='politics', function_call=None, similarity_score=None)"
+ ]
+ },
+ "execution_count": 15,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "router(\"don't you love politics?\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "['politics', 'chitchat']"
+ ]
+ },
+ "execution_count": 17,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "router.list_route_names()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "2025-02-03 15:41:17 - httpx - INFO - _client.py:1013 - _send_single_request() - HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
+ "2025-02-03 15:41:17 - semantic_router.utils.logger - ERROR - pinecone.py:575 - query() - retrying query with vector as str\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "RouteChoice(name='chitchat', function_call=None, similarity_score=None)"
+ ]
+ },
+ "execution_count": 18,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "router(\"how are things going?\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "10"
+ ]
+ },
+ "execution_count": 19,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "len(router.index)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can also view all of the records for a given route:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "['politics#64069085d9d6e98e5a80915f69fabe82bac6c742f801bc305c5001dce88f0d19',\n",
+ " 'politics#af8b76111f260cf44fb34f04fcf82927dcbe08e8f47c30f4d571379c1512fac8',\n",
+ " 'politics#d1bb40236c3d95b9c695bfa86b314b6da4eb87e136699563fccae47fccea23e2',\n",
+ " 'politics#ed0f3dd7bd5dea12e55b1953bcd2c562a5ab19f501f6d5ff8c8927652c3904b8',\n",
+ " 'politics#fc6d15f9e6075e6de82b3fbef6722b64353e4eadc8d663b7312a4ed60c43e6f6']"
+ ]
+ },
+ "execution_count": 20,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "router.index._get_route_ids(route_name=\"politics\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "And query:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "2025-02-03 15:41:25 - httpx - INFO - _client.py:1013 - _send_single_request() - HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
+ "2025-02-03 15:41:25 - semantic_router.utils.logger - ERROR - pinecone.py:575 - query() - retrying query with vector as str\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "'politics'"
+ ]
+ },
+ "execution_count": 21,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "router(\"don't you love politics?\").name"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "2025-02-03 15:19:14 - httpx - INFO - _client.py:1013 - _send_single_request() - HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
+ "2025-02-03 15:19:14 - semantic_router.utils.logger - ERROR - pinecone.py:572 - query() - retrying query with vector as str\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "'chitchat'"
+ ]
+ },
+ "execution_count": 22,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "router(\"how's the weather today?\").name"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "2025-02-03 15:41:32 - httpx - INFO - _client.py:1013 - _send_single_request() - HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
+ "2025-02-03 15:41:32 - semantic_router.utils.logger - ERROR - pinecone.py:575 - query() - retrying query with vector as str\n"
+ ]
+ }
+ ],
+ "source": [
+ "router(\"I'm interested in learning about llama 2\").name"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can delete or update routes."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "10"
+ ]
+ },
+ "execution_count": 24,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "len(router.index)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Deleting a Route from the Semantic Router\n",
+ "In this section, we demonstrate how to delete a specific route from the `SemanticRouter` instance. This is useful when you want to remove a route that is no longer needed or to update the routing logic dynamically.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "2025-02-03 15:41:39 - semantic_router.utils.logger - WARNING - pinecone.py:611 - _read_config() - Configuration for sr_lock parameter not found in index.\n",
+ "2025-02-03 15:41:39 - semantic_router.utils.logger - INFO - pinecone.py:477 - delete() - index is not None, deleting...\n",
+ "2025-02-03 15:41:39 - semantic_router.utils.logger - INFO - pinecone.py:491 - delete() - Deleted 5 vectors from index route-test.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "5"
+ ]
+ },
+ "execution_count": 23,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "import time\n",
+ "\n",
+ "router.delete(route_name=\"politics\")\n",
+ "time.sleep(1)\n",
+ "len(router.index)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "2025-02-03 15:41:49 - httpx - INFO - _client.py:1013 - _send_single_request() - HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
+ "2025-02-03 15:41:49 - semantic_router.utils.logger - ERROR - pinecone.py:575 - query() - retrying query with vector as str\n"
+ ]
+ }
+ ],
+ "source": [
+ "router(\"don't you love politics??\").name"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[Route(name='chitchat', utterances=['how are things going?', \"how's the weather today?\", 'the weather is horrendous', 'lovely weather today', \"let's go to the chippy\"], description=None, function_schemas=None, llm=None, score_threshold=None, metadata={})]"
+ ]
+ },
+ "execution_count": 25,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "router.index.get_routes()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Asynchronous Route Query with Semantic Router\n",
+ "\n",
+ "In this section, we explore how to perform an asynchronous query using the `SemanticRouter`. This approach is beneficial when you want to handle multiple queries concurrently without blocking the execution of your program."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "2025-02-03 15:42:01 - httpx - INFO - _client.py:1729 - _send_single_request() - HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "RouteChoice(name='chitchat', function_call=None, similarity_score=None)"
+ ]
+ },
+ "execution_count": 26,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "await router.acall(\"how are things going?\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'indexes': [{'name': 'route-test',\n",
+ " 'dimension': 1536,\n",
+ " 'metric': 'dotproduct',\n",
+ " 'host': 'localhost:5081',\n",
+ " 'deletion_protection': 'disabled',\n",
+ " 'spec': {'serverless': {'cloud': 'aws', 'region': 'us-east-1'}},\n",
+ " 'status': {'ready': True, 'state': 'Ready'}}]}"
+ ]
+ },
+ "execution_count": 27,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "await router.index._async_list_indexes()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Stop and remove Pinecone Local\n",
+ "\n",
+ "To stop and remove the resources for Pinecone Local, run the following command:\n",
+ "\n",
+ "```bash\n",
+ "docker compose down\n",
+ "docker stop pinecone-local\n",
+ "docker rm pinecone-local\n",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "py12",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.12.4"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs/indexes/postgres/postgres.ipynb b/docs/indexes/postgres/postgres.ipynb
index f0f868d56877e69d2e54e0bb77c6fe2c80d97be2..86931eadd135319ed0014382ffbc8bea63dab0cc 100644
--- a/docs/indexes/postgres/postgres.ipynb
+++ b/docs/indexes/postgres/postgres.ipynb
@@ -97,9 +97,9 @@
"import os\n",
"\n",
"# Set Postgres connection string\n",
- "os.environ[\"POSTGRES_CONNECTION_STRING\"] = (\n",
- " \"postgresql://admin:root@localhost:5432/semantic_router\"\n",
- ")\n",
+ "os.environ[\n",
+ " \"POSTGRES_CONNECTION_STRING\"\n",
+ "] = \"postgresql://admin:root@localhost:5432/semantic_router\"\n",
"# Initialize the Postgres index\n",
"postgres_index = PostgresIndex()"
]
diff --git a/docs/source/conf.py b/docs/source/conf.py
index 36f85ca79750be8ab53314f0f524109958b67c36..a16c1325337ff130dc52e8600f424db4e04a86b9 100644
--- a/docs/source/conf.py
+++ b/docs/source/conf.py
@@ -1,6 +1,6 @@
-from typing import List
import os
import sys
+from typing import List
sys.path.insert(0, os.path.abspath("../..")) # Source code dir relative to this file
@@ -13,9 +13,9 @@ sys.path.insert(0, os.path.abspath("../..")) # Source code dir relative to this
# https://www.sphinx-doc.org/en/master/usage/configuration.html#project-information
project = "Semantic Router"
-copyright = "2024, Aurelio AI"
+copyright = "2025, Aurelio AI"
author = "Aurelio AI"
-release = "0.1.0.dev5"
+release = "0.1.0.dev10"
# -- General configuration ---------------------------------------------------
# https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration
diff --git a/poetry.lock b/poetry.lock
deleted file mode 100644
index 1202d622c07a86c3e21d2d6b30bed1500ce7517c..0000000000000000000000000000000000000000
--- a/poetry.lock
+++ /dev/null
@@ -1,5565 +0,0 @@
-# This file is automatically @generated by Poetry 1.8.4 and should not be changed by hand.
-
-[[package]]
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-[[package]]
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-python-versions = ">=3.8"
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-
-[package.dependencies]
-idna = ">=2.0"
-multidict = ">=4.0"
-propcache = ">=0.2.0"
-
-[[package]]
-name = "zipp"
-version = "3.21.0"
-description = "Backport of pathlib-compatible object wrapper for zip files"
-optional = false
-python-versions = ">=3.9"
-files = [
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-]
-
-[package.extras]
-check = ["pytest-checkdocs (>=2.4)", "pytest-ruff (>=0.2.1)"]
-cover = ["pytest-cov"]
-doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
-enabler = ["pytest-enabler (>=2.2)"]
-test = ["big-O", "importlib-resources", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more-itertools", "pytest (>=6,!=8.1.*)", "pytest-ignore-flaky"]
-type = ["pytest-mypy"]
-
-[extras]
-bedrock = ["boto3", "botocore"]
-cohere = ["cohere"]
-docs = ["sphinx", "sphinxawesome-theme"]
-fastembed = ["fastembed"]
-google = ["google-cloud-aiplatform"]
-hybrid = ["pinecone-text"]
-local = ["llama-cpp-python", "tokenizers", "torch", "transformers"]
-mistralai = ["mistralai"]
-pinecone = ["pinecone"]
-postgres = []
-qdrant = ["qdrant-client"]
-vision = ["pillow", "torch", "torchvision", "transformers"]
-
-[metadata]
-lock-version = "2.0"
-python-versions = ">=3.9,<3.14"
-content-hash = "8637391565e137e9f5d9d52bd927845242b7dea9217901c1b69f7be6b0b31917"
diff --git a/pyproject.toml b/pyproject.toml
index d9216d4687cb587a19efb16bffb5fa713e7933cf..ccec0457191b2ad58dee5b03e8deb63be9ecebb1 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,75 +1,100 @@
-[tool.poetry]
+[project]
name = "semantic-router"
-version = "0.1.0.dev5"
+version = "0.1.0.dev10"
description = "Super fast semantic router for AI decision making"
-authors = ["Aurelio AI <hello@aurelio.ai>"]
+authors = [{ name = "Aurelio AI", email = "hello@aurelio.ai" }]
+requires-python = ">=3.9,<3.14"
readme = "README.md"
-packages = [{include = "semantic_router"}]
license = "MIT"
+dependencies = [
+ "pydantic>=2.10.2,<3",
+ "openai>=1.10.0,<2.0.0",
+ "numpy>=1.25.2,<2",
+ "colorlog>=6.8.0,<7",
+ "pyyaml>=6.0.1,<7",
+ "aurelio-sdk>=0.0.18,<0.0.19",
+ "colorama>=0.4.6,<0.5",
+ "regex>=2023.12.25",
+ "tiktoken>=0.6.0,<1.0.0",
+ "requests-mock>=1.12.1,<2",
+ "aiohttp>=3.10.11,<4",
+ "psycopg2-binary>=2.9.9,<3",
+ "tornado>=6.4.2,<7",
+ "urllib3>=1.25.4,<1.27"
+]
-[tool.poetry.dependencies]
-python = ">=3.9,<3.14"
-pydantic = "^2.10.2"
-openai = ">=1.10.0,<2.0.0"
-cohere = {version = ">=5.9.4,<6.00", optional = true}
-mistralai= {version = ">=0.0.12,<0.1.0", optional = true}
-numpy = "^1.25.2"
-colorlog = "^6.8.0"
-pyyaml = "^6.0.1"
-aurelio-sdk = {version = "^0.0.16"}
-pinecone-text = {version = ">=0.7.1,<0.10.0", optional = true}
-torch = {version = ">=2.1.0,<2.6.0", optional = true, python = "<3.13" }
-transformers = {version = ">=4.36.2", optional = true, python = "<3.13" }
-tokenizers = {version = ">=0.19", optional = true, python = "<3.13" }
-llama-cpp-python = {version = ">=0.2.28,<0.2.86", optional = true, python = "<3.13" }
-colorama = "^0.4.6"
-pinecone = {version=">=5.0.0", optional = true}
-regex = ">=2023.12.25"
-torchvision = { version = ">=0.17.0,<0.18.0", optional = true, python = "<3.13" }
-pillow = { version = ">=10.2.0,<11.0.0", optional = true, python = "<3.13" }
-tiktoken = ">=0.6.0,<1.0.0"
-qdrant-client = {version = "^1.11.1", optional = true}
-google-cloud-aiplatform = {version = "^1.45.0", optional = true}
-requests-mock = "^1.12.1"
-boto3 = { version = "^1.34.98", optional = true }
-botocore = {version = "^1.34.110", optional = true}
-aiohttp = "^3.10.11"
-fastembed = {version = "^0.3.0", optional = true, python = "<3.13" }
-psycopg2-binary = {version = "^2.9.9", optional = true}
-sphinx = {version = "^7.0.0", optional = true}
-sphinxawesome-theme = {version = "^5.2.0", optional = true}
-tornado = {version = "^6.4.2", optional = true}
+[project.optional-dependencies]
+local = [
+ "torch>=2.1.0,<2.6.0 ; python_version < '3.13'",
+ "transformers>=4.36.2 ; python_version < '3.13'",
+ "tokenizers>=0.19 ; python_version < '3.13'",
+ "llama-cpp-python>=0.2.28,<0.2.86 ; python_version < '3.13'",
+]
+pinecone = ["pinecone>=5.0.0,<6.0.0"]
+vision = [
+ "torch>=2.1.0 ; python_version < '3.13'",
+ "torchvision>=0.17.0 ; python_version < '3.13'",
+ "transformers>=4.36.2 ; python_version < '3.13'",
+ "pillow>=10.2.0,<11.0.0 ; python_version < '3.13'",
+]
+mistralai = ["mistralai>=0.0.12,<0.1.0"]
+qdrant = ["qdrant-client>=1.11.1,<2"]
+google = ["google-cloud-aiplatform>=1.45.0,<2"]
+bedrock = [
+ "boto3>=1.34.98,<2",
+ "botocore>=1.34.110,<2",
+]
+postgres = []
+fastembed = ["fastembed>=0.3.0,<0.4 ; python_version < '3.13'"]
+docs = ["pydoc-markdown>=4.8.2 ; python_version < '3.12'"]
+cohere = ["cohere>=5.9.4,<6.00"]
+all = [
+ "semantic-router[local]",
+ "semantic-router[pinecone]",
+ "semantic-router[vision]",
+ "semantic-router[mistralai]",
+ "semantic-router[qdrant]",
+ "semantic-router[google]",
+ "semantic-router[bedrock]",
+ "semantic-router[postgres]",
+ "semantic-router[fastembed]",
+ "semantic-router[cohere]",
+]
-[tool.poetry.extras]
-hybrid = ["pinecone-text"]
-local = ["torch", "transformers", "tokenizers", "huggingface-hub", "llama-cpp-python"]
-pinecone = ["pinecone"]
-vision = ["torch", "torchvision", "transformers", "pillow"]
-mistralai = ["mistralai"]
-qdrant = ["qdrant-client"]
-google = ["google-cloud-aiplatform"]
-bedrock = ["boto3", "botocore"]
-postgres = ["psycopg2"]
-fastembed = ["fastembed"]
-docs = ["sphinx", "sphinxawesome-theme"]
-cohere = ["cohere"]
+[tool.uv.sources]
+torch = [
+ { index = "pytorch-nightly", marker = "python_version >= '3.13' and sys_platform == 'darwin'" },
+ { index = "pytorch-cpu", marker = "python_version < '3.13' or sys_platform != 'darwin'" },
+]
+torchvision = [
+ { index = "pytorch-nightly", marker = "python_version >= '3.13' and sys_platform == 'darwin'" },
+ { index = "pytorch-cpu", marker = "python_version < '3.13' or sys_platform != 'darwin'" },
+]
-[tool.poetry.group.dev.dependencies]
-ipykernel = "^6.25.0"
-ruff = "^0.1.5"
-pytest = "^8.2"
-pytest-mock = "^3.12.0"
-pytest-cov = "^4.1.0"
-pytest-xdist = "^3.5.0"
-pytest-asyncio = "^0.24.0"
-mypy = "^1.7.1"
-types-pyyaml = "^6.0.12.12"
-types-requests = "^2.31.0"
-black = {extras = ["jupyter"], version = ">=23.12.1,<24.5.0"}
+[dependency-groups]
+dev = [
+ "ipykernel>=6.25.0,<7",
+ "ruff>=0.1.5,<0.2",
+ "pytest~=8.2",
+ "pytest-mock>=3.12.0,<4",
+ "pytest-cov>=4.1.0,<5",
+ "pytest-xdist>=3.5.0,<4",
+ "pytest-asyncio>=0.24.0,<0.25",
+ "mypy>=1.7.1,<2",
+ "types-pyyaml>=6.0.12.12,<7",
+ "types-requests>=2.31.0,<3",
+ "black[jupyter]>=23.1.0",
+]
+
+[tool.hatch.build.targets.sdist]
+include = ["semantic_router"]
+
+[tool.hatch.build.targets.wheel]
+include = ["semantic_router"]
[build-system]
-requires = ["poetry-core"]
-build-backend = "poetry.core.masonry.api"
+requires = ["hatchling"]
+build-backend = "hatchling.build"
[tool.ruff.per-file-ignores]
"*.ipynb" = ["ALL"]
@@ -79,3 +104,14 @@ line-length = 88
[tool.mypy]
ignore_missing_imports = true
+
+# PyTorch indexes
+[[tool.uv.index]]
+name = "pytorch-cpu"
+url = "https://download.pytorch.org/whl/cpu"
+explicit = true
+
+[[tool.uv.index]]
+name = "pytorch-nightly"
+url = "https://download.pytorch.org/whl/nightly/cpu"
+explicit = true
diff --git a/semantic_router/__init__.py b/semantic_router/__init__.py
index 50e24234dafda74d45c11b0ea4a2a450eb23457f..d9d8cba5af2fe6fdb0da5080315fc59915e9744f 100644
--- a/semantic_router/__init__.py
+++ b/semantic_router/__init__.py
@@ -1,6 +1,6 @@
-from semantic_router.routers import RouterConfig, SemanticRouter, HybridRouter
from semantic_router.route import Route
+from semantic_router.routers import HybridRouter, RouterConfig, SemanticRouter
__all__ = ["SemanticRouter", "HybridRouter", "Route", "RouterConfig"]
-__version__ = "0.1.0.dev5"
+__version__ = "0.1.0.dev10"
diff --git a/semantic_router/encoders/__init__.py b/semantic_router/encoders/__init__.py
index 07e468d893afe8fb2b8ecf69d1dcd8bbc2f4d8ee..5fdeb789098aa009996df1c6b264f1db1bbeaf82 100644
--- a/semantic_router/encoders/__init__.py
+++ b/semantic_router/encoders/__init__.py
@@ -8,8 +8,7 @@ from semantic_router.encoders.clip import CLIPEncoder
from semantic_router.encoders.cohere import CohereEncoder
from semantic_router.encoders.fastembed import FastEmbedEncoder
from semantic_router.encoders.google import GoogleEncoder
-from semantic_router.encoders.huggingface import HuggingFaceEncoder
-from semantic_router.encoders.huggingface import HFEndpointEncoder
+from semantic_router.encoders.huggingface import HFEndpointEncoder, HuggingFaceEncoder
from semantic_router.encoders.mistral import MistralEncoder
from semantic_router.encoders.openai import OpenAIEncoder
from semantic_router.encoders.tfidf import TfidfEncoder
diff --git a/semantic_router/encoders/aurelio.py b/semantic_router/encoders/aurelio.py
index c50c6c4e51d648221b2421cdf4dcfb3481ef6c35..0288a864592e276b323433bb73b142fdf057a8d5 100644
--- a/semantic_router/encoders/aurelio.py
+++ b/semantic_router/encoders/aurelio.py
@@ -1,14 +1,18 @@
import os
from typing import Any, List, Optional
-from pydantic import Field
-from aurelio_sdk import AurelioClient, AsyncAurelioClient, EmbeddingResponse
+from aurelio_sdk import AsyncAurelioClient, AurelioClient, EmbeddingResponse
+from pydantic import Field
-from semantic_router.encoders.base import SparseEncoder
+from semantic_router.encoders.base import AsymmetricSparseMixin, SparseEncoder
from semantic_router.schema import SparseEmbedding
-class AurelioSparseEncoder(SparseEncoder):
+class AurelioSparseEncoder(SparseEncoder, AsymmetricSparseMixin):
+ """Sparse encoder using Aurelio Platform's embedding API. Requires an API key from
+ https://platform.aurelio.ai
+ """
+
model: Optional[Any] = None
client: AurelioClient = Field(default_factory=AurelioClient, exclude=True)
async_client: AsyncAurelioClient = Field(
@@ -21,6 +25,13 @@ class AurelioSparseEncoder(SparseEncoder):
name: str | None = None,
api_key: Optional[str] = None,
):
+ """Initialize the AurelioSparseEncoder.
+
+ :param name: The name of the model to use.
+ :type name: str | None
+ :param api_key: The API key to use.
+ :type api_key: str | None
+ """
if name is None:
name = "bm25"
super().__init__(name=name)
@@ -32,16 +43,58 @@ class AurelioSparseEncoder(SparseEncoder):
self.async_client = AsyncAurelioClient(api_key=api_key)
def __call__(self, docs: list[str]) -> list[SparseEmbedding]:
- res: EmbeddingResponse = self.client.embedding(input=docs, model=self.name)
+ """Encode a list of queries using the Aurelio Platform embedding API. Documents
+ must be strings, sparse encoders do not support other types.
+ """
+ return self.encode_queries(docs)
+
+ def encode_queries(self, docs: List[str]) -> List[SparseEmbedding]:
+ res: EmbeddingResponse = self.client.embedding(
+ input=docs, model=self.name, input_type="queries"
+ )
embeds = [SparseEmbedding.from_aurelio(r.embedding) for r in res.data]
return embeds
- async def acall(self, docs: list[str]) -> list[SparseEmbedding]:
+ def encode_documents(self, docs: List[str]) -> List[SparseEmbedding]:
+ res: EmbeddingResponse = self.client.embedding(
+ input=docs, model=self.name, input_type="documents"
+ )
+ embeds = [SparseEmbedding.from_aurelio(r.embedding) for r in res.data]
+ return embeds
+
+ async def aencode_queries(self, docs: List[str]) -> list[SparseEmbedding]:
+ res: EmbeddingResponse = await self.async_client.embedding(
+ input=docs, model=self.name, input_type="queries"
+ )
+ embeds = [SparseEmbedding.from_aurelio(r.embedding) for r in res.data]
+ return embeds
+
+ async def aencode_documents(self, docs: List[str]) -> list[SparseEmbedding]:
res: EmbeddingResponse = await self.async_client.embedding(
- input=docs, model=self.name
+ input=docs, model=self.name, input_type="documents"
)
embeds = [SparseEmbedding.from_aurelio(r.embedding) for r in res.data]
return embeds
+ async def acall(self, docs: list[str]) -> list[SparseEmbedding]:
+ """Asynchronously encode a list of documents using the Aurelio Platform
+ embedding API. Documents must be strings, sparse encoders do not support other
+ types.
+
+ :param docs: The documents to encode.
+ :type docs: list[str]
+ :param input_type:
+ :type semantic_router.encoders.encode_input_type.EncodeInputType
+ :return: The encoded documents.
+ :rtype: list[SparseEmbedding]
+ """
+ return await self.aencode_queries(docs)
+
def fit(self, docs: List[str]):
+ """Fit the encoder to a list of documents. AurelioSparseEncoder does not support
+ fit yet.
+
+ :param docs: The documents to fit the encoder to.
+ :type docs: list[str]
+ """
raise NotImplementedError("AurelioSparseEncoder does not support fit.")
diff --git a/semantic_router/encoders/base.py b/semantic_router/encoders/base.py
index 993093c1a2132c58e769b3035ed7588aefe7a689..2d4ad1f90f3cc4fab57ce6b8df4cbf838a4cb2de 100644
--- a/semantic_router/encoders/base.py
+++ b/semantic_router/encoders/base.py
@@ -1,8 +1,9 @@
-from typing import Any, Coroutine, List, Optional
+from typing import Any, List, Optional
-from pydantic import BaseModel, Field, field_validator
import numpy as np
+from pydantic import BaseModel, Field, field_validator
+from semantic_router.route import Route
from semantic_router.schema import SparseEmbedding
@@ -15,17 +16,44 @@ class DenseEncoder(BaseModel):
arbitrary_types_allowed = True
@field_validator("score_threshold")
- def set_score_threshold(cls, v):
+ def set_score_threshold(cls, v: float | None) -> float | None:
+ """Set the score threshold. If None, the score threshold is not used.
+
+ :param v: The score threshold.
+ :type v: float | None
+ :return: The score threshold.
+ :rtype: float | None
+ """
return float(v) if v is not None else None
def __call__(self, docs: List[Any]) -> List[List[float]]:
+ """Encode a list of documents. Documents can be any type, but the encoder must
+ be built to handle that data type. Typically, these types are strings or
+ arrays representing images.
+
+ :param docs: The documents to encode.
+ :type docs: List[Any]
+ :return: The encoded documents.
+ :rtype: List[List[float]]
+ """
raise NotImplementedError("Subclasses must implement this method")
- def acall(self, docs: List[Any]) -> Coroutine[Any, Any, List[List[float]]]:
+ async def acall(self, docs: List[Any]) -> List[List[float]]:
+ """Encode a list of documents asynchronously. Documents can be any type, but the
+ encoder must be built to handle that data type. Typically, these types are
+ strings or arrays representing images.
+
+ :param docs: The documents to encode.
+ :type docs: List[Any]
+ :return: The encoded documents.
+ :rtype: List[List[float]]
+ """
raise NotImplementedError("Subclasses must implement this method")
class SparseEncoder(BaseModel):
+ """An encoder that encodes documents into a sparse format."""
+
name: str
type: str = Field(default="base")
@@ -33,21 +61,90 @@ class SparseEncoder(BaseModel):
arbitrary_types_allowed = True
def __call__(self, docs: List[str]) -> List[SparseEmbedding]:
+ """Sparsely encode a list of documents. Documents can be any type, but the encoder must
+ be built to handle that data type. Typically, these types are strings or
+ arrays representing images.
+
+ :param docs: The documents to encode.
+ :type docs: List[Any]
+ :return: The encoded documents.
+ :rtype: List[SparseEmbedding]
+ """
raise NotImplementedError("Subclasses must implement this method")
- async def acall(self, docs: List[str]) -> list[SparseEmbedding]:
+ async def acall(self, docs: List[Any]) -> List[SparseEmbedding]:
+ """Encode a list of documents asynchronously. Documents can be any type, but the
+ encoder must be built to handle that data type. Typically, these types are
+ strings or arrays representing images.
+
+ :param docs: The documents to encode.
+ :type docs: List[Any]
+ :return: The encoded documents.
+ :rtype: List[SparseEmbedding]
+ """
raise NotImplementedError("Subclasses must implement this method")
def _array_to_sparse_embeddings(
self, sparse_arrays: np.ndarray
) -> List[SparseEmbedding]:
- """Consumes several sparse vectors containing zero-values and returns a compact array."""
+ """Consumes several sparse vectors containing zero-values and returns a compact
+ array.
+
+ :param sparse_arrays: The sparse arrays to compact.
+ :type sparse_arrays: np.ndarray
+ :return: The compact array.
+ :rtype: List[SparseEmbedding]
+ """
if sparse_arrays.ndim != 2:
raise ValueError(f"Expected a 2D array, got a {sparse_arrays.ndim}D array.")
# get coordinates of non-zero values
coords = np.nonzero(sparse_arrays)
+ if coords[0].size == 0:
+ # Sparse Embeddings can be all zero, if query tokens do not appear in corpus at all
+ return [SparseEmbedding(embedding=np.empty((1, 2)))]
# create compact array
compact_array = np.array([coords[0], coords[1], sparse_arrays[coords]]).T
arr_range = range(compact_array[:, 0].max().astype(int) + 1)
arrs = [compact_array[compact_array[:, 0] == i, :][:, 1:3] for i in arr_range]
return [SparseEmbedding.from_compact_array(arr) for arr in arrs]
+
+
+class FittableMixin:
+ def fit(self, routes: list[Route]):
+ pass
+
+
+class AsymmetricDenseMixin:
+ def encode_queries(self, docs: List[str]) -> List[List[float]]:
+ """Convert query texts to dense embeddings optimized for querying"""
+ raise NotImplementedError("Subclasses must implement this method")
+
+ def encode_documents(self, docs: List[str]) -> List[List[float]]:
+ """Convert document texts to dense embeddings optimized for storage"""
+ raise NotImplementedError("Subclasses must implement this method")
+
+ async def aencode_queries(self, docs: List[str]) -> List[List[float]]:
+ """Async version of encode_queries"""
+ raise NotImplementedError("Subclasses must implement this method")
+
+ async def aencode_documents(self, docs: List[str]) -> List[List[float]]:
+ """Async version of encode_documents"""
+ raise NotImplementedError("Subclasses must implement this method")
+
+
+class AsymmetricSparseMixin:
+ def encode_queries(self, docs: List[str]) -> List[SparseEmbedding]:
+ """Convert query texts to dense embeddings optimized for querying"""
+ raise NotImplementedError("Subclasses must implement this method")
+
+ def encode_documents(self, docs: List[str]) -> List[SparseEmbedding]:
+ """Convert document texts to dense embeddings optimized for storage"""
+ raise NotImplementedError("Subclasses must implement this method")
+
+ async def aencode_queries(self, docs: List[str]) -> List[SparseEmbedding]:
+ """Async version of encode_queries"""
+ raise NotImplementedError("Subclasses must implement this method")
+
+ async def aencode_documents(self, docs: List[str]) -> List[SparseEmbedding]:
+ """Async version of encode_documents"""
+ raise NotImplementedError("Subclasses must implement this method")
diff --git a/semantic_router/encoders/bedrock.py b/semantic_router/encoders/bedrock.py
index 7adf60c717252b71da0d9f604aec43d2dc5226b9..cdab5b937903b956ac265ba251d50235bffb1f98 100644
--- a/semantic_router/encoders/bedrock.py
+++ b/semantic_router/encoders/bedrock.py
@@ -17,16 +17,40 @@ Classes:
"""
import json
-from typing import Dict, List, Optional, Any, Union
import os
from time import sleep
+from typing import Any, Dict, List, Optional, Union
+
import tiktoken
+
from semantic_router.encoders import DenseEncoder
from semantic_router.utils.defaults import EncoderDefault
from semantic_router.utils.logger import logger
class BedrockEncoder(DenseEncoder):
+ """Dense encoder using Amazon Bedrock embedding API. Requires an AWS Access Key ID
+ and AWS Secret Access Key.
+
+ The BedrockEncoder class is a subclass of DenseEncoder and utilizes the
+ TextEmbeddingModel from the Amazon's Bedrock Platform to generate embeddings for
+ given documents. It supports customization of the pre-trained model, score
+ threshold, and region.
+
+ Example usage:
+
+ ```python
+ from semantic_router.encoders.bedrock_encoder import BedrockEncoder
+
+ encoder = BedrockEncoder(
+ access_key_id="your-access-key-id",
+ secret_access_key="your-secret-key",
+ region="your-region"
+ )
+ embeddings = encoder(["document1", "document2"])
+ ```
+ """
+
client: Any = None
type: str = "bedrock"
input_type: Optional[str] = "search_query"
@@ -48,26 +72,32 @@ class BedrockEncoder(DenseEncoder):
):
"""Initializes the BedrockEncoder.
- Args:
- name: The name of the pre-trained model to use for embedding.
- If not provided, the default model specified in EncoderDefault will
- be used.
- score_threshold: The threshold for similarity scores.
- access_key_id: The AWS access key id for an IAM principle.
- If not provided, it will be retrieved from the access_key_id
- environment variable.
- secret_access_key: The secret access key for an IAM principle.
- If not provided, it will be retrieved from the AWS_SECRET_KEY
- environment variable.
- session_token: The session token for an IAM principle.
- If not provided, it will be retrieved from the AWS_SESSION_TOKEN
- environment variable.
- region: The location of the Bedrock resources.
- If not provided, it will be retrieved from the AWS_REGION
- environment variable, defaulting to "us-west-1"
-
- Raises:
- ValueError: If the Bedrock Platform client fails to initialize.
+ :param name: The name of the pre-trained model to use for embedding.
+ If not provided, the default model specified in EncoderDefault will
+ be used.
+ :type name: str
+ :param input_type: The type of input to use for the embedding.
+ If not provided, the default input type specified in EncoderDefault will
+ be used.
+ :type input_type: str
+ :param score_threshold: The threshold for similarity scores.
+ :type score_threshold: float
+ :param access_key_id: The AWS access key id for an IAM principle.
+ If not provided, it will be retrieved from the access_key_id
+ environment variable.
+ :type access_key_id: str
+ :param secret_access_key: The secret access key for an IAM principle.
+ If not provided, it will be retrieved from the AWS_SECRET_KEY
+ environment variable.
+ :type secret_access_key: str
+ :param session_token: The session token for an IAM principle.
+ If not provided, it will be retrieved from the AWS_SESSION_TOKEN
+ environment variable.
+ :param region: The location of the Bedrock resources.
+ If not provided, it will be retrieved from the AWS_REGION
+ environment variable, defaulting to "us-west-1"
+ :type region: str
+ :raises ValueError: If the Bedrock Platform client fails to initialize.
"""
super().__init__(name=name, score_threshold=score_threshold)
self.access_key_id = self.get_env_variable("AWS_ACCESS_KEY_ID", access_key_id)
@@ -94,16 +124,15 @@ class BedrockEncoder(DenseEncoder):
):
"""Initializes the Bedrock client.
- Args:
- access_key_id: The Amazon access key ID.
- secret_access_key: The Amazon secret key.
- region: The location of the AI Platform resources.
-
- Returns:
- An instance of the TextEmbeddingModel client.
-
- Raises:
- ImportError: If the required Bedrock libraries are not
+ :param access_key_id: The Amazon access key ID.
+ :type access_key_id: str
+ :param secret_access_key: The Amazon secret key.
+ :type secret_access_key: str
+ :param region: The location of the AI Platform resources.
+ :type region: str
+ :returns: An instance of the TextEmbeddingModel client.
+ :rtype: Any
+ :raises ImportError: If the required Bedrock libraries are not
installed.
ValueError: If the Bedrock client fails to initialize.
"""
@@ -143,16 +172,14 @@ class BedrockEncoder(DenseEncoder):
) -> List[List[float]]:
"""Generates embeddings for the given documents.
- Args:
- docs: A list of strings representing the documents to embed.
- model_kwargs: A dictionary of model-specific inference parameters.
-
- Returns:
- A list of lists, where each inner list contains the embedding values for a
+ :param docs: A list of strings representing the documents to embed.
+ :type docs: list[str]
+ :param model_kwargs: A dictionary of model-specific inference parameters.
+ :type model_kwargs: dict
+ :returns: A list of lists, where each inner list contains the embedding values for a
document.
-
- Raises:
- ValueError: If the Bedrock Platform client is not initialized or if the
+ :rtype: list[list[float]]
+ :raises ValueError: If the Bedrock Platform client is not initialized or if the
API call fails.
"""
try:
@@ -171,7 +198,6 @@ class BedrockEncoder(DenseEncoder):
embeddings = []
if self.name and "amazon" in self.name:
for doc in docs:
-
embedding_body = {}
if isinstance(doc, dict):
@@ -253,15 +279,14 @@ class BedrockEncoder(DenseEncoder):
raise ValueError("Bedrock call failed to return embeddings.")
def chunk_strings(self, strings, MAX_WORDS=20):
- """
- Breaks up a list of strings into smaller chunks.
-
- Args:
- strings (list): A list of strings to be chunked.
- max_chunk_size (int): The maximum size of each chunk. Default is 20.
-
- Returns:
- list: A list of lists, where each inner list contains a chunk of strings.
+ """Breaks up a list of strings into smaller chunks.
+
+ :param strings: A list of strings to be chunked.
+ :type strings: list
+ :param max_chunk_size: The maximum size of each chunk. Default is 20.
+ :type max_chunk_size: int
+ :returns: A list of lists, where each inner list contains a chunk of strings.
+ :rtype: list[list[str]]
"""
encoding = tiktoken.get_encoding("cl100k_base")
chunked_strings = []
@@ -278,17 +303,15 @@ class BedrockEncoder(DenseEncoder):
def get_env_variable(var_name, provided_value, default=None):
"""Retrieves environment variable or uses a provided value.
- Args:
- var_name (str): The name of the environment variable.
- provided_value (Optional[str]): The provided value to use if not None.
- default (Optional[str]): The default value if the environment variable is not set.
-
- Returns:
- str: The value of the environment variable or the provided/default value.
- None: Where AWS_SESSION_TOKEN is not set or provided
-
- Raises:
- ValueError: If no value is provided and the environment variable is not set.
+ :param var_name: The name of the environment variable.
+ :type var_name: str
+ :param provided_value: The provided value to use if not None.
+ :type provided_value: Optional[str]
+ :param default: The default value if the environment variable is not set.
+ :type default: Optional[str]
+ :returns: The value of the environment variable or the provided/default value.
+ :rtype: str
+ :raises ValueError: If no value is provided and the environment variable is not set.
"""
if provided_value is not None:
return provided_value
diff --git a/semantic_router/encoders/bm25.py b/semantic_router/encoders/bm25.py
index f42bf9c28937419b29ae4429151a1fe3120c0487..a72b8e16c534adb1115f40841e2803b3e091aa7f 100644
--- a/semantic_router/encoders/bm25.py
+++ b/semantic_router/encoders/bm25.py
@@ -1,73 +1,291 @@
-from typing import Any, Dict, List, Optional
+import asyncio
+from functools import partial
+from typing import Any, List
-from semantic_router.encoders.tfidf import TfidfEncoder
-from semantic_router.utils.logger import logger
-from semantic_router.schema import SparseEmbedding
+import numpy as np
+
+from semantic_router.encoders.base import (
+ AsymmetricSparseMixin,
+ FittableMixin,
+ SparseEncoder,
+)
from semantic_router.route import Route
+from semantic_router.schema import SparseEmbedding
+from semantic_router.tokenizers import BaseTokenizer, PretrainedTokenizer
+from semantic_router.utils.logger import logger
+
+
+class BM25Encoder(SparseEncoder, FittableMixin, AsymmetricSparseMixin):
+ """BM25Encoder, running a vectorized version of ATIRE BM25 algorithm
+
+ Concept:
+ - BM25 uses scoring between queries & corpus to retrieve the most relevant documents ∈ corpus
+ - most vector databases (VDB) store embedded documents and score them versus received queries for retrieval
+ - we need to break up the BM25 formula into `encode_queries` and `encode_documents`, with the latter to be stored in VDB
+ - dot product of `encode_queries(q)` and `encode_documents([D_0, D_1, ...])` is the BM25 score of the documents `[D_0, D_1, ...]` for the given query `q`
+ - we train a BM25 encoder's normalization parameters on a sufficiently large corpus to capture target language distribution
+ - these trained parameter allow us to balance TF & IDF of query & documents for retrieval (read more on how BM25 fixes issues with TF-IDF)
+ ATIRE Paper: https://www.cs.otago.ac.nz/research/student-publications/atire-opensource.pdf
+ Pinecone Implementation: https://github.com/pinecone-io/pinecone-text/blob/8399f9ff28c4652766c35165c0db9b0eff309077/pinecone_text/sparse/bm25_encoder.py
+
+ :param k1: normalizer parameter that limits how much a single query term `q_i ∈ q` can affect score for document `D_n`
+ :type k1: float
+ :param b: normalizer parameter that balances the effect of a single document length compared to the average document length
+ :type b: float
+ :param corpus_size: number of documents in the trained corpus
+ :type corpus_size: int, optional
+ :param _avg_doc_len: float representing the average document length in the trained corpus
+ :type _avg_doc_len: float, optional
+ :param _documents_containing_word: (1, tokenizer.vocab_size) shaped array, denoting how many documents contain `token ∈ vocab`
+ :type _documents_containing_word: class:`numpy.ndarray`, optional
+
+ """
-class BM25Encoder(TfidfEncoder):
- model: Optional[Any] = None
- idx_mapping: Optional[Dict[int, int]] = None
type: str = "sparse"
+ k1: float = 1.5
+ b: float = 0.75
+ corpus_size: int | None = None
+ _tokenizer: BaseTokenizer | None
+ _avg_doc_len: np.float64 | float | None
+ _documents_containing_word: np.ndarray | None
def __init__(
self,
+ tokenizer: BaseTokenizer | None = None,
name: str | None = None,
+ k1: float = 1.5,
+ b: float = 0.75,
+ corpus_size: int | None = None,
+ avg_doc_len: float | None = None,
use_default_params: bool = True,
- ):
+ ) -> None:
if name is None:
name = "bm25"
super().__init__(name=name)
- try:
- from pinecone_text.sparse import BM25Encoder as encoder
- except ImportError:
- raise ImportError(
- "Please install pinecone-text to use BM25Encoder. "
- "You can install it with: `pip install 'semantic-router[hybrid]'`"
- )
-
- self.model = encoder()
- if use_default_params:
- logger.info("Downloading and initializing default sBM25 model parameters.")
- self.model = encoder.default()
- self._set_idx_mapping()
-
- def _set_idx_mapping(self):
- # TODO JB: this is training the model somehow - not sure how...
- params = self.model.get_params()
- doc_freq = params["doc_freq"]
- if isinstance(doc_freq, dict):
- indices = doc_freq["indices"]
- self.idx_mapping = {int(idx): i for i, idx in enumerate(indices)}
+ self.k1 = k1
+ self.b = b
+ self.corpus_size = corpus_size
+ self._avg_doc_len = np.float64(avg_doc_len) if avg_doc_len else None
+ if use_default_params and not tokenizer:
+ logger.info("Initializing default BM25 model parameters.")
+ self._tokenizer = PretrainedTokenizer("bert-base-uncased")
+ elif tokenizer is not None:
+ self._tokenizer = tokenizer
else:
- raise TypeError("Expected a dictionary for 'doc_freq'")
+ raise ValueError(
+ "Tokenizer not provided. Provide a tokenizer or set `use_default_params` to True"
+ )
+
+ def _fit_validate(self, routes: List[Route]):
+ if not isinstance(routes, list) or not isinstance(routes[0], Route):
+ raise TypeError("`routes` parameter must be a list of Route objects.")
- def fit(self, routes: List[Route]):
+ def fit(self, routes: List[Route]) -> "BM25Encoder":
"""Trains the encoder weights on the provided routes.
:param routes: List of routes to train the encoder on.
:type routes: List[Route]
"""
- self._fit_validate(routes=routes)
- if self.model is None:
- raise ValueError("Model is not initialized.")
+ if not self._tokenizer:
+ raise ValueError(
+ "BM25 encoder not initialized. Provide a tokenizer or set `use_default_params` to True"
+ )
+ self._fit_validate(routes)
utterances = [utterance for route in routes for utterance in route.utterances]
- self.model.fit(corpus=utterances)
-
- def __call__(self, docs: List[str]) -> list[SparseEmbedding]:
- if self.model is None:
- raise ValueError("Model or index mapping is not initialized.")
- if len(docs) == 1:
- sparse_dicts = self.model.encode_queries(docs)
- elif len(docs) > 1:
- sparse_dicts = self.model.encode_documents(docs)
- else:
- raise ValueError("No documents to encode.")
+ utterance_ids = self._tokenizer.tokenize(utterances, pad=True)
+ corpus = self._tf(utterance_ids)
+
+ self.corpus_size = len(utterances)
+
+ # Calculate document lengths and average
+ doc_lengths = corpus.sum(axis=1)
+ self._avg_doc_len = doc_lengths.mean()
+
+ # Calculate document frequencies
+ documents_containing_word = np.atleast_2d((corpus > 0).sum(axis=0))
+
+ documents_containing_word[:, 0] *= 0
+ self._documents_containing_word = documents_containing_word
+
+ return self
+
+ def _tf(self, docs: np.ndarray) -> np.ndarray:
+ """Returns term frequency of query terms in trained corpus
+
+ :param docs: 2D shaped array of each document's token ids
+ :type docs: :class:`numpy.ndarray`
+ :return: Matrix where value @ (m, n) represents how many times token id `n` appears in document `m`
+ :rtype: :class:`numpy.ndarray`
+ """
+ if self._tokenizer is None:
+ raise ValueError(
+ "Tokenizer not provided. Provide a tokenizer or set `use_default_params` to True"
+ )
+ vocab_size = self._tokenizer.vocab_size
+ # `np.bincount` doesn't return a consistent shape, we need to ensure minlength
+ bincount = partial(np.bincount, minlength=vocab_size)
+ # Bincount returns element count
+ # e.g. [0, 1, 1, 3, 1] => [1, 3, 0, 1]
+ tf = np.apply_along_axis(bincount, 1, docs)
+
+ # only change the values of existing non-zero terms, so that the operation doesn't change the sparsity of the matrix, and we don't get a warning that our operation is expensive
+ # We use `0` as a padding, so ignore it's term frequency
+ tf[:, 0] *= 0 # type: ignore
+ return tf
+
+ def _df(self, queries: np.ndarray) -> np.ndarray:
+ """Returns the amount of times each token in the query appears in trained corpus
+
+ This is done in a faster, vectorized way, instead of looping through each query
+
+ :param queries: 2D shaped array of each query token ids
+ :type queries: :class:`numpy.ndarray`
+ :return: Matrix where value @ (m, n) represents how many times token id `n` in query `m` appears in the trained corpus
+ :rtype: :class:`numpy.ndarray`
+ """
+ if self._documents_containing_word is None:
+ raise ValueError(
+ "Encoder not fitted. `BM25Encoder.fit` a corpus, or `BM25Encoder.load` a pretrained encoder."
+ )
+ if self._tokenizer is None:
+ raise ValueError(
+ "Tokenizer not provided. Provide a tokenizer or set `use_default_params` to True"
+ )
+ n = queries.shape[0]
+ # Create row indices -> [[0], [1], [2], ...] (n, 1) shaped matrix
+ row_indices = np.arange(n)[:, None]
+
+ # Create a (len(queries), vocab_size) mask
+ mask = np.zeros(
+ (n, self._tokenizer.vocab_size), dtype=bool
+ ) # (n_queries, vocab_size)
+ # Fill the mask with `True` only for the token ids that appear in each query
+ mask[row_indices, queries] = True
+
+ # Repeat the `(1, vocab_size)` shaped _documents_containing_word matrix `len(queries)` times -> (len(queries), vocab_size) shape
+ # Multiply the repeated matrix with the mask to result in the document frequency of each token in each query
+ query_df = mask * self._documents_containing_word
+ return query_df
+
+ def encode_queries(self, queries: list[str]) -> list[SparseEmbedding]:
+ """Returns BM25 scores for queries using precomputed corpus scores.
+
+ :param queries: List of queries to encode
+ :type queries: list
+ :return: BM25 scores for each query against the corpus
+ :rtype: np.ndarray
+ """
+ if (
+ self.corpus_size is None
+ or self._avg_doc_len is None
+ or self._documents_containing_word is None
+ ):
+ raise ValueError(
+ "Encoder not fitted. Please `.fit` the model on a provided corpus or load a pretrained encoder"
+ )
+ if not self._tokenizer:
+ raise ValueError(
+ "BM25 encoder not initialized. Provide a tokenizer or set `use_default_params` to True"
+ )
+ if queries == []:
+ raise ValueError("No documents provided for encoding")
+
+ # Convert queries to token counts
+ queries_ids = self._tokenizer.tokenize(queries)
+ df = self._df(queries_ids) # (batch_size, vocab_size)
+ N = self.corpus_size
+ df = df + np.where(df > 0, 0.5, 0)
+ idf = np.divide(N + 1, df, out=np.zeros_like(df), where=df != 0)
+ idf = np.log(
+ idf, out=np.zeros_like(df), where=df != 0
+ ) # (batch_size, vocab_size)
+ idf_norm = np.divide(
+ idf, idf.sum(axis=1)[:, np.newaxis], out=np.zeros_like(idf), where=idf != 0
+ )
+
+ return self._array_to_sparse_embeddings(idf_norm)
+
+ def encode_documents(
+ self,
+ documents: list[str],
+ batch_size: int | None = None,
+ ) -> list[SparseEmbedding]:
+ r"""Returns document term frequency normed by itself & average trained corpus length
+ (This is the right-hand side of the BM25 equation, which gets matmul-ed with the query IDF component)
+
+ LaTeX: \frac{f(d_i, D)}{f(d_i, D) + k_1 \times (1 - b + b \times \frac{|D|}{avgdl})}
+ where:
+ f(d_i, D) is frequency of term `d_i ∈ D`
+ |D| is the document length
+ avgdl is average document length in trained corpus
+
+ :param documents: List of queries to encode
+ :type documents: list
+ :return: Encoded queries (as either sparse or dict)
+ :rtype: :class:`scipy.sparse.csr_matrix`, list[dict]
+ """
+ if (
+ self.corpus_size is None
+ or self._avg_doc_len is None
+ or self._documents_containing_word is None
+ ):
+ raise ValueError(
+ "Encoder not fitted. Please `.fit` the model on a provided corpus or load a pretrained encoder"
+ )
+ if not self._tokenizer:
+ raise ValueError(
+ "BM25 encoder not initialized. Provide a tokenizer or set `use_default_params` to True"
+ )
+ if documents == []:
+ raise ValueError("No documents provided for encoding")
+ batch_size = batch_size or len(documents)
+ queries_ids = self._tokenizer.tokenize(documents, pad=True)
+ tf = self._tf(queries_ids) # (batch_size, vocab_size)
+ tf_sum = tf.sum(axis=1) # (batch_size, 1)
+ tf_normed = tf / (
+ self.k1
+ * (1.0 - self.b * self.b * (tf_sum[:, np.newaxis] / self._avg_doc_len))
+ + tf
+ ) # (batch_size, vocab_size)
+
+ return self._array_to_sparse_embeddings(tf_normed)
+
+ def model(self, docs: List[str]) -> list[SparseEmbedding]:
+ """Encode documents using BM25, with different encoding for queries vs documents to be indexed.
+
+ :param docs: List of documents to encode
+ :param is_query: If True, use query encoding, else use document encoding
+ :return: List of sparse embeddings
+ """
+ if not self._tokenizer:
+ raise ValueError(
+ "Encoder not fitted. `BM25.index` a corpus, or `BM25.load` a pretrained encoder."
+ )
+ if (
+ self.corpus_size is None
+ or self._avg_doc_len is None
+ or self._documents_containing_word is None
+ ):
+ raise ValueError(
+ "Encoder not fitted. Please `.fit` the model on a provided corpus or load a pretrained encoder"
+ )
+
+ return self.encode_queries(docs)
+
+ async def aencode_queries(self, docs: List[str]) -> List[SparseEmbedding]:
+ # While this is a CPU-bound operation, and doesn't benefit from asyncio
+ # we provide this method to abide by the `SparseEncoder` superclass
+ return await asyncio.to_thread(lambda: self.encode_queries(docs))
+
+ async def aencode_documents(self, docs: List[str]) -> List[SparseEmbedding]:
+ # While this is a CPU-bound operation, and doesn't benefit from asyncio
+ # we provide this method to abide by the `SparseEncoder` superclass
+ return await asyncio.to_thread(lambda: self.encode_documents(docs))
+
+ def __call__(self, docs: List[str]) -> List[SparseEmbedding]:
+ return self.encode_queries(docs)
- embeds = []
- for i, output in enumerate(sparse_dicts):
- if isinstance(output, dict):
- embeds.append(SparseEmbedding.from_pinecone_dict(output))
- return embeds
+ async def acall(self, docs: List[Any]) -> List[SparseEmbedding]:
+ return await asyncio.to_thread(lambda: self.__call__(docs))
diff --git a/semantic_router/encoders/clip.py b/semantic_router/encoders/clip.py
index 065ff115bede8d4597b57a62857627ea8099525f..5ad351ef06cebb76e503fa15e1268152af910786 100644
--- a/semantic_router/encoders/clip.py
+++ b/semantic_router/encoders/clip.py
@@ -1,12 +1,37 @@
-from typing import Any, List, Optional
+from typing import Any, Dict, List, Optional
import numpy as np
from pydantic import PrivateAttr
-from typing import Dict
+
from semantic_router.encoders import DenseEncoder
class CLIPEncoder(DenseEncoder):
+ """Multi-modal dense encoder for text and images using CLIP-type models via
+ HuggingFace.
+
+ :param name: The name of the model to use.
+ :type name: str
+ :param tokenizer_kwargs: Keyword arguments for the tokenizer.
+ :type tokenizer_kwargs: Dict
+ :param processor_kwargs: Keyword arguments for the processor.
+ :type processor_kwargs: Dict
+ :param model_kwargs: Keyword arguments for the model.
+ :type model_kwargs: Dict
+ :param device: The device to use for the model.
+ :type device: Optional[str]
+ :param _tokenizer: The tokenizer for the model.
+ :type _tokenizer: Any
+ :param _processor: The processor for the model.
+ :type _processor: Any
+ :param _model: The model.
+ :type _model: Any
+ :param _torch: The torch library.
+ :type _torch: Any
+ :param _Image: The PIL library.
+ :type _Image: Any
+ """
+
name: str = "openai/clip-vit-base-patch16"
type: str = "huggingface"
tokenizer_kwargs: Dict = {}
@@ -20,6 +45,11 @@ class CLIPEncoder(DenseEncoder):
_Image: Any = PrivateAttr()
def __init__(self, **data):
+ """Initialize the CLIPEncoder.
+
+ :param **data: Keyword arguments for the encoder.
+ :type **data: Dict
+ """
if data.get("score_threshold") is None:
data["score_threshold"] = 0.2
super().__init__(**data)
@@ -31,6 +61,17 @@ class CLIPEncoder(DenseEncoder):
batch_size: int = 32,
normalize_embeddings: bool = True,
) -> List[List[float]]:
+ """Encode a list of documents. Can handle both text and images.
+
+ :param docs: The documents to encode.
+ :type docs: List[Any]
+ :param batch_size: The batch size for the encoding.
+ :type batch_size: int
+ :param normalize_embeddings: Whether to normalize the embeddings.
+ :type normalize_embeddings: bool
+ :returns: A list of embeddings.
+ :rtype: List[List[float]]
+ """
all_embeddings = []
if isinstance(docs[0], str):
text = True
@@ -50,6 +91,11 @@ class CLIPEncoder(DenseEncoder):
return all_embeddings
def _initialize_hf_model(self):
+ """Initialize the HuggingFace model.
+
+ :returns: A tuple of the tokenizer, processor, and model.
+ :rtype: Tuple[Any, Any, Any]
+ """
try:
from transformers import CLIPModel, CLIPProcessor, CLIPTokenizerFast
except ImportError:
@@ -92,6 +138,11 @@ class CLIPEncoder(DenseEncoder):
return tokenizer, processor, model
def _get_device(self) -> str:
+ """Get the device to use for the model. Returns either cuda, mps, or cpu.
+
+ :returns: The device to use for the model.
+ :rtype: str
+ """
if self.device:
device = self.device
elif self._torch.cuda.is_available():
@@ -103,6 +154,13 @@ class CLIPEncoder(DenseEncoder):
return device
def _encode_text(self, docs: List[str]) -> Any:
+ """Encode a list of text documents.
+
+ :param docs: The documents to encode.
+ :type docs: List[str]
+ :returns: The embeddings for the documents.
+ :rtype: Any
+ """
inputs = self._tokenizer(
docs, return_tensors="pt", padding=True, truncation=True
).to(self.device)
@@ -112,6 +170,13 @@ class CLIPEncoder(DenseEncoder):
return embeds
def _encode_image(self, images: List[Any]) -> Any:
+ """Encode a list of image documents.
+
+ :param images: The images to encode.
+ :type images: List[Any]
+ :returns: The embeddings for the images.
+ :rtype: Any
+ """
rgb_images = [self._ensure_rgb(img) for img in images]
inputs = self._processor(text=None, images=rgb_images, return_tensors="pt")[
"pixel_values"
@@ -122,6 +187,13 @@ class CLIPEncoder(DenseEncoder):
return embeds
def _ensure_rgb(self, img: Any):
+ """Ensure the image is in RGB format.
+
+ :param img: The image to ensure is in RGB format.
+ :type img: Any
+ :returns: The image in RGB format.
+ :rtype: Any
+ """
rgbimg = self._Image.new("RGB", img.size)
rgbimg.paste(img)
return rgbimg
diff --git a/semantic_router/encoders/cohere.py b/semantic_router/encoders/cohere.py
index e919bae110769f87f3c76ff58104ef14a30b34a7..dbfb9b1527d278add73e125a24fdc9fa72eb6cff 100644
--- a/semantic_router/encoders/cohere.py
+++ b/semantic_router/encoders/cohere.py
@@ -4,39 +4,54 @@ from typing import Any, List, Optional
from pydantic import PrivateAttr
from semantic_router.encoders import DenseEncoder
+from semantic_router.encoders.base import AsymmetricDenseMixin
from semantic_router.utils.defaults import EncoderDefault
-class CohereEncoder(DenseEncoder):
+class CohereEncoder(DenseEncoder, AsymmetricDenseMixin):
+ """Dense encoder that uses Cohere API to embed documents. Supports text only. Requires
+ a Cohere API key from https://dashboard.cohere.com/api-keys.
+ """
+
_client: Any = PrivateAttr()
+ _async_client: Any = PrivateAttr()
_embed_type: Any = PrivateAttr()
type: str = "cohere"
- input_type: Optional[str] = "search_query"
def __init__(
self,
name: Optional[str] = None,
cohere_api_key: Optional[str] = None,
score_threshold: float = 0.3,
- input_type: Optional[str] = "search_query",
):
+ """Initialize the Cohere encoder.
+
+ :param name: The name of the embedding model to use.
+ :type name: str
+ :param cohere_api_key: The API key for the Cohere client, can also
+ be set via the COHERE_API_KEY environment variable.
+ :type cohere_api_key: str
+ :param score_threshold: The threshold for the score of the embedding.
+ :type score_threshold: float
+ :param input_type: The type of input to embed.
+ :type input_type: str
+ """
if name is None:
name = EncoderDefault.COHERE.value["embedding_model"]
super().__init__(
name=name,
score_threshold=score_threshold,
- input_type=input_type, # type: ignore
)
- self.input_type = input_type
- self._client = self._initialize_client(cohere_api_key)
+ self._client, self._async_client = self._initialize_client(cohere_api_key)
def _initialize_client(self, cohere_api_key: Optional[str] = None):
"""Initializes the Cohere client.
:param cohere_api_key: The API key for the Cohere client, can also
- be set via the COHERE_API_KEY environment variable.
-
+ be set via the COHERE_API_KEY environment variable.
+ :type cohere_api_key: str
:return: An instance of the Cohere client.
+ :rtype: cohere.Client
"""
try:
import cohere
@@ -54,20 +69,85 @@ class CohereEncoder(DenseEncoder):
raise ValueError("Cohere API key cannot be 'None'.")
try:
client = cohere.Client(cohere_api_key)
+ async_client = cohere.AsyncClient(cohere_api_key)
except Exception as e:
raise ValueError(
f"Cohere API client failed to initialize. Error: {e}"
) from e
- return client
+ return client, async_client
def __call__(self, docs: List[str]) -> List[List[float]]:
+ """Embed a list of documents. Supports text only.
+
+ :param docs: The documents to embed.
+ :type docs: List[str]
+ :return: The vector embeddings of the documents.
+ :rtype: List[List[float]]
+ """
+ return self.encode_queries(docs)
+
+ async def acall(self, docs: List[Any]) -> List[List[float]]:
+ return await self.aencode_queries(docs)
+
+ def encode_queries(self, docs: List[str]) -> List[List[float]]:
+ if self._client is None:
+ raise ValueError("Cohere client is not initialized.")
+
+ try:
+ embeds = self._client.embed(
+ texts=docs, input_type="search_query", model=self.name
+ )
+ if isinstance(embeds, self._embed_type):
+ raise NotImplementedError(
+ "Handling of EmbedByTypeResponseEmbeddings is not implemented."
+ )
+ else:
+ return embeds.embeddings
+ except Exception as e:
+ raise ValueError(f"Cohere API call failed. Error: {e}") from e
+
+ def encode_documents(self, docs: List[str]) -> List[List[float]]:
if self._client is None:
raise ValueError("Cohere client is not initialized.")
+
try:
embeds = self._client.embed(
- texts=docs, input_type=self.input_type, model=self.name
+ texts=docs, input_type="search_document", model=self.name
+ )
+ if isinstance(embeds, self._embed_type):
+ raise NotImplementedError(
+ "Handling of EmbedByTypeResponseEmbeddings is not implemented."
+ )
+ else:
+ return embeds.embeddings
+ except Exception as e:
+ raise ValueError(f"Cohere API call failed. Error: {e}") from e
+
+ async def aencode_queries(self, docs: List[str]) -> List[List[float]]:
+ if self._async_client is None:
+ raise ValueError("Cohere client is not initialized.")
+
+ try:
+ embeds = await self._async_client.embed(
+ texts=docs, input_type="search_query", model=self.name
+ )
+ if isinstance(embeds, self._embed_type):
+ raise NotImplementedError(
+ "Handling of EmbedByTypeResponseEmbeddings is not implemented."
+ )
+ else:
+ return embeds.embeddings
+ except Exception as e:
+ raise ValueError(f"Cohere API call failed. Error: {e}") from e
+
+ async def aencode_documents(self, docs: List[str]) -> List[List[float]]:
+ if self._async_client is None:
+ raise ValueError("Cohere client is not initialized.")
+
+ try:
+ embeds = await self._async_client.embed(
+ texts=docs, input_type="search_document", model=self.name
)
- # Check for unsupported type.
if isinstance(embeds, self._embed_type):
raise NotImplementedError(
"Handling of EmbedByTypeResponseEmbeddings is not implemented."
diff --git a/semantic_router/encoders/encode_input_type.py b/semantic_router/encoders/encode_input_type.py
new file mode 100644
index 0000000000000000000000000000000000000000..1cfe9c8719e74f80627ca04a3367b56c960973cc
--- /dev/null
+++ b/semantic_router/encoders/encode_input_type.py
@@ -0,0 +1,3 @@
+from typing import Literal
+
+EncodeInputType = Literal["queries", "documents"]
diff --git a/semantic_router/encoders/fastembed.py b/semantic_router/encoders/fastembed.py
index 2c9977955994f100366d67ef75a419f83fd55bce..3d720fa64c0515d3bce89e29c3b3735c184c0f2a 100644
--- a/semantic_router/encoders/fastembed.py
+++ b/semantic_router/encoders/fastembed.py
@@ -7,6 +7,15 @@ from semantic_router.encoders import DenseEncoder
class FastEmbedEncoder(DenseEncoder):
+ """Dense encoder that uses local FastEmbed to embed documents. Supports text only.
+ Requires the fastembed package which can be installed with `pip install 'semantic-router[fastembed]'`
+
+ :param name: The name of the embedding model to use.
+ :param max_length: The maximum length of the input text.
+ :param cache_dir: The directory to cache the embedding model.
+ :param threads: The number of threads to use for the embedding.
+ """
+
type: str = "fastembed"
name: str = "BAAI/bge-small-en-v1.5"
max_length: int = 512
@@ -14,13 +23,18 @@ class FastEmbedEncoder(DenseEncoder):
threads: Optional[int] = None
_client: Any = PrivateAttr()
- def __init__(
- self, score_threshold: float = 0.5, **data
- ): # TODO default score_threshold not thoroughly tested, should optimize
+ def __init__(self, score_threshold: float = 0.5, **data):
+ """Initialize the FastEmbed encoder.
+
+ :param score_threshold: The threshold for the score of the embedding.
+ :type score_threshold: float
+ """
+ # TODO default score_threshold not thoroughly tested, should optimize
super().__init__(score_threshold=score_threshold, **data)
self._client = self._initialize_client()
def _initialize_client(self):
+ """Initialize the FastEmbed library. Requires the fastembed package."""
try:
from fastembed import TextEmbedding
except ImportError:
@@ -43,6 +57,14 @@ class FastEmbedEncoder(DenseEncoder):
return embedding
def __call__(self, docs: List[str]) -> List[List[float]]:
+ """Embed a list of documents. Supports text only.
+
+ :param docs: The documents to embed.
+ :type docs: List[str]
+ :raise ValueError: If the embedding fails.
+ :return: The vector embeddings of the documents.
+ :rtype: List[List[float]]
+ """
try:
embeds: List[np.ndarray] = list(self._client.embed(docs))
embeddings: List[List[float]] = [e.tolist() for e in embeds]
diff --git a/semantic_router/encoders/google.py b/semantic_router/encoders/google.py
index 5d50a0e14ea573269202e04086ed21f44997e310..5284a747eb785f2c1abe7ba6ffadf24a8030f932 100644
--- a/semantic_router/encoders/google.py
+++ b/semantic_router/encoders/google.py
@@ -1,21 +1,3 @@
-"""
-This module provides the GoogleEncoder class for generating embeddings using Google's AI Platform.
-
-The GoogleEncoder class is a subclass of DenseEncoder and utilizes the TextEmbeddingModel from the
-Google AI Platform to generate embeddings for given documents. It requires a Google Cloud project ID
-and supports customization of the pre-trained model, score threshold, location, and API endpoint.
-
-Example usage:
-
- from semantic_router.encoders.google_encoder import GoogleEncoder
-
- encoder = GoogleEncoder(project_id="your-project-id")
- embeddings = encoder(["document1", "document2"])
-
-Classes:
- GoogleEncoder: A class for generating embeddings using Google's AI Platform.
-"""
-
import os
from typing import Any, List, Optional
@@ -26,6 +8,19 @@ from semantic_router.utils.defaults import EncoderDefault
class GoogleEncoder(DenseEncoder):
"""GoogleEncoder class for generating embeddings using Google's AI Platform.
+ The GoogleEncoder class is a subclass of DenseEncoder and utilizes the TextEmbeddingModel from the
+ Google AI Platform to generate embeddings for given documents. It requires a Google Cloud project ID
+ and supports customization of the pre-trained model, score threshold, location, and API endpoint.
+
+ Example usage:
+
+ ```python
+ from semantic_router.encoders.google_encoder import GoogleEncoder
+
+ encoder = GoogleEncoder(project_id="your-project-id")
+ embeddings = encoder(["document1", "document2"])
+ ```
+
Attributes:
client: An instance of the TextEmbeddingModel client.
type: The type of the encoder, which is "google".
@@ -44,23 +39,25 @@ class GoogleEncoder(DenseEncoder):
):
"""Initializes the GoogleEncoder.
- Args:
- model_name: The name of the pre-trained model to use for embedding.
- If not provided, the default model specified in EncoderDefault will
- be used.
- score_threshold: The threshold for similarity scores.
- project_id: The Google Cloud project ID.
- If not provided, it will be retrieved from the GOOGLE_PROJECT_ID
- environment variable.
- location: The location of the AI Platform resources.
- If not provided, it will be retrieved from the GOOGLE_LOCATION
- environment variable, defaulting to "us-central1".
- api_endpoint: The API endpoint for the AI Platform.
- If not provided, it will be retrieved from the GOOGLE_API_ENDPOINT
- environment variable.
-
- Raises:
- ValueError: If the Google Project ID is not provided or if the AI Platform
+ :param model_name: The name of the pre-trained model to use for embedding.
+ If not provided, the default model specified in EncoderDefault will
+ be used.
+ :type model_name: str
+ :param score_threshold: The threshold for similarity scores.
+ :type score_threshold: float
+ :param project_id: The Google Cloud project ID.
+ If not provided, it will be retrieved from the GOOGLE_PROJECT_ID
+ environment variable.
+ :type project_id: str
+ :param location: The location of the AI Platform resources.
+ If not provided, it will be retrieved from the GOOGLE_LOCATION
+ environment variable, defaulting to "us-central1".
+ :type location: str
+ :param api_endpoint: The API endpoint for the AI Platform.
+ If not provided, it will be retrieved from the GOOGLE_API_ENDPOINT
+ environment variable.
+ :type api_endpoint: str
+ :raise ValueError: If the Google Project ID is not provided or if the AI Platform
client fails to initialize.
"""
if name is None:
@@ -73,18 +70,17 @@ class GoogleEncoder(DenseEncoder):
def _initialize_client(self, project_id, location, api_endpoint):
"""Initializes the Google AI Platform client.
- Args:
- project_id: The Google Cloud project ID.
- location: The location of the AI Platform resources.
- api_endpoint: The API endpoint for the AI Platform.
-
- Returns:
- An instance of the TextEmbeddingModel client.
-
- Raises:
- ImportError: If the required Google Cloud or Vertex AI libraries are not
+ :param project_id: The Google Cloud project ID.
+ :type project_id: str
+ :param location: The location of the AI Platform resources.
+ :type location: str
+ :param api_endpoint: The API endpoint for the AI Platform.
+ :type api_endpoint: str
+ :return: An instance of the TextEmbeddingModel client.
+ :rtype: TextEmbeddingModel
+ :raise ImportError: If the required Google Cloud or Vertex AI libraries are not
installed.
- ValueError: If the Google Project ID is not provided or if the AI Platform
+ :raise ValueError: If the Google Project ID is not provided or if the AI Platform
client fails to initialize.
"""
try:
@@ -119,15 +115,12 @@ class GoogleEncoder(DenseEncoder):
def __call__(self, docs: List[str]) -> List[List[float]]:
"""Generates embeddings for the given documents.
- Args:
- docs: A list of strings representing the documents to embed.
-
- Returns:
- A list of lists, where each inner list contains the embedding values for a
+ :param docs: A list of strings representing the documents to embed.
+ :type docs: List[str]
+ :return: A list of lists, where each inner list contains the embedding values for a
document.
-
- Raises:
- ValueError: If the Google AI Platform client is not initialized or if the
+ :rtype: List[List[float]]
+ :raise ValueError: If the Google AI Platform client is not initialized or if the
API call fails.
"""
if self.client is None:
diff --git a/semantic_router/encoders/huggingface.py b/semantic_router/encoders/huggingface.py
index f553a082f663bb6dc697c9ecd80eb581fb21be73..3cd973b3a64a08da1a68780e9f042e6f9f8313cb 100644
--- a/semantic_router/encoders/huggingface.py
+++ b/semantic_router/encoders/huggingface.py
@@ -1,9 +1,9 @@
"""
This module provides the HFEndpointEncoder class to embeddings models using Huggingface's endpoint.
-The HFEndpointEncoder class is a subclass of DenseEncoder and utilizes a specified Huggingface
-endpoint to generate embeddings for given documents. It requires the URL of the Huggingface
-API endpoint and an API key for authentication. The class supports customization of the score
+The HFEndpointEncoder class is a subclass of DenseEncoder and utilizes a specified Huggingface
+endpoint to generate embeddings for given documents. It requires the URL of the Huggingface
+API endpoint and an API key for authentication. The class supports customization of the score
threshold for filtering or processing the embeddings.
Example usage:
@@ -20,18 +20,39 @@ Classes:
HFEndpointEncoder: A class for generating embeddings using a Huggingface endpoint.
"""
-import requests
-import time
import os
-from typing import Any, List, Optional, Dict
+import time
+from typing import Any, Dict, List, Optional
+import requests
from pydantic import PrivateAttr
from semantic_router.encoders import DenseEncoder
from semantic_router.utils.logger import logger
+# TODO: this should support local models, and we should have another class for remote
+# inference endpoint models
+
+
class HuggingFaceEncoder(DenseEncoder):
+ """HuggingFace encoder class for local embedding models. Models can be trained and
+ loaded from private repositories, or from the Huggingface Hub. The class supports
+ customization of the score threshold for filtering or processing the embeddings.
+
+ Example usage:
+
+ ```python
+ from semantic_router.encoders import HuggingFaceEncoder
+
+ encoder = HuggingFaceEncoder(
+ name="sentence-transformers/all-MiniLM-L6-v2",
+ device="cuda"
+ )
+ embeddings = encoder(["document1", "document2"])
+ ```
+ """
+
name: str = "sentence-transformers/all-MiniLM-L6-v2"
type: str = "huggingface"
tokenizer_kwargs: Dict = {}
@@ -50,7 +71,7 @@ class HuggingFaceEncoder(DenseEncoder):
def _initialize_hf_model(self):
try:
from transformers import AutoModel, AutoTokenizer
- except ImportError:
+ except (ImportError, RuntimeError):
raise ImportError(
"Please install transformers to use HuggingFaceEncoder. "
"You can install it with: "
@@ -59,7 +80,7 @@ class HuggingFaceEncoder(DenseEncoder):
try:
import torch
- except ImportError:
+ except RuntimeError:
raise ImportError(
"Please install Pytorch to use HuggingFaceEncoder. "
"You can install it with: "
@@ -92,6 +113,12 @@ class HuggingFaceEncoder(DenseEncoder):
normalize_embeddings: bool = True,
pooling_strategy: str = "mean",
) -> List[List[float]]:
+ """Encode a list of documents into embeddings using the local Hugging Face model.
+
+ :param docs: A list of documents to encode.
+ :type docs: List[str]
+ :param batch_size: The batch size for encoding.
+ """
all_embeddings = []
for i in range(0, len(docs), batch_size):
batch_docs = docs[i : i + batch_size]
@@ -124,6 +151,12 @@ class HuggingFaceEncoder(DenseEncoder):
return all_embeddings
def _mean_pooling(self, model_output, attention_mask):
+ """Perform mean pooling on the token embeddings.
+
+ :param model_output: The output of the model.
+ :type model_output: torch.Tensor
+ :param attention_mask: The attention mask.
+ """
token_embeddings = model_output[0]
input_mask_expanded = (
attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
@@ -133,6 +166,12 @@ class HuggingFaceEncoder(DenseEncoder):
) / self._torch.clamp(input_mask_expanded.sum(1), min=1e-9)
def _max_pooling(self, model_output, attention_mask):
+ """Perform max pooling on the token embeddings.
+
+ :param model_output: The output of the model.
+ :type model_output: torch.Tensor
+ :param attention_mask: The attention mask.
+ """
token_embeddings = model_output[0]
input_mask_expanded = (
attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
@@ -142,13 +181,24 @@ class HuggingFaceEncoder(DenseEncoder):
class HFEndpointEncoder(DenseEncoder):
- """
- A class to encode documents using a Hugging Face transformer model endpoint.
+ """HFEndpointEncoder class to embeddings models using Huggingface's inference endpoints.
+
+ The HFEndpointEncoder class is a subclass of DenseEncoder and utilizes a specified
+ Huggingface endpoint to generate embeddings for given documents. It requires the URL
+ of the Huggingface API endpoint and an API key for authentication. The class supports
+ customization of the score threshold for filtering or processing the embeddings.
+
+ Example usage:
+
+ ```python
+ from semantic_router.encoders import HFEndpointEncoder
- Attributes:
- huggingface_url (str): The URL of the Hugging Face API endpoint.
- huggingface_api_key (str): The API key for authenticating with the Hugging Face API.
- score_threshold (float): A threshold value used for filtering or processing the embeddings.
+ encoder = HFEndpointEncoder(
+ huggingface_url="https://api-inference.huggingface.co/models/BAAI/bge-large-en-v1.5",
+ huggingface_api_key="your-hugging-face-api-key"
+ )
+ embeddings = encoder(["document1", "document2"])
+ ```
"""
name: str = "hugging_face_custom_endpoint"
@@ -162,21 +212,17 @@ class HFEndpointEncoder(DenseEncoder):
huggingface_api_key: Optional[str] = None,
score_threshold: float = 0.8,
):
- """
- Initializes the HFEndpointEncoder with the specified parameters.
-
- Args:
- name (str, optional): The name of the encoder. Defaults to
- "hugging_face_custom_endpoint".
- huggingface_url (str, optional): The URL of the Hugging Face API endpoint.
- Cannot be None.
- huggingface_api_key (str, optional): The API key for the Hugging Face API.
- Cannot be None.
- score_threshold (float, optional): A threshold for processing the embeddings.
- Defaults to 0.8.
-
- Raises:
- ValueError: If either `huggingface_url` or `huggingface_api_key` is None.
+ """Initializes the HFEndpointEncoder with the specified parameters.
+
+ :param name: The name of the encoder.
+ :type name: str
+ :param huggingface_url: The URL of the Hugging Face API endpoint.
+ :type huggingface_url: str
+ :param huggingface_api_key: The API key for the Hugging Face API.
+ :type huggingface_api_key: str
+ :param score_threshold: A threshold for processing the embeddings.
+ :type score_threshold: float
+ :raise ValueError: If either `huggingface_url` or `huggingface_api_key` is None.
"""
huggingface_url = huggingface_url or os.getenv("HF_API_URL")
huggingface_api_key = huggingface_api_key or os.getenv("HF_API_KEY")
@@ -201,17 +247,13 @@ class HFEndpointEncoder(DenseEncoder):
) from e
def __call__(self, docs: List[str]) -> List[List[float]]:
- """
- Encodes a list of documents into embeddings using the Hugging Face API.
-
- Args:
- docs (List[str]): A list of documents to encode.
+ """Encodes a list of documents into embeddings using the Hugging Face API.
- Returns:
- List[List[float]]: A list of embeddings for the given documents.
-
- Raises:
- ValueError: If no embeddings are returned for a document.
+ :param docs: A list of documents to encode.
+ :type docs: List[str]
+ :return: A list of embeddings for the given documents.
+ :rtype: List[List[float]]
+ :raise ValueError: If no embeddings are returned for a document.
"""
embeddings = []
for d in docs:
@@ -228,17 +270,13 @@ class HFEndpointEncoder(DenseEncoder):
return embeddings
def query(self, payload, max_retries=3, retry_interval=5):
- """
- Sends a query to the Hugging Face API and returns the response.
-
- Args:
- payload (dict): The payload to send in the request.
-
- Returns:
- dict: The response from the Hugging Face API.
+ """Sends a query to the Hugging Face API and returns the response.
- Raises:
- ValueError: If the query fails or the response status is not 200.
+ :param payload: The payload to send in the request.
+ :type payload: dict
+ :return: The response from the Hugging Face API.
+ :rtype: dict
+ :raise ValueError: If the query fails or the response status is not 200.
"""
headers = {
"Accept": "application/json",
diff --git a/semantic_router/encoders/mistral.py b/semantic_router/encoders/mistral.py
index 6c3a2f5ed64f2a2c581b1aa8241fb81930c07c42..f1267a7b27e6226124d8b372555e259cdff3325d 100644
--- a/semantic_router/encoders/mistral.py
+++ b/semantic_router/encoders/mistral.py
@@ -11,7 +11,8 @@ from semantic_router.utils.defaults import EncoderDefault
class MistralEncoder(DenseEncoder):
- """Class to encode text using MistralAI"""
+ """Class to encode text using MistralAI. Requires a MistralAI API key from
+ https://console.mistral.ai/api-keys/"""
_client: Any = PrivateAttr()
_mistralai: Any = PrivateAttr()
@@ -23,12 +24,27 @@ class MistralEncoder(DenseEncoder):
mistralai_api_key: Optional[str] = None,
score_threshold: float = 0.82,
):
+ """Initialize the MistralEncoder.
+
+ :param name: The name of the embedding model to use.
+ :type name: str
+ :param mistralai_api_key: The MistralAI API key.
+ :type mistralai_api_key: str
+ :param score_threshold: The score threshold for the embeddings.
+ """
if name is None:
name = EncoderDefault.MISTRAL.value["embedding_model"]
super().__init__(name=name, score_threshold=score_threshold)
self._client, self._mistralai = self._initialize_client(mistralai_api_key)
def _initialize_client(self, api_key):
+ """Initialize the MistralAI client.
+
+ :param api_key: The MistralAI API key.
+ :type api_key: str
+ :return: The MistralAI client.
+ :rtype: MistralClient
+ """
try:
import mistralai
from mistralai.client import MistralClient
@@ -49,6 +65,13 @@ class MistralEncoder(DenseEncoder):
return client, mistralai
def __call__(self, docs: List[str]) -> List[List[float]]:
+ """Encode a list of documents into embeddings using MistralAI.
+
+ :param docs: The documents to encode.
+ :type docs: List[str]
+ :return: The embeddings for the documents.
+ :rtype: List[List[float]]
+ """
if self._client is None:
raise ValueError("Mistral client not initialized")
embeds = None
diff --git a/semantic_router/encoders/openai.py b/semantic_router/encoders/openai.py
index 1ac5e56e4e519ffb8f71ba18f7fdcb86d3168f23..fc248b3467a45660550ac763801821ce269fad80 100644
--- a/semantic_router/encoders/openai.py
+++ b/semantic_router/encoders/openai.py
@@ -1,21 +1,20 @@
-from asyncio import sleep as asleep
import os
+from asyncio import sleep as asleep
from time import sleep
from typing import Any, List, Optional, Union
-from pydantic import PrivateAttr
import openai
+import tiktoken
from openai import OpenAIError
from openai._types import NotGiven
from openai.types import CreateEmbeddingResponse
-import tiktoken
+from pydantic import PrivateAttr
from semantic_router.encoders import DenseEncoder
from semantic_router.schema import EncoderInfo
from semantic_router.utils.defaults import EncoderDefault
from semantic_router.utils.logger import logger
-
model_configs = {
"text-embedding-ada-002": EncoderInfo(
name="text-embedding-ada-002",
@@ -36,6 +35,13 @@ model_configs = {
class OpenAIEncoder(DenseEncoder):
+ """OpenAI encoder class for generating embeddings using OpenAI API.
+
+ The OpenAIEncoder class is a subclass of DenseEncoder and utilizes the OpenAI API
+ to generate embeddings for given documents. It requires an OpenAI API key and
+ supports customization of the score threshold for filtering or processing the embeddings.
+ """
+
_client: Optional[openai.Client] = PrivateAttr(default=None)
_async_client: Optional[openai.AsyncClient] = PrivateAttr(default=None)
dimensions: Union[int, NotGiven] = NotGiven()
@@ -54,6 +60,23 @@ class OpenAIEncoder(DenseEncoder):
dimensions: Union[int, NotGiven] = NotGiven(),
max_retries: int = 3,
):
+ """Initialize the OpenAIEncoder.
+
+ :param name: The name of the embedding model to use.
+ :type name: str
+ :param openai_base_url: The base URL for the OpenAI API.
+ :type openai_base_url: str
+ :param openai_api_key: The OpenAI API key.
+ :type openai_api_key: str
+ :param openai_org_id: The OpenAI organization ID.
+ :type openai_org_id: str
+ :param score_threshold: The score threshold for the embeddings.
+ :type score_threshold: float
+ :param dimensions: The dimensions of the embeddings.
+ :type dimensions: int
+ :param max_retries: The maximum number of retries for the OpenAI API call.
+ :type max_retries: int
+ """
if name is None:
name = EncoderDefault.OPENAI.value["embedding_model"]
if score_threshold is None and name in model_configs:
@@ -147,6 +170,13 @@ class OpenAIEncoder(DenseEncoder):
return embeddings
def _truncate(self, text: str) -> str:
+ """Truncate a document to the token limit.
+
+ :param text: The document to truncate.
+ :type text: str
+ :return: The truncated document.
+ :rtype: str
+ """
# we use encode_ordinary as faster equivalent to encode(text, disallowed_special=())
tokens = self._token_encoder.encode_ordinary(text)
if len(tokens) > self.token_limit:
@@ -160,6 +190,13 @@ class OpenAIEncoder(DenseEncoder):
return text
async def acall(self, docs: List[str], truncate: bool = True) -> List[List[float]]:
+ """Encode a list of text documents into embeddings using OpenAI API asynchronously.
+
+ :param docs: List of text documents to encode.
+ :param truncate: Whether to truncate the documents to token limit. If
+ False and a document exceeds the token limit, an error will be
+ raised.
+ :return: List of embeddings for each document."""
if self._async_client is None:
raise ValueError("OpenAI async client is not initialized.")
embeds = None
diff --git a/semantic_router/encoders/tfidf.py b/semantic_router/encoders/tfidf.py
index 656ee117e54b60a293c0b425452065258bb5db96..ddc0ff0442784ea19c7ad6767589a2831ac50ad6 100644
--- a/semantic_router/encoders/tfidf.py
+++ b/semantic_router/encoders/tfidf.py
@@ -1,3 +1,4 @@
+import asyncio
import string
from collections import Counter
from typing import Dict, List
@@ -5,11 +6,12 @@ from typing import Dict, List
import numpy as np
from semantic_router.encoders import SparseEncoder
+from semantic_router.encoders.base import FittableMixin
from semantic_router.route import Route
from semantic_router.schema import SparseEmbedding
-class TfidfEncoder(SparseEncoder):
+class TfidfEncoder(SparseEncoder, FittableMixin):
idf: np.ndarray = np.array([])
# TODO: add option to use default params like with BM25Encoder
word_index: Dict = {}
@@ -32,6 +34,9 @@ class TfidfEncoder(SparseEncoder):
tfidf = tf * self.idf
return self._array_to_sparse_embeddings(tfidf)
+ async def acall(self, docs: List[str]) -> List[SparseEmbedding]:
+ return await asyncio.to_thread(lambda: self.__call__(docs))
+
def fit(self, routes: List[Route]):
"""Trains the encoder weights on the provided routes.
diff --git a/semantic_router/encoders/vit.py b/semantic_router/encoders/vit.py
index dec768e43d3eff3a8eb6a428ea4e5773a488c9ad..1888e27a66a77fada138ef363bc08d32f68a0d13 100644
--- a/semantic_router/encoders/vit.py
+++ b/semantic_router/encoders/vit.py
@@ -6,6 +6,13 @@ from semantic_router.encoders import DenseEncoder
class VitEncoder(DenseEncoder):
+ """Encoder for Vision Transformer models.
+
+ This class provides functionality to encode images using a Vision Transformer
+ model via Hugging Face. It supports various image processing and model initialization
+ options.
+ """
+
name: str = "google/vit-base-patch16-224"
type: str = "huggingface"
processor_kwargs: Dict = {}
@@ -18,12 +25,22 @@ class VitEncoder(DenseEncoder):
_Image: Any = PrivateAttr()
def __init__(self, **data):
+ """Initialize the VitEncoder.
+
+ :param **data: Additional keyword arguments for the encoder.
+ :type **data: dict
+ """
if data.get("score_threshold") is None:
data["score_threshold"] = 0.5
super().__init__(**data)
self._processor, self._model = self._initialize_hf_model()
def _initialize_hf_model(self):
+ """Initialize the Hugging Face model.
+
+ :return: The processor and model.
+ :rtype: tuple
+ """
try:
from transformers import ViTImageProcessor, ViTModel
except ImportError:
@@ -68,6 +85,11 @@ class VitEncoder(DenseEncoder):
return processor, model
def _get_device(self) -> str:
+ """Get the device to use for the model.
+
+ :return: The device to use for the model.
+ :rtype: str
+ """
if self.device:
device = self.device
elif self._torch.cuda.is_available():
@@ -79,12 +101,26 @@ class VitEncoder(DenseEncoder):
return device
def _process_images(self, images: List[Any]):
+ """Process the images for the model.
+
+ :param images: The images to process.
+ :type images: List[Any]
+ :return: The processed images.
+ :rtype: Any
+ """
rgb_images = [self._ensure_rgb(img) for img in images]
processed_images = self._processor(images=rgb_images, return_tensors="pt")
processed_images = processed_images.to(self.device)
return processed_images
def _ensure_rgb(self, img: Any):
+ """Ensure the image is in RGB format.
+
+ :param img: The image to ensure is in RGB format.
+ :type img: Any
+ :return: The image in RGB format.
+ :rtype: Any
+ """
rgbimg = self._Image.new("RGB", img.size)
rgbimg.paste(img)
return rgbimg
@@ -94,6 +130,15 @@ class VitEncoder(DenseEncoder):
imgs: List[Any],
batch_size: int = 32,
) -> List[List[float]]:
+ """Encode a list of images into embeddings using the Vision Transformer model.
+
+ :param imgs: The images to encode.
+ :type imgs: List[Any]
+ :param batch_size: The batch size for encoding.
+ :type batch_size: int
+ :return: The embeddings for the images.
+ :rtype: List[List[float]]
+ """
all_embeddings = []
for i in range(0, len(imgs), batch_size):
batch_imgs = imgs[i : i + batch_size]
diff --git a/semantic_router/encoders/zure.py b/semantic_router/encoders/zure.py
index fd8594a986ea5911cd78c4761b304f72fb6b4b2e..faab1c90d59336980fa9509620dd9ed2d70502ff 100644
--- a/semantic_router/encoders/zure.py
+++ b/semantic_router/encoders/zure.py
@@ -1,11 +1,11 @@
-from asyncio import sleep as asleep
import os
+from asyncio import sleep as asleep
from time import sleep
from typing import List, Optional, Union
import openai
-from openai._types import NotGiven
from openai import OpenAIError
+from openai._types import NotGiven
from openai.types import CreateEmbeddingResponse
from semantic_router.encoders import DenseEncoder
@@ -14,6 +14,12 @@ from semantic_router.utils.logger import logger
class AzureOpenAIEncoder(DenseEncoder):
+ """Encoder for Azure OpenAI API.
+
+ This class provides functionality to encode text documents using the Azure OpenAI API.
+ It supports customization of the score threshold for filtering or processing the embeddings.
+ """
+
client: Optional[openai.AzureOpenAI] = None
async_client: Optional[openai.AsyncAzureOpenAI] = None
dimensions: Union[int, NotGiven] = NotGiven()
@@ -36,6 +42,25 @@ class AzureOpenAIEncoder(DenseEncoder):
dimensions: Union[int, NotGiven] = NotGiven(),
max_retries: int = 3,
):
+ """Initialize the AzureOpenAIEncoder.
+
+ :param api_key: The API key for the Azure OpenAI API.
+ :type api_key: str
+ :param deployment_name: The name of the deployment to use.
+ :type deployment_name: str
+ :param azure_endpoint: The endpoint for the Azure OpenAI API.
+ :type azure_endpoint: str
+ :param api_version: The version of the API to use.
+ :type api_version: str
+ :param model: The model to use.
+ :type model: str
+ :param score_threshold: The score threshold for the embeddings.
+ :type score_threshold: float
+ :param dimensions: The dimensions of the embeddings.
+ :type dimensions: int
+ :param max_retries: The maximum number of retries for the API call.
+ :type max_retries: int
+ """
name = deployment_name
if name is None:
name = EncoderDefault.AZURE.value["embedding_model"]
@@ -98,6 +123,13 @@ class AzureOpenAIEncoder(DenseEncoder):
) from e
def __call__(self, docs: List[str]) -> List[List[float]]:
+ """Encode a list of documents into embeddings using the Azure OpenAI API.
+
+ :param docs: The documents to encode.
+ :type docs: List[str]
+ :return: The embeddings for the documents.
+ :rtype: List[List[float]]
+ """
if self.client is None:
raise ValueError("Azure OpenAI client is not initialized.")
embeds = None
@@ -136,10 +168,16 @@ class AzureOpenAIEncoder(DenseEncoder):
return embeddings
async def acall(self, docs: List[str]) -> List[List[float]]:
+ """Encode a list of documents into embeddings using the Azure OpenAI API asynchronously.
+
+ :param docs: The documents to encode.
+ :type docs: List[str]
+ :return: The embeddings for the documents.
+ :rtype: List[List[float]]
+ """
if self.async_client is None:
raise ValueError("Azure OpenAI async client is not initialized.")
embeds = None
-
# Exponential backoff
for j in range(self.max_retries + 1):
try:
diff --git a/semantic_router/index/base.py b/semantic_router/index/base.py
index d98ae19e404d49040f08ac828cc252298fa70dcd..3d6a3afad1ea3a7796df9ab7c2297afc314629f1 100644
--- a/semantic_router/index/base.py
+++ b/semantic_router/index/base.py
@@ -1,20 +1,25 @@
import asyncio
-from datetime import datetime
-import time
-from typing import Any, List, Optional, Tuple, Union, Dict
import json
+import time
+from datetime import datetime
+from typing import Any, Dict, List, Optional, Tuple, Union
import numpy as np
from pydantic import BaseModel
-from semantic_router.schema import ConfigParameter, SparseEmbedding, Utterance
from semantic_router.route import Route
+from semantic_router.schema import ConfigParameter, SparseEmbedding, Utterance
from semantic_router.utils.logger import logger
-
RETRY_WAIT_TIME = 2.5
+class IndexConfig(BaseModel):
+ type: str
+ dimensions: int
+ vectors: int
+
+
class BaseIndex(BaseModel):
"""
Base class for indices using Pydantic's BaseModel.
@@ -38,9 +43,21 @@ class BaseIndex(BaseModel):
utterances: List[Any],
function_schemas: Optional[List[Dict[str, Any]]] = None,
metadata_list: List[Dict[str, Any]] = [],
+ **kwargs,
):
"""Add embeddings to the index.
This method should be implemented by subclasses.
+
+ :param embeddings: List of embeddings to add to the index.
+ :type embeddings: List[List[float]]
+ :param routes: List of routes to add to the index.
+ :type routes: List[str]
+ :param utterances: List of utterances to add to the index.
+ :type utterances: List[str]
+ :param function_schemas: List of function schemas to add to the index.
+ :type function_schemas: Optional[List[Dict[str, Any]]]
+ :param metadata_list: List of metadata to add to the index.
+ :type metadata_list: List[Dict[str, Any]]
"""
raise NotImplementedError("This method should be implemented by subclasses.")
@@ -51,9 +68,21 @@ class BaseIndex(BaseModel):
utterances: List[str],
function_schemas: Optional[Optional[List[Dict[str, Any]]]] = None,
metadata_list: List[Dict[str, Any]] = [],
+ **kwargs,
):
"""Add vectors to the index asynchronously.
This method should be implemented by subclasses.
+
+ :param embeddings: List of embeddings to add to the index.
+ :type embeddings: List[List[float]]
+ :param routes: List of routes to add to the index.
+ :type routes: List[str]
+ :param utterances: List of utterances to add to the index.
+ :type utterances: List[str]
+ :param function_schemas: List of function schemas to add to the index.
+ :type function_schemas: Optional[List[Dict[str, Any]]]
+ :param metadata_list: List of metadata to add to the index.
+ :type metadata_list: List[Dict[str, Any]]
"""
logger.warning("Async method not implemented.")
return self.add(
@@ -62,36 +91,47 @@ class BaseIndex(BaseModel):
utterances=utterances,
function_schemas=function_schemas,
metadata_list=metadata_list,
+ **kwargs,
)
- def get_utterances(self) -> List[Utterance]:
+ def get_utterances(self, include_metadata: bool = False) -> List[Utterance]:
"""Gets a list of route and utterance objects currently stored in the
index, including additional metadata.
- :return: A list of tuples, each containing route, utterance, function
- schema and additional metadata.
- :rtype: List[Tuple]
+ :param include_metadata: Whether to include function schemas and metadata in
+ the returned Utterance objects.
+ :type include_metadata: bool
+ :return: A list of Utterance objects.
+ :rtype: List[Utterance]
"""
if self.index is None:
logger.warning("Index is None, could not retrieve utterances.")
return []
- _, metadata = self._get_all(include_metadata=True)
+ _, metadata = self._get_all(include_metadata=True) # include_metadata required
route_tuples = parse_route_info(metadata=metadata)
+ if not include_metadata:
+ # we remove the metadata from the tuples (ie only keep 0, 1 items)
+ route_tuples = [x[:2] for x in route_tuples]
return [Utterance.from_tuple(x) for x in route_tuples]
- async def aget_utterances(self) -> List[Utterance]:
+ async def aget_utterances(self, include_metadata: bool = False) -> List[Utterance]:
"""Gets a list of route and utterance objects currently stored in the
index, including additional metadata.
- :return: A list of tuples, each containing route, utterance, function
- schema and additional metadata.
- :rtype: List[Tuple]
+ :param include_metadata: Whether to include function schemas and metadata in
+ the returned Utterance objects.
+ :type include_metadata: bool
+ :return: A list of Utterance objects.
+ :rtype: List[Utterance]
"""
if self.index is None:
logger.warning("Index is None, could not retrieve utterances.")
return []
_, metadata = await self._async_get_all(include_metadata=True)
route_tuples = parse_route_info(metadata=metadata)
+ if not include_metadata:
+ # we remove the metadata from the tuples (ie only keep 0, 1 items)
+ route_tuples = [x[:2] for x in route_tuples]
return [Utterance.from_tuple(x) for x in route_tuples]
def get_routes(self) -> List[Route]:
@@ -100,7 +140,7 @@ class BaseIndex(BaseModel):
:return: A list of Route objects.
:rtype: List[Route]
"""
- utterances = self.get_utterances()
+ utterances = self.get_utterances(include_metadata=True)
routes_dict: Dict[str, Route] = {}
# first create a dictionary of route names to Route objects
for utt in utterances:
@@ -125,6 +165,9 @@ class BaseIndex(BaseModel):
"""
Remove embeddings in a routes syncing process from the index.
This method should be implemented by subclasses.
+
+ :param routes_to_delete: Dictionary of routes to delete.
+ :type routes_to_delete: dict
"""
raise NotImplementedError("This method should be implemented by subclasses.")
@@ -132,22 +175,50 @@ class BaseIndex(BaseModel):
"""
Remove embeddings in a routes syncing process from the index asynchronously.
This method should be implemented by subclasses.
+
+ :param routes_to_delete: Dictionary of routes to delete.
+ :type routes_to_delete: dict
"""
logger.warning("Async method not implemented.")
return self._remove_and_sync(routes_to_delete=routes_to_delete)
def delete(self, route_name: str):
+ """Deletes route by route name.
+ This method should be implemented by subclasses.
+
+ :param route_name: Name of the route to delete.
+ :type route_name: str
"""
- Deletes route by route name.
+ raise NotImplementedError("This method should be implemented by subclasses.")
+
+ async def adelete(self, route_name: str) -> list[str]:
+ """Asynchronously delete specified route from index if it exists. Returns the IDs
+ of the vectors deleted.
This method should be implemented by subclasses.
+
+ :param route_name: Name of the route to delete.
+ :type route_name: str
+ :return: List of IDs of the vectors deleted.
+ :rtype: list[str]
"""
raise NotImplementedError("This method should be implemented by subclasses.")
- def describe(self) -> Dict:
+ def describe(self) -> IndexConfig:
+ """Returns an IndexConfig object with index details such as type, dimensions,
+ and total vector count.
+ This method should be implemented by subclasses.
+
+ :return: An IndexConfig object.
+ :rtype: IndexConfig
"""
- Returns a dictionary with index details such as type, dimensions, and total
- vector count.
+ raise NotImplementedError("This method should be implemented by subclasses.")
+
+ def is_ready(self) -> bool:
+ """Checks if the index is ready to be used.
This method should be implemented by subclasses.
+
+ :return: True if the index is ready, False otherwise.
+ :rtype: bool
"""
raise NotImplementedError("This method should be implemented by subclasses.")
@@ -158,9 +229,19 @@ class BaseIndex(BaseModel):
route_filter: Optional[List[str]] = None,
sparse_vector: dict[int, float] | SparseEmbedding | None = None,
) -> Tuple[np.ndarray, List[str]]:
- """
- Search the index for the query_vector and return top_k results.
+ """Search the index for the query_vector and return top_k results.
This method should be implemented by subclasses.
+
+ :param vector: The vector to search for.
+ :type vector: np.ndarray
+ :param top_k: The number of results to return.
+ :type top_k: int
+ :param route_filter: The routes to filter the search by.
+ :type route_filter: Optional[List[str]]
+ :param sparse_vector: The sparse vector to search for.
+ :type sparse_vector: dict[int, float] | SparseEmbedding | None
+ :return: A tuple containing the query vector and a list of route names.
+ :rtype: Tuple[np.ndarray, List[str]]
"""
raise NotImplementedError("This method should be implemented by subclasses.")
@@ -171,9 +252,19 @@ class BaseIndex(BaseModel):
route_filter: Optional[List[str]] = None,
sparse_vector: dict[int, float] | SparseEmbedding | None = None,
) -> Tuple[np.ndarray, List[str]]:
- """
- Search the index for the query_vector and return top_k results.
+ """Search the index for the query_vector and return top_k results.
This method should be implemented by subclasses.
+
+ :param vector: The vector to search for.
+ :type vector: np.ndarray
+ :param top_k: The number of results to return.
+ :type top_k: int
+ :param route_filter: The routes to filter the search by.
+ :type route_filter: Optional[List[str]]
+ :param sparse_vector: The sparse vector to search for.
+ :type sparse_vector: dict[int, float] | SparseEmbedding | None
+ :return: A tuple containing the query vector and a list of route names.
+ :rtype: Tuple[np.ndarray, List[str]]
"""
raise NotImplementedError("This method should be implemented by subclasses.")
@@ -189,8 +280,10 @@ class BaseIndex(BaseModel):
raise NotImplementedError("This method should be implemented by subclasses.")
def delete_all(self):
- """
- Deletes all records from the index.
+ """Deletes all records from the index.
+ This method should be implemented by subclasses.
+
+ :raises NotImplementedError: If the method is not implemented by the subclass.
"""
logger.warning("This method should be implemented by subclasses.")
self.index = None
@@ -198,9 +291,10 @@ class BaseIndex(BaseModel):
self.utterances = None
def delete_index(self):
- """
- Deletes or resets the index.
+ """Deletes or resets the index.
This method should be implemented by subclasses.
+
+ :raises NotImplementedError: If the method is not implemented by the subclass.
"""
logger.warning("This method should be implemented by subclasses.")
self.index = None
@@ -238,7 +332,7 @@ class BaseIndex(BaseModel):
:return: The config parameter that was read.
:rtype: ConfigParameter
"""
- logger.warning("Async method not implemented.")
+ logger.warning("_async_read_config method not implemented.")
return self._read_config(field=field, scope=scope)
def _write_config(self, config: ConfigParameter) -> ConfigParameter:
@@ -332,6 +426,11 @@ class BaseIndex(BaseModel):
if self._is_locked(scope=scope) != value:
# in this case, we can set the lock value
break
+ elif not value:
+ # if unlocking, we can break immediately — often with Pinecone the
+ # lock/unlocked state takes a few seconds to update, so locking then
+ # unlocking quickly will fail without this check
+ break
if (datetime.now() - start_time).total_seconds() < wait:
# wait for a few seconds before checking again
time.sleep(RETRY_WAIT_TIME)
@@ -374,9 +473,7 @@ class BaseIndex(BaseModel):
return lock_param
def _get_all(self, prefix: Optional[str] = None, include_metadata: bool = False):
- """
- Retrieves all vector IDs from the index.
-
+ """Retrieves all vector IDs from the index.
This method should be implemented by subclasses.
:param prefix: The prefix to filter the vectors by.
@@ -392,7 +489,6 @@ class BaseIndex(BaseModel):
self, prefix: Optional[str] = None, include_metadata: bool = False
) -> tuple[list[str], list[dict]]:
"""Retrieves all vector IDs from the index asynchronously.
-
This method should be implemented by subclasses.
:param prefix: The prefix to filter the vectors by.
diff --git a/semantic_router/index/hybrid_local.py b/semantic_router/index/hybrid_local.py
index d4096edbf59413643e6b4bec72ac218549fde8a9..2a1441563853632fb135516162b7009997386aab 100644
--- a/semantic_router/index/hybrid_local.py
+++ b/semantic_router/index/hybrid_local.py
@@ -1,12 +1,11 @@
-from typing import List, Optional, Tuple, Dict
+from typing import Any, Dict, List, Optional, Tuple
import numpy as np
from numpy.linalg import norm
-from semantic_router.schema import ConfigParameter, SparseEmbedding, Utterance
from semantic_router.index.local import LocalIndex
+from semantic_router.schema import ConfigParameter, SparseEmbedding, Utterance
from semantic_router.utils.logger import logger
-from typing import Any
class HybridLocalIndex(LocalIndex):
@@ -25,7 +24,23 @@ class HybridLocalIndex(LocalIndex):
function_schemas: Optional[List[Dict[str, Any]]] = None,
metadata_list: List[Dict[str, Any]] = [],
sparse_embeddings: Optional[List[SparseEmbedding]] = None,
+ **kwargs,
):
+ """Add embeddings to the index.
+
+ :param embeddings: List of embeddings to add to the index.
+ :type embeddings: List[List[float]]
+ :param routes: List of routes to add to the index.
+ :type routes: List[str]
+ :param utterances: List of utterances to add to the index.
+ :type utterances: List[str]
+ :param function_schemas: List of function schemas to add to the index.
+ :type function_schemas: Optional[List[Dict[str, Any]]]
+ :param metadata_list: List of metadata to add to the index.
+ :type metadata_list: List[Dict[str, Any]]
+ :param sparse_embeddings: List of sparse embeddings to add to the index.
+ :type sparse_embeddings: Optional[List[SparseEmbedding]]
+ """
if sparse_embeddings is None:
raise ValueError("Sparse embeddings are required for HybridLocalIndex.")
if function_schemas is not None:
@@ -56,32 +71,45 @@ class HybridLocalIndex(LocalIndex):
self.routes = np.concatenate([self.routes, routes_arr])
self.utterances = np.concatenate([self.utterances, utterances_arr])
- def get_utterances(self) -> List[Utterance]:
+ def get_utterances(self, include_metadata: bool = False) -> List[Utterance]:
"""Gets a list of route and utterance objects currently stored in the index.
- Returns:
- List[Tuple]: A list of (route_name, utterance) objects.
+ :param include_metadata: Whether to include function schemas and metadata in
+ the returned Utterance objects - HybridLocalIndex doesn't include metadata so
+ this parameter is ignored.
+ :type include_metadata: bool
+ :return: A list of Utterance objects.
+ :rtype: List[Utterance]
"""
if self.routes is None or self.utterances is None:
return []
return [Utterance.from_tuple(x) for x in zip(self.routes, self.utterances)]
- def describe(self) -> Dict:
- return {
- "type": self.type,
- "dimensions": self.index.shape[1] if self.index is not None else 0,
- "vectors": self.index.shape[0] if self.index is not None else 0,
- }
-
def _sparse_dot_product(
self, vec_a: dict[int, float], vec_b: dict[int, float]
) -> float:
+ """Calculate the dot product of two sparse vectors.
+
+ :param vec_a: The first sparse vector.
+ :type vec_a: dict[int, float]
+ :param vec_b: The second sparse vector.
+ :type vec_b: dict[int, float]
+ :return: The dot product of the two sparse vectors.
+ :rtype: float
+ """
# switch vecs to ensure first is smallest for more efficiency
if len(vec_a) > len(vec_b):
vec_a, vec_b = vec_b, vec_a
return sum(vec_a[i] * vec_b.get(i, 0) for i in vec_a)
def _sparse_index_dot_product(self, vec_a: dict[int, float]) -> list[float]:
+ """Calculate the dot product of a sparse vector and a list of sparse vectors.
+
+ :param vec_a: The sparse vector.
+ :type vec_a: dict[int, float]
+ :return: A list of dot products.
+ :rtype: list[float]
+ """
if self.sparse_index is None:
raise ValueError("self.sparse_index is not populated.")
dot_products = [
@@ -166,14 +194,26 @@ class HybridLocalIndex(LocalIndex):
)
def aget_routes(self):
+ """Get all routes from the index.
+
+ :return: A list of routes.
+ :rtype: List[str]
+ """
logger.error(f"Sync remove is not implemented for {self.__class__.__name__}.")
def _write_config(self, config: ConfigParameter):
+ """Write the config to the index.
+
+ :param config: The config to write to the index.
+ :type config: ConfigParameter
+ """
logger.warning(f"No config is written for {self.__class__.__name__}.")
def delete(self, route_name: str):
- """
- Delete all records of a specific route from the index.
+ """Delete all records of a specific route from the index.
+
+ :param route_name: The name of the route to delete.
+ :type route_name: str
"""
if (
self.index is not None
@@ -191,15 +231,23 @@ class HybridLocalIndex(LocalIndex):
)
def delete_index(self):
- """
- Deletes the index, effectively clearing it and setting it to None.
+ """Deletes the index, effectively clearing it and setting it to None.
+
+ :return: None
+ :rtype: None
"""
self.index = None
self.routes = None
self.utterances = None
def _get_indices_for_route(self, route_name: str):
- """Gets an array of indices for a specific route."""
+ """Gets an array of indices for a specific route.
+
+ :param route_name: The name of the route to get indices for.
+ :type route_name: str
+ :return: An array of indices for the route.
+ :rtype: np.ndarray
+ """
if self.routes is None:
raise ValueError("Routes are not populated.")
idx = [i for i, route in enumerate(self.routes) if route == route_name]
diff --git a/semantic_router/index/local.py b/semantic_router/index/local.py
index 76d44d82b50108136f5a6776621d75aca428c451..1c70daae29897142495bfe93c503d4ed39213bb1 100644
--- a/semantic_router/index/local.py
+++ b/semantic_router/index/local.py
@@ -1,12 +1,11 @@
-from typing import List, Optional, Tuple, Dict
+from typing import Any, Dict, List, Optional, Tuple
import numpy as np
-from semantic_router.schema import ConfigParameter, SparseEmbedding, Utterance
-from semantic_router.index.base import BaseIndex
+from semantic_router.index.base import BaseIndex, IndexConfig
from semantic_router.linear import similarity_matrix, top_scores
+from semantic_router.schema import ConfigParameter, SparseEmbedding, Utterance
from semantic_router.utils.logger import logger
-from typing import Any
class LocalIndex(BaseIndex):
@@ -26,7 +25,21 @@ class LocalIndex(BaseIndex):
utterances: List[str],
function_schemas: Optional[List[Dict[str, Any]]] = None,
metadata_list: List[Dict[str, Any]] = [],
+ **kwargs,
):
+ """Add embeddings to the index.
+
+ :param embeddings: List of embeddings to add to the index.
+ :type embeddings: List[List[float]]
+ :param routes: List of routes to add to the index.
+ :type routes: List[str]
+ :param utterances: List of utterances to add to the index.
+ :type utterances: List[str]
+ :param function_schemas: List of function schemas to add to the index.
+ :type function_schemas: Optional[List[Dict[str, Any]]]
+ :param metadata_list: List of metadata to add to the index.
+ :type metadata_list: List[Dict[str, Any]]
+ """
embeds = np.array(embeddings) # type: ignore
routes_arr = np.array(routes)
if isinstance(utterances[0], str):
@@ -43,6 +56,13 @@ class LocalIndex(BaseIndex):
self.utterances = np.concatenate([self.utterances, utterances_arr])
def _remove_and_sync(self, routes_to_delete: dict) -> np.ndarray:
+ """Remove and sync the index.
+
+ :param routes_to_delete: Dictionary of routes to delete.
+ :type routes_to_delete: dict
+ :return: A numpy array of the removed route utterances.
+ :rtype: np.ndarray
+ """
if self.index is None or self.routes is None or self.utterances is None:
raise ValueError("Index, routes, or utterances are not populated.")
# TODO JB: implement routes and utterances as a numpy array
@@ -63,23 +83,38 @@ class LocalIndex(BaseIndex):
# return what was removed
return route_utterances[~mask]
- def get_utterances(self) -> List[Utterance]:
- """
- Gets a list of route and utterance objects currently stored in the index.
+ def get_utterances(self, include_metadata: bool = False) -> List[Utterance]:
+ """Gets a list of route and utterance objects currently stored in the index.
- Returns:
- List[Tuple]: A list of (route_name, utterance) objects.
+ :param include_metadata: Whether to include function schemas and metadata in
+ the returned Utterance objects - LocalIndex doesn't include metadata so this
+ parameter is ignored.
+ :return: A list of Utterance objects.
+ :rtype: List[Utterance]
"""
if self.routes is None or self.utterances is None:
return []
return [Utterance.from_tuple(x) for x in zip(self.routes, self.utterances)]
- def describe(self) -> Dict:
- return {
- "type": self.type,
- "dimensions": self.index.shape[1] if self.index is not None else 0,
- "vectors": self.index.shape[0] if self.index is not None else 0,
- }
+ def describe(self) -> IndexConfig:
+ """Describe the index.
+
+ :return: An IndexConfig object.
+ :rtype: IndexConfig
+ """
+ return IndexConfig(
+ type=self.type,
+ dimensions=self.index.shape[1] if self.index is not None else 0,
+ vectors=self.index.shape[0] if self.index is not None else 0,
+ )
+
+ def is_ready(self) -> bool:
+ """Checks if the index is ready to be used.
+
+ :return: True if the index is ready, False otherwise.
+ :rtype: bool
+ """
+ return self.index is not None and self.routes is not None
def query(
self,
@@ -88,8 +123,18 @@ class LocalIndex(BaseIndex):
route_filter: Optional[List[str]] = None,
sparse_vector: dict[int, float] | SparseEmbedding | None = None,
) -> Tuple[np.ndarray, List[str]]:
- """
- Search the index for the query and return top_k results.
+ """Search the index for the query and return top_k results.
+
+ :param vector: The vector to search for.
+ :type vector: np.ndarray
+ :param top_k: The number of results to return.
+ :type top_k: int
+ :param route_filter: The routes to filter the search by.
+ :type route_filter: Optional[List[str]]
+ :param sparse_vector: The sparse vector to search for.
+ :type sparse_vector: dict[int, float] | SparseEmbedding | None
+ :return: A tuple containing the query vector and a list of route names.
+ :rtype: Tuple[np.ndarray, List[str]]
"""
if self.index is None or self.routes is None:
raise ValueError("Index or routes are not populated.")
@@ -118,8 +163,18 @@ class LocalIndex(BaseIndex):
route_filter: Optional[List[str]] = None,
sparse_vector: dict[int, float] | SparseEmbedding | None = None,
) -> Tuple[np.ndarray, List[str]]:
- """
- Search the index for the query and return top_k results.
+ """Search the index for the query and return top_k results.
+
+ :param vector: The vector to search for.
+ :type vector: np.ndarray
+ :param top_k: The number of results to return.
+ :type top_k: int
+ :param route_filter: The routes to filter the search by.
+ :type route_filter: Optional[List[str]]
+ :param sparse_vector: The sparse vector to search for.
+ :type sparse_vector: dict[int, float] | SparseEmbedding | None
+ :return: A tuple containing the query vector and a list of route names.
+ :rtype: Tuple[np.ndarray, List[str]]
"""
if self.index is None or self.routes is None:
raise ValueError("Index or routes are not populated.")
@@ -142,14 +197,26 @@ class LocalIndex(BaseIndex):
return scores, route_names
def aget_routes(self):
+ """Get all routes from the index.
+
+ :return: A list of routes.
+ :rtype: List[str]
+ """
logger.error("Sync remove is not implemented for LocalIndex.")
def _write_config(self, config: ConfigParameter):
+ """Write the config to the index.
+
+ :param config: The config to write to the index.
+ :type config: ConfigParameter
+ """
logger.warning("No config is written for LocalIndex.")
def delete(self, route_name: str):
- """
- Delete all records of a specific route from the index.
+ """Delete all records of a specific route from the index.
+
+ :param route_name: The name of the route to delete.
+ :type route_name: str
"""
if (
self.index is not None
@@ -167,15 +234,23 @@ class LocalIndex(BaseIndex):
)
def delete_index(self):
- """
- Deletes the index, effectively clearing it and setting it to None.
+ """Deletes the index, effectively clearing it and setting it to None.
+
+ :return: None
+ :rtype: None
"""
self.index = None
self.routes = None
self.utterances = None
def _get_indices_for_route(self, route_name: str):
- """Gets an array of indices for a specific route."""
+ """Gets an array of indices for a specific route.
+
+ :param route_name: The name of the route to get indices for.
+ :type route_name: str
+ :return: An array of indices for the route.
+ :rtype: np.ndarray
+ """
if self.routes is None:
raise ValueError("Routes are not populated.")
idx = [i for i, route in enumerate(self.routes) if route == route_name]
diff --git a/semantic_router/index/pinecone.py b/semantic_router/index/pinecone.py
index 7e7def05ed241680cfda470d07d9558adf46ec5d..723d8b5ffc50cefc1eb03930d25c741f63e20c7f 100644
--- a/semantic_router/index/pinecone.py
+++ b/semantic_router/index/pinecone.py
@@ -1,16 +1,17 @@
-import aiohttp
import asyncio
import hashlib
+import json
import os
import time
-import json
-
-from typing import Any, Dict, List, Optional, Union, Tuple
+import requests
+from json.decoder import JSONDecodeError
+from typing import Any, Dict, List, Optional, Tuple, Union
+import aiohttp
import numpy as np
from pydantic import BaseModel, Field
-from semantic_router.index.base import BaseIndex
+from semantic_router.index.base import BaseIndex, IndexConfig
from semantic_router.schema import ConfigParameter, SparseEmbedding
from semantic_router.utils.logger import logger
@@ -19,16 +20,94 @@ def clean_route_name(route_name: str) -> str:
return route_name.strip().replace(" ", "-")
+class DeleteRequest(BaseModel):
+ ids: list[str] | None = None
+ delete_all: bool = False
+ namespace: str | None = None
+ filter: dict[str, Any] | None = None
+
+
+def build_records(
+ embeddings: List[List[float]],
+ routes: List[str],
+ utterances: List[str],
+ function_schemas: Optional[Optional[List[Dict[str, Any]]]] = None,
+ metadata_list: List[Dict[str, Any]] = [],
+ sparse_embeddings: Optional[Optional[List[SparseEmbedding]]] = None,
+) -> List[Dict]:
+ """Build records for Pinecone upsert.
+
+ :param embeddings: List of embeddings to upsert.
+ :type embeddings: List[List[float]]
+ :param routes: List of routes to upsert.
+ :type routes: List[str]
+ :param utterances: List of utterances to upsert.
+ :type utterances: List[str]
+ :param function_schemas: List of function schemas to upsert.
+ :type function_schemas: Optional[List[Dict[str, Any]]]
+ :param metadata_list: List of metadata to upsert.
+ :type metadata_list: List[Dict[str, Any]]
+ :param sparse_embeddings: List of sparse embeddings to upsert.
+ :type sparse_embeddings: Optional[List[SparseEmbedding]]
+ :return: List of records to upsert.
+ :rtype: List[Dict]
+ """
+ if function_schemas is None:
+ function_schemas = [{}] * len(embeddings)
+ if sparse_embeddings is None:
+ vectors_to_upsert = [
+ PineconeRecord(
+ values=vector,
+ route=route,
+ utterance=utterance,
+ function_schema=json.dumps(function_schema),
+ metadata=metadata,
+ ).to_dict()
+ for vector, route, utterance, function_schema, metadata in zip(
+ embeddings,
+ routes,
+ utterances,
+ function_schemas,
+ metadata_list,
+ )
+ ]
+ else:
+ vectors_to_upsert = [
+ PineconeRecord(
+ values=vector,
+ sparse_values=sparse_emb.to_pinecone(),
+ route=route,
+ utterance=utterance,
+ function_schema=json.dumps(function_schema),
+ metadata=metadata,
+ ).to_dict()
+ for vector, route, utterance, function_schema, metadata, sparse_emb in zip(
+ embeddings,
+ routes,
+ utterances,
+ function_schemas,
+ metadata_list,
+ sparse_embeddings,
+ )
+ ]
+ return vectors_to_upsert
+
+
class PineconeRecord(BaseModel):
id: str = ""
values: List[float]
- sparse_values: Optional[dict[int, float]] = None
+ sparse_values: Optional[dict[str, list]] = None
route: str
utterance: str
function_schema: str = "{}"
metadata: Dict[str, Any] = {} # Additional metadata dictionary
def __init__(self, **data):
+ """Initialize PineconeRecord.
+
+ :param **data: Keyword arguments to pass to the BaseModel constructor.
+ :type **data: dict
+ """
super().__init__(**data)
clean_route = clean_route_name(self.route)
# Use SHA-256 for a more secure hash
@@ -43,16 +122,18 @@ class PineconeRecord(BaseModel):
)
def to_dict(self):
+ """Convert PineconeRecord to a dictionary.
+
+ :return: Dictionary representation of the PineconeRecord.
+ :rtype: dict
+ """
d = {
"id": self.id,
"values": self.values,
"metadata": self.metadata,
}
if self.sparse_values:
- d["sparse_values"] = {
- "indices": list(self.sparse_values.keys()),
- "values": list(self.sparse_values.values()),
- }
+ d["sparse_values"] = self.sparse_values
return d
@@ -63,14 +144,15 @@ class PineconeIndex(BaseIndex):
dimensions: Union[int, None] = None
metric: str = "dotproduct"
cloud: str = "aws"
- region: str = "us-west-2"
+ region: str = "us-east-1"
host: str = ""
client: Any = Field(default=None, exclude=True)
- async_client: Any = Field(default=None, exclude=True)
index: Optional[Any] = Field(default=None, exclude=True)
ServerlessSpec: Any = Field(default=None, exclude=True)
namespace: Optional[str] = ""
- base_url: Optional[str] = "https://api.pinecone.io"
+ base_url: Optional[str] = None
+ headers: dict[str, str] = {}
+ index_host: Optional[str] = "http://localhost:5080"
def __init__(
self,
@@ -79,13 +161,56 @@ class PineconeIndex(BaseIndex):
dimensions: Optional[int] = None,
metric: str = "dotproduct",
cloud: str = "aws",
- region: str = "us-west-2",
+ region: str = "us-east-1",
host: str = "",
namespace: Optional[str] = "",
base_url: Optional[str] = "https://api.pinecone.io",
init_async_index: bool = False,
):
+ """Initialize PineconeIndex.
+
+ :param api_key: Pinecone API key.
+ :type api_key: Optional[str]
+ :param index_name: Name of the index.
+ :type index_name: str
+ :param dimensions: Dimensions of the index.
+ :type dimensions: Optional[int]
+ :param metric: Metric of the index.
+ :type metric: str
+ :param cloud: Cloud provider of the index.
+ :type cloud: str
+ :param region: Region of the index.
+ :type region: str
+ :param host: Host of the index.
+ :type host: str
+ :param namespace: Namespace of the index.
+ :type namespace: Optional[str]
+ :param base_url: Base URL of the Pinecone API.
+ :type base_url: Optional[str]
+ :param init_async_index: Whether to initialize the index asynchronously.
+ :type init_async_index: bool
+ """
super().__init__()
+ self.api_key = api_key or os.getenv("PINECONE_API_KEY")
+ if not self.api_key:
+ raise ValueError("Pinecone API key is required.")
+
+ self.headers = {
+ "Api-Key": self.api_key,
+ "Content-Type": "application/json",
+ "User-Agent": "source_tag=semanticrouter",
+ }
+
+ if base_url is not None or os.getenv("PINECONE_API_BASE_URL"):
+ logger.info("Using pinecone remote API.")
+ if os.getenv("PINECONE_API_BASE_URL"):
+ self.base_url = os.getenv("PINECONE_API_BASE_URL")
+ else:
+ self.base_url = base_url
+
+ if self.base_url and "api.pinecone.io" in self.base_url:
+ self.headers["X-Pinecone-API-Version"] = "2024-07"
+
self.index_name = index_name
self.dimensions = dimensions
self.metric = metric
@@ -96,21 +221,24 @@ class PineconeIndex(BaseIndex):
raise ValueError("Namespace 'sr_config' is reserved for internal use.")
self.namespace = namespace
self.type = "pinecone"
- self.api_key = api_key or os.getenv("PINECONE_API_KEY")
- self.base_url = base_url
- if self.api_key is None:
- raise ValueError("Pinecone API key is required.")
+ logger.warning(
+ "Default region changed from us-west-2 to us-east-1 in v0.1.0.dev6"
+ )
self.client = self._initialize_client(api_key=self.api_key)
- if init_async_index:
- self.async_client = self._initialize_async_client(api_key=self.api_key)
- else:
- self.async_client = None
+
# try initializing index
self.index = self._init_index()
def _initialize_client(self, api_key: Optional[str] = None):
+ """Initialize the Pinecone client.
+
+ :param api_key: Pinecone API key.
+ :type api_key: Optional[str]
+ :return: Pinecone client.
+ :rtype: Pinecone
+ """
try:
from pinecone import Pinecone, ServerlessSpec
@@ -131,19 +259,25 @@ class PineconeIndex(BaseIndex):
return Pinecone(**pinecone_args)
- def _initialize_async_client(self, api_key: Optional[str] = None):
- api_key = api_key or self.api_key
- if api_key is None:
- raise ValueError("Pinecone API key is required.")
- async_client = aiohttp.ClientSession(
- headers={
- "Api-Key": api_key,
- "Content-Type": "application/json",
- "X-Pinecone-API-Version": "2024-07",
- "User-Agent": "source_tag=semanticrouter",
- }
- )
- return async_client
+ def _calculate_index_host(self):
+ """Calculate the index host. Used to differentiate between normal
+ Pinecone and Pinecone Local instance.
+
+ :return: None
+ :rtype: None
+ """
+ if self.index_host and self.base_url:
+ if "api.pinecone.io" in self.base_url:
+ if not self.index_host.startswith("http"):
+ self.index_host = f"https://{self.index_host}"
+ else:
+ if "http" not in self.index_host:
+ self.index_host = f"http://{self.base_url.split(':')[-2].strip('/')}:{self.index_host.split(':')[-1]}"
+ elif not self.index_host.startswith("http://"):
+ if "localhost" in self.index_host:
+ self.index_host = f"http://{self.base_url.split(':')[-2].strip('/')}:{self.index_host.split(':')[-1]}"
+ else:
+ self.index_host = f"http://{self.index_host}"
def _init_index(self, force_create: bool = False) -> Union[Any, None]:
"""Initializing the index can be done after the object has been created
@@ -158,71 +292,137 @@ class PineconeIndex(BaseIndex):
dimensions are not given (which will raise an error).
:type force_create: bool, optional
"""
- index_exists = self.index_name in self.client.list_indexes().names()
dimensions_given = self.dimensions is not None
- if dimensions_given and not index_exists:
- # if the index doesn't exist and we have dimension value
- # we create the index
- self.client.create_index(
- name=self.index_name,
- dimension=self.dimensions,
- metric=self.metric,
- spec=self.ServerlessSpec(cloud=self.cloud, region=self.region),
- )
- # wait for index to be created
- while not self.client.describe_index(self.index_name).status["ready"]:
- time.sleep(1)
- index = self.client.Index(self.index_name)
- time.sleep(0.5)
- elif index_exists:
- # if the index exists we just return it
- index = self.client.Index(self.index_name)
- # grab the dimensions from the index
- self.dimensions = index.describe_index_stats()["dimension"]
- elif force_create and not dimensions_given:
- raise ValueError(
- "Cannot create an index without specifying the dimensions."
- )
+ if self.index is None:
+ index_exists = self.index_name in self.client.list_indexes().names()
+ if dimensions_given and not index_exists:
+ # if the index doesn't exist and we have dimension value
+ # we create the index
+ self.client.create_index(
+ name=self.index_name,
+ dimension=self.dimensions,
+ metric=self.metric,
+ spec=self.ServerlessSpec(cloud=self.cloud, region=self.region),
+ )
+ # wait for index to be created
+ while not self.client.describe_index(self.index_name).status["ready"]:
+ time.sleep(0.2)
+ index = self.client.Index(self.index_name)
+ self.index = index
+ time.sleep(0.2)
+ elif index_exists:
+ # if the index exists we just return it
+ # index = self.client.Index(self.index_name)
+
+ self.index_host = self.client.describe_index(self.index_name).host
+ self._calculate_index_host()
+ index = self.client.Index(self.index_name, host=self.index_host)
+ self.index = index
+
+ # grab the dimensions from the index
+ self.dimensions = index.describe_index_stats()["dimension"]
+ elif force_create and not dimensions_given:
+ raise ValueError(
+ "Cannot create an index without specifying the dimensions."
+ )
+ else:
+ # if the index doesn't exist and we don't have the dimensions
+ # we return None
+ logger.warning(
+ "Index could not be initialized. Init parameters: "
+ f"{self.index_name=}, {self.dimensions=}, {self.metric=}, "
+ f"{self.cloud=}, {self.region=}, {self.host=}, {self.namespace=}, "
+ f"{force_create=}"
+ )
+ index = None
else:
- # if the index doesn't exist and we don't have the dimensions
- # we return None
- logger.warning("Index could not be initialized.")
- index = None
- if index is not None:
- self.host = self.client.describe_index(self.index_name)["host"]
+ index = self.index
+ if self.index is not None and self.host == "":
+ # if the index exists we just return it
+ self.index_host = self.client.describe_index(self.index_name).host
+
+ if self.index_host and self.base_url:
+ self._calculate_index_host()
+ index = self.client.Index(self.index_name, host=self.index_host)
+ self.host = self.index_host
return index
async def _init_async_index(self, force_create: bool = False):
+ """Initializing the index can be done after the object has been created
+ to allow for the user to set the dimensions and other parameters.
+
+ If the index doesn't exist and the dimensions are given, the index will
+ be created. If the index exists, it will be returned. If the index doesn't
+ exist and the dimensions are not given, the index will not be created and
+ None will be returned.
+
+ This method is used to initialize the index asynchronously.
+
+ :param force_create: If True, the index will be created even if the
+ dimensions are not given (which will raise an error).
+ :type force_create: bool, optional
+ """
index_stats = None
- indexes = await self._async_list_indexes()
- index_names = [i["name"] for i in indexes["indexes"]]
- index_exists = self.index_name in index_names
- if self.dimensions is not None and not index_exists:
- await self._async_create_index(
- name=self.index_name,
- dimension=self.dimensions,
- metric=self.metric,
- cloud=self.cloud,
- region=self.region,
- )
- # TODO describe index and async sleep
- index_ready = "false"
- while index_ready != "true":
+ # first try getting dimensions if needed
+ if self.dimensions is None:
+ # check if the index exists
+ indexes = await self._async_list_indexes()
+ index_names = [i["name"] for i in indexes["indexes"]]
+ index_exists = self.index_name in index_names
+ if index_exists:
+ # we can get the dimensions from the index
+ index_stats = await self._async_describe_index(self.index_name)
+ self.dimensions = index_stats["dimension"]
+ elif index_exists and not force_create:
+ # if the index doesn't exist and we don't have the dimensions
+ # we raise warning
+ logger.warning(
+ "Index could not be initialized. Init parameters: "
+ f"{self.index_name=}, {self.dimensions=}, {self.metric=}, "
+ f"{self.cloud=}, {self.region=}, {self.host=}, {self.namespace=}, "
+ f"{force_create=}"
+ )
+ elif force_create:
+ raise ValueError(
+ "Index could not be initialized. Init parameters: "
+ f"{self.index_name=}, {self.dimensions=}, {self.metric=}, "
+ )
+ else:
+ raise NotImplementedError(
+ "Unexpected init conditions. Please report this issue in GitHub."
+ )
+ # now check if we have dimensions
+ if self.dimensions:
+ # check if the index exists
+ indexes = await self._async_list_indexes()
+ index_names = [i["name"] for i in indexes["indexes"]]
+ index_exists = self.index_name in index_names
+ # if the index doesn't exist, we create it
+ if not index_exists:
+ # confirm if the index exists
index_stats = await self._async_describe_index(self.index_name)
index_ready = index_stats["status"]["ready"]
- await asyncio.sleep(1)
- elif index_exists:
- index_stats = await self._async_describe_index(self.index_name)
- # grab dimensions for the index
- self.dimensions = index_stats["dimension"]
- elif force_create and self.dimensions is None:
- raise ValueError(
- "Cannot create an index without specifying the dimensions."
- )
- else:
- # if the index doesn't exist and we don't have the dimensions
- # we raise warning
- logger.warning("Index could not be initialized.")
+ if index_ready == "true":
+ # if the index is ready, we return it
+ return index_stats
+ else:
+ # if the index is not ready, we create it
+ await self._async_create_index(
+ name=self.index_name,
+ dimension=self.dimensions,
+ metric=self.metric,
+ cloud=self.cloud,
+ region=self.region,
+ )
+ index_ready = "false"
+ while index_ready != "true":
+ index_stats = await self._async_describe_index(self.index_name)
+ index_ready = index_stats["status"]["ready"]
+ await asyncio.sleep(0.1)
+ return index_stats
+ else:
+ # if the index exists, we return it
+ return index_stats
self.host = index_stats["host"] if index_stats else ""
def _batch_upsert(self, batch: List[Dict]):
@@ -236,17 +436,6 @@ class PineconeIndex(BaseIndex):
else:
raise ValueError("Index is None, could not upsert.")
- async def _async_batch_upsert(self, batch: List[Dict]):
- """Helper method for upserting a single batch of records asynchronously.
-
- :param batch: The batch of records to upsert.
- :type batch: List[Dict]
- """
- if self.index is not None:
- await self.index.upsert(vectors=batch, namespace=self.namespace)
- else:
- raise ValueError("Index is None, could not upsert.")
-
def add(
self,
embeddings: List[List[float]],
@@ -255,35 +444,37 @@ class PineconeIndex(BaseIndex):
function_schemas: Optional[Optional[List[Dict[str, Any]]]] = None,
metadata_list: List[Dict[str, Any]] = [],
batch_size: int = 100,
- sparse_embeddings: Optional[Optional[List[dict[int, float]]]] = None,
+ sparse_embeddings: Optional[Optional[List[SparseEmbedding]]] = None,
+ **kwargs,
):
- """Add vectors to Pinecone in batches."""
+ """Add vectors to Pinecone in batches.
+
+ :param embeddings: List of embeddings to upsert.
+ :type embeddings: List[List[float]]
+ :param routes: List of routes to upsert.
+ :type routes: List[str]
+ :param utterances: List of utterances to upsert.
+ :type utterances: List[str]
+ :param function_schemas: List of function schemas to upsert.
+ :type function_schemas: Optional[List[Dict[str, Any]]]
+ :param metadata_list: List of metadata to upsert.
+ :type metadata_list: List[Dict[str, Any]]
+ :param batch_size: Number of vectors to upsert in a single batch.
+ :type batch_size: int, optional
+ :param sparse_embeddings: List of sparse embeddings to upsert.
+ :type sparse_embeddings: Optional[List[SparseEmbedding]]
+ """
if self.index is None:
self.dimensions = self.dimensions or len(embeddings[0])
self.index = self._init_index(force_create=True)
- if function_schemas is None:
- function_schemas = [{}] * len(embeddings)
- if sparse_embeddings is None:
- sparse_embeddings = [{}] * len(embeddings)
- vectors_to_upsert = [
- PineconeRecord(
- values=vector,
- sparse_values=sparse_dict,
- route=route,
- utterance=utterance,
- function_schema=json.dumps(function_schema),
- metadata=metadata,
- ).to_dict()
- for vector, route, utterance, function_schema, metadata, sparse_dict in zip(
- embeddings,
- routes,
- utterances,
- function_schemas,
- metadata_list,
- sparse_embeddings,
- )
- ]
-
+ vectors_to_upsert = build_records(
+ embeddings=embeddings,
+ routes=routes,
+ utterances=utterances,
+ function_schemas=function_schemas,
+ metadata_list=metadata_list,
+ sparse_embeddings=sparse_embeddings,
+ )
for i in range(0, len(vectors_to_upsert), batch_size):
batch = vectors_to_upsert[i : i + batch_size]
self._batch_upsert(batch)
@@ -296,40 +487,51 @@ class PineconeIndex(BaseIndex):
function_schemas: Optional[Optional[List[Dict[str, Any]]]] = None,
metadata_list: List[Dict[str, Any]] = [],
batch_size: int = 100,
- sparse_embeddings: Optional[Optional[List[dict[int, float]]]] = None,
+ sparse_embeddings: Optional[Optional[List[SparseEmbedding]]] = None,
+ **kwargs,
):
- """Add vectors to Pinecone in batches."""
+ """Add vectors to Pinecone in batches.
+
+ :param embeddings: List of embeddings to upsert.
+ :type embeddings: List[List[float]]
+ :param routes: List of routes to upsert.
+ :type routes: List[str]
+ :param utterances: List of utterances to upsert.
+ :type utterances: List[str]
+ :param function_schemas: List of function schemas to upsert.
+ :type function_schemas: Optional[List[Dict[str, Any]]]
+ :param metadata_list: List of metadata to upsert.
+ :type metadata_list: List[Dict[str, Any]]
+ :param batch_size: Number of vectors to upsert in a single batch.
+ :type batch_size: int, optional
+ :param sparse_embeddings: List of sparse embeddings to upsert.
+ :type sparse_embeddings: Optional[List[SparseEmbedding]]
+ """
if self.index is None:
self.dimensions = self.dimensions or len(embeddings[0])
self.index = await self._init_async_index(force_create=True)
- if function_schemas is None:
- function_schemas = [{}] * len(embeddings)
- if sparse_embeddings is None:
- sparse_embeddings = [{}] * len(embeddings)
- vectors_to_upsert = [
- PineconeRecord(
- values=vector,
- sparse_values=sparse_dict,
- route=route,
- utterance=utterance,
- function_schema=json.dumps(function_schema),
- metadata=metadata,
- ).to_dict()
- for vector, route, utterance, function_schema, metadata, sparse_dict in zip(
- embeddings,
- routes,
- utterances,
- function_schemas,
- metadata_list,
- sparse_embeddings,
- )
- ]
+ vectors_to_upsert = build_records(
+ embeddings=embeddings,
+ routes=routes,
+ utterances=utterances,
+ function_schemas=function_schemas,
+ metadata_list=metadata_list,
+ sparse_embeddings=sparse_embeddings,
+ )
for i in range(0, len(vectors_to_upsert), batch_size):
batch = vectors_to_upsert[i : i + batch_size]
- await self._async_batch_upsert(batch)
+ await self._async_upsert(
+ vectors=batch,
+ namespace=self.namespace or "",
+ )
def _remove_and_sync(self, routes_to_delete: dict):
+ """Remove specified routes from index if they exist.
+
+ :param routes_to_delete: Routes to delete.
+ :type routes_to_delete: dict
+ """
for route, utterances in routes_to_delete.items():
remote_routes = self._get_routes_with_ids(route_name=route)
ids_to_delete = [
@@ -342,6 +544,13 @@ class PineconeIndex(BaseIndex):
self.index.delete(ids=ids_to_delete, namespace=self.namespace)
async def _async_remove_and_sync(self, routes_to_delete: dict):
+ """Remove specified routes from index if they exist.
+
+ This method is asyncronous.
+
+ :param routes_to_delete: Routes to delete.
+ :type routes_to_delete: dict
+ """
for route, utterances in routes_to_delete.items():
remote_routes = await self._async_get_routes_with_ids(route_name=route)
ids_to_delete = [
@@ -356,16 +565,37 @@ class PineconeIndex(BaseIndex):
)
def _get_route_ids(self, route_name: str):
+ """Get the IDs of the routes in the index.
+
+ :param route_name: Name of the route to get the IDs for.
+ :type route_name: str
+ :return: List of IDs of the routes.
+ :rtype: list[str]
+ """
clean_route = clean_route_name(route_name)
ids, _ = self._get_all(prefix=f"{clean_route}#")
return ids
async def _async_get_route_ids(self, route_name: str):
+ """Get the IDs of the routes in the index.
+
+ :param route_name: Name of the route to get the IDs for.
+ :type route_name: str
+ :return: List of IDs of the routes.
+ :rtype: list[str]
+ """
clean_route = clean_route_name(route_name)
ids, _ = await self._async_get_all(prefix=f"{clean_route}#")
return ids
def _get_routes_with_ids(self, route_name: str):
+ """Get the routes with their IDs from the index.
+
+ :param route_name: Name of the route to get the routes with their IDs for.
+ :type route_name: str
+ :return: List of routes with their IDs.
+ :rtype: list[dict]
+ """
clean_route = clean_route_name(route_name)
ids, metadata = self._get_all(prefix=f"{clean_route}#", include_metadata=True)
route_tuples = []
@@ -380,6 +610,13 @@ class PineconeIndex(BaseIndex):
return route_tuples
async def _async_get_routes_with_ids(self, route_name: str):
+ """Get the routes with their IDs from the index.
+
+ :param route_name: Name of the route to get the routes with their IDs for.
+ :type route_name: str
+ :return: List of routes with their IDs.
+ :rtype: list[dict]
+ """
clean_route = clean_route_name(route_name)
ids, metadata = await self._async_get_all(
prefix=f"{clean_route}#", include_metadata=True
@@ -392,8 +629,7 @@ class PineconeIndex(BaseIndex):
return route_tuples
def _get_all(self, prefix: Optional[str] = None, include_metadata: bool = False):
- """
- Retrieves all vector IDs from the Pinecone index using pagination.
+ """Retrieves all vector IDs from the Pinecone index using pagination.
:param prefix: The prefix to filter the vectors by.
:type prefix: Optional[str]
@@ -423,26 +659,97 @@ class PineconeIndex(BaseIndex):
return all_vector_ids, metadata
- def delete(self, route_name: str):
+ def delete(self, route_name: str) -> list[str]:
+ """Delete specified route from index if it exists. Returns the IDs of the vectors
+ deleted.
+
+ :param route_name: Name of the route to delete.
+ :type route_name: str
+ :return: List of IDs of the vectors deleted.
+ :rtype: list[str]
+ """
route_vec_ids = self._get_route_ids(route_name=route_name)
if self.index is not None:
- self.index.delete(ids=route_vec_ids, namespace=self.namespace)
+ logger.info("index is not None, deleting...")
+ if self.base_url and "api.pinecone.io" in self.base_url:
+ self.index.delete(ids=route_vec_ids, namespace=self.namespace)
+ else:
+ response = requests.post(
+ f"{self.index_host}/vectors/delete",
+ json=DeleteRequest(
+ ids=route_vec_ids,
+ delete_all=True,
+ namespace=self.namespace,
+ ).model_dump(exclude_none=True),
+ timeout=10,
+ )
+ if response.status_code == 200:
+ logger.info(
+ f"Deleted {len(route_vec_ids)} vectors from index {self.index_name}."
+ )
+ else:
+ error_message = response.text
+ raise Exception(
+ f"Failed to delete vectors: {response.status_code} : {error_message}"
+ )
+ return route_vec_ids
+ else:
+ raise ValueError("Index is None, could not delete.")
+
+ async def adelete(self, route_name: str) -> list[str]:
+ """Asynchronously delete specified route from index if it exists. Returns the IDs
+ of the vectors deleted.
+
+ :param route_name: Name of the route to delete.
+ :type route_name: str
+ :return: List of IDs of the vectors deleted.
+ :rtype: list[str]
+ """
+ route_vec_ids = await self._async_get_route_ids(route_name=route_name)
+ if self.index is not None:
+ await self._async_delete(ids=route_vec_ids, namespace=self.namespace or "")
+ return route_vec_ids
else:
raise ValueError("Index is None, could not delete.")
def delete_all(self):
- self.index.delete(delete_all=True, namespace=self.namespace)
+ """Delete all routes from index if it exists.
- def describe(self) -> Dict:
+ :return: None
+ :rtype: None
+ """
+ if self.index is not None:
+ self.index.delete(delete_all=True, namespace=self.namespace)
+ else:
+ raise ValueError("Index is None, could not delete.")
+
+ def describe(self) -> IndexConfig:
+ """Describe the index.
+
+ :return: IndexConfig
+ :rtype: IndexConfig
+ """
if self.index is not None:
stats = self.index.describe_index_stats()
- return {
- "type": self.type,
- "dimensions": stats["dimension"],
- "vectors": stats["namespaces"][self.namespace]["vector_count"],
- }
+ return IndexConfig(
+ type=self.type,
+ dimensions=stats["dimension"],
+ vectors=stats["namespaces"][self.namespace]["vector_count"],
+ )
else:
- raise ValueError("Index is None, cannot describe index stats.")
+ return IndexConfig(
+ type=self.type,
+ dimensions=self.dimensions or 0,
+ vectors=0,
+ )
+
+ def is_ready(self) -> bool:
+ """Checks if the index is ready to be used.
+
+ :return: True if the index is ready, False otherwise.
+ :rtype: bool
+ """
+ return self.index is not None
def query(
self,
@@ -475,24 +782,45 @@ class PineconeIndex(BaseIndex):
else:
filter_query = None
if sparse_vector is not None:
+ logger.error(f"sparse_vector exists:{sparse_vector}")
if isinstance(sparse_vector, dict):
sparse_vector = SparseEmbedding.from_dict(sparse_vector)
if isinstance(sparse_vector, SparseEmbedding):
# unnecessary if-statement but mypy didn't like this otherwise
sparse_vector = sparse_vector.to_pinecone()
- results = self.index.query(
- vector=[query_vector_list],
- sparse_vector=sparse_vector,
- top_k=top_k,
- filter=filter_query,
- include_metadata=True,
- namespace=self.namespace,
- )
+ try:
+ results = self.index.query(
+ vector=[query_vector_list],
+ sparse_vector=sparse_vector,
+ top_k=top_k,
+ filter=filter_query,
+ include_metadata=True,
+ namespace=self.namespace,
+ )
+ except Exception:
+ logger.error("retrying query with vector as str")
+ results = self.index.query(
+ vector=query_vector_list,
+ sparse_vector=sparse_vector,
+ top_k=top_k,
+ filter=filter_query,
+ include_metadata=True,
+ namespace=self.namespace,
+ )
scores = [result["score"] for result in results["matches"]]
route_names = [result["metadata"]["sr_route"] for result in results["matches"]]
return np.array(scores), route_names
def _read_config(self, field: str, scope: str | None = None) -> ConfigParameter:
+ """Read a config parameter from the index.
+
+ :param field: The field to read.
+ :type field: str
+ :param scope: The scope to read.
+ :type scope: str | None
+ :return: The config parameter that was read.
+ :rtype: ConfigParameter
+ """
scope = scope or self.namespace
if self.index is None:
return ConfigParameter(
@@ -505,7 +833,7 @@ class PineconeIndex(BaseIndex):
ids=[config_id],
namespace="sr_config",
)
- if config_record["vectors"]:
+ if config_record.get("vectors"):
return ConfigParameter(
field=field,
value=config_record["vectors"][config_id]["metadata"]["value"],
@@ -522,6 +850,49 @@ class PineconeIndex(BaseIndex):
scope=scope,
)
+ async def _async_read_config(
+ self, field: str, scope: str | None = None
+ ) -> ConfigParameter:
+ """Read a config parameter from the index asynchronously.
+
+ :param field: The field to read.
+ :type field: str
+ :param scope: The scope to read.
+ :type scope: str | None
+ :return: The config parameter that was read.
+ :rtype: ConfigParameter
+ """
+ scope = scope or self.namespace
+ if self.index is None:
+ return ConfigParameter(
+ field=field,
+ value="",
+ scope=scope,
+ )
+ config_id = f"{field}#{scope}"
+ config_record = await self._async_fetch_metadata(
+ vector_id=config_id, namespace="sr_config"
+ )
+ if config_record:
+ try:
+ return ConfigParameter(
+ field=field,
+ value=config_record["value"],
+ created_at=config_record["created_at"],
+ scope=scope,
+ )
+ except KeyError:
+ raise ValueError(
+ f"Found invalid config record during sync: {config_record}"
+ )
+ else:
+ logger.warning(f"Configuration for {field} parameter not found in index.")
+ return ConfigParameter(
+ field=field,
+ value="",
+ scope=scope,
+ )
+
def _write_config(self, config: ConfigParameter) -> ConfigParameter:
"""Method to write a config parameter to the remote Pinecone index.
@@ -550,8 +921,9 @@ class PineconeIndex(BaseIndex):
raise ValueError("Index has not been initialized.")
if self.dimensions is None:
raise ValueError("Must set PineconeIndex.dimensions before writing config.")
- self.index.upsert(
- vectors=[config.to_pinecone(dimensions=self.dimensions)],
+ pinecone_config = config.to_pinecone(dimensions=self.dimensions)
+ await self._async_upsert(
+ vectors=[pinecone_config],
namespace="sr_config",
)
return config
@@ -563,8 +935,7 @@ class PineconeIndex(BaseIndex):
route_filter: Optional[List[str]] = None,
sparse_vector: dict[int, float] | SparseEmbedding | None = None,
) -> Tuple[np.ndarray, List[str]]:
- """
- Asynchronously search the index for the query vector and return the top_k results.
+ """Asynchronously search the index for the query vector and return the top_k results.
:param vector: The query vector to search for.
:type vector: np.ndarray
@@ -580,8 +951,8 @@ class PineconeIndex(BaseIndex):
:rtype: Tuple[np.ndarray, List[str]]
:raises ValueError: If the index is not populated.
"""
- if self.async_client is None or self.host == "":
- raise ValueError("Async client or host are not initialized.")
+ if self.host == "":
+ raise ValueError("Host is not initialized.")
query_vector_list = vector.tolist()
if route_filter is not None:
filter_query = {"sr_route": {"$in": route_filter}}
@@ -614,12 +985,17 @@ class PineconeIndex(BaseIndex):
:return: A list of (route_name, utterance) objects.
:rtype: List[Tuple]
"""
- if self.async_client is None or self.host == "":
- raise ValueError("Async client or host are not initialized.")
+ if self.host == "":
+ raise ValueError("Host is not initialized.")
return await self._async_get_routes()
def delete_index(self):
+ """Delete the index.
+
+ :return: None
+ :rtype: None
+ """
self.client.delete_index(self.index_name)
self.index = None
@@ -633,6 +1009,21 @@ class PineconeIndex(BaseIndex):
top_k: int = 5,
include_metadata: bool = False,
):
+ """Asynchronously query the index for the query vector and return the top_k results.
+
+ :param vector: The query vector to search for.
+ :type vector: list[float]
+ :param sparse_vector: The sparse vector to search for.
+ :type sparse_vector: dict[str, Any] | None
+ :param namespace: The namespace to search for.
+ :type namespace: str
+ :param filter: The filter to search for.
+ :type filter: Optional[dict]
+ :param top_k: The number of top results to return, defaults to 5.
+ :type top_k: int, optional
+ :param include_metadata: Whether to include metadata in the results, defaults to False.
+ :type include_metadata: bool, optional
+ """
params = {
"vector": vector,
"sparse_vector": sparse_vector,
@@ -640,33 +1031,100 @@ class PineconeIndex(BaseIndex):
"filter": filter,
"top_k": top_k,
"include_metadata": include_metadata,
+ "topK": top_k,
+ "includeMetadata": include_metadata,
}
if self.host == "":
raise ValueError("self.host is not initialized.")
- async with self.async_client.post(
- f"https://{self.host}/query",
- json=params,
- ) as response:
- return await response.json(content_type=None)
+ elif self.base_url and "api.pinecone.io" in self.base_url:
+ if not self.host.startswith("http"):
+ logger.error(f"host exists:{self.host}")
+
+ self.host = f"https://{self.host}"
+ elif self.host.startswith("localhost") and self.base_url:
+ self.host = f"http://{self.base_url.split(':')[-2].strip('/')}:{self.host.split(':')[-1]}"
+
+ async with aiohttp.ClientSession() as session:
+ async with session.post(
+ f"{self.host}/query",
+ json=params,
+ headers=self.headers,
+ ) as response:
+ if response.status != 200:
+ error_text = await response.text()
+ logger.error(f"Error in query response: {error_text}")
+ return {} # or handle the error as needed
+
+ try:
+ return await response.json(content_type=None)
+ except JSONDecodeError as e:
+ logger.error(f"JSON decode error: {e}")
+ return {}
+
+ async def _is_async_ready(self, client_only: bool = False) -> bool:
+ """Checks if class attributes exist to be used for async operations.
+
+ :param client_only: Whether to check only the client attributes. If False
+ attributes will be checked for both client and index operations. If True
+ only attributes for client operations will be checked. Defaults to False.
+ :type client_only: bool, optional
+ :return: True if the class attributes exist, False otherwise.
+ :rtype: bool
+ """
+ # first check client only attributes
+ if not (self.cloud or self.region or self.base_url):
+ return False
+ if not client_only:
+ # now check index attributes
+ if not (self.index_name or self.dimensions or self.metric or self.host):
+ return False
+ return True
async def _async_list_indexes(self):
- async with self.async_client.get(f"{self.base_url}/indexes") as response:
- return await response.json(content_type=None)
+ """Asynchronously lists all indexes within the current Pinecone project.
+
+ :return: List of indexes.
+ :rtype: list[dict]
+ """
+ async with aiohttp.ClientSession() as session:
+ async with session.get(
+ f"{self.base_url}/indexes",
+ headers=self.headers,
+ ) as response:
+ return await response.json(content_type=None)
async def _async_upsert(
self,
vectors: list[dict],
namespace: str = "",
):
+ """Asynchronously upserts vectors into the index.
+
+ :param vectors: The vectors to upsert.
+ :type vectors: list[dict]
+ :param namespace: The namespace to upsert the vectors into.
+ :type namespace: str
+ """
params = {
"vectors": vectors,
"namespace": namespace,
}
- async with self.async_client.post(
- f"{self.base_url}/vectors/upsert",
- json=params,
- ) as response:
- return await response.json(content_type=None)
+
+ if self.base_url and "api.pinecone.io" in self.base_url:
+ if not self.host.startswith("http"):
+ logger.error(f"host exists:{self.host}")
+ self.host = f"https://{self.host}"
+
+ elif self.host.startswith("localhost") and self.base_url:
+ self.host = f"http://{self.base_url.split(':')[-2].strip('/')}:{self.host.split(':')[-1]}"
+ async with aiohttp.ClientSession() as session:
+ async with session.post(
+ f"{self.host}/vectors/upsert",
+ json=params,
+ headers=self.headers,
+ ) as response:
+ res = await response.json(content_type=None)
+ return res
async def _async_create_index(
self,
@@ -676,32 +1134,72 @@ class PineconeIndex(BaseIndex):
region: str,
metric: str = "dotproduct",
):
+ """Asynchronously creates a new index in Pinecone.
+
+ :param name: The name of the index to create.
+ :type name: str
+ :param dimension: The dimension of the index.
+ :type dimension: int
+ :param cloud: The cloud provider to create the index on.
+ :type cloud: str
+ :param region: The region to create the index in.
+ :type region: str
+ :param metric: The metric to use for the index, defaults to "dotproduct".
+ :type metric: str, optional
+ """
params = {
"name": name,
"dimension": dimension,
"metric": metric,
"spec": {"serverless": {"cloud": cloud, "region": region}},
}
- async with self.async_client.post(
- f"{self.base_url}/indexes",
- headers={"Api-Key": self.api_key},
- json=params,
- ) as response:
- return await response.json(content_type=None)
+ async with aiohttp.ClientSession() as session:
+ async with session.post(
+ f"{self.base_url}/indexes",
+ json=params,
+ headers=self.headers,
+ ) as response:
+ return await response.json(content_type=None)
async def _async_delete(self, ids: list[str], namespace: str = ""):
+ """Asynchronously deletes vectors from the index.
+
+ :param ids: The IDs of the vectors to delete.
+ :type ids: list[str]
+ :param namespace: The namespace to delete the vectors from.
+ :type namespace: str
+ """
params = {
"ids": ids,
"namespace": namespace,
}
- async with self.async_client.post(
- f"{self.base_url}/vectors/delete", json=params
- ) as response:
- return await response.json(content_type=None)
+ if self.base_url and "api.pinecone.io" in self.base_url:
+ if not self.host.startswith("http"):
+ logger.error(f"host exists:{self.host}")
+ self.host = f"https://{self.host}"
+ elif self.host.startswith("localhost") and self.base_url:
+ self.host = f"http://{self.base_url.split(':')[-2].strip('/')}:{self.host.split(':')[-1]}"
+
+ async with aiohttp.ClientSession() as session:
+ async with session.post(
+ f"{self.host}/vectors/delete",
+ json=params,
+ headers=self.headers,
+ ) as response:
+ return await response.json(content_type=None)
async def _async_describe_index(self, name: str):
- async with self.async_client.get(f"{self.base_url}/indexes/{name}") as response:
- return await response.json(content_type=None)
+ """Asynchronously describes the index.
+
+ :param name: The name of the index to describe.
+ :type name: str
+ """
+ async with aiohttp.ClientSession() as session:
+ async with session.get(
+ f"{self.base_url}/indexes/{name}",
+ headers=self.headers,
+ ) as response:
+ return await response.json(content_type=None)
async def _async_get_all(
self, prefix: Optional[str] = None, include_metadata: bool = False
@@ -729,83 +1227,110 @@ class PineconeIndex(BaseIndex):
else:
prefix_str = ""
- list_url = f"https://{self.host}/vectors/list{prefix_str}"
+ if self.base_url and "api.pinecone.io" in self.base_url:
+ if not self.host.startswith("http"):
+ logger.error(f"host exists:{self.host}")
+ self.host = f"https://{self.host}"
+
+ elif self.host.startswith("localhost") and self.base_url:
+ self.host = f"http://{self.base_url.split(':')[-2].strip('/')}:{self.host.split(':')[-1]}"
+
+ list_url = f"{self.host}/vectors/list{prefix_str}"
params: dict = {}
if self.namespace:
params["namespace"] = self.namespace
metadata = []
- while True:
- if next_page_token:
- params["paginationToken"] = next_page_token
-
- async with self.async_client.get(
- list_url, params=params, headers={"Api-Key": self.api_key}
- ) as response:
- if response.status != 200:
- error_text = await response.text()
- logger.error(f"Error fetching vectors: {error_text}")
+ async with aiohttp.ClientSession() as session:
+ while True:
+ if next_page_token:
+ params["paginationToken"] = next_page_token
+
+ async with session.get(
+ list_url,
+ params=params,
+ headers=self.headers,
+ ) as response:
+ if response.status != 200:
+ error_text = await response.text()
+ logger.error(f"Error fetching vectors: {error_text}")
+ break
+
+ response_data = await response.json(content_type=None)
+
+ vector_ids = [vec["id"] for vec in response_data.get("vectors", [])]
+ if not vector_ids:
break
+ all_vector_ids.extend(vector_ids)
- response_data = await response.json(content_type=None)
+ if include_metadata:
+ metadata_tasks = [
+ self._async_fetch_metadata(id) for id in vector_ids
+ ]
+ metadata_results = await asyncio.gather(*metadata_tasks)
+ metadata.extend(metadata_results)
- vector_ids = [vec["id"] for vec in response_data.get("vectors", [])]
- if not vector_ids:
- break
- all_vector_ids.extend(vector_ids)
-
- if include_metadata:
- metadata_tasks = [self._async_fetch_metadata(id) for id in vector_ids]
- metadata_results = await asyncio.gather(*metadata_tasks)
- metadata.extend(metadata_results)
-
- next_page_token = response_data.get("pagination", {}).get("next")
- if not next_page_token:
- break
+ next_page_token = response_data.get("pagination", {}).get("next")
+ if not next_page_token:
+ break
return all_vector_ids, metadata
- async def _async_fetch_metadata(self, vector_id: str) -> dict:
+ async def _async_fetch_metadata(
+ self,
+ vector_id: str,
+ namespace: str | None = None,
+ ) -> dict:
"""Fetch metadata for a single vector ID asynchronously using the
- async_client.
+ ClientSession.
:param vector_id: The ID of the vector to fetch metadata for.
:type vector_id: str
+ :param namespace: The namespace to fetch metadata for.
+ :type namespace: str | None
:return: A dictionary containing the metadata for the vector.
:rtype: dict
"""
if self.host == "":
raise ValueError("self.host is not initialized.")
- url = f"https://{self.host}/vectors/fetch"
+ if self.base_url and "api.pinecone.io" in self.base_url:
+ if not self.host.startswith("http"):
+ logger.error(f"host exists:{self.host}")
+ self.host = f"https://{self.host}"
+ elif self.host.startswith("localhost") and self.base_url:
+ self.host = f"http://{self.base_url.split(':')[-2].strip('/')}:{self.host.split(':')[-1]}"
+
+ url = f"{self.host}/vectors/fetch"
params = {
"ids": [vector_id],
}
- if self.namespace:
+ if namespace:
+ params["namespace"] = [namespace]
+ elif self.namespace:
params["namespace"] = [self.namespace]
- headers = {
- "Api-Key": self.api_key,
- }
-
- async with self.async_client.get(
- url, params=params, headers=headers
- ) as response:
- if response.status != 200:
- error_text = await response.text()
- logger.error(f"Error fetching metadata: {error_text}")
- return {}
-
- try:
- response_data = await response.json(content_type=None)
- except Exception as e:
- logger.warning(f"No metadata found for vector {vector_id}: {e}")
- return {}
-
- return (
- response_data.get("vectors", {}).get(vector_id, {}).get("metadata", {})
- )
+ async with aiohttp.ClientSession() as session:
+ async with session.get(
+ url, params=params, headers=self.headers
+ ) as response:
+ if response.status != 200:
+ error_text = await response.text()
+ logger.error(f"Error fetching metadata: {error_text}")
+ return {}
+
+ try:
+ response_data = await response.json(content_type=None)
+ except Exception as e:
+ logger.warning(f"No metadata found for vector {vector_id}: {e}")
+ return {}
+
+ return (
+ response_data.get("vectors", {})
+ .get(vector_id, {})
+ .get("metadata", {})
+ )
def __len__(self):
namespace_stats = self.index.describe_index_stats()["namespaces"].get(
diff --git a/semantic_router/index/postgres.py b/semantic_router/index/postgres.py
index 71ea32e85fb84531f85178fd74663ad8bce61bcb..dda6149e626ea1a2b9af7ee515a1e99c6b52b1d1 100644
--- a/semantic_router/index/postgres.py
+++ b/semantic_router/index/postgres.py
@@ -1,12 +1,12 @@
import os
import uuid
from enum import Enum
-from typing import Any, Dict, List, Optional, Tuple, TYPE_CHECKING, Union
+from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
import numpy as np
from pydantic import BaseModel, Field
-from semantic_router.index.base import BaseIndex
+from semantic_router.index.base import BaseIndex, IndexConfig
from semantic_router.schema import ConfigParameter, Metric, SparseEmbedding
from semantic_router.utils.logger import logger
@@ -15,9 +15,7 @@ if TYPE_CHECKING:
class MetricPgVecOperatorMap(Enum):
- """
- Enum to map the metric to PostgreSQL vector operators.
- """
+ """Enum to map the metric to PostgreSQL vector operators."""
cosine = "<=>"
dotproduct = "<#>" # inner product
@@ -26,8 +24,7 @@ class MetricPgVecOperatorMap(Enum):
def parse_vector(vector_str: Union[str, Any]) -> List[float]:
- """
- Parses a vector from a string or other representation.
+ """Parses a vector from a string or other representation.
:param vector_str: The string or object representation of a vector.
:type vector_str: Union[str, Any]
@@ -42,8 +39,7 @@ def parse_vector(vector_str: Union[str, Any]) -> List[float]:
def clean_route_name(route_name: str) -> str:
- """
- Cleans and formats the route name by stripping spaces and replacing them with hyphens.
+ """Cleans and formats the route name by stripping spaces and replacing them with hyphens.
:param route_name: The original route name.
:type route_name: str
@@ -54,9 +50,7 @@ def clean_route_name(route_name: str) -> str:
class PostgresIndexRecord(BaseModel):
- """
- Model to represent a record in the Postgres index.
- """
+ """Model to represent a record in the Postgres index."""
id: str = ""
route: str
@@ -64,8 +58,7 @@ class PostgresIndexRecord(BaseModel):
vector: List[float]
def __init__(self, **data) -> None:
- """
- Initializes a new Postgres index record with given data.
+ """Initializes a new Postgres index record with given data.
:param data: Field values for the record.
:type data: dict
@@ -81,8 +74,7 @@ class PostgresIndexRecord(BaseModel):
self.id = clean_route + "#" + str(hashed_uuid)
def to_dict(self) -> Dict:
- """
- Converts the record to a dictionary.
+ """Converts the record to a dictionary.
:return: A dictionary representation of the record.
:rtype: Dict
@@ -96,9 +88,7 @@ class PostgresIndexRecord(BaseModel):
class PostgresIndex(BaseIndex):
- """
- Postgres implementation of Index.
- """
+ """Postgres implementation of Index."""
connection_string: Optional[str] = None
index_prefix: str = "semantic_router_"
@@ -118,8 +108,7 @@ class PostgresIndex(BaseIndex):
namespace: Optional[str] = "",
dimensions: int | None = None,
):
- """
- Initializes the Postgres index with the specified parameters.
+ """Initializes the Postgres index with the specified parameters.
:param connection_string: The connection string for the PostgreSQL database.
:type connection_string: Optional[str]
@@ -170,8 +159,7 @@ class PostgresIndex(BaseIndex):
return f"{self.index_prefix}{self.index_name}"
def _get_metric_operator(self) -> str:
- """
- Returns the PostgreSQL operator for the specified metric.
+ """Returns the PostgreSQL operator for the specified metric.
:return: The PostgreSQL operator.
:rtype: str
@@ -179,8 +167,7 @@ class PostgresIndex(BaseIndex):
return MetricPgVecOperatorMap[self.metric.value].value
def _get_score_query(self, embeddings_str: str) -> str:
- """
- Creates the select statement required to return the embeddings distance.
+ """Creates the select statement required to return the embeddings distance.
:param embeddings_str: The string representation of the embeddings.
:type embeddings_str: str
@@ -200,8 +187,7 @@ class PostgresIndex(BaseIndex):
raise ValueError(f"Unsupported metric: {self.metric}")
def setup_index(self) -> None:
- """
- Sets up the index by creating the table and vector extension if they do not exist.
+ """Sets up the index by creating the table and vector extension if they do not exist.
:raises ValueError: If the existing table's vector dimensions do not match the expected dimensions.
:raises TypeError: If the database connection is not established.
@@ -229,8 +215,8 @@ class PostgresIndex(BaseIndex):
self.conn.commit()
def _check_embeddings_dimensions(self) -> bool:
- """
- Checks if the length of the vector embeddings in the table matches the expected dimensions, or if no table exists.
+ """Checks if the length of the vector embeddings in the table matches the expected
+ dimensions, or if no table exists.
:return: True if the dimensions match or the table does not exist, False otherwise.
:rtype: bool
@@ -273,9 +259,9 @@ class PostgresIndex(BaseIndex):
utterances: List[str],
function_schemas: Optional[List[Dict[str, Any]]] = None,
metadata_list: List[Dict[str, Any]] = [],
+ **kwargs,
) -> None:
- """
- Adds vectors to the index.
+ """Adds records to the index.
:param embeddings: A list of vector embeddings to add.
:type embeddings: List[List[float]]
@@ -283,6 +269,10 @@ class PostgresIndex(BaseIndex):
:type routes: List[str]
:param utterances: A list of utterances corresponding to the embeddings.
:type utterances: List[Any]
+ :param function_schemas: A list of function schemas corresponding to the embeddings.
+ :type function_schemas: Optional[List[Dict[str, Any]]]
+ :param metadata_list: A list of metadata corresponding to the embeddings.
+ :type metadata_list: List[Dict[str, Any]]
:raises ValueError: If the vector embeddings being added do not match the expected dimensions.
:raises TypeError: If the database connection is not established.
"""
@@ -309,8 +299,7 @@ class PostgresIndex(BaseIndex):
self.conn.commit()
def delete(self, route_name: str) -> None:
- """
- Deletes records with the specified route name.
+ """Deletes records with the specified route name.
:param route_name: The name of the route to delete records for.
:type route_name: str
@@ -323,17 +312,20 @@ class PostgresIndex(BaseIndex):
cur.execute(f"DELETE FROM {table_name} WHERE route = '{route_name}'")
self.conn.commit()
- def describe(self) -> Dict:
- """
- Describes the index by returning its type, dimensions, and total vector count.
+ def describe(self) -> IndexConfig:
+ """Describes the index by returning its type, dimensions, and total vector count.
- :return: A dictionary containing the index's type, dimensions, and total vector count.
- :rtype: Dict
- :raises TypeError: If the database connection is not established.
+ :return: An IndexConfig object containing the index's type, dimensions, and total vector count.
+ :rtype: IndexConfig
"""
table_name = self._get_table_name()
if not isinstance(self.conn, psycopg2.extensions.connection):
- raise TypeError("Index has not established a connection to Postgres")
+ logger.warning("Index has not established a connection to Postgres")
+ return IndexConfig(
+ type=self.type,
+ dimensions=self.dimensions or 0,
+ vectors=0,
+ )
with self.conn.cursor() as cur:
cur.execute(f"SELECT COUNT(*) FROM {table_name}")
count = cur.fetchone()
@@ -341,11 +333,19 @@ class PostgresIndex(BaseIndex):
count = 0
else:
count = count[0] # Extract the actual count from the tuple
- return {
- "type": self.type,
- "dimensions": self.dimensions,
- "total_vector_count": count,
- }
+ return IndexConfig(
+ type=self.type,
+ dimensions=self.dimensions or 0,
+ vectors=count,
+ )
+
+ def is_ready(self) -> bool:
+ """Checks if the index is ready to be used.
+
+ :return: True if the index is ready, False otherwise.
+ :rtype: bool
+ """
+ return isinstance(self.conn, psycopg2.extensions.connection)
def query(
self,
@@ -354,8 +354,7 @@ class PostgresIndex(BaseIndex):
route_filter: Optional[List[str]] = None,
sparse_vector: dict[int, float] | SparseEmbedding | None = None,
) -> Tuple[np.ndarray, List[str]]:
- """
- Searches the index for the query vector and returns the top_k results.
+ """Searches the index for the query vector and returns the top_k results.
:param vector: The query vector.
:type vector: np.ndarray
@@ -363,6 +362,8 @@ class PostgresIndex(BaseIndex):
:type top_k: int
:param route_filter: Optional list of routes to filter the results by.
:type route_filter: Optional[List[str]]
+ :param sparse_vector: Optional sparse vector to filter the results by.
+ :type sparse_vector: dict[int, float] | SparseEmbedding | None
:return: A tuple containing the scores and routes of the top_k results.
:rtype: Tuple[np.ndarray, List[str]]
:raises TypeError: If the database connection is not established.
@@ -385,8 +386,7 @@ class PostgresIndex(BaseIndex):
]
def _get_route_ids(self, route_name: str):
- """
- Retrieves all vector IDs for a specific route.
+ """Retrieves all vector IDs for a specific route.
:param route_name: The name of the route to retrieve IDs for.
:type route_name: str
@@ -400,8 +400,7 @@ class PostgresIndex(BaseIndex):
def _get_all(
self, route_name: Optional[str] = None, include_metadata: bool = False
):
- """
- Retrieves all vector IDs and optionally metadata from the Postgres index.
+ """Retrieves all vector IDs and optionally metadata from the Postgres index.
:param route_name: Optional route name to filter the results by.
:type route_name: Optional[str]
@@ -437,8 +436,7 @@ class PostgresIndex(BaseIndex):
return all_vector_ids, metadata
def delete_all(self):
- """
- Deletes all records from the Postgres index.
+ """Deletes all records from the Postgres index.
:raises TypeError: If the database connection is not established.
"""
@@ -450,8 +448,7 @@ class PostgresIndex(BaseIndex):
self.conn.commit()
def delete_index(self) -> None:
- """
- Deletes the entire table for the index.
+ """Deletes the entire table for the index.
:raises TypeError: If the database connection is not established.
"""
@@ -463,14 +460,25 @@ class PostgresIndex(BaseIndex):
self.conn.commit()
def aget_routes(self):
+ """Asynchronously get all routes from the index.
+
+ Not yet implemented for PostgresIndex.
+
+ :return: A list of routes.
+ :rtype: List[str]
+ """
raise NotImplementedError("Async get is not implemented for PostgresIndex.")
def _write_config(self, config: ConfigParameter):
+ """Write the config to the index.
+
+ :param config: The config to write to the index.
+ :type config: ConfigParameter
+ """
logger.warning("No config is written for PostgresIndex.")
def __len__(self):
- """
- Returns the total number of vectors in the index.
+ """Returns the total number of vectors in the index.
:return: The total number of vectors.
:rtype: int
@@ -487,8 +495,6 @@ class PostgresIndex(BaseIndex):
return count[0]
class Config:
- """
- Configuration for the Pydantic BaseModel.
- """
+ """Configuration for the Pydantic BaseModel."""
arbitrary_types_allowed = True
diff --git a/semantic_router/index/qdrant.py b/semantic_router/index/qdrant.py
index 1b23753ba886db48947a14108d1fe7a0ced56c42..7e6f0748668fef90d28135a625ef697b152a0fe9 100644
--- a/semantic_router/index/qdrant.py
+++ b/semantic_router/index/qdrant.py
@@ -3,7 +3,7 @@ from typing import Any, Dict, List, Optional, Tuple, Union
import numpy as np
from pydantic import Field
-from semantic_router.index.base import BaseIndex
+from semantic_router.index.base import BaseIndex, IndexConfig
from semantic_router.schema import ConfigParameter, Metric, SparseEmbedding, Utterance
from semantic_router.utils.logger import logger
@@ -95,6 +95,11 @@ class QdrantIndex(BaseIndex):
self.client, self.aclient = self._initialize_clients()
def _initialize_clients(self):
+ """Initialize the clients for the Qdrant index.
+
+ :return: A tuple of the sync and async clients.
+ :rtype: Tuple[QdrantClient, Optional[AsyncQdrantClient]]
+ """
try:
from qdrant_client import AsyncQdrantClient, QdrantClient
@@ -142,6 +147,11 @@ class QdrantIndex(BaseIndex):
) from e
def _init_collection(self) -> None:
+ """Initialize the collection for the Qdrant index.
+
+ :return: None
+ :rtype: None
+ """
from qdrant_client import QdrantClient, models
self.client: QdrantClient
@@ -160,6 +170,11 @@ class QdrantIndex(BaseIndex):
)
def _remove_and_sync(self, routes_to_delete: dict):
+ """Remove and sync the index.
+
+ :param routes_to_delete: The routes to delete.
+ :type routes_to_delete: dict
+ """
logger.error("Sync remove is not implemented for QdrantIndex.")
def add(
@@ -170,7 +185,23 @@ class QdrantIndex(BaseIndex):
function_schemas: Optional[List[Dict[str, Any]]] = None,
metadata_list: List[Dict[str, Any]] = [],
batch_size: int = DEFAULT_UPLOAD_BATCH_SIZE,
+ **kwargs,
):
+ """Add records to the index.
+
+ :param embeddings: The embeddings to add.
+ :type embeddings: List[List[float]]
+ :param routes: The routes to add.
+ :type routes: List[str]
+ :param utterances: The utterances to add.
+ :type utterances: List[str]
+ :param function_schemas: The function schemas to add.
+ :type function_schemas: Optional[List[Dict[str, Any]]]
+ :param metadata_list: The metadata to add.
+ :type metadata_list: List[Dict[str, Any]]
+ :param batch_size: The batch size to use for the upload.
+ :type batch_size: int
+ """
self.dimensions = self.dimensions or len(embeddings[0])
self._init_collection()
@@ -187,13 +218,20 @@ class QdrantIndex(BaseIndex):
batch_size=batch_size,
)
- def get_utterances(self) -> List[Utterance]:
- """
- Gets a list of route and utterance objects currently stored in the index.
+ def get_utterances(self, include_metadata: bool = False) -> List[Utterance]:
+ """Gets a list of route and utterance objects currently stored in the index.
- Returns:
- List[Tuple]: A list of (route_name, utterance, function_schema, metadata) objects.
+ :param include_metadata: Whether to include function schemas and metadata in
+ the returned Utterance objects - QdrantIndex does not currently support this
+ parameter so it is ignored. If required for your use-case please reach out to
+ semantic-router maintainers on GitHub via an issue or PR.
+ :type include_metadata: bool
+ :return: A list of Utterance objects.
+ :rtype: List[Utterance]
"""
+ # Check if collection exists first
+ if not self.client.collection_exists(self.index_name):
+ return []
from qdrant_client import grpc
@@ -231,6 +269,11 @@ class QdrantIndex(BaseIndex):
return utterances
def delete(self, route_name: str):
+ """Delete records from the index.
+
+ :param route_name: The name of the route to delete.
+ :type route_name: str
+ """
from qdrant_client import models
self.client.delete(
@@ -245,14 +288,27 @@ class QdrantIndex(BaseIndex):
),
)
- def describe(self) -> Dict:
+ def describe(self) -> IndexConfig:
+ """Describe the index.
+
+ :return: The index configuration.
+ :rtype: IndexConfig
+ """
collection_info = self.client.get_collection(self.index_name)
- return {
- "type": self.type,
- "dimensions": collection_info.config.params.vectors.size,
- "vectors": collection_info.points_count,
- }
+ return IndexConfig(
+ type=self.type,
+ dimensions=collection_info.config.params.vectors.size,
+ vectors=collection_info.points_count,
+ )
+
+ def is_ready(self) -> bool:
+ """Checks if the index is ready to be used.
+
+ :return: True if the index is ready, False otherwise.
+ :rtype: bool
+ """
+ return self.client.collection_exists(self.index_name)
def query(
self,
@@ -261,6 +317,19 @@ class QdrantIndex(BaseIndex):
route_filter: Optional[List[str]] = None,
sparse_vector: dict[int, float] | SparseEmbedding | None = None,
) -> Tuple[np.ndarray, List[str]]:
+ """Query the index.
+
+ :param vector: The vector to query.
+ :type vector: np.ndarray
+ :param top_k: The number of results to return.
+ :type top_k: int
+ :param route_filter: The route filter to apply.
+ :type route_filter: Optional[List[str]]
+ :param sparse_vector: The sparse vector to query.
+ :type sparse_vector: dict[int, float] | SparseEmbedding | None
+ :return: A tuple of the scores and route names.
+ :rtype: Tuple[np.ndarray, List[str]]
+ """
from qdrant_client import QdrantClient, models
self.client: QdrantClient
@@ -295,6 +364,19 @@ class QdrantIndex(BaseIndex):
route_filter: Optional[List[str]] = None,
sparse_vector: dict[int, float] | SparseEmbedding | None = None,
) -> Tuple[np.ndarray, List[str]]:
+ """Asynchronously query the index.
+
+ :param vector: The vector to query.
+ :type vector: np.ndarray
+ :param top_k: The number of results to return.
+ :type top_k: int
+ :param route_filter: The route filter to apply.
+ :type route_filter: Optional[List[str]]
+ :param sparse_vector: The sparse vector to query.
+ :type sparse_vector: dict[int, float] | SparseEmbedding | None
+ :return: A tuple of the scores and route names.
+ :rtype: Tuple[np.ndarray, List[str]]
+ """
from qdrant_client import AsyncQdrantClient, models
self.aclient: Optional[AsyncQdrantClient]
@@ -327,12 +409,29 @@ class QdrantIndex(BaseIndex):
return np.array(scores), route_names
def aget_routes(self):
+ """Asynchronously get all routes from the index.
+
+ :return: A list of routes.
+ :rtype: List[str]
+ """
logger.error("Sync remove is not implemented for QdrantIndex.")
def delete_index(self):
+ """Delete the index.
+
+ :return: None
+ :rtype: None
+ """
self.client.delete_collection(self.index_name)
def convert_metric(self, metric: Metric):
+ """Convert the metric to a Qdrant distance metric.
+
+ :param metric: The metric to convert.
+ :type metric: Metric
+ :return: The converted metric.
+ :rtype: Distance
+ """
from qdrant_client.models import Distance
mapping = {
@@ -348,6 +447,11 @@ class QdrantIndex(BaseIndex):
return mapping[metric]
def _write_config(self, config: ConfigParameter):
+ """Write the config to the index.
+
+ :param config: The config to write to the index.
+ :type config: ConfigParameter
+ """
logger.warning("No config is written for QdrantIndex.")
def __len__(self):
diff --git a/semantic_router/linear.py b/semantic_router/linear.py
index 1c13262fbe55bfe4cd92f75c61fb33899d60337e..a308dc5a800d8ecfe35c211356e27d5d9b5d64c1 100644
--- a/semantic_router/linear.py
+++ b/semantic_router/linear.py
@@ -7,12 +7,10 @@ from numpy.linalg import norm
def similarity_matrix(xq: np.ndarray, index: np.ndarray) -> np.ndarray:
"""Compute the similarity scores between a query vector and a set of vectors.
- Args:
- xq: A query vector (1d ndarray)
- index: A set of vectors.
-
- Returns:
- The similarity between the query vector and the set of vectors.
+ :param xq: A query vector (1d ndarray)
+ :param index: A set of vectors.
+ :return: The similarity between the query vector and the set of vectors.
+ :rtype: np.ndarray
"""
index_norm = norm(index, axis=1)
@@ -22,7 +20,13 @@ def similarity_matrix(xq: np.ndarray, index: np.ndarray) -> np.ndarray:
def top_scores(sim: np.ndarray, top_k: int = 5) -> Tuple[np.ndarray, np.ndarray]:
- # get indices of top_k records
+ """Get the top scores and indices from a similarity matrix.
+
+ :param sim: A similarity matrix.
+ :param top_k: The number of top scores to get.
+ :return: The top scores and indices.
+ :rtype: Tuple[np.ndarray, np.ndarray]
+ """
top_k = min(top_k, sim.shape[0])
idx = np.argpartition(sim, -top_k)[-top_k:]
scores = sim[idx]
diff --git a/semantic_router/llms/base.py b/semantic_router/llms/base.py
index 02df2764502d2d2bdd70135406c5e27c5a900fda..a3e6c12232596da5bf49d60b3a6b25a8b6fcb70f 100644
--- a/semantic_router/llms/base.py
+++ b/semantic_router/llms/base.py
@@ -1,5 +1,5 @@
import json
-from typing import Any, List, Optional, Dict
+from typing import Any, Dict, List, Optional
from pydantic import BaseModel
@@ -8,6 +8,12 @@ from semantic_router.utils.logger import logger
class BaseLLM(BaseModel):
+ """Base class for LLMs typically used by dynamic routes.
+
+ This class provides a base implementation for LLMs. It defines the common
+ configuration and methods for all LLM classes.
+ """
+
name: str
temperature: Optional[float] = 0.0
max_tokens: Optional[int] = None
@@ -16,15 +22,39 @@ class BaseLLM(BaseModel):
arbitrary_types_allowed = True
def __init__(self, name: str, **kwargs):
+ """Initialize the BaseLLM.
+
+ :param name: The name of the LLM.
+ :type name: str
+ :param **kwargs: Additional keyword arguments for the LLM.
+ :type **kwargs: dict
+ """
super().__init__(name=name, **kwargs)
def __call__(self, messages: List[Message]) -> Optional[str]:
+ """Call the LLM.
+
+ Must be implemented by subclasses.
+
+ :param messages: The messages to pass to the LLM.
+ :type messages: List[Message]
+ :return: The response from the LLM.
+ :rtype: Optional[str]
+ """
raise NotImplementedError("Subclasses must implement this method")
def _check_for_mandatory_inputs(
self, inputs: dict[str, Any], mandatory_params: List[str]
) -> bool:
- """Check for mandatory parameters in inputs"""
+ """Check for mandatory parameters in inputs.
+
+ :param inputs: The inputs to check for mandatory parameters.
+ :type inputs: dict[str, Any]
+ :param mandatory_params: The mandatory parameters to check for.
+ :type mandatory_params: List[str]
+ :return: True if all mandatory parameters are present, False otherwise.
+ :rtype: bool
+ """
for name in mandatory_params:
if name not in inputs:
logger.error(f"Mandatory input {name} missing from query")
@@ -34,7 +64,15 @@ class BaseLLM(BaseModel):
def _check_for_extra_inputs(
self, inputs: dict[str, Any], all_params: List[str]
) -> bool:
- """Check for extra parameters not defined in the signature"""
+ """Check for extra parameters not defined in the signature.
+
+ :param inputs: The inputs to check for extra parameters.
+ :type inputs: dict[str, Any]
+ :param all_params: The all parameters to check for.
+ :type all_params: List[str]
+ :return: True if all extra parameters are present, False otherwise.
+ :rtype: bool
+ """
input_keys = set(inputs.keys())
param_keys = set(all_params)
if not input_keys.issubset(param_keys):
@@ -49,7 +87,15 @@ class BaseLLM(BaseModel):
self, inputs: List[Dict[str, Any]], function_schemas: List[Dict[str, Any]]
) -> bool:
"""Determine if the functions chosen by the LLM exist within the function_schemas,
- and if the input arguments are valid for those functions."""
+ and if the input arguments are valid for those functions.
+
+ :param inputs: The inputs to check for validity.
+ :type inputs: List[Dict[str, Any]]
+ :param function_schemas: The function schemas to check against.
+ :type function_schemas: List[Dict[str, Any]]
+ :return: True if the inputs are valid, False otherwise.
+ :rtype: bool
+ """
try:
# Currently only supporting single functions for most LLMs in Dynamic Routes.
if len(inputs) != 1:
@@ -72,7 +118,15 @@ class BaseLLM(BaseModel):
def _validate_single_function_inputs(
self, inputs: Dict[str, Any], function_schema: Dict[str, Any]
) -> bool:
- """Validate the extracted inputs against the function schema"""
+ """Validate the extracted inputs against the function schema.
+
+ :param inputs: The inputs to validate.
+ :type inputs: Dict[str, Any]
+ :param function_schema: The function schema to validate against.
+ :type function_schema: Dict[str, Any]
+ :return: True if the inputs are valid, False otherwise.
+ :rtype: bool
+ """
try:
# Extract parameter names and determine if they are optional
signature = function_schema["signature"]
@@ -107,7 +161,13 @@ class BaseLLM(BaseModel):
return False
def _extract_parameter_info(self, signature: str) -> tuple[List[str], List[str]]:
- """Extract parameter names and types from the function signature."""
+ """Extract parameter names and types from the function signature.
+
+ :param signature: The function signature to extract parameter names and types from.
+ :type signature: str
+ :return: A tuple of parameter names and types.
+ :rtype: tuple[List[str], List[str]]
+ """
param_info = [param.strip() for param in signature[1:-1].split(",")]
param_names = [info.split(":")[0].strip() for info in param_info]
param_types = [
@@ -118,6 +178,15 @@ class BaseLLM(BaseModel):
def extract_function_inputs(
self, query: str, function_schemas: List[Dict[str, Any]]
) -> List[Dict[str, Any]]:
+ """Extract the function inputs from the query.
+
+ :param query: The query to extract the function inputs from.
+ :type query: str
+ :param function_schemas: The function schemas to extract the function inputs from.
+ :type function_schemas: List[Dict[str, Any]]
+ :return: The function inputs.
+ :rtype: List[Dict[str, Any]]
+ """
logger.info("Extracting function input...")
prompt = f"""
diff --git a/semantic_router/llms/cohere.py b/semantic_router/llms/cohere.py
index d37f979d623613c91bbc9a433b6b03d7a9f9cb3f..707887e389ba360b03b456a61419f4461e52f8ed 100644
--- a/semantic_router/llms/cohere.py
+++ b/semantic_router/llms/cohere.py
@@ -8,6 +8,12 @@ from semantic_router.schema import Message
class CohereLLM(BaseLLM):
+ """LLM for Cohere. Requires a Cohere API key from https://dashboard.cohere.com/api-keys.
+
+ This class provides functionality to interact with the Cohere API for generating text responses.
+ It extends the BaseLLM class and implements the __call__ method to generate text responses.
+ """
+
_client: Any = PrivateAttr()
def __init__(
@@ -15,12 +21,27 @@ class CohereLLM(BaseLLM):
name: Optional[str] = None,
cohere_api_key: Optional[str] = None,
):
+ """Initialize the CohereLLM.
+
+ :param name: The name of the Cohere model to use can also be set via the
+ COHERE_CHAT_MODEL_NAME environment variable.
+ :type name: Optional[str]
+ :param cohere_api_key: The API key for the Cohere client. Can also be set via the
+ COHERE_API_KEY environment variable.
+ :type cohere_api_key: Optional[str]
+ """
if name is None:
name = os.getenv("COHERE_CHAT_MODEL_NAME", "command")
super().__init__(name=name)
self._client = self._initialize_client(cohere_api_key)
def _initialize_client(self, cohere_api_key: Optional[str] = None):
+ """Initialize the Cohere client.
+
+ :param cohere_api_key: The API key for the Cohere client. Can also be set via the
+ COHERE_API_KEY environment variable.
+ :type cohere_api_key: Optional[str]
+ """
try:
import cohere
except ImportError:
@@ -41,6 +62,13 @@ class CohereLLM(BaseLLM):
return client
def __call__(self, messages: List[Message]) -> str:
+ """Call the Cohere client.
+
+ :param messages: The messages to pass to the Cohere client.
+ :type messages: List[Message]
+ :return: The response from the Cohere client.
+ :rtype: str
+ """
if self._client is None:
raise ValueError("Cohere client is not initialized.")
try:
diff --git a/semantic_router/llms/llamacpp.py b/semantic_router/llms/llamacpp.py
index 8257593c6f250a8bc1ded5f3af0dbc30a8b44141..6940907bfad0de63466b9a624d3a7f15050dbb82 100644
--- a/semantic_router/llms/llamacpp.py
+++ b/semantic_router/llms/llamacpp.py
@@ -1,6 +1,6 @@
from contextlib import contextmanager
from pathlib import Path
-from typing import Any, Optional, List, Dict
+from typing import Any, Dict, List, Optional
from pydantic import PrivateAttr
@@ -10,6 +10,10 @@ from semantic_router.utils.logger import logger
class LlamaCppLLM(BaseLLM):
+ """LLM for LlamaCPP. Enables fully local LLM use, helpful for local implementation of
+ dynamic routes.
+ """
+
llm: Any
grammar: Optional[Any] = None
_llama_cpp: Any = PrivateAttr()
@@ -22,6 +26,19 @@ class LlamaCppLLM(BaseLLM):
max_tokens: Optional[int] = 200,
grammar: Optional[Any] = None,
):
+ """Initialize the LlamaCPPLLM.
+
+ :param llm: The LLM to use.
+ :type llm: Any
+ :param name: The name of the LLM.
+ :type name: str
+ :param temperature: The temperature of the LLM.
+ :type temperature: float
+ :param max_tokens: The maximum number of tokens to generate.
+ :type max_tokens: Optional[int]
+ :param grammar: The grammar to use.
+ :type grammar: Optional[Any]
+ """
super().__init__(
name=name,
llm=llm,
@@ -48,6 +65,13 @@ class LlamaCppLLM(BaseLLM):
self,
messages: List[Message],
) -> str:
+ """Call the LlamaCPPLLM.
+
+ :param messages: The messages to pass to the LlamaCPPLLM.
+ :type messages: List[Message]
+ :return: The response from the LlamaCPPLLM.
+ :rtype: str
+ """
try:
completion = self.llm.create_chat_completion(
messages=[m.to_llamacpp() for m in messages],
@@ -68,6 +92,11 @@ class LlamaCppLLM(BaseLLM):
@contextmanager
def _grammar(self):
+ """Context manager for the grammar.
+
+ :return: The grammar.
+ :rtype: Any
+ """
grammar_path = Path(__file__).parent.joinpath("grammars", "json.gbnf")
assert grammar_path.exists(), f"{grammar_path}\ndoes not exist"
try:
@@ -79,6 +108,15 @@ class LlamaCppLLM(BaseLLM):
def extract_function_inputs(
self, query: str, function_schemas: List[Dict[str, Any]]
) -> List[Dict[str, Any]]:
+ """Extract the function inputs from the query.
+
+ :param query: The query to extract the function inputs from.
+ :type query: str
+ :param function_schemas: The function schemas to extract the function inputs from.
+ :type function_schemas: List[Dict[str, Any]]
+ :return: The function inputs.
+ :rtype: List[Dict[str, Any]]
+ """
with self._grammar():
return super().extract_function_inputs(
query=query, function_schemas=function_schemas
diff --git a/semantic_router/llms/mistral.py b/semantic_router/llms/mistral.py
index 370fba65756224ded1af6841382f02f63578851d..d66fa5576541418c80879d530ef723be6d98a376 100644
--- a/semantic_router/llms/mistral.py
+++ b/semantic_router/llms/mistral.py
@@ -10,6 +10,8 @@ from semantic_router.utils.logger import logger
class MistralAILLM(BaseLLM):
+ """LLM for MistralAI. Requires a MistralAI API key from https://console.mistral.ai/api-keys/"""
+
_client: Any = PrivateAttr()
_mistralai: Any = PrivateAttr()
@@ -20,6 +22,17 @@ class MistralAILLM(BaseLLM):
temperature: float = 0.01,
max_tokens: int = 200,
):
+ """Initialize the MistralAILLM.
+
+ :param name: The name of the MistralAI model to use.
+ :type name: Optional[str]
+ :param mistralai_api_key: The MistralAI API key.
+ :type mistralai_api_key: Optional[str]
+ :param temperature: The temperature of the LLM.
+ :type temperature: float
+ :param max_tokens: The maximum number of tokens to generate.
+ :type max_tokens: int
+ """
if name is None:
name = EncoderDefault.MISTRAL.value["language_model"]
super().__init__(name=name)
@@ -28,6 +41,13 @@ class MistralAILLM(BaseLLM):
self.max_tokens = max_tokens
def _initialize_client(self, api_key):
+ """Initialize the MistralAI client.
+
+ :param api_key: The MistralAI API key.
+ :type api_key: Optional[str]
+ :return: The MistralAI client.
+ :rtype: MistralClient
+ """
try:
import mistralai
from mistralai.client import MistralClient
@@ -49,9 +69,15 @@ class MistralAILLM(BaseLLM):
return client, mistralai
def __call__(self, messages: List[Message]) -> str:
+ """Call the MistralAILLM.
+
+ :param messages: The messages to pass to the MistralAILLM.
+ :type messages: List[Message]
+ :return: The response from the MistralAILLM.
+ :rtype: str
+ """
if self._client is None:
raise ValueError("MistralAI client is not initialized.")
-
chat_messages = [
self._mistralai.models.chat_completion.ChatMessage(
role=m.role, content=m.content
diff --git a/semantic_router/llms/ollama.py b/semantic_router/llms/ollama.py
index f6e9779ea65197e08f47135af8fec0c2dba8f295..aa2a38a132d087bb8b3da32fafc9ba18d950ec52 100644
--- a/semantic_router/llms/ollama.py
+++ b/semantic_router/llms/ollama.py
@@ -8,6 +8,10 @@ from semantic_router.utils.logger import logger
class OllamaLLM(BaseLLM):
+ """LLM for Ollama. Enables fully local LLM use, helpful for local implementation of
+ dynamic routes.
+ """
+
stream: bool = False
def __init__(
@@ -17,6 +21,17 @@ class OllamaLLM(BaseLLM):
max_tokens: Optional[int] = 200,
stream: bool = False,
):
+ """Initialize the OllamaLLM.
+
+ :param name: The name of the Ollama model to use.
+ :type name: str
+ :param temperature: The temperature of the LLM.
+ :type temperature: float
+ :param max_tokens: The maximum number of tokens to generate.
+ :type max_tokens: Optional[int]
+ :param stream: Whether to stream the response.
+ :type stream: bool
+ """
super().__init__(name=name)
self.temperature = temperature
self.max_tokens = max_tokens
@@ -30,6 +45,19 @@ class OllamaLLM(BaseLLM):
max_tokens: Optional[int] = None,
stream: Optional[bool] = None,
) -> str:
+ """Call the OllamaLLM.
+
+ :param messages: The messages to pass to the OllamaLLM.
+ :type messages: List[Message]
+ :param temperature: The temperature of the LLM.
+ :type temperature: Optional[float]
+ :param name: The name of the Ollama model to use.
+ :type name: Optional[str]
+ :param max_tokens: The maximum number of tokens to generate.
+ :type max_tokens: Optional[int]
+ :param stream: Whether to stream the response.
+ :type stream: Optional[bool]
+ """
# Use instance defaults if not overridden
temperature = temperature if temperature is not None else self.temperature
name = name if name is not None else self.name
diff --git a/semantic_router/llms/openai.py b/semantic_router/llms/openai.py
index 7dc565e8877a46d74e5fd86d4e3d05e04c52175c..169128477eb330531e52d8522bf6002c8bd6760b 100644
--- a/semantic_router/llms/openai.py
+++ b/semantic_router/llms/openai.py
@@ -1,27 +1,29 @@
+import inspect
+import json
import os
-from typing import List, Optional, Any, Callable, Dict, Union
-from pydantic import PrivateAttr
+import re
+from typing import Any, Callable, Dict, List, Optional, Union
import openai
-from openai._types import NotGiven, NOT_GIVEN
+from openai._types import NOT_GIVEN, NotGiven
+from openai.types.chat.chat_completion_message_tool_call import (
+ ChatCompletionMessageToolCall,
+)
+from pydantic import PrivateAttr
from semantic_router.llms import BaseLLM
from semantic_router.schema import Message
from semantic_router.utils.defaults import EncoderDefault
-from semantic_router.utils.logger import logger
-import json
from semantic_router.utils.function_call import (
- get_schema,
convert_python_type_to_json_type,
+ get_schema,
)
-import inspect
-import re
-from openai.types.chat.chat_completion_message_tool_call import (
- ChatCompletionMessageToolCall,
-)
+from semantic_router.utils.logger import logger
class OpenAILLM(BaseLLM):
+ """LLM for OpenAI. Requires an OpenAI API key from https://platform.openai.com/api-keys."""
+
_client: Optional[openai.OpenAI] = PrivateAttr(default=None)
_async_client: Optional[openai.AsyncOpenAI] = PrivateAttr(default=None)
@@ -32,6 +34,17 @@ class OpenAILLM(BaseLLM):
temperature: float = 0.01,
max_tokens: int = 200,
):
+ """Initialize the OpenAILLM.
+
+ :param name: The name of the OpenAI model to use.
+ :type name: Optional[str]
+ :param openai_api_key: The OpenAI API key.
+ :type openai_api_key: Optional[str]
+ :param temperature: The temperature of the LLM.
+ :type temperature: float
+ :param max_tokens: The maximum number of tokens to generate.
+ :type max_tokens: int
+ """
if name is None:
name = EncoderDefault.OPENAI.value["language_model"]
super().__init__(name=name)
@@ -51,6 +64,13 @@ class OpenAILLM(BaseLLM):
def _extract_tool_calls_info(
self, tool_calls: List[ChatCompletionMessageToolCall]
) -> List[Dict[str, Any]]:
+ """Extract the tool calls information from the tool calls.
+
+ :param tool_calls: The tool calls to extract the information from.
+ :type tool_calls: List[ChatCompletionMessageToolCall]
+ :return: The tool calls information.
+ :rtype: List[Dict[str, Any]]
+ """
tool_calls_info = []
for tool_call in tool_calls:
if tool_call.function.arguments is None:
@@ -68,6 +88,13 @@ class OpenAILLM(BaseLLM):
async def async_extract_tool_calls_info(
self, tool_calls: List[ChatCompletionMessageToolCall]
) -> List[Dict[str, Any]]:
+ """Extract the tool calls information from the tool calls.
+
+ :param tool_calls: The tool calls to extract the information from.
+ :type tool_calls: List[ChatCompletionMessageToolCall]
+ :return: The tool calls information.
+ :rtype: List[Dict[str, Any]]
+ """
tool_calls_info = []
for tool_call in tool_calls:
if tool_call.function.arguments is None:
@@ -87,6 +114,15 @@ class OpenAILLM(BaseLLM):
messages: List[Message],
function_schemas: Optional[List[Dict[str, Any]]] = None,
) -> str:
+ """Call the OpenAILLM.
+
+ :param messages: The messages to pass to the OpenAILLM.
+ :type messages: List[Message]
+ :param function_schemas: The function schemas to pass to the OpenAILLM.
+ :type function_schemas: Optional[List[Dict[str, Any]]]
+ :return: The response from the OpenAILLM.
+ :rtype: str
+ """
if self._client is None:
raise ValueError("OpenAI client is not initialized.")
try:
@@ -131,6 +167,15 @@ class OpenAILLM(BaseLLM):
messages: List[Message],
function_schemas: Optional[List[Dict[str, Any]]] = None,
) -> str:
+ """Call the OpenAILLM asynchronously.
+
+ :param messages: The messages to pass to the OpenAILLM.
+ :type messages: List[Message]
+ :param function_schemas: The function schemas to pass to the OpenAILLM.
+ :type function_schemas: Optional[List[Dict[str, Any]]]
+ :return: The response from the OpenAILLM.
+ :rtype: str
+ """
if self._async_client is None:
raise ValueError("OpenAI async_client is not initialized.")
try:
@@ -173,6 +218,15 @@ class OpenAILLM(BaseLLM):
def extract_function_inputs(
self, query: str, function_schemas: List[Dict[str, Any]]
) -> List[Dict[str, Any]]:
+ """Extract the function inputs from the query.
+
+ :param query: The query to extract the function inputs from.
+ :type query: str
+ :param function_schemas: The function schemas to extract the function inputs from.
+ :type function_schemas: List[Dict[str, Any]]
+ :return: The function inputs.
+ :rtype: List[Dict[str, Any]]
+ """
system_prompt = "You are an intelligent AI. Given a command or request from the user, call the function to complete the request."
messages = [
Message(role="system", content=system_prompt),
@@ -190,6 +244,15 @@ class OpenAILLM(BaseLLM):
async def async_extract_function_inputs(
self, query: str, function_schemas: List[Dict[str, Any]]
) -> List[Dict[str, Any]]:
+ """Extract the function inputs from the query asynchronously.
+
+ :param query: The query to extract the function inputs from.
+ :type query: str
+ :param function_schemas: The function schemas to extract the function inputs from.
+ :type function_schemas: List[Dict[str, Any]]
+ :return: The function inputs.
+ :rtype: List[Dict[str, Any]]
+ """
system_prompt = "You are an intelligent AI. Given a command or request from the user, call the function to complete the request."
messages = [
Message(role="system", content=system_prompt),
@@ -208,7 +271,15 @@ class OpenAILLM(BaseLLM):
self, inputs: List[Dict[str, Any]], function_schemas: List[Dict[str, Any]]
) -> bool:
"""Determine if the functions chosen by the LLM exist within the function_schemas,
- and if the input arguments are valid for those functions."""
+ and if the input arguments are valid for those functions.
+
+ :param inputs: The inputs to check for validity.
+ :type inputs: List[Dict[str, Any]]
+ :param function_schemas: The function schemas to check against.
+ :type function_schemas: List[Dict[str, Any]]
+ :return: True if the inputs are valid, False otherwise.
+ :rtype: bool
+ """
try:
for input_dict in inputs:
# Check if 'function_name' and 'arguments' keys exist in each input dictionary
@@ -251,7 +322,14 @@ class OpenAILLM(BaseLLM):
def _validate_single_function_inputs(
self, inputs: Dict[str, Any], function_schema: Dict[str, Any]
) -> bool:
- """Validate the extracted inputs against the function schema"""
+ """Validate the extracted inputs against the function schema.
+
+ :param inputs: The inputs to validate.
+ :type inputs: Dict[str, Any]
+ :param function_schema: The function schema to validate against.
+ :type function_schema: Dict[str, Any]
+ :return: True if the inputs are valid, False otherwise.
+ """
try:
# Access the parameters and their properties from the function schema directly
parameters = function_schema["parameters"]["properties"]
@@ -283,6 +361,13 @@ class OpenAILLM(BaseLLM):
def get_schemas_openai(items: List[Callable]) -> List[Dict[str, Any]]:
+ """Get function schemas for the OpenAI LLM from a list of functions.
+
+ :param items: The functions to get function schemas for.
+ :type items: List[Callable]
+ :return: The schemas for the OpenAI LLM.
+ :rtype: List[Dict[str, Any]]
+ """
schemas = []
for item in items:
if not callable(item):
diff --git a/semantic_router/llms/openrouter.py b/semantic_router/llms/openrouter.py
index 0376236486c06f32775730bb332f13c5f87b0ab8..523528089425fa13b71cb7fdbfd8c278eea71840 100644
--- a/semantic_router/llms/openrouter.py
+++ b/semantic_router/llms/openrouter.py
@@ -1,9 +1,8 @@
import os
from typing import List, Optional
-from pydantic import PrivateAttr
-
import openai
+from pydantic import PrivateAttr
from semantic_router.llms import BaseLLM
from semantic_router.schema import Message
@@ -11,6 +10,9 @@ from semantic_router.utils.logger import logger
class OpenRouterLLM(BaseLLM):
+ """LLM for OpenRouter. Requires an OpenRouter API key, see here for more information
+ https://openrouter.ai/docs/api-reference/authentication#using-an-api-key"""
+
_client: Optional[openai.OpenAI] = PrivateAttr(default=None)
_base_url: str = PrivateAttr(default="https://openrouter.ai/api/v1")
@@ -22,6 +24,19 @@ class OpenRouterLLM(BaseLLM):
temperature: float = 0.01,
max_tokens: int = 200,
):
+ """Initialize the OpenRouterLLM.
+
+ :param name: The name of the OpenRouter model to use.
+ :type name: Optional[str]
+ :param openrouter_api_key: The OpenRouter API key.
+ :type openrouter_api_key: Optional[str]
+ :param base_url: The base URL for the OpenRouter API.
+ :type base_url: str
+ :param temperature: The temperature of the LLM.
+ :type temperature: float
+ :param max_tokens: The maximum number of tokens to generate.
+ :type max_tokens: int
+ """
if name is None:
name = os.getenv(
"OPENROUTER_CHAT_MODEL_NAME", "mistralai/mistral-7b-instruct"
@@ -41,6 +56,13 @@ class OpenRouterLLM(BaseLLM):
self.max_tokens = max_tokens
def __call__(self, messages: List[Message]) -> str:
+ """Call the OpenRouterLLM.
+
+ :param messages: The messages to pass to the OpenRouterLLM.
+ :type messages: List[Message]
+ :return: The response from the OpenRouterLLM.
+ :rtype: str
+ """
if self._client is None:
raise ValueError("OpenRouter client is not initialized.")
try:
diff --git a/semantic_router/llms/zure.py b/semantic_router/llms/zure.py
index ba833d044785b775b0da123db554a79f55dd5be8..143ad63b6195c70291c6d2b3940e5138eedfd857 100644
--- a/semantic_router/llms/zure.py
+++ b/semantic_router/llms/zure.py
@@ -1,8 +1,8 @@
import os
from typing import List, Optional
-from pydantic import PrivateAttr
import openai
+from pydantic import PrivateAttr
from semantic_router.llms import BaseLLM
from semantic_router.schema import Message
@@ -11,6 +11,8 @@ from semantic_router.utils.logger import logger
class AzureOpenAILLM(BaseLLM):
+ """LLM for Azure OpenAI. Requires an Azure OpenAI API key."""
+
_client: Optional[openai.AzureOpenAI] = PrivateAttr(default=None)
def __init__(
@@ -22,6 +24,21 @@ class AzureOpenAILLM(BaseLLM):
max_tokens: int = 200,
api_version="2023-07-01-preview",
):
+ """Initialize the AzureOpenAILLM.
+
+ :param name: The name of the Azure OpenAI model to use.
+ :type name: Optional[str]
+ :param openai_api_key: The Azure OpenAI API key.
+ :type openai_api_key: Optional[str]
+ :param azure_endpoint: The Azure OpenAI endpoint.
+ :type azure_endpoint: Optional[str]
+ :param temperature: The temperature of the LLM.
+ :type temperature: float
+ :param max_tokens: The maximum number of tokens to generate.
+ :type max_tokens: int
+ :param api_version: The API version to use.
+ :type api_version: str
+ """
if name is None:
name = EncoderDefault.AZURE.value["language_model"]
super().__init__(name=name)
@@ -41,6 +58,13 @@ class AzureOpenAILLM(BaseLLM):
self.max_tokens = max_tokens
def __call__(self, messages: List[Message]) -> str:
+ """Call the AzureOpenAILLM.
+
+ :param messages: The messages to pass to the AzureOpenAILLM.
+ :type messages: List[Message]
+ :return: The response from the AzureOpenAILLM.
+ :rtype: str
+ """
if self._client is None:
raise ValueError("AzureOpenAI client is not initialized.")
try:
diff --git a/semantic_router/route.py b/semantic_router/route.py
index b1fc5e8b699bcaa7a117fabc7f8654a2ae4f4f9c..4df7f1fcf70d9daca719d6ef8db58eeb8b5e632c 100644
--- a/semantic_router/route.py
+++ b/semantic_router/route.py
@@ -11,6 +11,13 @@ from semantic_router.utils.logger import logger
def is_valid(route_config: str) -> bool:
+ """Check if the route config is valid.
+
+ :param route_config: The route config to check.
+ :type route_config: str
+ :return: Whether the route config is valid.
+ :rtype: bool
+ """
try:
output_json = json.loads(route_config)
required_keys = ["name", "utterances"]
@@ -39,6 +46,24 @@ def is_valid(route_config: str) -> bool:
class Route(BaseModel):
+ """A route for the semantic router.
+
+ :param name: The name of the route.
+ :type name: str
+ :param utterances: The utterances of the route.
+ :type utterances: Union[List[str], List[Any]]
+ :param description: The description of the route.
+ :type description: Optional[str]
+ :param function_schemas: The function schemas of the route.
+ :type function_schemas: Optional[List[Dict[str, Any]]]
+ :param llm: The LLM to use.
+ :type llm: Optional[BaseLLM]
+ :param score_threshold: The score threshold of the route.
+ :type score_threshold: Optional[float]
+ :param metadata: The metadata of the route.
+ :type metadata: Optional[Dict[str, Any]]
+ """
+
name: str
utterances: Union[List[str], List[Any]]
description: Optional[str] = None
@@ -51,6 +76,14 @@ class Route(BaseModel):
arbitrary_types_allowed = True
def __call__(self, query: Optional[str] = None) -> RouteChoice:
+ """Call the route. If dynamic routes have been provided the query must have been
+ provided and the llm attribute must be set.
+
+ :param query: The query to pass to the route.
+ :type query: Optional[str]
+ :return: The route choice.
+ :rtype: RouteChoice
+ """
if self.function_schemas:
if not self.llm:
raise ValueError(
@@ -73,6 +106,14 @@ class Route(BaseModel):
return RouteChoice(name=self.name, function_call=func_call)
async def acall(self, query: Optional[str] = None) -> RouteChoice:
+ """Asynchronous call the route. If dynamic routes have been provided the query
+ must have been provided and the llm attribute must be set.
+
+ :param query: The query to pass to the route.
+ :type query: Optional[str]
+ :return: The route choice.
+ :rtype: RouteChoice
+ """
if self.function_schemas:
if not self.llm:
raise ValueError(
@@ -95,6 +136,11 @@ class Route(BaseModel):
return RouteChoice(name=self.name, function_call=func_call)
def to_dict(self) -> Dict[str, Any]:
+ """Convert the route to a dictionary.
+
+ :return: The dictionary representation of the route.
+ :rtype: Dict[str, Any]
+ """
data = self.dict()
if self.llm is not None:
data["llm"] = {
@@ -106,14 +152,27 @@ class Route(BaseModel):
@classmethod
def from_dict(cls, data: Dict[str, Any]):
+ """Create a Route object from a dictionary.
+
+ :param data: The dictionary to create the route from.
+ :type data: Dict[str, Any]
+ :return: The created route.
+ :rtype: Route
+ """
return cls(**data)
@classmethod
def from_dynamic_route(
cls, llm: BaseLLM, entities: List[Union[BaseModel, Callable]], route_name: str
):
- """
- Generate a dynamic Route object from a list of functions or Pydantic models using LLM
+ """Generate a dynamic Route object from a list of functions or Pydantic models
+ using an LLM.
+
+ :param llm: The LLM to use.
+ :type llm: BaseLLM
+ :param entities: The entities to use.
+ :type entities: List[Union[BaseModel, Callable]]
+ :param route_name: The name of the route.
"""
schemas = function_call.get_schema_list(items=entities)
dynamic_route = cls._generate_dynamic_route(
@@ -124,6 +183,14 @@ class Route(BaseModel):
@classmethod
def _parse_route_config(cls, config: str) -> str:
+ """Parse the route config from the LLM output using regex. Expects the output
+ content to be wrapped in <config></config> tags.
+
+ :param config: The LLM output.
+ :type config: str
+ :return: The parsed route config.
+ :rtype: str
+ """
# Regular expression to match content inside <config></config>
config_pattern = r"<config>(.*?)</config>"
match = re.search(config_pattern, config, re.DOTALL)
@@ -138,8 +205,14 @@ class Route(BaseModel):
def _generate_dynamic_route(
cls, llm: BaseLLM, function_schemas: List[Dict[str, Any]], route_name: str
):
- logger.info("Generating dynamic route...")
+ """Generate a dynamic Route object from a list of function schemas using an LLM.
+ :param llm: The LLM to use.
+ :type llm: BaseLLM
+ :param function_schemas: The function schemas to use.
+ :type function_schemas: List[Dict[str, Any]]
+ :param route_name: The name of the route.
+ """
formatted_schemas = "\n".join(
[json.dumps(schema, indent=4) for schema in function_schemas]
)
@@ -176,8 +249,6 @@ class Route(BaseModel):
route_config = cls._parse_route_config(config=output)
- logger.info(f"Generated route config:\n{route_config}")
-
if is_valid(route_config):
route_config_dict = json.loads(route_config)
route_config_dict["llm"] = llm
diff --git a/semantic_router/routers/__init__.py b/semantic_router/routers/__init__.py
index 79987406ed8c430993f714f433e6df892b53e299..754ea17d7c4751d52aa4ff6024e7856a67f31e9a 100644
--- a/semantic_router/routers/__init__.py
+++ b/semantic_router/routers/__init__.py
@@ -1,6 +1,6 @@
from semantic_router.routers.base import BaseRouter, RouterConfig
-from semantic_router.routers.semantic import SemanticRouter
from semantic_router.routers.hybrid import HybridRouter
+from semantic_router.routers.semantic import SemanticRouter
__all__ = [
"BaseRouter",
diff --git a/semantic_router/routers/base.py b/semantic_router/routers/base.py
index 0d47f939b3c8b84a759edc0ed7bca21f9e150b04..a6e27b40197f1a80e8d6e86e884fbcc42d23678a 100644
--- a/semantic_router/routers/base.py
+++ b/semantic_router/routers/base.py
@@ -1,20 +1,27 @@
+import hashlib
import importlib
import json
import os
import random
-import hashlib
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
-from typing_extensions import deprecated
-from pydantic import BaseModel, Field
import numpy as np
import yaml # type: ignore
+from pydantic import BaseModel, Field
from tqdm.auto import tqdm
+from typing_extensions import deprecated
-from semantic_router.encoders import AutoEncoder, DenseEncoder, OpenAIEncoder
+from semantic_router.encoders import (
+ AutoEncoder,
+ DenseEncoder,
+ OpenAIEncoder,
+ SparseEncoder,
+)
+from semantic_router.encoders.encode_input_type import EncodeInputType
from semantic_router.index.base import BaseIndex
from semantic_router.index.local import LocalIndex
from semantic_router.index.pinecone import PineconeIndex
+from semantic_router.index.qdrant import QdrantIndex
from semantic_router.llms import BaseLLM, OpenAILLM
from semantic_router.route import Route
from semantic_router.schema import (
@@ -59,12 +66,9 @@ def is_valid(layer_config: str) -> bool:
class RouterConfig:
- """
- Generates a RouterConfig object that can be used for initializing a
- Routers.
- """
+ """Generates a RouterConfig object that can be used for initializing routers."""
- routes: List[Route] = []
+ routes: List[Route] = Field(default_factory=list)
class Config:
arbitrary_types_allowed = True
@@ -75,6 +79,15 @@ class RouterConfig:
encoder_type: str = "openai",
encoder_name: Optional[str] = None,
):
+ """Initialize a RouterConfig object.
+
+ :param routes: A list of routes.
+ :type routes: List[Route]
+ :param encoder_type: The type of encoder to use.
+ :type encoder_type: str
+ :param encoder_name: The name of the encoder to use.
+ :type encoder_name: Optional[str]
+ """
self.encoder_type = encoder_type
if encoder_name is None:
for encode_type in EncoderType:
@@ -93,6 +106,12 @@ class RouterConfig:
@classmethod
def from_file(cls, path: str) -> "RouterConfig":
+ """Initialize a RouterConfig from a file. Expects a JSON or YAML file with file
+ extension .json, .yaml, or .yml.
+
+ :param path: The path to the file to load the RouterConfig from.
+ :type path: str
+ """
logger.info(f"Loading route config from {path}")
_, ext = os.path.splitext(path)
with open(path, "r") as f:
@@ -192,7 +211,7 @@ class RouterConfig:
:param encoder_name: The name of the encoder to use, defaults to None.
:type encoder_name: Optional[str], optional
"""
- remote_routes = index.get_utterances()
+ remote_routes = index.get_utterances(include_metadata=True)
return cls.from_tuples(
route_tuples=[utt.to_tuple() for utt in remote_routes],
encoder_type=encoder_type,
@@ -200,6 +219,11 @@ class RouterConfig:
)
def to_dict(self) -> Dict[str, Any]:
+ """Convert the RouterConfig to a dictionary.
+
+ :return: A dictionary representation of the RouterConfig.
+ :rtype: Dict[str, Any]
+ """
return {
"encoder_type": self.encoder_type,
"encoder_name": self.encoder_name,
@@ -207,7 +231,11 @@ class RouterConfig:
}
def to_file(self, path: str):
- """Save the routes to a file in JSON or YAML format"""
+ """Save the routes to a file in JSON or YAML format.
+
+ :param path: The path to save the RouterConfig to.
+ :type path: str
+ """
logger.info(f"Saving route config to {path}")
_, ext = os.path.splitext(path)
@@ -260,6 +288,13 @@ class RouterConfig:
logger.info(f"Added route `{route.name}`")
def get(self, name: str) -> Optional[Route]:
+ """Get a route from the RouterConfig by name.
+
+ :param name: The name of the route to get.
+ :type name: str
+ :return: The route if found, otherwise None.
+ :rtype: Optional[Route]
+ """
for route in self.routes:
if route.name == name:
return route
@@ -267,6 +302,11 @@ class RouterConfig:
return None
def remove(self, name: str):
+ """Remove a route from the RouterConfig by name.
+
+ :param name: The name of the route to remove.
+ :type name: str
+ """
if name not in [route.name for route in self.routes]:
logger.error(f"Route `{name}` not found")
else:
@@ -274,6 +314,11 @@ class RouterConfig:
logger.info(f"Removed route `{name}`")
def get_hash(self) -> ConfigParameter:
+ """Get the hash of the RouterConfig. Used for syncing.
+
+ :return: The hash of the RouterConfig.
+ :rtype: ConfigParameter
+ """
layer = self.to_dict()
return ConfigParameter(
field="sr_hash",
@@ -282,6 +327,13 @@ class RouterConfig:
def xq_reshape(xq: List[float] | np.ndarray) -> np.ndarray:
+ """Reshape the query vector to be a 2D numpy array.
+
+ :param xq: The query vector.
+ :type xq: List[float] | np.ndarray
+ :return: The reshaped query vector.
+ :rtype: np.ndarray
+ """
# convert to numpy array if not already
if not isinstance(xq, np.ndarray):
xq = np.array(xq)
@@ -296,10 +348,13 @@ def xq_reshape(xq: List[float] | np.ndarray) -> np.ndarray:
class BaseRouter(BaseModel):
+ """Base class for all routers."""
+
encoder: DenseEncoder = Field(default_factory=OpenAIEncoder)
+ sparse_encoder: Optional[SparseEncoder] = Field(default=None)
index: BaseIndex = Field(default_factory=BaseIndex)
score_threshold: Optional[float] = Field(default=None)
- routes: List[Route] = []
+ routes: List[Route] = Field(default_factory=list)
llm: Optional[BaseLLM] = None
top_k: int = 5
aggregation: str = "mean"
@@ -312,15 +367,38 @@ class BaseRouter(BaseModel):
def __init__(
self,
encoder: Optional[DenseEncoder] = None,
+ sparse_encoder: Optional[SparseEncoder] = None,
llm: Optional[BaseLLM] = None,
- routes: List[Route] = [],
+ routes: Optional[List[Route]] = None,
index: Optional[BaseIndex] = None, # type: ignore
top_k: int = 5,
aggregation: str = "mean",
auto_sync: Optional[str] = None,
):
+ """Initialize a BaseRouter object. Expected to be used as a base class only,
+ not directly instantiated.
+
+ :param encoder: The encoder to use.
+ :type encoder: Optional[DenseEncoder]
+ :param sparse_encoder: The sparse encoder to use.
+ :type sparse_encoder: Optional[SparseEncoder]
+ :param llm: The LLM to use.
+ :type llm: Optional[BaseLLM]
+ :param routes: The routes to use.
+ :type routes: Optional[List[Route]]
+ :param index: The index to use.
+ :type index: Optional[BaseIndex]
+ :param top_k: The number of routes to return.
+ :type top_k: int
+ :param aggregation: The aggregation method to use.
+ :type aggregation: str
+ :param auto_sync: The auto sync mode to use.
+ :type auto_sync: Optional[str]
+ """
+ routes = routes.copy() if routes else []
super().__init__(
encoder=encoder,
+ sparse_encoder=sparse_encoder,
llm=llm,
routes=routes,
index=index,
@@ -329,8 +407,9 @@ class BaseRouter(BaseModel):
auto_sync=auto_sync,
)
self.encoder = self._get_encoder(encoder=encoder)
+ self.sparse_encoder = self._get_sparse_encoder(sparse_encoder=sparse_encoder)
self.llm = llm
- self.routes = routes.copy() if routes else []
+ self.routes = routes
# initialize index
self.index = self._get_index(index=index)
# set score threshold using default method
@@ -350,8 +429,17 @@ class BaseRouter(BaseModel):
for route in self.routes:
if route.score_threshold is None:
route.score_threshold = self.score_threshold
+ # initialize index
+ self._init_index_state()
def _get_index(self, index: Optional[BaseIndex]) -> BaseIndex:
+ """Get the index to use.
+
+ :param index: The index to use.
+ :type index: Optional[BaseIndex]
+ :return: The index to use.
+ :rtype: BaseIndex
+ """
if index is None:
logger.warning("No index provided. Using default LocalIndex.")
index = LocalIndex()
@@ -360,6 +448,13 @@ class BaseRouter(BaseModel):
return index
def _get_encoder(self, encoder: Optional[DenseEncoder]) -> DenseEncoder:
+ """Get the dense encoder to be used for creating dense vector embeddings.
+
+ :param encoder: The encoder to use.
+ :type encoder: Optional[DenseEncoder]
+ :return: The encoder to use.
+ :rtype: DenseEncoder
+ """
if encoder is None:
logger.warning("No encoder provided. Using default OpenAIEncoder.")
encoder = OpenAIEncoder()
@@ -367,6 +462,22 @@ class BaseRouter(BaseModel):
encoder = encoder
return encoder
+ def _get_sparse_encoder(
+ self, sparse_encoder: Optional[SparseEncoder]
+ ) -> Optional[SparseEncoder]:
+ """Get the sparse encoder to be used for creating sparse vector embeddings.
+
+ :param sparse_encoder: The sparse encoder to use.
+ :type sparse_encoder: Optional[SparseEncoder]
+ :return: The sparse encoder to use.
+ :rtype: Optional[SparseEncoder]
+ """
+ if sparse_encoder is None:
+ return None
+ raise NotImplementedError(
+ f"Sparse encoder not implemented for {self.__class__.__name__}"
+ )
+
def _init_index_state(self):
"""Initializes an index (where required) and runs auto_sync if active."""
# initialize index now, check if we need dimensions
@@ -375,11 +486,15 @@ class BaseRouter(BaseModel):
self.index.dimensions = dims
# now init index
if isinstance(self.index, PineconeIndex):
+ # _init_index will not create index if already exists — it will also check
+ # for required attributes like self.index.host and self.index.dimensions and
+ # fetch them if not set
self.index.index = self.index._init_index(force_create=True)
+
# run auto sync if active
if self.auto_sync:
local_utterances = self.to_config().to_utterances()
- remote_utterances = self.index.get_utterances()
+ remote_utterances = self.index.get_utterances(include_metadata=True)
diff = UtteranceDiff.from_utterances(
local_utterances=local_utterances,
remote_utterances=remote_utterances,
@@ -403,6 +518,13 @@ class BaseRouter(BaseModel):
)
def check_for_matching_routes(self, top_class: str) -> Optional[Route]:
+ """Check for a matching route in the routes list.
+
+ :param top_class: The top class to check for.
+ :type top_class: str
+ :return: The matching route if found, otherwise None.
+ :rtype: Optional[Route]
+ """
matching_route = next(
(route for route in self.routes if route.name == top_class), None
)
@@ -421,15 +543,41 @@ class BaseRouter(BaseModel):
simulate_static: bool = False,
route_filter: Optional[List[str]] = None,
) -> RouteChoice:
+ """Call the router to get a route choice.
+
+ :param text: The text to route.
+ :type text: Optional[str]
+ :param vector: The vector to route.
+ :type vector: Optional[List[float] | np.ndarray]
+ :param simulate_static: Whether to simulate a static route.
+ :type simulate_static: bool
+ :param route_filter: The route filter to use.
+ :type route_filter: Optional[List[str]]
+ :return: The route choice.
+ :rtype: RouteChoice
+ """
+ if not self.index.is_ready():
+ raise ValueError("Index is not ready.")
# if no vector provided, encode text to get vector
if vector is None:
if text is None:
raise ValueError("Either text or vector must be provided")
- vector = self._encode(text=[text])
+ vector = self._encode(text=[text], input_type="queries")
# convert to numpy array if not already
vector = xq_reshape(vector)
- # calculate semantics
- route, top_class_scores = self._retrieve_top_route(vector, route_filter)
+ # get scores and routes
+ scores, routes = self.index.query(
+ vector=vector[0], top_k=self.top_k, route_filter=route_filter
+ )
+ query_results = [
+ {"route": d, "score": s.item()} for d, s in zip(routes, scores)
+ ]
+ # decide most relevant routes
+ top_class, top_class_scores = self._semantic_classify(
+ query_results=query_results
+ )
+ # TODO do we need this check?
+ route = self.check_for_matching_routes(top_class)
passed = self._check_threshold(top_class_scores, route)
if passed and route is not None and not simulate_static:
if route.function_schemas and text is None:
@@ -466,17 +614,43 @@ class BaseRouter(BaseModel):
simulate_static: bool = False,
route_filter: Optional[List[str]] = None,
) -> RouteChoice:
+ """Asynchronously call the router to get a route choice.
+
+ :param text: The text to route.
+ :type text: Optional[str]
+ :param vector: The vector to route.
+ :type vector: Optional[List[float] | np.ndarray]
+ :param simulate_static: Whether to simulate a static route (ie avoid dynamic route
+ LLM calls during fit or evaluate).
+ :type simulate_static: bool
+ :param route_filter: The route filter to use.
+ :type route_filter: Optional[List[str]]
+ :return: The route choice.
+ :rtype: RouteChoice
+ """
+ if not self.index.is_ready():
+ # TODO: need async version for qdrant
+ raise ValueError("Index is not ready.")
# if no vector provided, encode text to get vector
if vector is None:
if text is None:
raise ValueError("Either text or vector must be provided")
- vector = await self._async_encode(text=[text])
+ vector = await self._async_encode(text=[text], input_type="queries")
# convert to numpy array if not already
vector = xq_reshape(vector)
- # calculate semantics
- route, top_class_scores = await self._async_retrieve_top_route(
- vector, route_filter
+ # get scores and routes
+ scores, routes = await self.index.aquery(
+ vector=vector[0], top_k=self.top_k, route_filter=route_filter
+ )
+ query_results = [
+ {"route": d, "score": s.item()} for d, s in zip(routes, scores)
+ ]
+ # decide most relevant routes
+ top_class, top_class_scores = await self._async_semantic_classify(
+ query_results=query_results
)
+ # TODO do we need this check?
+ route = self.check_for_matching_routes(top_class)
passed = self._check_threshold(top_class_scores, route)
if passed and route is not None and not simulate_static:
if route.function_schemas and text is None:
@@ -503,65 +677,19 @@ class BaseRouter(BaseModel):
# if no route passes threshold, return empty route choice
return RouteChoice()
- # TODO: add multiple routes return to __call__ and acall
- @deprecated("This method is deprecated. Use `__call__` instead.")
- def retrieve_multiple_routes(
- self,
- text: Optional[str] = None,
- vector: Optional[List[float] | np.ndarray] = None,
- ) -> List[RouteChoice]:
- if vector is None:
- if text is None:
- raise ValueError("Either text or vector must be provided")
- vector = self._encode(text=[text])
- # convert to numpy array if not already
- vector = xq_reshape(vector)
- # get relevant utterances
- results = self._retrieve(xq=vector)
- # decide most relevant routes
- categories_with_scores = self._semantic_classify_multiple_routes(results)
- return [
- RouteChoice(name=category, similarity_score=score)
- for category, score in categories_with_scores
- ]
-
- # route_choices = []
- # TODO JB: do we need this check? Maybe we should be returning directly
- # for category, score in categories_with_scores:
- # route = self.check_for_matching_routes(category)
- # if route:
- # route_choice = RouteChoice(name=route.name, similarity_score=score)
- # route_choices.append(route_choice)
+ def _index_ready(self) -> bool:
+ """Method to check if the index is ready to be used.
- # return route_choices
-
- def _retrieve_top_route(
- self, vector: np.ndarray, route_filter: Optional[List[str]] = None
- ) -> Tuple[Optional[Route], List[float]]:
- """
- Retrieve the top matching route based on the given vector.
- Returns a tuple of the route (if any) and the scores of the top class.
+ :return: True if the index is ready, False otherwise.
+ :rtype: bool
"""
- # get relevant results (scores and routes)
- results = self._retrieve(xq=vector, top_k=self.top_k, route_filter=route_filter)
- # decide most relevant routes
- top_class, top_class_scores = self._semantic_classify(results)
- # TODO do we need this check?
- route = self.check_for_matching_routes(top_class)
- return route, top_class_scores
-
- async def _async_retrieve_top_route(
- self, vector: np.ndarray, route_filter: Optional[List[str]] = None
- ) -> Tuple[Optional[Route], List[float]]:
- # get relevant results (scores and routes)
- results = await self._async_retrieve(
- xq=vector, top_k=self.top_k, route_filter=route_filter
- )
- # decide most relevant routes
- top_class, top_class_scores = await self._async_semantic_classify(results)
- # TODO do we need this check?
- route = self.check_for_matching_routes(top_class)
- return route, top_class_scores
+ if self.index.index is None or self.routes is None:
+ return False
+ if isinstance(self.index, QdrantIndex):
+ info = self.index.describe()
+ if info.vectors == 0:
+ return False
+ return True
def sync(self, sync_mode: str, force: bool = False, wait: int = 0) -> List[str]:
"""Runs a sync of the local routes with the remote index.
@@ -596,7 +724,7 @@ class BaseRouter(BaseModel):
try:
# first creating a diff
local_utterances = self.to_config().to_utterances()
- remote_utterances = self.index.get_utterances()
+ remote_utterances = self.index.get_utterances(include_metadata=True)
diff = UtteranceDiff.from_utterances(
local_utterances=local_utterances,
remote_utterances=remote_utterances,
@@ -652,7 +780,9 @@ class BaseRouter(BaseModel):
try:
# first creating a diff
local_utterances = self.to_config().to_utterances()
- remote_utterances = await self.index.aget_utterances()
+ remote_utterances = await self.index.aget_utterances(
+ include_metadata=True
+ )
diff = UtteranceDiff.from_utterances(
local_utterances=local_utterances,
remote_utterances=remote_utterances,
@@ -693,7 +823,8 @@ class BaseRouter(BaseModel):
routes=[utt.route for utt in strategy["remote"]["upsert"]],
utterances=utterances_text,
function_schemas=[
- utt.function_schemas for utt in strategy["remote"]["upsert"] # type: ignore
+ utt.function_schemas # type: ignore
+ for utt in strategy["remote"]["upsert"]
],
metadata_list=[utt.metadata for utt in strategy["remote"]["upsert"]],
)
@@ -726,7 +857,8 @@ class BaseRouter(BaseModel):
routes=[utt.route for utt in strategy["remote"]["upsert"]],
utterances=utterances_text,
function_schemas=[
- utt.function_schemas for utt in strategy["remote"]["upsert"] # type: ignore
+ utt.function_schemas # type: ignore
+ for utt in strategy["remote"]["upsert"]
],
metadata_list=[utt.metadata for utt in strategy["remote"]["upsert"]],
)
@@ -799,10 +931,15 @@ class BaseRouter(BaseModel):
self.routes = new_routes
def _check_threshold(self, scores: List[float], route: Optional[Route]) -> bool:
+ """Check if the route's score passes the specified threshold.
+
+ :param scores: The scores to check.
+ :type scores: List[float]
+ :param route: The route to check.
+ :type route: Optional[Route]
+ :return: True if the route's score passes the threshold, otherwise False.
+ :rtype: bool
"""
- Check if the route's score passes the specified threshold.
- """
- # TODO JB: do we need this?
if route is None:
return False
threshold = (
@@ -821,6 +958,13 @@ class BaseRouter(BaseModel):
@classmethod
def from_json(cls, file_path: str):
+ """Load a RouterConfig from a JSON file.
+
+ :param file_path: The path to the JSON file.
+ :type file_path: str
+ :return: The RouterConfig object.
+ :rtype: RouterConfig
+ """
config = RouterConfig.from_file(file_path)
encoder = AutoEncoder(type=config.encoder_type, name=config.encoder_name).model
if isinstance(encoder, DenseEncoder):
@@ -830,6 +974,13 @@ class BaseRouter(BaseModel):
@classmethod
def from_yaml(cls, file_path: str):
+ """Load a RouterConfig from a YAML file.
+
+ :param file_path: The path to the YAML file.
+ :type file_path: str
+ :return: The RouterConfig object.
+ :rtype: RouterConfig
+ """
config = RouterConfig.from_file(file_path)
encoder = AutoEncoder(type=config.encoder_type, name=config.encoder_name).model
if isinstance(encoder, DenseEncoder):
@@ -839,6 +990,13 @@ class BaseRouter(BaseModel):
@classmethod
def from_config(cls, config: RouterConfig, index: Optional[BaseIndex] = None):
+ """Create a Router from a RouterConfig object.
+
+ :param config: The RouterConfig object.
+ :type config: RouterConfig
+ :param index: The index to use.
+ :type index: Optional[BaseIndex]
+ """
encoder = AutoEncoder(type=config.encoder_type, name=config.encoder_name).model
if isinstance(encoder, DenseEncoder):
return cls(encoder=encoder, routes=config.routes, index=index)
@@ -878,7 +1036,15 @@ class BaseRouter(BaseModel):
The name must exist within the local SemanticRouter, if not a
KeyError will be raised.
+
+ :param name: The name of the route to update.
+ :type name: str
+ :param threshold: The threshold to update.
+ :type threshold: Optional[float]
+ :param utterances: The utterances to update.
+ :type utterances: Optional[List[str]]
"""
+ # TODO JB: should modify update to take a Route object
current_local_hash = self._get_hash()
current_remote_hash = self.index._read_hash()
if current_remote_hash.value == "":
@@ -949,8 +1115,46 @@ class BaseRouter(BaseModel):
"to see details."
)
+ async def adelete(self, route_name: str):
+ """Deletes a route given a specific route name asynchronously.
+
+ :param route_name: the name of the route to be deleted
+ :type str:
+ """
+ # ensure index is not locked
+ if await self.index._ais_locked():
+ raise ValueError("Index is locked. Cannot delete route.")
+ current_local_hash = self._get_hash()
+ current_remote_hash = await self.index._async_read_hash()
+ if current_remote_hash.value == "":
+ # if remote hash is empty, the index is to be initialized
+ current_remote_hash = current_local_hash
+
+ if route_name not in [route.name for route in self.routes]:
+ err_msg = f"Route `{route_name}` not found in {self.__class__.__name__}"
+ logger.warning(err_msg)
+ try:
+ await self.index.adelete(route_name=route_name)
+ except Exception as e:
+ logger.error(f"Failed to delete route from the index: {e}")
+ else:
+ self.routes = [route for route in self.routes if route.name != route_name]
+ await self.index.adelete(route_name=route_name)
+
+ if current_local_hash.value == current_remote_hash.value:
+ await self._async_write_hash() # update current hash in index
+ else:
+ logger.warning(
+ "Local and remote route layers were not aligned. Remote hash "
+ f"not updated. Use `{self.__class__.__name__}.get_utterance_diff()` "
+ "to see details."
+ )
+
def _refresh_routes(self):
- """Pulls out the latest routes from the index."""
+ """Pulls out the latest routes from the index.
+
+ Not yet implemented for BaseRouter.
+ """
raise NotImplementedError("This method has not yet been implemented.")
route_mapping = {route.name: route for route in self.routes}
index_routes = self.index.get_utterances()
@@ -967,16 +1171,31 @@ class BaseRouter(BaseModel):
self.routes.append(route)
def _get_hash(self) -> ConfigParameter:
+ """Get the hash of the current routes.
+
+ :return: The hash of the current routes.
+ :rtype: ConfigParameter
+ """
config = self.to_config()
return config.get_hash()
def _write_hash(self) -> ConfigParameter:
+ """Write the hash of the current routes to the index.
+
+ :return: The hash of the current routes.
+ :rtype: ConfigParameter
+ """
config = self.to_config()
hash_config = config.get_hash()
self.index._write_config(config=hash_config)
return hash_config
async def _async_write_hash(self) -> ConfigParameter:
+ """Write the hash of the current routes to the index asynchronously.
+
+ :return: The hash of the current routes.
+ :rtype: ConfigParameter
+ """
config = self.to_config()
hash_config = config.get_hash()
await self.index._async_write_config(config=hash_config)
@@ -1034,9 +1253,15 @@ class BaseRouter(BaseModel):
This diff tells us that the remote has "route2: utterance3" and
"route2: utterance4", which do not exist locally.
+
+ :param include_metadata: Whether to include metadata in the diff.
+ :type include_metadata: bool
+ :return: A list of strings showing the difference between the local and remote
+ utterances.
+ :rtype: List[str]
"""
# first we get remote and local utterances
- remote_utterances = self.index.get_utterances()
+ remote_utterances = self.index.get_utterances(include_metadata=include_metadata)
local_utterances = self.to_config().to_utterances()
diff_obj = UtteranceDiff.from_utterances(
@@ -1064,9 +1289,17 @@ class BaseRouter(BaseModel):
This diff tells us that the remote has "route2: utterance3" and
"route2: utterance4", which do not exist locally.
+
+ :param include_metadata: Whether to include metadata in the diff.
+ :type include_metadata: bool
+ :return: A list of strings showing the difference between the local and remote
+ utterances.
+ :rtype: List[str]
"""
# first we get remote and local utterances
- remote_utterances = await self.index.aget_utterances()
+ remote_utterances = await self.index.aget_utterances(
+ include_metadata=include_metadata
+ )
local_utterances = self.to_config().to_utterances()
diff_obj = UtteranceDiff.from_utterances(
@@ -1077,10 +1310,23 @@ class BaseRouter(BaseModel):
def _extract_routes_details(
self, routes: List[Route], include_metadata: bool = False
) -> Tuple:
+ """Extract the routes details.
+
+ :param routes: The routes to extract the details from.
+ :type routes: List[Route]
+ :param include_metadata: Whether to include metadata in the details.
+ :type include_metadata: bool
+ :return: A tuple of the route names, utterances, and function schemas.
+ """
+
route_names = [route.name for route in routes for _ in route.utterances]
utterances = [utterance for route in routes for utterance in route.utterances]
function_schemas = [
- route.function_schemas[0] if route.function_schemas is not None else {}
+ (
+ route.function_schemas[0]
+ if route.function_schemas and len(route.function_schemas) > 0
+ else {}
+ )
for route in routes
for _ in route.utterances
]
@@ -1090,53 +1336,52 @@ class BaseRouter(BaseModel):
return route_names, utterances, function_schemas, metadata
return route_names, utterances, function_schemas
- def _encode(self, text: list[str]) -> Any:
+ def _encode(
+ self,
+ text: list[str],
+ input_type: EncodeInputType,
+ ) -> Any:
"""Generates embeddings for a given text.
Must be implemented by a subclass.
:param text: The text to encode.
:type text: list[str]
+ :param input_type: Specify whether encoding 'queries' or 'documents', used in asymmetric retrieval
+ :type input_type: semantic_router.encoders.encode_input_type.EncodeInputType
:return: The embeddings of the text.
:rtype: Any
"""
# TODO: should encode "content" rather than text
raise NotImplementedError("This method should be implemented by subclasses.")
- async def _async_encode(self, text: list[str]) -> Any:
+ async def _async_encode(
+ self,
+ text: list[str],
+ input_type: EncodeInputType,
+ ) -> Any:
"""Asynchronously generates embeddings for a given text.
Must be implemented by a subclass.
:param text: The text to encode.
:type text: list[str]
+ :param input_type: Specify whether encoding 'queries' or 'documents', used in asymmetric retrieval
+ :type input_type: semantic_router.encoders.encode_input_type.EncodeInputType
:return: The embeddings of the text.
:rtype: Any
"""
# TODO: should encode "content" rather than text
raise NotImplementedError("This method should be implemented by subclasses.")
- def _retrieve(
- self, xq: Any, top_k: int = 5, route_filter: Optional[List[str]] = None
- ) -> List[Dict]:
- """Given a query vector, retrieve the top_k most similar records."""
- # get scores and routes
- scores, routes = self.index.query(
- vector=xq[0], top_k=top_k, route_filter=route_filter
- )
- return [{"route": d, "score": s.item()} for d, s in zip(routes, scores)]
-
- async def _async_retrieve(
- self, xq: Any, top_k: int = 5, route_filter: Optional[List[str]] = None
- ) -> List[Dict]:
- """Given a query vector, retrieve the top_k most similar records."""
- # get scores and routes
- scores, routes = await self.index.aquery(
- vector=xq[0], top_k=top_k, route_filter=route_filter
- )
- return [{"route": d, "score": s.item()} for d, s in zip(routes, scores)]
-
def _set_aggregation_method(self, aggregation: str = "sum"):
+ """Set the aggregation method.
+
+ :param aggregation: The aggregation method to use.
+ :type aggregation: str
+ :return: The aggregation method.
+ :rtype: Callable
+ """
# TODO is this really needed?
if aggregation == "sum":
return lambda x: sum(x)
@@ -1149,6 +1394,7 @@ class BaseRouter(BaseModel):
f"Unsupported aggregation method chosen: {aggregation}. Choose either 'SUM', 'MEAN', or 'MAX'."
)
+ # TODO JB allow return of multiple routes
def _semantic_classify(self, query_results: List[Dict]) -> Tuple[str, List[float]]:
"""Classify the query results into a single class based on the highest total score.
If no classification is found, return an empty string and an empty list.
@@ -1210,15 +1456,31 @@ class BaseRouter(BaseModel):
return "", []
def get(self, name: str) -> Optional[Route]:
+ """Get a route by name.
+
+ :param name: The name of the route to get.
+ :type name: str
+ :return: The route.
+ :rtype: Optional[Route]
+ """
for route in self.routes:
if route.name == name:
return route
logger.error(f"Route `{name}` not found")
return None
+ @deprecated("This method is deprecated. Use `semantic_classify` instead.")
def _semantic_classify_multiple_routes(
self, query_results: List[Dict]
) -> List[Tuple[str, float]]:
+ """Classify the query results into multiple routes based on the highest total score.
+
+ :param query_results: The query results to classify. Expected format is a list of
+ dictionaries with "route" and "score" keys.
+ :type query_results: List[Dict]
+ :return: A list of tuples containing the route name and its associated scores.
+ :rtype: List[Tuple[str, float]]
+ """
scores_by_class = self.group_scores_by_class(query_results)
# Filter classes based on threshold and find max score for each
@@ -1242,6 +1504,14 @@ class BaseRouter(BaseModel):
def group_scores_by_class(
self, query_results: List[Dict]
) -> Dict[str, List[float]]:
+ """Group the scores by class.
+
+ :param query_results: The query results to group. Expected format is a list of
+ dictionaries with "route" and "score" keys.
+ :type query_results: List[Dict]
+ :return: A dictionary of route names and their associated scores.
+ :rtype: Dict[str, List[float]]
+ """
scores_by_class: Dict[str, List[float]] = {}
for result in query_results:
score = result["score"]
@@ -1255,6 +1525,14 @@ class BaseRouter(BaseModel):
async def async_group_scores_by_class(
self, query_results: List[Dict]
) -> Dict[str, List[float]]:
+ """Group the scores by class asynchronously.
+
+ :param query_results: The query results to group. Expected format is a list of
+ dictionaries with "route" and "score" keys.
+ :type query_results: List[Dict]
+ :return: A dictionary of route names and their associated scores.
+ :rtype: Dict[str, List[float]]
+ """
scores_by_class: Dict[str, List[float]] = {}
for result in query_results:
score = result["score"]
@@ -1279,6 +1557,7 @@ class BaseRouter(BaseModel):
if threshold is None:
return True
if scores:
+ # TODO JB is this correct?
return max(scores) > threshold
else:
return False
@@ -1320,6 +1599,11 @@ class BaseRouter(BaseModel):
logger.error(f"Route `{route_name}` not found")
def to_config(self) -> RouterConfig:
+ """Convert the router to a RouterConfig object.
+
+ :return: The RouterConfig object.
+ :rtype: RouterConfig
+ """
return RouterConfig(
encoder_type=self.encoder.type,
encoder_name=self.encoder.name,
@@ -1327,14 +1611,29 @@ class BaseRouter(BaseModel):
)
def to_json(self, file_path: str):
+ """Convert the router to a JSON file.
+
+ :param file_path: The path to the JSON file.
+ :type file_path: str
+ """
config = self.to_config()
config.to_file(file_path)
def to_yaml(self, file_path: str):
+ """Convert the router to a YAML file.
+
+ :param file_path: The path to the YAML file.
+ :type file_path: str
+ """
config = self.to_config()
config.to_file(file_path)
def get_thresholds(self) -> Dict[str, float]:
+ """Get the score thresholds for each route.
+
+ :return: A dictionary of route names and their associated thresholds.
+ :rtype: Dict[str, float]
+ """
thresholds = {
route.name: route.score_threshold or self.score_threshold or 0.0
for route in self.routes
@@ -1349,16 +1648,34 @@ class BaseRouter(BaseModel):
max_iter: int = 500,
local_execution: bool = False,
):
+ """Fit the router to the data. Works best with a large number of examples for each
+ route and with many `None` utterances.
+
+ :param X: The input data.
+ :type X: List[str]
+ :param y: The output data.
+ :type y: List[str]
+ :param batch_size: The batch size to use for fitting.
+ :type batch_size: int
+ :param max_iter: The maximum number of iterations to use for fitting.
+ :type max_iter: int
+ :param local_execution: Whether to execute the fitting locally.
+ :type local_execution: bool
+ """
original_index = self.index
if local_execution:
# Switch to a local index for fitting
from semantic_router.index.local import LocalIndex
- remote_routes = self.index.get_utterances()
+ remote_utterances = self.index.get_utterances(include_metadata=True)
# TODO Enhance by retrieving directly the vectors instead of embedding all utterances again
- routes, utterances, function_schemas, metadata = map(
- list, zip(*remote_routes)
- )
+ routes = []
+ utterances = []
+ metadata = []
+ for utterance in remote_utterances:
+ routes.append(utterance.route)
+ utterances.append(utterance.utterance)
+ metadata.append(utterance.metadata)
embeddings = self.encoder(utterances)
self.index = LocalIndex()
self.index.add(
@@ -1374,7 +1691,7 @@ class BaseRouter(BaseModel):
emb = np.array(self.encoder(X[i : i + batch_size]))
Xq.extend(emb)
# initial eval (we will iterate from here)
- best_acc = self._vec_evaluate(Xq=np.array(Xq), y=y)
+ best_acc = self._vec_evaluate(Xq_d=np.array(Xq), y=y)
best_thresholds = self.get_thresholds()
# begin fit
for _ in (pbar := tqdm(range(max_iter), desc="Training")):
@@ -1387,7 +1704,7 @@ class BaseRouter(BaseModel):
# update current route layer
self._update_thresholds(route_thresholds=thresholds)
# evaluate
- acc = self._vec_evaluate(Xq=Xq, y=y)
+ acc = self._vec_evaluate(Xq_d=Xq, y=y)
# update best
if acc > best_acc:
best_acc = acc
@@ -1400,31 +1717,52 @@ class BaseRouter(BaseModel):
self.index = original_index
def evaluate(self, X: List[str], y: List[str], batch_size: int = 500) -> float:
- """
- Evaluate the accuracy of the route selection.
+ """Evaluate the accuracy of the route selection.
+
+ :param X: The input data.
+ :type X: List[str]
+ :param y: The output data.
+ :type y: List[str]
+ :param batch_size: The batch size to use for evaluation.
+ :type batch_size: int
+ :return: The accuracy of the route selection.
+ :rtype: float
"""
Xq: List[List[float]] = []
for i in tqdm(range(0, len(X), batch_size), desc="Generating embeddings"):
emb = np.array(self.encoder(X[i : i + batch_size]))
Xq.extend(emb)
- accuracy = self._vec_evaluate(Xq=np.array(Xq), y=y)
+ accuracy = self._vec_evaluate(Xq_d=np.array(Xq), y=y)
return accuracy
- def _vec_evaluate(self, Xq: Union[List[float], Any], y: List[str]) -> float:
- """
- Evaluate the accuracy of the route selection.
+ def _vec_evaluate(
+ self, Xq_d: Union[List[float], Any], y: List[str], **kwargs
+ ) -> float:
+ """Evaluate the accuracy of the route selection.
+
+ :param Xq_d: The input data.
+ :type Xq_d: Union[List[float], Any]
+ :param y: The output data.
+ :type y: List[str]
+ :return: The accuracy of the route selection.
+ :rtype: float
"""
correct = 0
- for xq, target_route in zip(Xq, y):
+ for xq, target_route in zip(Xq_d, y):
# We treate dynamic routes as static here, because when evaluating we use only vectors, and dynamic routes expect strings by default.
route_choice = self(vector=xq, simulate_static=True)
if route_choice.name == target_route:
correct += 1
- accuracy = correct / len(Xq)
+ accuracy = correct / len(Xq_d)
return accuracy
def _get_route_names(self) -> List[str]:
+ """Get the names of the routes.
+
+ :return: The names of the routes.
+ :rtype: List[str]
+ """
return [route.name for route in self.routes]
@@ -1432,7 +1770,15 @@ def threshold_random_search(
route_layer: BaseRouter,
search_range: Union[int, float],
) -> Dict[str, float]:
- """Performs a random search iteration given a route layer and a search range."""
+ """Performs a random search iteration given a route layer and a search range.
+
+ :param route_layer: The route layer to search.
+ :type route_layer: BaseRouter
+ :param search_range: The search range to use.
+ :type search_range: Union[int, float]
+ :return: A dictionary of route names and their associated thresholds.
+ :rtype: Dict[str, float]
+ """
# extract the route names
routes = route_layer.get_thresholds()
route_names = list(routes.keys())
diff --git a/semantic_router/routers/hybrid.py b/semantic_router/routers/hybrid.py
index 54901d5e50a0116be8a88496869b17ec00aa06e8..69520d5253e0d77115f70b40e0943ca472d0b42a 100644
--- a/semantic_router/routers/hybrid.py
+++ b/semantic_router/routers/hybrid.py
@@ -1,21 +1,27 @@
-from typing import Dict, List, Optional
import asyncio
-from pydantic import Field
+from typing import Any, Dict, List, Optional, Union
import numpy as np
+from pydantic import Field
+from tqdm.auto import tqdm
from semantic_router.encoders import (
+ BM25Encoder,
DenseEncoder,
SparseEncoder,
- BM25Encoder,
- TfidfEncoder,
)
-from semantic_router.route import Route
+from semantic_router.encoders.base import (
+ AsymmetricDenseMixin,
+ AsymmetricSparseMixin,
+ FittableMixin,
+)
+from semantic_router.encoders.encode_input_type import EncodeInputType
from semantic_router.index import BaseIndex, HybridLocalIndex
+from semantic_router.llms import BaseLLM
+from semantic_router.route import Route
+from semantic_router.routers.base import BaseRouter, threshold_random_search, xq_reshape
from semantic_router.schema import RouteChoice, SparseEmbedding, Utterance
from semantic_router.utils.logger import logger
-from semantic_router.routers.base import BaseRouter, xq_reshape
-from semantic_router.llms import BaseLLM
class HybridRouter(BaseRouter):
@@ -30,19 +36,32 @@ class HybridRouter(BaseRouter):
encoder: DenseEncoder,
sparse_encoder: Optional[SparseEncoder] = None,
llm: Optional[BaseLLM] = None,
- routes: List[Route] = [],
+ routes: Optional[List[Route]] = None,
index: Optional[HybridLocalIndex] = None,
top_k: int = 5,
aggregation: str = "mean",
auto_sync: Optional[str] = None,
alpha: float = 0.3,
):
+ """Initialize the HybridRouter.
+
+ :param encoder: The dense encoder to use.
+ :type encoder: DenseEncoder
+ :param sparse_encoder: The sparse encoder to use.
+ :type sparse_encoder: Optional[SparseEncoder]
+ """
if index is None:
logger.warning("No index provided. Using default HybridLocalIndex.")
index = HybridLocalIndex()
encoder = self._get_encoder(encoder=encoder)
+ # initialize sparse encoder
+ sparse_encoder = self._get_sparse_encoder(sparse_encoder=sparse_encoder)
+ # fit sparse encoder if needed
+ if isinstance(sparse_encoder, FittableMixin) and routes:
+ sparse_encoder.fit(routes)
super().__init__(
encoder=encoder,
+ sparse_encoder=sparse_encoder,
llm=llm,
routes=routes,
index=index,
@@ -50,20 +69,23 @@ class HybridRouter(BaseRouter):
aggregation=aggregation,
auto_sync=auto_sync,
)
- # initialize sparse encoder
- self.sparse_encoder = self._get_sparse_encoder(sparse_encoder=sparse_encoder)
# set alpha
self.alpha = alpha
- # fit sparse encoder if needed
- if (
- isinstance(self.sparse_encoder, TfidfEncoder)
- and hasattr(self.sparse_encoder, "fit")
- and self.routes
- ):
- self.sparse_encoder.fit(self.routes)
- # run initialize index now if auto sync is active
- if self.auto_sync:
- self._init_index_state()
+
+ def _set_score_threshold(self):
+ """Set the score threshold for the HybridRouter. Unlike the base router the
+ encoder score threshold is not used directly. Instead, the dense encoder
+ score threshold is multiplied by the alpha value, resulting in a lower
+ score threshold. This is done to account for the difference in returned
+ scores from the hybrid router.
+ """
+ if self.encoder.score_threshold is not None:
+ self.score_threshold = self.encoder.score_threshold * self.alpha
+ if self.score_threshold is None:
+ logger.warning(
+ "No score threshold value found in encoder. Using the default "
+ "'None' value can lead to unexpected results."
+ )
def add(self, routes: List[Route] | Route):
"""Add a route to the local HybridRouter and index.
@@ -71,6 +93,9 @@ class HybridRouter(BaseRouter):
:param route: The route to add.
:type route: Route
"""
+
+ if self.sparse_encoder is None:
+ raise ValueError("Sparse Encoder not initialised.")
# TODO: merge into single method within BaseRouter
current_local_hash = self._get_hash()
current_remote_hash = self.index._read_hash()
@@ -79,23 +104,29 @@ class HybridRouter(BaseRouter):
current_remote_hash = current_local_hash
if isinstance(routes, Route):
routes = [routes]
+
+ self.routes.extend(routes)
+ if isinstance(self.sparse_encoder, FittableMixin) and self.routes:
+ self.sparse_encoder.fit(self.routes)
# create embeddings for all routes
- route_names, all_utterances, all_function_schemas, all_metadata = (
- self._extract_routes_details(routes, include_metadata=True)
- )
+ (
+ route_names,
+ all_utterances,
+ all_function_schemas,
+ all_metadata,
+ ) = self._extract_routes_details(routes, include_metadata=True)
# TODO: to merge, self._encode should probably output a special
# TODO Embedding type that can be either dense or hybrid
- dense_emb, sparse_emb = self._encode(all_utterances)
+ dense_emb, sparse_emb = self._encode(all_utterances, input_type="documents")
self.index.add(
embeddings=dense_emb.tolist(),
routes=route_names,
utterances=all_utterances,
function_schemas=all_function_schemas,
metadata_list=all_metadata,
- sparse_embeddings=sparse_emb, # type: ignore
+ sparse_embeddings=sparse_emb,
)
- self.routes.extend(routes)
if current_local_hash.value == current_remote_hash.value:
self._write_hash() # update current hash in index
else:
@@ -112,6 +143,8 @@ class HybridRouter(BaseRouter):
:param strategy: The sync strategy to execute.
:type strategy: Dict[str, Dict[str, List[Utterance]]]
"""
+ if self.sparse_encoder is None:
+ raise ValueError("Sparse Encoder not initialised.")
if strategy["remote"]["delete"]:
data_to_delete = {} # type: ignore
for utt_obj in strategy["remote"]["delete"]:
@@ -120,16 +153,19 @@ class HybridRouter(BaseRouter):
self.index._remove_and_sync(data_to_delete)
if strategy["remote"]["upsert"]:
utterances_text = [utt.utterance for utt in strategy["remote"]["upsert"]]
- dense_emb, sparse_emb = self._encode(utterances_text)
+ dense_emb, sparse_emb = self._encode(
+ utterances_text, input_type="documents"
+ )
self.index.add(
embeddings=dense_emb.tolist(),
routes=[utt.route for utt in strategy["remote"]["upsert"]],
utterances=utterances_text,
function_schemas=[
- utt.function_schemas for utt in strategy["remote"]["upsert"] # type: ignore
+ utt.function_schemas # type: ignore
+ for utt in strategy["remote"]["upsert"]
],
metadata_list=[utt.metadata for utt in strategy["remote"]["upsert"]],
- sparse_embeddings=sparse_emb, # type: ignore
+ sparse_embeddings=sparse_emb,
)
if strategy["local"]["delete"]:
self._local_delete(utterances=strategy["local"]["delete"])
@@ -137,8 +173,17 @@ class HybridRouter(BaseRouter):
self._local_upsert(utterances=strategy["local"]["upsert"])
# update hash
self._write_hash()
+ if isinstance(self.sparse_encoder, FittableMixin) and self.routes:
+ self.sparse_encoder.fit(self.routes)
def _get_index(self, index: Optional[BaseIndex]) -> BaseIndex:
+ """Get the index.
+
+ :param index: The index to get.
+ :type index: Optional[BaseIndex]
+ :return: The index.
+ :rtype: BaseIndex
+ """
if index is None:
logger.warning("No index provided. Using default HybridLocalIndex.")
index = HybridLocalIndex()
@@ -149,6 +194,13 @@ class HybridRouter(BaseRouter):
def _get_sparse_encoder(
self, sparse_encoder: Optional[SparseEncoder]
) -> SparseEncoder:
+ """Get the sparse encoder.
+
+ :param sparse_encoder: The sparse encoder to get.
+ :type sparse_encoder: Optional[SparseEncoder]
+ :return: The sparse encoder.
+ :rtype: Optional[SparseEncoder]
+ """
if sparse_encoder is None:
logger.warning("No sparse_encoder provided. Using default BM25Encoder.")
sparse_encoder = BM25Encoder()
@@ -156,37 +208,80 @@ class HybridRouter(BaseRouter):
sparse_encoder = sparse_encoder
return sparse_encoder
- def _encode(self, text: list[str]) -> tuple[np.ndarray, list[SparseEmbedding]]:
+ def _encode(
+ self, text: list[str], input_type: EncodeInputType
+ ) -> tuple[np.ndarray, list[SparseEmbedding]]:
"""Given some text, generates dense and sparse embeddings, then scales them
using the chosen alpha value.
+
+ :param text: List of texts to encode
+ :type text: List[str]
+ :param input_type: Specify whether encoding 'queries' or 'documents', used in asymmetric retrieval
+ :type input_type: semantic_router.encoders.encode_input_type.EncodeInputType
+ :return: Tuple of dense and sparse embeddings
"""
if self.sparse_encoder is None:
raise ValueError("self.sparse_encoder is not set.")
- # TODO: should encode "content" rather than text
- # TODO: add alpha as a parameter
- # create dense query vector
- xq_d = np.array(self.encoder(text))
- # xq_d = np.squeeze(xq_d) # Reduce to 1d array.
- # create sparse query vector dict
- xq_s = self.sparse_encoder(text)
- # xq_s = np.squeeze(xq_s)
- # convex scaling
+
+ if isinstance(self.encoder, AsymmetricDenseMixin):
+ match input_type:
+ case "queries":
+ dense_v = self.encoder.encode_queries(text)
+ case "documents":
+ dense_v = self.encoder.encode_documents(text)
+ else:
+ dense_v = self.encoder(text)
+ xq_d = np.array(dense_v) # type: ignore
+
+ if isinstance(self.sparse_encoder, AsymmetricSparseMixin):
+ match input_type:
+ case "queries":
+ xq_s = self.sparse_encoder.encode_queries(text)
+ case "documents":
+ xq_s = self.sparse_encoder.encode_documents(text)
+ else:
+ xq_s = self.sparse_encoder(text)
+
+ # Convex scaling
xq_d, xq_s = self._convex_scaling(dense=xq_d, sparse=xq_s)
return xq_d, xq_s
async def _async_encode(
- self, text: List[str]
+ self, text: List[str], input_type: EncodeInputType
) -> tuple[np.ndarray, list[SparseEmbedding]]:
"""Given some text, generates dense and sparse embeddings, then scales them
using the chosen alpha value.
+
+ :param text: The text to encode.
+ :type text: List[str]
+ :param input_type: Specify whether encoding 'queries' or 'documents', used in asymmetric retrieval
+ :type input_type: semantic_router.encoders.encode_input_type.EncodeInputType
+ :return: A tuple of the dense and sparse embeddings.
+ :rtype: tuple[np.ndarray, list[SparseEmbedding]]
"""
if self.sparse_encoder is None:
raise ValueError("self.sparse_encoder is not set.")
# TODO: should encode "content" rather than text
# TODO: add alpha as a parameter
# async encode both dense and sparse
- dense_coro = self.encoder.acall(text)
- sparse_coro = self.sparse_encoder.acall(text)
+
+ if isinstance(self.encoder, AsymmetricDenseMixin):
+ match input_type:
+ case "queries":
+ dense_coro = self.encoder.aencode_queries(text)
+ case "documents":
+ dense_coro = self.encoder.aencode_documents(text)
+ else:
+ dense_coro = self.encoder.acall(text)
+
+ if isinstance(self.sparse_encoder, AsymmetricSparseMixin):
+ match input_type:
+ case "queries":
+ sparse_coro = self.sparse_encoder.aencode_queries(text)
+ case "documents":
+ sparse_coro = self.sparse_encoder.aencode_documents(text)
+ else:
+ sparse_coro = self.sparse_encoder.acall(text)
dense_vec, xq_s = await asyncio.gather(dense_coro, sparse_coro)
# create dense query vector
xq_d = np.array(dense_vec)
@@ -202,12 +297,33 @@ class HybridRouter(BaseRouter):
route_filter: Optional[List[str]] = None,
sparse_vector: dict[int, float] | SparseEmbedding | None = None,
) -> RouteChoice:
+ """Call the HybridRouter.
+
+ :param text: The text to encode.
+ :type text: Optional[str]
+ :param vector: The vector to encode.
+ :type vector: Optional[List[float] | np.ndarray]
+ :param simulate_static: Whether to simulate a static route.
+ :type simulate_static: bool
+ :param route_filter: The route filter to use.
+ :type route_filter: Optional[List[str]]
+ :param sparse_vector: The sparse vector to use.
+ :type sparse_vector: dict[int, float] | SparseEmbedding | None
+ """
+ if not self.index.is_ready():
+ raise ValueError("Index is not ready.")
+ if self.sparse_encoder is None:
+ raise ValueError
potential_sparse_vector: List[SparseEmbedding] | None = None
# if no vector provided, encode text to get vector
if vector is None:
if text is None:
raise ValueError("Either text or vector must be provided")
- vector, potential_sparse_vector = self._encode(text=[text])
+ xq_d = np.array(self.encoder([text]))
+ xq_s = self.sparse_encoder([text])
+ vector, potential_sparse_vector = self._convex_scaling(
+ dense=xq_d, sparse=xq_s
+ )
# convert to numpy array if not already
vector = xq_reshape(vector)
if sparse_vector is None:
@@ -220,16 +336,18 @@ class HybridRouter(BaseRouter):
raise ValueError("Sparse vector is required for HybridLocalIndex.")
# TODO: add alpha as a parameter
scores, route_names = self.index.query(
- vector=vector,
+ vector=vector[0],
top_k=self.top_k,
route_filter=route_filter,
sparse_vector=sparse_vector,
)
+ query_results = [
+ {"route": d, "score": s.item()} for d, s in zip(route_names, scores)
+ ]
+ # TODO JB we should probably make _semantic_classify consume arrays rather than
+ # needing to convert to list here
top_class, top_class_scores = self._semantic_classify(
- [
- {"score": score, "route": route}
- for score, route in zip(scores, route_names)
- ]
+ query_results=query_results
)
passed = self._pass_threshold(top_class_scores, self.score_threshold)
if passed:
@@ -240,6 +358,13 @@ class HybridRouter(BaseRouter):
def _convex_scaling(
self, dense: np.ndarray, sparse: list[SparseEmbedding]
) -> tuple[np.ndarray, list[SparseEmbedding]]:
+ """Convex scaling of the dense and sparse vectors.
+
+ :param dense: The dense vector to scale.
+ :type dense: np.ndarray
+ :param sparse: The sparse vector to scale.
+ :type sparse: list[SparseEmbedding]
+ """
# TODO: better way to do this?
sparse_dicts = [sparse_vec.to_dict() for sparse_vec in sparse]
# scale sparse and dense vecs
@@ -252,3 +377,162 @@ class HybridRouter(BaseRouter):
)
)
return scaled_dense, scaled_sparse
+
+ def fit(
+ self,
+ X: List[str],
+ y: List[str],
+ batch_size: int = 500,
+ max_iter: int = 500,
+ local_execution: bool = False,
+ ):
+ """Fit the HybridRouter.
+
+ :param X: The input data.
+ :type X: List[str]
+ :param y: The output data.
+ :type y: List[str]
+ :param batch_size: The batch size to use for fitting.
+ :type batch_size: int
+ :param max_iter: The maximum number of iterations to use for fitting.
+ :type max_iter: int
+ :param local_execution: Whether to execute the fitting locally.
+ :type local_execution: bool
+ """
+ original_index = self.index
+ if self.sparse_encoder is None:
+ raise ValueError("Sparse encoder is not set.")
+ if local_execution:
+ # Switch to a local index for fitting
+ from semantic_router.index.hybrid_local import HybridLocalIndex
+
+ remote_utterances = self.index.get_utterances(include_metadata=True)
+ # TODO Enhance by retrieving directly the vectors instead of embedding all utterances again
+ routes = []
+ utterances = []
+ metadata = []
+ for utterance in remote_utterances:
+ routes.append(utterance.route)
+ utterances.append(utterance.utterance)
+ metadata.append(utterance.metadata)
+ embeddings = (
+ self.encoder(utterances)
+ if not isinstance(self.encoder, AsymmetricDenseMixin)
+ else self.encoder.encode_documents(utterances)
+ )
+ sparse_embeddings = (
+ self.sparse_encoder(utterances)
+ if not isinstance(self.sparse_encoder, AsymmetricSparseMixin)
+ else self.sparse_encoder.encode_documents(utterances)
+ )
+ self.index = HybridLocalIndex()
+ self.index.add(
+ embeddings=embeddings,
+ sparse_embeddings=sparse_embeddings,
+ routes=routes,
+ utterances=utterances,
+ metadata_list=metadata,
+ )
+
+ # convert inputs into array
+ Xq_d: List[List[float]] = []
+ Xq_s: List[SparseEmbedding] = []
+ for i in tqdm(range(0, len(X), batch_size), desc="Generating embeddings"):
+ emb_d = np.array(
+ self.encoder(X[i : i + batch_size])
+ if not isinstance(self.encoder, AsymmetricDenseMixin)
+ else self.encoder.encode_queries(X[i : i + batch_size])
+ )
+ # TODO JB: for some reason the sparse encoder is receiving a tuple
+ # like `("Hello",)`
+ emb_s = (
+ self.sparse_encoder(X[i : i + batch_size])
+ if not isinstance(self.sparse_encoder, AsymmetricSparseMixin)
+ else self.sparse_encoder.encode_queries(X[i : i + batch_size])
+ )
+ Xq_d.extend(emb_d)
+ Xq_s.extend(emb_s)
+ # initial eval (we will iterate from here)
+ best_acc = self._vec_evaluate(Xq_d=np.array(Xq_d), Xq_s=Xq_s, y=y)
+ best_thresholds = self.get_thresholds()
+ # begin fit
+ for _ in (pbar := tqdm(range(max_iter), desc="Training")):
+ pbar.set_postfix({"acc": round(best_acc, 2)})
+ # Find the best score threshold for each route
+ thresholds = threshold_random_search(
+ route_layer=self,
+ search_range=0.8,
+ )
+ # update current route layer
+ self._update_thresholds(route_thresholds=thresholds)
+ # evaluate
+ acc = self._vec_evaluate(Xq_d=np.array(Xq_d), Xq_s=Xq_s, y=y)
+ # update best
+ if acc > best_acc:
+ best_acc = acc
+ best_thresholds = thresholds
+ # update route layer to best thresholds
+ self._update_thresholds(route_thresholds=best_thresholds)
+
+ if local_execution:
+ # Switch back to the original index
+ self.index = original_index
+
+ def evaluate(self, X: List[str], y: List[str], batch_size: int = 500) -> float:
+ """Evaluate the accuracy of the route selection.
+
+ :param X: The input data.
+ :type X: List[str]
+ :param y: The output data.
+ :type y: List[str]
+ :param batch_size: The batch size to use for evaluation.
+ :type batch_size: int
+ :return: The accuracy of the route selection.
+ :rtype: float
+ """
+ if self.sparse_encoder is None:
+ raise ValueError("Sparse encoder is not set.")
+ Xq_d: List[List[float]] = []
+ Xq_s: List[SparseEmbedding] = []
+ for i in tqdm(range(0, len(X), batch_size), desc="Generating embeddings"):
+ emb_d = np.array(
+ self.encoder(X[i : i + batch_size])
+ if not isinstance(self.encoder, AsymmetricDenseMixin)
+ else self.encoder.encode_queries(X[i : i + batch_size])
+ )
+ emb_s = (
+ self.sparse_encoder(X[i : i + batch_size])
+ if not isinstance(self.sparse_encoder, AsymmetricSparseMixin)
+ else self.sparse_encoder.encode_queries(X[i : i + batch_size])
+ )
+ Xq_d.extend(emb_d)
+ Xq_s.extend(emb_s)
+
+ accuracy = self._vec_evaluate(Xq_d=np.array(Xq_d), Xq_s=Xq_s, y=y)
+ return accuracy
+
+ def _vec_evaluate( # type: ignore
+ self,
+ Xq_d: Union[List[float], Any],
+ Xq_s: list[SparseEmbedding],
+ y: List[str],
+ ) -> float:
+ """Evaluate the accuracy of the route selection.
+
+ :param Xq_d: The dense vectors to evaluate.
+ :type Xq_d: Union[List[float], Any]
+ :param Xq_s: The sparse vectors to evaluate.
+ :type Xq_s: list[SparseEmbedding]
+ :param y: The output data.
+ :type y: List[str]
+ :return: The accuracy of the route selection.
+ :rtype: float
+ """
+ correct = 0
+ for xq_d, xq_s, target_route in zip(Xq_d, Xq_s, y):
+ # We treate dynamic routes as static here, because when evaluating we use only vectors, and dynamic routes expect strings by default.
+ route_choice = self(vector=xq_d, sparse_vector=xq_s, simulate_static=True)
+ if route_choice.name == target_route:
+ correct += 1
+ accuracy = correct / len(Xq_d)
+ return accuracy
diff --git a/semantic_router/routers/semantic.py b/semantic_router/routers/semantic.py
index 41c92d538828409f6d36cab74086096fba52e844..8b939f33115fcf089ff4103c1ebbd7ca194b4bf8 100644
--- a/semantic_router/routers/semantic.py
+++ b/semantic_router/routers/semantic.py
@@ -3,14 +3,18 @@ from typing import Any, List, Optional
import numpy as np
from semantic_router.encoders import DenseEncoder
+from semantic_router.encoders.base import AsymmetricDenseMixin
+from semantic_router.encoders.encode_input_type import EncodeInputType
from semantic_router.index.base import BaseIndex
from semantic_router.llms import BaseLLM
-from semantic_router.utils.logger import logger
from semantic_router.route import Route
from semantic_router.routers.base import BaseRouter
+from semantic_router.utils.logger import logger
class SemanticRouter(BaseRouter):
+ """A router that uses a dense encoder to encode routes and utterances."""
+
def __init__(
self,
encoder: Optional[DenseEncoder] = None,
@@ -32,20 +36,61 @@ class SemanticRouter(BaseRouter):
aggregation=aggregation,
auto_sync=auto_sync,
)
- # run initialize index now if auto sync is active
- if self.auto_sync:
- self._init_index_state()
- def _encode(self, text: list[str]) -> Any:
- """Given some text, encode it."""
+ def _encode(self, text: list[str], input_type: EncodeInputType) -> Any:
+ """Given some text, encode it.
+
+ :param text: The text to encode.
+ :type text: list[str]
+ :param input_type: Specify whether encoding 'queries' or 'documents', used in asymmetric retrieval
+ :type input_type: semantic_router.encoders.encode_input_type.EncodeInputType
+ :return: The encoded text.
+ :rtype: Any
+ """
# create query vector
- xq = np.array(self.encoder(text))
+ match input_type:
+ case "queries":
+ xq = np.array(
+ self.encoder(text)
+ if not isinstance(self.encoder, AsymmetricDenseMixin)
+ else self.encoder.encode_queries(text)
+ )
+ case "documents":
+ xq = np.array(
+ self.encoder(text)
+ if not isinstance(self.encoder, AsymmetricDenseMixin)
+ else self.encoder.encode_documents(text)
+ )
return xq
- async def _async_encode(self, text: list[str]) -> Any:
- """Given some text, encode it."""
+ async def _async_encode(self, text: list[str], input_type: EncodeInputType) -> Any:
+ """Given some text, encode it.
+
+ :param text: The text to encode.
+ :type text: list[str]
+ :param input_type: Specify whether encoding 'queries' or 'documents', used in asymmetric retrieval
+ :type input_type: semantic_router.encoders.encode_input_type.EncodeInputType
+ :return: The encoded text.
+ :rtype: Any
+ """
# create query vector
- xq = np.array(await self.encoder.acall(docs=text))
+ match input_type:
+ case "queries":
+ xq = np.array(
+ await (
+ self.encoder.acall(docs=text)
+ if not isinstance(self.encoder, AsymmetricDenseMixin)
+ else self.encoder.aencode_queries(docs=text)
+ )
+ )
+ case "documents":
+ xq = np.array(
+ await (
+ self.encoder.acall(docs=text)
+ if not isinstance(self.encoder, AsymmetricDenseMixin)
+ else self.encoder.aencode_documents(docs=text)
+ )
+ )
return xq
def add(self, routes: List[Route] | Route):
@@ -62,10 +107,13 @@ class SemanticRouter(BaseRouter):
if isinstance(routes, Route):
routes = [routes]
# create embeddings for all routes
- route_names, all_utterances, all_function_schemas, all_metadata = (
- self._extract_routes_details(routes, include_metadata=True)
- )
- dense_emb = self._encode(all_utterances)
+ (
+ route_names,
+ all_utterances,
+ all_function_schemas,
+ all_metadata,
+ ) = self._extract_routes_details(routes, include_metadata=True)
+ dense_emb = self._encode(all_utterances, input_type="documents")
self.index.add(
embeddings=dense_emb.tolist(),
routes=route_names,
diff --git a/semantic_router/schema.py b/semantic_router/schema.py
index 273043f55ced8f5fdedb2b91e2ef9b0d35ee3d17..d5a68081169fd516ce68f8944002efeaffc8d809 100644
--- a/semantic_router/schema.py
+++ b/semantic_router/schema.py
@@ -1,15 +1,19 @@
+import json
from datetime import datetime, timezone
from difflib import Differ
from enum import Enum
-import json
+from typing import Any, Dict, List, Optional, Tuple, Union
+
import numpy as np
-from typing import List, Optional, Union, Any, Dict, Tuple
+from aurelio_sdk.schema import SparseEmbedding as BM25SparseEmbedding
from pydantic import BaseModel, Field
+
from semantic_router.utils.logger import logger
-from aurelio_sdk.schema import BM25Embedding
class EncoderType(Enum):
+ """The type of encoder."""
+
AURELIO = "aurelio"
AZURE = "azure"
COHERE = "cohere"
@@ -26,40 +30,57 @@ class EncoderType(Enum):
class EncoderInfo(BaseModel):
+ """Information about an encoder."""
+
name: str
token_limit: int
threshold: Optional[float] = None
class RouteChoice(BaseModel):
+ """A route choice typically output by the routers."""
+
name: Optional[str] = None
function_call: Optional[List[Dict]] = None
similarity_score: Optional[float] = None
class Message(BaseModel):
+ """A message in a conversation, includes the role and content fields."""
+
role: str
content: str
def to_openai(self):
- if self.role.lower() not in ["user", "assistant", "system"]:
- raise ValueError("Role must be either 'user', 'assistant' or 'system'")
+ """Convert the message to an OpenAI-compatible format."""
+ if self.role.lower() not in ["user", "assistant", "system", "tool"]:
+ raise ValueError(
+ "Role must be either 'user', 'assistant', 'system' or 'tool'"
+ )
return {"role": self.role, "content": self.content}
def to_cohere(self):
+ """Convert the message to a Cohere-compatible format."""
return {"role": self.role, "message": self.content}
def to_llamacpp(self):
+ """Convert the message to a LlamaCPP-compatible format."""
return {"role": self.role, "content": self.content}
def to_mistral(self):
+ """Convert the message to a Mistral-compatible format."""
return {"role": self.role, "content": self.content}
def __str__(self):
+ """Convert the message to a string."""
return f"{self.role}: {self.content}"
class ConfigParameter(BaseModel):
+ """A configuration parameter for a route. Used for remote router metadata such as
+ router hashes, sync locks, etc.
+ """
+
field: str
value: str
scope: Optional[str] = None
@@ -68,6 +89,15 @@ class ConfigParameter(BaseModel):
)
def to_pinecone(self, dimensions: int):
+ """Convert the configuration parameter to a Pinecone-compatible format. Should
+ be used when upserting configuration parameters to a separate config namespace
+ within your Pinecone index.
+
+ :param dimensions: The dimensions of the Pinecone index.
+ :type dimensions: int
+ :return: A Pinecone-compatible configuration parameter.
+ :rtype: dict
+ """
namespace = self.scope or ""
return {
"id": f"{self.field}#{namespace}",
@@ -82,6 +112,10 @@ class ConfigParameter(BaseModel):
class Utterance(BaseModel):
+ """An utterance in a conversation, includes the route, utterance, function
+ schemas, metadata, and diff tag.
+ """
+
route: str
utterance: Union[str, Any]
function_schemas: Optional[List[Dict]] = None
@@ -127,6 +161,14 @@ class Utterance(BaseModel):
)
def to_str(self, include_metadata: bool = False):
+ """Convert an Utterance object to a string. Used for comparisons during sync
+ check operations.
+
+ :param include_metadata: Whether to include metadata in the string.
+ :type include_metadata: bool
+ :return: A string representation of the Utterance object.
+ :rtype: str
+ """
if include_metadata:
# we sort the dicts to ensure consistent order as we need this to compare
# stringified function schemas accurately
@@ -147,9 +189,7 @@ class Utterance(BaseModel):
class SyncMode(Enum):
- """Synchronization modes for local (route layer) and remote (index)
- instances.
- """
+ """Synchronization modes for local (route layer) and remote (index) instances."""
ERROR = "error"
REMOTE = "remote"
@@ -163,12 +203,23 @@ SYNC_MODES = [x.value for x in SyncMode]
class UtteranceDiff(BaseModel):
+ """A list of Utterance objects that represent the differences between local and
+ remote utterances.
+ """
+
diff: List[Utterance]
@classmethod
def from_utterances(
cls, local_utterances: List[Utterance], remote_utterances: List[Utterance]
):
+ """Create a UtteranceDiff object from two lists of Utterance objects.
+
+ :param local_utterances: A list of Utterance objects.
+ :type local_utterances: List[Utterance]
+ :param remote_utterances: A list of Utterance objects.
+ :type remote_utterances: List[Utterance]
+ """
local_utterances_map = {
x.to_str(include_metadata=True): x for x in local_utterances
}
@@ -220,14 +271,18 @@ class UtteranceDiff(BaseModel):
This diff tells us that the remote has "route2: utterance3" and
"route2: utterance4", which do not exist locally.
+
+ :param include_metadata: Whether to include metadata in the string.
+ :type include_metadata: bool
+ :return: A list of diff strings.
+ :rtype: List[str]
"""
return [x.to_diff_str(include_metadata=include_metadata) for x in self.diff]
def get_tag(self, diff_tag: str) -> List[Utterance]:
"""Get all utterances with a given diff tag.
- :param diff_tag: The diff tag to filter by. Must be one of "+", "-", or
- " ".
+ :param diff_tag: The diff tag to filter by. Must be one of "+", "-", or " ".
:type diff_tag: str
:return: A list of Utterance objects.
:rtype: List[Utterance]
@@ -237,8 +292,7 @@ class UtteranceDiff(BaseModel):
return [x for x in self.diff if x.diff_tag == diff_tag]
def get_sync_strategy(self, sync_mode: str) -> dict:
- """Generates the optimal synchronization plan for local and remote
- instances.
+ """Generates the optimal synchronization plan for local and remote instances.
:param sync_mode: The mode to sync the routes with the remote index.
:type sync_mode: str
@@ -415,6 +469,8 @@ class UtteranceDiff(BaseModel):
class Metric(Enum):
+ """The metric to use in vector-based similarity search indexes."""
+
COSINE = "cosine"
DOTPRODUCT = "dotproduct"
EUCLIDEAN = "euclidean"
@@ -433,6 +489,13 @@ class SparseEmbedding(BaseModel):
@classmethod
def from_compact_array(cls, array: np.ndarray):
+ """Create a SparseEmbedding object from a compact array.
+
+ :param array: A compact array.
+ :type array: np.ndarray
+ :return: A SparseEmbedding object.
+ :rtype: SparseEmbedding
+ """
if array.ndim != 2 or array.shape[1] != 2:
raise ValueError(
f"Expected a 2D array with 2 columns, got a {array.ndim}D array with {array.shape[1]} columns. "
@@ -442,32 +505,69 @@ class SparseEmbedding(BaseModel):
@classmethod
def from_vector(cls, vector: np.ndarray):
- """Consumes an array of sparse vectors containing zero-values."""
+ """Consumes an array of sparse vectors containing zero-values.
+
+ :param vector: A sparse vector.
+ :type vector: np.ndarray
+ :return: A SparseEmbedding object.
+ :rtype: SparseEmbedding
+ """
if vector.ndim != 1:
raise ValueError(f"Expected a 1D array, got a {vector.ndim}D array.")
return cls.from_compact_array(np.array([np.arange(len(vector)), vector]).T)
@classmethod
- def from_aurelio(cls, embedding: BM25Embedding):
+ def from_aurelio(cls, embedding: BM25SparseEmbedding):
+ """Create a SparseEmbedding object from an AurelioSparseEmbedding object.
+
+ :param embedding: An AurelioSparseEmbedding object.
+ :type embedding: BM25SparseEmbedding
+ :return: A SparseEmbedding object.
+ :rtype: SparseEmbedding
+ """
arr = np.array([embedding.indices, embedding.values]).T
return cls.from_compact_array(arr)
@classmethod
def from_dict(cls, sparse_dict: dict):
+ """Create a SparseEmbedding object from a dictionary.
+
+ :param sparse_dict: A dictionary of sparse values.
+ :type sparse_dict: dict
+ :return: A SparseEmbedding object.
+ :rtype: SparseEmbedding
+ """
arr = np.array([list(sparse_dict.keys()), list(sparse_dict.values())]).T
return cls.from_compact_array(arr)
@classmethod
def from_pinecone_dict(cls, sparse_dict: dict):
+ """Create a SparseEmbedding object from a Pinecone dictionary.
+
+ :param sparse_dict: A Pinecone dictionary.
+ :type sparse_dict: dict
+ :return: A SparseEmbedding object.
+ :rtype: SparseEmbedding
+ """
arr = np.array([sparse_dict["indices"], sparse_dict["values"]]).T
return cls.from_compact_array(arr)
def to_dict(self):
+ """Convert a SparseEmbedding object to a dictionary.
+
+ :return: A dictionary of sparse values.
+ :rtype: dict
+ """
return {
i: v for i, v in zip(self.embedding[:, 0].astype(int), self.embedding[:, 1])
}
def to_pinecone(self):
+ """Convert a SparseEmbedding object to a Pinecone dictionary.
+
+ :return: A Pinecone dictionary.
+ :rtype: dict
+ """
return {
"indices": self.embedding[:, 0].astype(int).tolist(),
"values": self.embedding[:, 1].tolist(),
@@ -475,6 +575,11 @@ class SparseEmbedding(BaseModel):
# dictionary interface
def items(self):
+ """Return a list of (index, value) tuples from the SparseEmbedding object.
+
+ :return: A list of (index, value) tuples.
+ :rtype: list
+ """
return [
(i, v)
for i, v in zip(self.embedding[:, 0].astype(int), self.embedding[:, 1])
diff --git a/semantic_router/tokenizers.py b/semantic_router/tokenizers.py
new file mode 100644
index 0000000000000000000000000000000000000000..0c1d5b34d3b6c235c541d71fc70cb95eb6e103d5
--- /dev/null
+++ b/semantic_router/tokenizers.py
@@ -0,0 +1,169 @@
+import json
+from pathlib import Path
+
+import numpy as np
+from tokenizers import Tokenizer
+from tokenizers.normalizers import Sequence
+
+
+class BaseTokenizer:
+ """Abstract Tokenizer class"""
+
+ @property
+ def vocab_size(self) -> int:
+ """Returns the vocabulary size of the tokenizer
+
+ :return: Vocabulary size of tokenizer
+ :rtype: int
+ """
+ raise NotImplementedError
+
+ @property
+ def config(self) -> dict:
+ """The tokenizer config
+
+ :return: dictionary of tokenizer config
+ :rtype: dict
+ """
+ raise NotImplementedError
+
+ def save(self, path: str | Path) -> None:
+ """Saves the configuration of the tokenizer
+
+ Saves these files:
+ - tokenizer.json: saved configuration of the tokenizer
+
+ :param path: Path to save the tokenizer to
+ :type path: str, :class:`pathlib.Path`
+ """
+ if isinstance(path, str):
+ path = Path(path)
+
+ with open(path, "w") as fp:
+ json.dump(self.config, fp)
+
+ @classmethod
+ def load(cls, path: str | Path) -> "BaseTokenizer":
+ """Returns a :class:`bm25_engine.tokenizer.BaseTokenizer` object from saved configuration
+
+ Requires these files:
+ - tokenizer.json: saved configuration of the tokenizer
+
+ :param path: Path to load the tokenizer from
+ :type path: str, :class:`pathlib.Path`
+ :returns: Configured BaseTokenizer
+ :rtype: BaseTokenizer
+ """
+ if isinstance(path, str):
+ path = Path(path)
+
+ with open(path) as fp:
+ config = json.load(fp)
+ return cls(**config)
+
+ def tokenize(self, texts: str | list[str], pad: bool = True) -> np.ndarray:
+ raise NotImplementedError
+
+
+class HashTokenizer(BaseTokenizer):
+ def __init__(self) -> None:
+ super().__init__()
+
+
+class PretrainedTokenizer(BaseTokenizer):
+ """Wrapper for HuggingFace tokenizers, representing a pretrained tokenizer (i.e. bert-base-uncased).
+ Extends the :class:`semantic_router.tokenizers.BaseTokenizer` class.
+
+ :param tokenizer: Binding for HuggingFace Rust tokenizers
+ :type tokenizer: class:`tokenizers.Tokenizer`
+ :param add_special_tokens: Whether to accept special tokens from the tokenizer (i.e. `[PAD]`)
+ :type add_special_tokens: bool
+ :param pad: Whether to pad the input to a consistent length (using `[PAD]` tokens)
+ :type pad: bool
+ :param model_ident: HuggingFace ID of the model (i.e. `bert-base-uncased`)
+ :type model_ident: str
+ """
+
+ tokenizer: Tokenizer
+ add_special_tokens: bool
+ pad: bool
+ model_ident: str
+
+ def __init__(
+ self,
+ model_ident: str,
+ custom_normalizer: Sequence | None = None,
+ add_special_tokens: bool = False,
+ pad: bool = True,
+ ) -> None:
+ """Constructor method"""
+ super().__init__()
+ self.add_special_tokens = add_special_tokens
+ self.model_ident = model_ident
+ self.tokenizer = Tokenizer.from_pretrained(model_ident)
+ self.pad = pad
+ if custom_normalizer:
+ self.tokenizer.normalizer = custom_normalizer # type: ignore
+ if pad:
+ self.tokenizer.enable_padding(direction="right", pad_id=0)
+
+ @property
+ def vocab_size(self):
+ """Returns the vocabulary size of the tokenizer
+
+ :return: Vocabulary size of tokenizer
+ :rtype: int
+ """
+ return self.tokenizer.get_vocab_size()
+
+ @property
+ def config(self) -> dict:
+ """The tokenizer config
+
+ :return: dictionary of tokenizer config
+ :rtype: dict
+ """
+ return {
+ "model_ident": self.model_ident,
+ "add_special_tokens": self.add_special_tokens,
+ "pad": self.pad,
+ }
+
+ def tokenize(self, texts: str | list[str], pad: bool = True) -> np.ndarray:
+ """Tokenizes a string or list of strings into a 2D :class:`numpy.ndarray` of token ids
+
+ :param texts: Texts to be tokenized
+ :type texts: str, list
+ :param pad: unused here (configured in the constructor)
+ :type pad: bool
+ :return: 2D numpy array representing token ids
+ :rtype: class:`numpy.ndarray`
+ """
+ if isinstance(texts, str):
+ texts = [texts]
+
+ encodings = self.tokenizer.encode_batch_fast(
+ texts, add_special_tokens=self.add_special_tokens
+ )
+
+ return np.array([e.ids for e in encodings])
+
+
+class TokenizerFactory:
+ """Tokenizer factory class"""
+
+ @staticmethod
+ def get(type_: str, **tokenizer_kwargs) -> BaseTokenizer:
+ r"""Get a configured :class:`bm25_engine.tokenizer.BaseTokenizer`
+
+ :param type_: Tokenizer type to instantiate
+ :type type_: str
+ :param \**kwargs: kwargs to be passed to Tokenizer constructor
+ :return: Tokenizer
+ :rtype: `bm25_engine.tokenizer.BaseTokenizer`
+ """
+ match type_:
+ case "pretrained":
+ return PretrainedTokenizer(**tokenizer_kwargs)
+ case _:
+ return PretrainedTokenizer(**tokenizer_kwargs)
diff --git a/semantic_router/utils/defaults.py b/semantic_router/utils/defaults.py
index 151a9935db79e42df254d8b7b119a503cdd77e13..7915ac9d7125582fdc6c0be871cacd0cf9abd71e 100644
--- a/semantic_router/utils/defaults.py
+++ b/semantic_router/utils/defaults.py
@@ -3,6 +3,8 @@ from enum import Enum
class EncoderDefault(Enum):
+ """Default model names for each encoder type."""
+
FASTEMBED = {
"embedding_model": "BAAI/bge-small-en-v1.5",
"language_model": "BAAI/bge-small-en-v1.5",
diff --git a/semantic_router/utils/function_call.py b/semantic_router/utils/function_call.py
index f5fd5a87fd100482cd65f3502c3f8f67349e36c2..8fd450c01aa7154be27a5f8bc48c889218f7d77a 100644
--- a/semantic_router/utils/function_call.py
+++ b/semantic_router/utils/function_call.py
@@ -1,15 +1,28 @@
import inspect
from typing import Any, Callable, Dict, List, Optional, Union
-from pydantic import BaseModel
+from pydantic import BaseModel, Field
from semantic_router.llms import BaseLLM
from semantic_router.schema import Message, RouteChoice
from semantic_router.utils.logger import logger
-from pydantic import Field
class Parameter(BaseModel):
+ """Parameter for a function.
+
+ :param name: The name of the parameter.
+ :type name: str
+ :param description: The description of the parameter.
+ :type description: Optional[str]
+ :param type: The type of the parameter.
+ :type type: str
+ :param default: The default value of the parameter.
+ :type default: Any
+ :param required: Whether the parameter is required.
+ :type required: bool
+ """
+
class Config:
arbitrary_types_allowed = True
@@ -22,6 +35,11 @@ class Parameter(BaseModel):
required: bool = Field(description="Whether the parameter is required")
def to_ollama(self):
+ """Convert the parameter to a dictionary for an Ollama-compatible function schema.
+
+ :return: The parameter in dictionary format.
+ :rtype: Dict[str, Any]
+ """
return {
self.name: {
"description": self.description,
@@ -42,6 +60,11 @@ class FunctionSchema:
parameters: List[Parameter] = Field(description="The parameters of the function")
def __init__(self, function: Union[Callable, BaseModel]):
+ """Initialize the FunctionSchema.
+
+ :param function: The function to consume.
+ :type function: Union[Callable, BaseModel]
+ """
self.function = function
if callable(function):
self._process_function(function)
@@ -51,6 +74,11 @@ class FunctionSchema:
raise TypeError("Function must be a Callable or BaseModel")
def _process_function(self, function: Callable):
+ """Process the function to get the name, description, signature, and output.
+
+ :param function: The function to process.
+ :type function: Callable
+ """
self.name = function.__name__
self.description = str(inspect.getdoc(function))
self.signature = str(inspect.signature(function))
@@ -68,6 +96,11 @@ class FunctionSchema:
self.parameters = parameters
def to_ollama(self):
+ """Convert the FunctionSchema to an Ollama-compatible function schema dictionary.
+
+ :return: The function schema in dictionary format.
+ :rtype: Dict[str, Any]
+ """
schema_dict = {
"type": "function",
"function": {
@@ -95,6 +128,13 @@ class FunctionSchema:
return schema_dict
def _ollama_type_mapping(self, param_type: str) -> str:
+ """Map the parameter type to an Ollama-compatible type.
+
+ :param param_type: The type of the parameter.
+ :type param_type: str
+ :return: The Ollama-compatible type.
+ :rtype: str
+ """
if param_type == "int":
return "number"
elif param_type == "float":
@@ -108,6 +148,13 @@ class FunctionSchema:
def get_schema_list(items: List[Union[BaseModel, Callable]]) -> List[Dict[str, Any]]:
+ """Get a list of function schemas from a list of functions or Pydantic BaseModels.
+
+ :param items: The functions or BaseModels to get the schemas for.
+ :type items: List[Union[BaseModel, Callable]]
+ :return: A list of function schemas.
+ :rtype: List[Dict[str, Any]]
+ """
schemas = []
for item in items:
schema = get_schema(item)
@@ -116,6 +163,13 @@ def get_schema_list(items: List[Union[BaseModel, Callable]]) -> List[Dict[str, A
def get_schema(item: Union[BaseModel, Callable]) -> Dict[str, Any]:
+ """Get a function schema from a function or Pydantic BaseModel.
+
+ :param item: The function or BaseModel to get the schema for.
+ :type item: Union[BaseModel, Callable]
+ :return: The function schema.
+ :rtype: Dict[str, Any]
+ """
if isinstance(item, BaseModel):
signature_parts = []
for field_name, field_model in item.__annotations__.items():
@@ -148,6 +202,13 @@ def get_schema(item: Union[BaseModel, Callable]) -> Dict[str, Any]:
def convert_python_type_to_json_type(param_type: str) -> str:
+ """Convert a Python type to a JSON type.
+
+ :param param_type: The type of the parameter.
+ :type param_type: str
+ :return: The JSON type.
+ :rtype: str
+ """
if param_type == "int":
return "number"
if param_type == "float":
@@ -168,6 +229,19 @@ def convert_python_type_to_json_type(param_type: str) -> str:
async def route_and_execute(
query: str, llm: BaseLLM, functions: List[Callable], layer
) -> Any:
+ """Route and execute a function.
+
+ :param query: The query to route and execute.
+ :type query: str
+ :param llm: The LLM to use.
+ :type llm: BaseLLM
+ :param functions: The functions to execute.
+ :type functions: List[Callable]
+ :param layer: The layer to use.
+ :type layer: Layer
+ :return: The result of the function.
+ :rtype: Any
+ """
route_choice: RouteChoice = layer(query)
for function in functions:
diff --git a/semantic_router/utils/llm.py b/semantic_router/utils/llm.py
deleted file mode 100644
index 5402e47fe27f8695447ab1c5fcc01fd4b8080729..0000000000000000000000000000000000000000
--- a/semantic_router/utils/llm.py
+++ /dev/null
@@ -1,65 +0,0 @@
-import os
-from typing import Optional
-
-import openai
-
-from semantic_router.utils.logger import logger
-
-
-def llm(prompt: str) -> Optional[str]:
- try:
- client = openai.OpenAI(
- base_url="https://openrouter.ai/api/v1",
- api_key=os.getenv("OPENROUTER_API_KEY"),
- )
-
- completion = client.chat.completions.create(
- model="mistralai/mistral-7b-instruct",
- messages=[
- {
- "role": "user",
- "content": prompt,
- },
- ],
- temperature=0.01,
- max_tokens=200,
- )
-
- output = completion.choices[0].message.content
-
- if not output:
- raise Exception("No output generated")
- return output
- except Exception as e:
- logger.error(f"LLM error: {e}")
- raise Exception(f"LLM error: {e}") from e
-
-
-# TODO integrate async LLM function
-# async def allm(prompt: str) -> Optional[str]:
-# try:
-# client = openai.AsyncOpenAI(
-# base_url="https://openrouter.ai/api/v1",
-# api_key=os.getenv("OPENROUTER_API_KEY"),
-# )
-
-# completion = await client.chat.completions.create(
-# model="mistralai/mistral-7b-instruct",
-# messages=[
-# {
-# "role": "user",
-# "content": prompt,
-# },
-# ],
-# temperature=0.01,
-# max_tokens=200,
-# )
-
-# output = completion.choices[0].message.content
-
-# if not output:
-# raise Exception("No output generated")
-# return output
-# except Exception as e:
-# logger.error(f"LLM error: {e}")
-# raise Exception(f"LLM error: {e}") from e
diff --git a/semantic_router/utils/logger.py b/semantic_router/utils/logger.py
index 2c1980d86801453184e63348ee82d56ea21291b6..30aedc2c111c9c3cd3abc8c7c6aec2793a8d5ff3 100644
--- a/semantic_router/utils/logger.py
+++ b/semantic_router/utils/logger.py
@@ -4,6 +4,8 @@ import colorlog
class CustomFormatter(colorlog.ColoredFormatter):
+ """Custom formatter for the logger."""
+
def __init__(self):
super().__init__(
"%(log_color)s%(asctime)s %(levelname)s %(name)s %(message)s",
@@ -21,6 +23,7 @@ class CustomFormatter(colorlog.ColoredFormatter):
def add_coloured_handler(logger):
+ """Add a coloured handler to the logger."""
formatter = CustomFormatter()
console_handler = logging.StreamHandler()
console_handler.setFormatter(formatter)
@@ -29,6 +32,7 @@ def add_coloured_handler(logger):
def setup_custom_logger(name):
+ """Setup a custom logger."""
logger = logging.getLogger(name)
if not logger.hasHandlers():
diff --git a/tests/functional/encoders/test_bm25_functional.py b/tests/functional/encoders/test_bm25_functional.py
new file mode 100644
index 0000000000000000000000000000000000000000..5e8fc1114a3243f673dd1cf120604bacce8c1eaa
--- /dev/null
+++ b/tests/functional/encoders/test_bm25_functional.py
@@ -0,0 +1,55 @@
+import numpy as np
+import pytest
+
+from semantic_router import Route
+from semantic_router.encoders.bm25 import BM25Encoder
+
+UTTERANCES = [
+ "A high weight in tf–idf is reached by a high term frequency",
+ "(in the given document) and a low document frequency of the term",
+ "in the whole collection of documents; the weights hence tend to filter",
+ "out common terms. Since the ratio inside the idf's log function is always",
+ "greater than or equal to 1, the value of idf (and tf–idf) is greater than or equal",
+ "to 0. As a term appears in more documents, the ratio inside the logarithm approaches",
+ "1, bringing the idf and tf–idf closer to 0.",
+]
+QUERIES = ["weights", "ratio logarithm"]
+
+
+@pytest.fixture
+def bm25_encoder():
+ sparse_encoder = BM25Encoder(use_default_params=True)
+ sparse_encoder.fit([Route(name="test_route", utterances=UTTERANCES)])
+ return sparse_encoder
+
+
+class TestBM25Encoder:
+ def _sparse_to_vector(self, sparse_embedding, vocab_size):
+ """Re-constructs the full (sparse_embedding.shape[0], vocab_size) array"""
+ return (
+ np.eye(vocab_size)[sparse_embedding[:, 0].astype(np.uint).tolist()]
+ * np.atleast_2d(sparse_embedding[:, 1]).T
+ ).sum(axis=0)
+
+ def test_bm25_scoring(self, bm25_encoder):
+ vocab_size = bm25_encoder._tokenizer.vocab_size
+ expected = np.array(
+ [
+ [0.00000, 0.00000, 0.54575, 0.00000, 0.00000, 0.00000, 0.00000],
+ [0.00000, 0.00000, 0.00000, 0.18864, 0.00000, 0.67897, 0.00000],
+ ]
+ )
+ q_e = np.stack(
+ [
+ self._sparse_to_vector(v.embedding, vocab_size=vocab_size)
+ for v in bm25_encoder.encode_queries(QUERIES)
+ ]
+ )
+ d_e = np.stack(
+ [
+ self._sparse_to_vector(v.embedding, vocab_size=vocab_size)
+ for v in bm25_encoder.encode_documents(UTTERANCES)
+ ]
+ )
+ scores = q_e @ d_e.T
+ assert np.allclose(scores, expected, rtol=1e-4), expected
diff --git a/tests/integration/encoders/test_openai_integration.py b/tests/integration/encoders/test_openai_integration.py
index 8c557d809d54413ae28a0676d5abe56026d29022..63c6968a101b15a3a187962bd0d0af713c915c89 100644
--- a/tests/integration/encoders/test_openai_integration.py
+++ b/tests/integration/encoders/test_openai_integration.py
@@ -1,6 +1,8 @@
import os
+
import pytest
from openai import OpenAIError
+
from semantic_router.encoders.base import DenseEncoder
from semantic_router.encoders.openai import OpenAIEncoder
diff --git a/tests/unit/encoders/test_azure.py b/tests/unit/encoders/test_azure.py
index 5a6841d7edf5038aae4a9a7124331a760279b626..01a495a44ec1f4e58f9ee0a0c45f211d62226d76 100644
--- a/tests/unit/encoders/test_azure.py
+++ b/tests/unit/encoders/test_azure.py
@@ -1,5 +1,6 @@
-import pytest
from unittest.mock import AsyncMock, Mock, patch
+
+import pytest
from openai import OpenAIError
from openai.types import CreateEmbeddingResponse, Embedding
from openai.types.create_embedding_response import Usage
diff --git a/tests/unit/encoders/test_bedrock.py b/tests/unit/encoders/test_bedrock.py
index 619325ed0ac43e7f54cf7eacd14a68a21a22ee8f..a1f40cc2121ba518ec2c07fe68997739dc7814de 100644
--- a/tests/unit/encoders/test_bedrock.py
+++ b/tests/unit/encoders/test_bedrock.py
@@ -1,7 +1,9 @@
-import os
-import pytest
import json
+import os
from io import BytesIO
+
+import pytest
+
from semantic_router.encoders import BedrockEncoder
diff --git a/tests/unit/encoders/test_bm25.py b/tests/unit/encoders/test_bm25.py
index a22b0cc2eb5c9e13c8a31038b76b18fe0b6e152c..408412eb5b64e9306279d1f7eaf61889e7d25dd3 100644
--- a/tests/unit/encoders/test_bm25.py
+++ b/tests/unit/encoders/test_bm25.py
@@ -1,11 +1,8 @@
-import pytest
import numpy as np
+import pytest
from semantic_router.encoders import BM25Encoder
from semantic_router.route import Route
-import nltk
-
-nltk.download("punkt_tab")
UTTERANCES = [
"Hello we need this text to be a little longer for our sparse encoders",
@@ -18,7 +15,7 @@ UTTERANCES = [
@pytest.fixture
def bm25_encoder():
- sparse_encoder = BM25Encoder(use_default_params=False)
+ sparse_encoder = BM25Encoder(use_default_params=True)
sparse_encoder.fit(
[
Route(
@@ -44,11 +41,11 @@ def routes():
class TestBM25Encoder:
def test_initialization(self, bm25_encoder):
- assert bm25_encoder.model is not None
+ assert bm25_encoder._tokenizer is not None
def test_fit(self, bm25_encoder, routes):
bm25_encoder.fit(routes)
- assert bm25_encoder.model is not None
+ assert bm25_encoder._tokenizer is not None
def test_fit_with_strings(self, bm25_encoder):
route_strings = ["test a", "test b", "test c"]
@@ -74,11 +71,51 @@ class TestBM25Encoder:
), "Each item in result should be an array"
def test_call_method_with_uninitialized_model_or_mapping(self, bm25_encoder):
- bm25_encoder.model = None
+ bm25_encoder._tokenizer = None
with pytest.raises(ValueError):
bm25_encoder(["test"])
def test_fit_with_uninitialized_model(self, bm25_encoder, routes):
- bm25_encoder.model = None
+ bm25_encoder._tokenizer = None
with pytest.raises(ValueError):
bm25_encoder.fit(routes)
+
+ def test_encode_queries(self, bm25_encoder):
+ queries = ["quick brown", "lazy dog", "hello world"]
+ results = bm25_encoder.encode_queries(queries)
+
+ assert len(results) == len(queries)
+ assert all([isinstance(result.embedding, np.ndarray) for result in results])
+
+ def test_encode_queries_empty_list(self, bm25_encoder):
+ with pytest.raises(ValueError, match="No documents provided for encoding"):
+ bm25_encoder.encode_queries([])
+
+ def test_encode_queries_unfitted(self):
+ encoder = BM25Encoder(use_default_params=True)
+ with pytest.raises(ValueError, match="Encoder not fitted"):
+ encoder.encode_queries(["test query"])
+
+ def test_encode_documents(self, bm25_encoder):
+ documents = ["quick brown", "lazy dog", "hello world"]
+ results = bm25_encoder.encode_documents(documents)
+
+ assert len(results) == len(documents)
+ assert all([isinstance(result.embedding, np.ndarray) for result in results])
+
+ def test_encode_documents_empty_list(self, bm25_encoder):
+ with pytest.raises(ValueError, match="No documents provided for encoding"):
+ bm25_encoder.encode_documents([])
+
+ def test_encode_documents_unfitted(self):
+ encoder = BM25Encoder(use_default_params=True)
+ with pytest.raises(ValueError, match="Encoder not fitted"):
+ encoder.encode_documents(["test document"])
+
+ def test_encode_documents_batch_size(self, bm25_encoder):
+ documents = ["quick brown", "lazy dog", "hello world", "test document"]
+ batch_size = 2
+ results = bm25_encoder.encode_documents(documents, batch_size=batch_size)
+
+ assert len(results) == len(documents)
+ assert all(isinstance(result.embedding, np.ndarray) for result in results)
diff --git a/tests/unit/encoders/test_clip.py b/tests/unit/encoders/test_clip.py
index 297761519f29870723970da7f6ece10e98ed9ff8..ac87e6a09fa03b9c3c31e4a1c55c7576dc179168 100644
--- a/tests/unit/encoders/test_clip.py
+++ b/tests/unit/encoders/test_clip.py
@@ -1,12 +1,14 @@
import os
+
import numpy as np
import pytest
_ = pytest.importorskip("torch")
+from unittest.mock import patch # noqa: E402
+
import torch # noqa: E402
from PIL import Image # noqa: E402
-from unittest.mock import patch # noqa: E402
from semantic_router.encoders import CLIPEncoder # noqa: E402
diff --git a/tests/unit/encoders/test_fastembed.py b/tests/unit/encoders/test_fastembed.py
index f46303c1bb069653c792fcf00c05ee9ced112923..a8ad3438e25eebd04edf62990a44bf1733b1abb2 100644
--- a/tests/unit/encoders/test_fastembed.py
+++ b/tests/unit/encoders/test_fastembed.py
@@ -1,7 +1,7 @@
-from semantic_router.encoders import FastEmbedEncoder
-
import pytest
+from semantic_router.encoders import FastEmbedEncoder
+
_ = pytest.importorskip("fastembed")
diff --git a/tests/unit/encoders/test_huggingface.py b/tests/unit/encoders/test_huggingface.py
index 7f496e39f8117dbe4242070e59517acf751ca9a0..76f28f5bb8564de0c434c778b09a80200c913b4d 100644
--- a/tests/unit/encoders/test_huggingface.py
+++ b/tests/unit/encoders/test_huggingface.py
@@ -1,6 +1,6 @@
+import os
from unittest.mock import patch
-import os
import numpy as np
import pytest
@@ -26,7 +26,9 @@ class TestHuggingFaceEncoder:
with pytest.raises(ImportError) as error:
HuggingFaceEncoder()
- assert "Please install Pytorch to use HuggingFaceEncoder" in str(error.value)
+ assert "Please install transformers to use HuggingFaceEncoder" in str(
+ error.value
+ )
@pytest.mark.skipif(
os.environ.get("RUN_HF_TESTS") is None, reason="Set RUN_HF_TESTS=1 to run"
diff --git a/tests/unit/encoders/test_mistral.py b/tests/unit/encoders/test_mistral.py
index a236ad167139b5d5994ed8ad2d3610e4b49fc349..25dba6b759bc2a033c64ae0055e0b1b45d626f48 100644
--- a/tests/unit/encoders/test_mistral.py
+++ b/tests/unit/encoders/test_mistral.py
@@ -1,7 +1,6 @@
from unittest.mock import patch
import pytest
-
from mistralai.exceptions import MistralException
from mistralai.models.embeddings import EmbeddingObject, EmbeddingResponse, UsageInfo
diff --git a/tests/unit/encoders/test_openai.py b/tests/unit/encoders/test_openai.py
index 96171cce5b462f76dfbd88d7795bfc684ca100c8..a564f2e63a100469dbb78e26278cd9f4a9c4d750 100644
--- a/tests/unit/encoders/test_openai.py
+++ b/tests/unit/encoders/test_openai.py
@@ -1,5 +1,6 @@
-import pytest
from unittest.mock import AsyncMock, Mock, patch
+
+import pytest
from openai import OpenAIError
from openai.types import CreateEmbeddingResponse, Embedding
from openai.types.create_embedding_response import Usage
diff --git a/tests/unit/encoders/test_vit.py b/tests/unit/encoders/test_vit.py
index 622b22689f327054392a98cb336ec3f40adee6cd..c2488a1e43b1126a762f7ebcca0825f55a097ece 100644
--- a/tests/unit/encoders/test_vit.py
+++ b/tests/unit/encoders/test_vit.py
@@ -1,6 +1,6 @@
+import os
from unittest.mock import patch
-import os
import numpy as np
import pytest
diff --git a/tests/unit/llms/test_llm_base.py b/tests/unit/llms/test_llm_base.py
index 7fe8c0f06e3d153f17c589c086267690c703aa9a..c849901dc4346a3b665015cb9679c1f834096967 100644
--- a/tests/unit/llms/test_llm_base.py
+++ b/tests/unit/llms/test_llm_base.py
@@ -1,6 +1,8 @@
+from unittest.mock import patch
+
import pytest
+
from semantic_router.llms import BaseLLM
-from unittest.mock import patch
class TestBaseLLM:
diff --git a/tests/unit/test_function_schema.py b/tests/unit/test_function_schema.py
index 94a3f8e8b182dfd8064c0548ef3ba634440ed462..fbd2d7c6ca378683780bd08d86f94e1e8b119be9 100644
--- a/tests/unit/test_function_schema.py
+++ b/tests/unit/test_function_schema.py
@@ -1,4 +1,5 @@
import inspect
+
from semantic_router.utils.function_call import FunctionSchema
diff --git a/tests/unit/test_hybrid_layer.py b/tests/unit/test_hybrid_layer.py
deleted file mode 100644
index b12ea2f5b12d199f809664a8b56630b0da9ae2a1..0000000000000000000000000000000000000000
--- a/tests/unit/test_hybrid_layer.py
+++ /dev/null
@@ -1,270 +0,0 @@
-import pytest
-
-from semantic_router.encoders import (
- AzureOpenAIEncoder,
- DenseEncoder,
- BM25Encoder,
- CohereEncoder,
- OpenAIEncoder,
- TfidfEncoder,
-)
-from semantic_router.routers import HybridRouter
-from semantic_router.route import Route
-
-
-UTTERANCES = [
- "Hello we need this text to be a little longer for our sparse encoders",
- "In this case they need to learn from recurring tokens, ie words.",
- "We give ourselves several examples from our encoders to learn from.",
- "But given this is only an example we don't need too many",
- "Just enough to test that our sparse encoders work as expected",
-]
-
-
-def mock_encoder_call(utterances):
- # Define a mapping of utterances to return values
- mock_responses = {
- UTTERANCES[0]: [0.1, 0.2, 0.3],
- UTTERANCES[1]: [0.4, 0.5, 0.6],
- UTTERANCES[2]: [0.7, 0.8, 0.9],
- UTTERANCES[3]: [1.0, 1.1, 1.2],
- UTTERANCES[4]: [1.3, 1.4, 1.5],
- }
- return [mock_responses.get(u, [0, 0, 0]) for u in utterances]
-
-
-@pytest.fixture
-def base_encoder(mocker):
- mock_base_encoder = DenseEncoder(name="test-encoder", score_threshold=0.5)
- mocker.patch.object(DenseEncoder, "__call__", return_value=[[0.1, 0.2, 0.3]])
- return mock_base_encoder
-
-
-@pytest.fixture
-def cohere_encoder(mocker):
- mocker.patch.object(CohereEncoder, "__call__", side_effect=mock_encoder_call)
- return CohereEncoder(name="test-cohere-encoder", cohere_api_key="test_api_key")
-
-
-@pytest.fixture
-def openai_encoder(mocker):
- mocker.patch.object(OpenAIEncoder, "__call__", side_effect=mock_encoder_call)
- return OpenAIEncoder(name="text-embedding-3-small", openai_api_key="test_api_key")
-
-
-@pytest.fixture
-def azure_encoder(mocker):
- mocker.patch.object(AzureOpenAIEncoder, "__call__", side_effect=mock_encoder_call)
- return AzureOpenAIEncoder(
- deployment_name="test-deployment",
- azure_endpoint="test_endpoint",
- api_key="test_api_key",
- api_version="test_version",
- model="test_model",
- )
-
-
-@pytest.fixture
-def bm25_encoder():
- # mocker.patch.object(BM25Encoder, "__call__", side_effect=mock_encoder_call)
- return BM25Encoder(name="test-bm25-encoder")
-
-
-@pytest.fixture
-def tfidf_encoder():
- # mocker.patch.object(TfidfEncoder, "__call__", side_effect=mock_encoder_call)
- return TfidfEncoder(name="test-tfidf-encoder")
-
-
-@pytest.fixture
-def routes():
- return [
- Route(name="Route 1", utterances=[UTTERANCES[0], UTTERANCES[1]]),
- Route(name="Route 2", utterances=[UTTERANCES[2], UTTERANCES[3], UTTERANCES[4]]),
- ]
-
-
-sparse_encoder = TfidfEncoder()
-sparse_encoder.fit(
- [
- Route(
- name="Route 1",
- utterances=[
- "The quick brown fox jumps over the lazy dog",
- "some other useful text containing words like fox and dog",
- ],
- ),
- Route(name="Route 2", utterances=["Hello, world!"]),
- ]
-)
-
-
-class TestHybridRouter:
- def test_initialization(self, openai_encoder, routes):
- route_layer = HybridRouter(
- encoder=openai_encoder,
- sparse_encoder=sparse_encoder,
- routes=routes,
- top_k=10,
- alpha=0.8,
- )
- assert route_layer.index is not None and route_layer.routes is not None
- assert openai_encoder.score_threshold == 0.3
- assert route_layer.score_threshold == 0.3
- assert route_layer.top_k == 10
- assert route_layer.alpha == 0.8
- assert route_layer.index.route_names is None
- assert len(route_layer.routes) == 2
-
- def test_initialization_different_encoders(self, cohere_encoder, openai_encoder):
- route_layer_cohere = HybridRouter(
- encoder=cohere_encoder, sparse_encoder=sparse_encoder
- )
- assert route_layer_cohere.score_threshold == 0.3
-
- route_layer_openai = HybridRouter(
- encoder=openai_encoder, sparse_encoder=sparse_encoder
- )
- assert route_layer_openai.score_threshold == 0.3
-
- def test_add_route(self, openai_encoder, routes):
- route_layer = HybridRouter(
- encoder=openai_encoder, sparse_encoder=sparse_encoder
- )
- route_layer.add(routes=routes[0])
- assert route_layer.index is not None, "route_layer.index is None"
- assert route_layer.routes is not None, "route_layer.routes is None"
- assert len(route_layer.routes) == 1, "route_layer.routes is not 1"
-
- def test_add_multiple_routes(self, openai_encoder, routes):
- route_layer = HybridRouter(
- encoder=openai_encoder, sparse_encoder=sparse_encoder
- )
- route_layer.add(routes=routes)
- assert route_layer.index is not None, "route_layer.index is None"
- assert route_layer.routes is not None, "route_layer.routes is None"
- assert len(route_layer.routes) == 2, "route_layer.routes is not 2"
-
- def test_query_and_classification(self, openai_encoder, routes):
- route_layer = HybridRouter(
- encoder=openai_encoder,
- sparse_encoder=sparse_encoder,
- routes=routes,
- auto_sync="local",
- )
- route_layer.set_threshold(0.0)
- query_result = route_layer(UTTERANCES[0])
- assert query_result.name in ["Route 1", "Route 2"]
-
- def test_query_with_no_index(self, openai_encoder):
- route_layer = HybridRouter(
- encoder=openai_encoder, sparse_encoder=sparse_encoder
- )
- assert isinstance(route_layer.sparse_encoder, BM25Encoder) or isinstance(
- route_layer.sparse_encoder, TfidfEncoder
- ), (
- f"route_layer.sparse_encoder is {route_layer.sparse_encoder.__class__.__name__} "
- "not BM25Encoder or TfidfEncoder"
- )
- assert route_layer("Anything").name is None
-
- def test_semantic_classify(self, openai_encoder, routes):
- route_layer = HybridRouter(
- encoder=openai_encoder, sparse_encoder=sparse_encoder, routes=routes
- )
- classification, score = route_layer._semantic_classify(
- [
- {"route": "Route 1", "score": 0.9},
- {"route": "Route 2", "score": 0.1},
- ]
- )
- assert classification == "Route 1"
- assert score == [0.9]
-
- def test_semantic_classify_multiple_routes(self, openai_encoder, routes):
- route_layer = HybridRouter(
- encoder=openai_encoder, sparse_encoder=sparse_encoder, routes=routes
- )
- classification, score = route_layer._semantic_classify(
- [
- {"route": "Route 1", "score": 0.9},
- {"route": "Route 2", "score": 0.1},
- {"route": "Route 1", "score": 0.8},
- ]
- )
- assert classification == "Route 1"
- assert score == [0.9, 0.8]
-
- def test_pass_threshold(self, openai_encoder):
- route_layer = HybridRouter(
- encoder=openai_encoder, sparse_encoder=sparse_encoder
- )
- assert not route_layer._pass_threshold([], 0.5)
- assert route_layer._pass_threshold([0.6, 0.7], 0.5)
-
- def test_failover_score_threshold(self, base_encoder):
- route_layer = HybridRouter(encoder=base_encoder, sparse_encoder=sparse_encoder)
- assert base_encoder.score_threshold == 0.50
- assert route_layer.score_threshold == 0.50
-
- def test_add_route_tfidf(self, cohere_encoder, tfidf_encoder, routes):
- hybrid_route_layer = HybridRouter(
- encoder=cohere_encoder,
- sparse_encoder=tfidf_encoder,
- routes=routes[:-1],
- auto_sync="local",
- )
- hybrid_route_layer.add(routes=routes[-1])
- all_utterances = [
- utterance for route in routes for utterance in route.utterances
- ]
- assert hybrid_route_layer.index.sparse_index is not None, "sparse_index is None"
- assert len(hybrid_route_layer.index.sparse_index) == len(
- all_utterances
- ), "sparse_index length mismatch"
-
- def test_setting_aggregation_methods(self, openai_encoder, routes):
- for agg in ["sum", "mean", "max"]:
- route_layer = HybridRouter(
- encoder=openai_encoder,
- sparse_encoder=sparse_encoder,
- routes=routes,
- aggregation=agg,
- )
- assert route_layer.aggregation == agg
-
- def test_semantic_classify_multiple_routes_with_different_aggregation(
- self, openai_encoder, routes
- ):
- route_scores = [
- {"route": "Route 1", "score": 0.5},
- {"route": "Route 1", "score": 0.5},
- {"route": "Route 1", "score": 0.5},
- {"route": "Route 1", "score": 0.5},
- {"route": "Route 2", "score": 0.4},
- {"route": "Route 2", "score": 0.6},
- {"route": "Route 2", "score": 0.8},
- {"route": "Route 3", "score": 0.1},
- {"route": "Route 3", "score": 1.0},
- ]
- for agg in ["sum", "mean", "max"]:
- route_layer = HybridRouter(
- encoder=openai_encoder,
- sparse_encoder=sparse_encoder,
- routes=routes,
- aggregation=agg,
- )
- classification, score = route_layer._semantic_classify(route_scores)
-
- if agg == "sum":
- assert classification == "Route 1"
- assert score == [0.5, 0.5, 0.5, 0.5]
- elif agg == "mean":
- assert classification == "Route 2"
- assert score == [0.4, 0.6, 0.8]
- elif agg == "max":
- assert classification == "Route 3"
- assert score == [0.1, 1.0]
-
-
-# Add more tests for edge cases and error handling as needed.
diff --git a/tests/unit/test_router.py b/tests/unit/test_router.py
index 1f743f1c8581fedc6232e57742b90c06d6671fe3..8b09b065d884d43f893a4b0372849d045cbdc484 100644
--- a/tests/unit/test_router.py
+++ b/tests/unit/test_router.py
@@ -1,22 +1,67 @@
import importlib
import os
import tempfile
-from unittest.mock import mock_open, patch
+import time
from datetime import datetime
+from functools import wraps
+from platform import python_version
+from typing import Any, List, Optional
+from unittest.mock import mock_open, patch
+
+import numpy as np
import pytest
-import time
-from typing import Optional
-from semantic_router.encoders import DenseEncoder, CohereEncoder, OpenAIEncoder
+
+from semantic_router.encoders import (
+ CohereEncoder,
+ DenseEncoder,
+ OpenAIEncoder,
+)
+from semantic_router.encoders.base import (
+ AsymmetricDenseMixin,
+ AsymmetricSparseMixin,
+ SparseEncoder,
+)
from semantic_router.index.local import LocalIndex
from semantic_router.index.pinecone import PineconeIndex
from semantic_router.index.qdrant import QdrantIndex
-from semantic_router.routers import RouterConfig, SemanticRouter
-from semantic_router.llms.base import BaseLLM
+from semantic_router.llms import BaseLLM, OpenAILLM
from semantic_router.route import Route
-from platform import python_version
+from semantic_router.routers import HybridRouter, RouterConfig, SemanticRouter
+from semantic_router.schema import SparseEmbedding
+from semantic_router.utils.logger import logger
+
+PINECONE_SLEEP = 8
+RETRY_COUNT = 10
+
+
+# retry decorator for PineconeIndex cases (which need delay)
+def retry(max_retries: int = 5, delay: int = 8):
+ """Retry decorator, currently used for PineconeIndex which often needs some time
+ to be populated and have all correct data. Once full Pinecone mock is built we
+ should remove this decorator.
+
+ :param max_retries: Maximum number of retries.
+ :param delay: Delay between retries in seconds.
+ """
+
+ def decorator(func):
+ @wraps(func)
+ def wrapper(*args, **kwargs):
+ count = 0
+ last_exception = None
+ while count < max_retries:
+ try:
+ return func(*args, **kwargs)
+ except Exception as e:
+ logger.warning(f"Attempt {count} | Error in {func.__name__}: {e}")
+ last_exception = e
+ count += 1
+ time.sleep(delay)
+ raise last_exception
+ return wrapper
-PINECONE_SLEEP = 6
+ return decorator
def mock_encoder_call(utterances):
@@ -41,13 +86,24 @@ def init_index(
index_cls,
dimensions: Optional[int] = None,
namespace: Optional[str] = "",
+ index_name: Optional[str] = None,
):
"""We use this function to initialize indexes with different names to avoid
issues during testing.
"""
if index_cls is PineconeIndex:
+ if index_name:
+ if not dimensions and "OpenAIEncoder" in index_name:
+ dimensions = 1536
+
+ elif not dimensions and "CohereEncoder" in index_name:
+ dimensions = 1024
+
+ # we specify different index names to avoid dimensionality issues between different encoders
+ index_name = TEST_ID if not index_name else f"{TEST_ID}-{index_name.lower()}"
+
index = index_cls(
- index_name=TEST_ID, dimensions=dimensions, namespace=namespace
+ index_name=index_name, dimensions=dimensions, namespace=namespace
)
else:
index = index_cls()
@@ -108,13 +164,48 @@ def base_encoder():
@pytest.fixture
def cohere_encoder(mocker):
mocker.patch.object(CohereEncoder, "__call__", side_effect=mock_encoder_call)
+
+ # Mock async call
+ async def async_mock_encoder_call(docs=None, utterances=None):
+ # Handle either docs or utterances parameter
+ texts = docs if docs is not None else utterances
+ return mock_encoder_call(texts)
+
+ mocker.patch.object(CohereEncoder, "acall", side_effect=async_mock_encoder_call)
return CohereEncoder(name="test-cohere-encoder", cohere_api_key="test_api_key")
@pytest.fixture
def openai_encoder(mocker):
+ # Mock the OpenAI client creation and API calls
+ mocker.patch("openai.OpenAI")
+ # Mock the __call__ method
mocker.patch.object(OpenAIEncoder, "__call__", side_effect=mock_encoder_call)
- return OpenAIEncoder(name="text-embedding-3-small", openai_api_key="test_api_key")
+
+ # Mock async call
+ async def async_mock_encoder_call(docs=None, utterances=None):
+ # Handle either docs or utterances parameter
+ texts = docs if docs is not None else utterances
+ return mock_encoder_call(texts)
+
+ mocker.patch.object(OpenAIEncoder, "acall", side_effect=async_mock_encoder_call)
+ # Create and return the mocked encoder
+ encoder = OpenAIEncoder(name="text-embedding-3-small")
+ return encoder
+
+
+@pytest.fixture
+def mock_openai_llm(mocker):
+ # Mock the OpenAI LLM
+ mocker.patch.object(OpenAILLM, "__call__", return_value="mocked response")
+
+ # also async
+ async def async_mock_llm_call(messages=None, **kwargs):
+ return "mocked response"
+
+ mocker.patch.object(OpenAILLM, "acall", side_effect=async_mock_llm_call)
+
+ return OpenAILLM(name="fake-model-v1")
@pytest.fixture
@@ -201,317 +292,662 @@ def get_test_encoders():
return encoders
+def get_test_routers():
+ routers = [SemanticRouter, HybridRouter]
+ return routers
+
+
@pytest.mark.parametrize(
- "index_cls,encoder_cls",
+ "index_cls,encoder_cls,router_cls",
[
- (index, encoder)
+ (index, encoder, router)
for index in get_test_indexes()
for encoder in get_test_encoders()
+ for router in get_test_routers()
],
)
class TestIndexEncoders:
- def test_initialization(self, routes, openai_encoder, index_cls, encoder_cls):
- index = init_index(index_cls)
- route_layer = SemanticRouter(
- encoder=encoder_cls(),
+ def test_initialization(self, routes, index_cls, encoder_cls, router_cls):
+ encoder = encoder_cls()
+ index = init_index(index_cls, index_name=encoder.__class__.__name__)
+ route_layer = router_cls(
+ encoder=encoder,
routes=routes,
index=index,
auto_sync="local",
top_k=10,
)
- if index_cls is PineconeIndex:
- time.sleep(PINECONE_SLEEP) # allow for index to be populated
-
- assert openai_encoder.score_threshold == 0.3
- assert route_layer.score_threshold == 0.3
+ score_threshold = route_layer.score_threshold
+ if isinstance(route_layer, HybridRouter):
+ assert score_threshold == encoder.score_threshold * route_layer.alpha
+ else:
+ assert score_threshold == encoder.score_threshold
assert route_layer.top_k == 10
- assert len(route_layer.index) == 5
+
+ @retry(max_retries=RETRY_COUNT, delay=PINECONE_SLEEP)
+ def check_index_populated():
+ assert len(route_layer.index) == 5
+
+ check_index_populated()
+
assert (
len(set(route_layer._get_route_names()))
if route_layer._get_route_names() is not None
else 0 == 2
)
- def test_initialization_different_encoders(self, encoder_cls, index_cls):
- index = init_index(index_cls)
+ def test_initialization_different_encoders(
+ self, encoder_cls, index_cls, router_cls
+ ):
encoder = encoder_cls()
- route_layer = SemanticRouter(encoder=encoder, index=index)
- assert route_layer.score_threshold == encoder.score_threshold
+ index = init_index(index_cls, index_name=encoder.__class__.__name__)
+ route_layer = router_cls(encoder=encoder, index=index)
+ score_threshold = route_layer.score_threshold
+ if isinstance(route_layer, HybridRouter):
+ assert score_threshold == encoder.score_threshold * route_layer.alpha
+ else:
+ assert score_threshold == encoder.score_threshold
+
+ def test_initialization_no_encoder(self, index_cls, encoder_cls, router_cls):
+ route_layer_none = router_cls(encoder=None)
+ score_threshold = route_layer_none.score_threshold
+ if isinstance(route_layer_none, HybridRouter):
+ assert score_threshold == 0.3 * route_layer_none.alpha
+ else:
+ assert score_threshold == 0.3
+
+
+class TestRouterConfig:
+ def test_from_file_json(self, tmp_path):
+ # Create a temporary JSON file with layer configuration
+ config_path = tmp_path / "config.json"
+ config_path.write_text(
+ layer_json()
+ ) # Assuming layer_json() returns a valid JSON string
+
+ # Load the RouterConfig from the temporary file
+ layer_config = RouterConfig.from_file(str(config_path))
+
+ # Assertions to verify the loaded configuration
+ assert layer_config.encoder_type == "cohere"
+ assert layer_config.encoder_name == "embed-english-v3.0"
+ assert len(layer_config.routes) == 2
+ assert layer_config.routes[0].name == "politics"
+
+ def test_from_file_yaml(self, tmp_path):
+ # Create a temporary YAML file with layer configuration
+ config_path = tmp_path / "config.yaml"
+ config_path.write_text(
+ layer_yaml()
+ ) # Assuming layer_yaml() returns a valid YAML string
+
+ # Load the RouterConfig from the temporary file
+ layer_config = RouterConfig.from_file(str(config_path))
+
+ # Assertions to verify the loaded configuration
+ assert layer_config.encoder_type == "cohere"
+ assert layer_config.encoder_name == "embed-english-v3.0"
+ assert len(layer_config.routes) == 2
+ assert layer_config.routes[0].name == "politics"
+
+ def test_from_file_invalid_path(self):
+ with pytest.raises(FileNotFoundError) as excinfo:
+ RouterConfig.from_file("nonexistent_path.json")
+ assert "[Errno 2] No such file or directory: 'nonexistent_path.json'" in str(
+ excinfo.value
+ )
+
+ def test_from_file_unsupported_type(self, tmp_path):
+ # Create a temporary unsupported file
+ config_path = tmp_path / "config.unsupported"
+ config_path.write_text(layer_json())
+
+ with pytest.raises(ValueError) as excinfo:
+ RouterConfig.from_file(str(config_path))
+ assert "Unsupported file type" in str(excinfo.value)
+
+ def test_from_file_invalid_config(self, tmp_path):
+ # Define an invalid configuration JSON
+ invalid_config_json = """
+ {
+ "encoder_type": "cohere",
+ "encoder_name": "embed-english-v3.0",
+ "routes": "This should be a list, not a string"
+ }"""
+
+ # Write the invalid configuration to a temporary JSON file
+ config_path = tmp_path / "invalid_config.json"
+ with open(config_path, "w") as file:
+ file.write(invalid_config_json)
+
+ # Patch the is_valid function to return False for this test
+ with patch("semantic_router.routers.base.is_valid", return_value=False):
+ # Attempt to load the RouterConfig from the temporary file
+ # and assert that it raises an exception due to invalid configuration
+ with pytest.raises(Exception) as excinfo:
+ RouterConfig.from_file(str(config_path))
+ assert "Invalid config JSON or YAML" in str(
+ excinfo.value
+ ), "Loading an invalid configuration should raise an exception."
+
+ def test_from_file_with_llm(self, tmp_path):
+ llm_config_json = """
+ {
+ "encoder_type": "cohere",
+ "encoder_name": "embed-english-v3.0",
+ "routes": [
+ {
+ "name": "llm_route",
+ "utterances": ["tell me a joke", "say something funny"],
+ "llm": {
+ "module": "semantic_router.llms.base",
+ "class": "BaseLLM",
+ "model": "fake-model-v1"
+ }
+ }
+ ]
+ }"""
+
+ config_path = tmp_path / "config_with_llm.json"
+ with open(config_path, "w") as file:
+ file.write(llm_config_json)
- def test_initialization_no_encoder(self, openai_encoder, index_cls, encoder_cls):
- os.environ["OPENAI_API_KEY"] = "test_api_key"
- route_layer_none = SemanticRouter(encoder=None)
- assert route_layer_none.score_threshold == openai_encoder.score_threshold
+ # Load the RouterConfig from the temporary file
+ layer_config = RouterConfig.from_file(str(config_path))
+
+ # Using BaseLLM because trying to create a usable Mock LLM is a nightmare.
+ assert isinstance(
+ layer_config.routes[0].llm, BaseLLM
+ ), "LLM should be instantiated and associated with the route based on the "
+ "config"
+ assert (
+ layer_config.routes[0].llm.name == "fake-model-v1"
+ ), "LLM instance should have the 'name' attribute set correctly"
+
+ def test_init(self):
+ layer_config = RouterConfig()
+ assert layer_config.routes == []
+
+ def test_to_file_json(self):
+ route = Route(name="test", utterances=["utterance"])
+ layer_config = RouterConfig(routes=[route])
+ with patch("builtins.open", mock_open()) as mocked_open:
+ layer_config.to_file("data/test_output.json")
+ mocked_open.assert_called_once_with("data/test_output.json", "w")
+
+ def test_to_file_yaml(self):
+ route = Route(name="test", utterances=["utterance"])
+ layer_config = RouterConfig(routes=[route])
+ with patch("builtins.open", mock_open()) as mocked_open:
+ layer_config.to_file("data/test_output.yaml")
+ mocked_open.assert_called_once_with("data/test_output.yaml", "w")
+
+ def test_to_file_invalid(self):
+ route = Route(name="test", utterances=["utterance"])
+ layer_config = RouterConfig(routes=[route])
+ with pytest.raises(ValueError):
+ layer_config.to_file("test_output.txt")
+
+ def test_from_file_invalid(self):
+ with open("test.txt", "w") as f:
+ f.write("dummy content")
+ with pytest.raises(ValueError):
+ RouterConfig.from_file("test.txt")
+ os.remove("test.txt")
+
+ def test_to_dict(self):
+ route = Route(name="test", utterances=["utterance"])
+ layer_config = RouterConfig(routes=[route])
+ assert layer_config.to_dict()["routes"] == [route.to_dict()]
+
+ def test_add(self):
+ route = Route(name="test", utterances=["utterance"])
+ route2 = Route(name="test2", utterances=["utterance2"])
+ layer_config = RouterConfig()
+ layer_config.add(route)
+ # confirm route added
+ assert layer_config.routes == [route]
+ # add second route and check updates
+ layer_config.add(route2)
+ assert layer_config.routes == [route, route2]
+
+ def test_get(self):
+ route = Route(name="test", utterances=["utterance"])
+ layer_config = RouterConfig(routes=[route])
+ assert layer_config.get("test") == route
+
+ def test_get_not_found(self):
+ route = Route(name="test", utterances=["utterance"])
+ layer_config = RouterConfig(routes=[route])
+ assert layer_config.get("not_found") is None
+
+ def test_remove(self):
+ route = Route(name="test", utterances=["utterance"])
+ layer_config = RouterConfig(routes=[route])
+ layer_config.remove("test")
+ assert layer_config.routes == []
+
+ def test_setting_aggregation_methods(self, openai_encoder, routes):
+ for agg in ["sum", "mean", "max"]:
+ route_layer = SemanticRouter(
+ encoder=openai_encoder,
+ routes=routes,
+ aggregation=agg,
+ )
+ assert route_layer.aggregation == agg
+
+ def test_semantic_classify_multiple_routes_with_different_aggregation(
+ self, openai_encoder, routes
+ ):
+ route_scores = [
+ {"route": "Route 1", "score": 0.5},
+ {"route": "Route 1", "score": 0.5},
+ {"route": "Route 1", "score": 0.5},
+ {"route": "Route 1", "score": 0.5},
+ {"route": "Route 2", "score": 0.4},
+ {"route": "Route 2", "score": 0.6},
+ {"route": "Route 2", "score": 0.8},
+ {"route": "Route 3", "score": 0.1},
+ {"route": "Route 3", "score": 1.0},
+ ]
+ for agg in ["sum", "mean", "max"]:
+ route_layer = SemanticRouter(
+ encoder=openai_encoder,
+ routes=routes,
+ aggregation=agg,
+ )
+ classification, score = route_layer._semantic_classify(route_scores)
+
+ if agg == "sum":
+ assert classification == "Route 1"
+ assert score == [0.5, 0.5, 0.5, 0.5]
+ elif agg == "mean":
+ assert classification == "Route 2"
+ assert score == [0.4, 0.6, 0.8]
+ elif agg == "max":
+ assert classification == "Route 3"
+ assert score == [0.1, 1.0]
-@pytest.mark.parametrize("index_cls", get_test_indexes())
+@pytest.mark.parametrize(
+ "index_cls,encoder_cls,router_cls",
+ [
+ (index, encoder, router)
+ for index in get_test_indexes()
+ for encoder in [OpenAIEncoder]
+ for router in get_test_routers()
+ ],
+)
class TestSemanticRouter:
def test_initialization_dynamic_route(
- self, dynamic_routes, openai_encoder, index_cls
+ self, dynamic_routes, index_cls, encoder_cls, router_cls
):
- index = init_index(index_cls)
- route_layer = SemanticRouter(
- encoder=openai_encoder,
+ encoder = encoder_cls()
+ index = init_index(index_cls, index_name=encoder.__class__.__name__)
+ route_layer = router_cls(
+ encoder=encoder,
routes=dynamic_routes,
index=index,
auto_sync="local",
)
- assert route_layer.score_threshold == openai_encoder.score_threshold
+ score_threshold = route_layer.score_threshold
+ if isinstance(route_layer, HybridRouter):
+ assert score_threshold == encoder.score_threshold * route_layer.alpha
+ else:
+ assert score_threshold == encoder.score_threshold
def test_add_single_utterance(
- self, routes, route_single_utterance, openai_encoder, index_cls
+ self, routes, route_single_utterance, index_cls, encoder_cls, router_cls
):
- index = init_index(index_cls)
- route_layer = SemanticRouter(
- encoder=openai_encoder,
+ encoder = encoder_cls()
+ index = init_index(index_cls, index_name=encoder.__class__.__name__)
+ route_layer = router_cls(
+ encoder=encoder,
routes=routes,
index=index,
auto_sync="local",
)
route_layer.add(routes=route_single_utterance)
- assert route_layer.score_threshold == openai_encoder.score_threshold
- if index_cls is PineconeIndex:
- time.sleep(PINECONE_SLEEP) # allow for index to be updated
- _ = route_layer("Hello")
- assert len(route_layer.index.get_utterances()) == 6
+ score_threshold = route_layer.score_threshold
+ if isinstance(route_layer, HybridRouter):
+ assert score_threshold == encoder.score_threshold * route_layer.alpha
+ else:
+ assert score_threshold == encoder.score_threshold
+
+ @retry(max_retries=RETRY_COUNT, delay=PINECONE_SLEEP)
+ def check_index_populated():
+ _ = route_layer("Hello")
+ assert len(route_layer.index.get_utterances()) == 6
+
+ check_index_populated()
def test_init_and_add_single_utterance(
- self, route_single_utterance, openai_encoder, index_cls
+ self, route_single_utterance, index_cls, encoder_cls, router_cls
):
- index = init_index(index_cls)
- route_layer = SemanticRouter(
- encoder=openai_encoder,
+ encoder = encoder_cls()
+ index = init_index(index_cls, index_name=encoder.__class__.__name__)
+ route_layer = router_cls(
+ encoder=encoder,
index=index,
auto_sync="local",
)
if index_cls is PineconeIndex:
time.sleep(PINECONE_SLEEP) # allow for index to be updated
route_layer.add(routes=route_single_utterance)
- assert route_layer.score_threshold == openai_encoder.score_threshold
- _ = route_layer("Hello")
- assert len(route_layer.index.get_utterances()) == 1
+ score_threshold = route_layer.score_threshold
+ if isinstance(route_layer, HybridRouter):
+ assert score_threshold == encoder.score_threshold * route_layer.alpha
+ else:
+ assert score_threshold == encoder.score_threshold
- def test_delete_index(self, openai_encoder, routes, index_cls):
- # TODO merge .delete_index() and .delete_all() and get working
- index = init_index(index_cls)
- route_layer = SemanticRouter(
- encoder=openai_encoder,
+ @retry(max_retries=RETRY_COUNT, delay=PINECONE_SLEEP)
+ def check_index_populated():
+ _ = route_layer("Hello")
+ assert len(route_layer.index.get_utterances()) == 1
+
+ check_index_populated()
+
+ def test_delete_index(self, routes, index_cls, encoder_cls, router_cls):
+ # TODO merge .delete_index() and .delete_all() and get working
+ encoder = encoder_cls()
+ index = init_index(index_cls, index_name=encoder.__class__.__name__)
+ route_layer = router_cls(
+ encoder=encoder,
routes=routes,
index=index,
auto_sync="local",
)
- if index_cls is PineconeIndex:
- time.sleep(PINECONE_SLEEP) # allow for index to be populated
- route_layer.index.delete_index()
- if index_cls is PineconeIndex:
- time.sleep(PINECONE_SLEEP) # allow for index to be updated
- assert route_layer.index.get_utterances() == []
- def test_add_route(self, routes, openai_encoder, index_cls):
- index = init_index(index_cls)
- route_layer = SemanticRouter(
- encoder=openai_encoder, routes=[], index=index, auto_sync="local"
- )
- if index_cls is PineconeIndex:
- time.sleep(PINECONE_SLEEP) # allow for index to be updated
+ # delete index
+ @retry(max_retries=RETRY_COUNT, delay=PINECONE_SLEEP)
+ def delete_index():
+ route_layer.index.delete_index()
+ # assert index empty
+ assert route_layer.index.get_utterances() == []
+ delete_index()
+
+ def test_add_route(self, routes, index_cls, encoder_cls, router_cls):
+ encoder = encoder_cls()
+ index = init_index(index_cls, index_name=encoder.__class__.__name__)
+ route_layer = router_cls(
+ encoder=encoder, routes=[], index=index, auto_sync="local"
+ )
# Initially, the local routes list should be empty
assert route_layer.routes == []
+
# same for the remote index
- assert route_layer.index.get_utterances() == []
+ @retry(max_retries=RETRY_COUNT, delay=PINECONE_SLEEP)
+ def check_index_empty():
+ assert route_layer.index.get_utterances() == []
+ check_index_empty()
# Add route1 and check
route_layer.add(routes=routes[0])
- if index_cls is PineconeIndex:
- time.sleep(PINECONE_SLEEP) # allow for index to be populated
- assert route_layer.routes == [routes[0]]
- assert route_layer.index is not None
- assert len(route_layer.index.get_utterances()) == 2
+
+ @retry(max_retries=RETRY_COUNT, delay=PINECONE_SLEEP)
+ def check_index_populated1():
+ assert route_layer.routes == [routes[0]]
+ assert route_layer.index is not None
+ assert len(route_layer.index.get_utterances()) == 2
+
+ check_index_populated1()
# Add route2 and check
route_layer.add(routes=routes[1])
- if index_cls is PineconeIndex:
- time.sleep(PINECONE_SLEEP) # allow for index to be populated
- assert route_layer.routes == [routes[0], routes[1]]
- assert len(route_layer.index.get_utterances()) == 5
-
- def test_list_route_names(self, openai_encoder, routes, index_cls):
- index = init_index(index_cls)
- route_layer = SemanticRouter(
- encoder=openai_encoder,
+
+ @retry(max_retries=RETRY_COUNT, delay=PINECONE_SLEEP)
+ def check_index_populated2():
+ assert route_layer.routes == [routes[0], routes[1]]
+ assert len(route_layer.index.get_utterances()) == 5
+
+ check_index_populated2()
+
+ def test_list_route_names(self, routes, index_cls, encoder_cls, router_cls):
+ encoder = encoder_cls()
+ index = init_index(index_cls, index_name=encoder.__class__.__name__)
+ route_layer = router_cls(
+ encoder=encoder,
routes=routes,
index=index,
auto_sync="local",
)
- if index_cls is PineconeIndex:
- time.sleep(PINECONE_SLEEP) # allow for index to be populated
- route_names = route_layer.list_route_names()
- assert set(route_names) == {
- route.name for route in routes
- }, "The list of route names should match the names of the routes added."
-
- def test_delete_route(self, openai_encoder, routes, index_cls):
- index = init_index(index_cls)
- route_layer = SemanticRouter(
- encoder=openai_encoder,
+
+ @retry(max_retries=RETRY_COUNT, delay=PINECONE_SLEEP)
+ def check_route_names():
+ route_names = route_layer.list_route_names()
+ assert set(route_names) == {
+ route.name for route in routes
+ }, "The list of route names should match the names of the routes added."
+
+ check_route_names()
+
+ def test_delete_route(self, routes, index_cls, encoder_cls, router_cls):
+ encoder = encoder_cls()
+ index = init_index(index_cls, index_name=encoder.__class__.__name__)
+ route_layer = router_cls(
+ encoder=encoder,
routes=routes,
index=index,
auto_sync="local",
)
- if index_cls is PineconeIndex:
- time.sleep(PINECONE_SLEEP) # allow for index to be populated
+
# Delete a route by name
- route_to_delete = routes[0].name
- route_layer.delete(route_to_delete)
- if index_cls is PineconeIndex:
- time.sleep(PINECONE_SLEEP) # allow for index to be populated
- # Ensure the route is no longer in the route layer
- assert (
- route_to_delete not in route_layer.list_route_names()
- ), "The route should be deleted from the route layer."
- # Ensure the route's utterances are no longer in the index
- for utterance in routes[0].utterances:
+ @retry(max_retries=RETRY_COUNT, delay=PINECONE_SLEEP)
+ def delete_route_by_name():
+ route_to_delete = routes[0].name
+ route_layer.delete(route_to_delete)
+ # Ensure the route is no longer in the route layer
assert (
- utterance not in route_layer.index
- ), "The route's utterances should be deleted from the index."
+ route_to_delete not in route_layer.list_route_names()
+ ), "The route should be deleted from the route layer."
+ # Ensure the route's utterances are no longer in the index
+ for utterance in routes[0].utterances:
+ assert (
+ utterance not in route_layer.index
+ ), "The route's utterances should be deleted from the index."
- def test_remove_route_not_found(self, openai_encoder, routes, index_cls):
- index = init_index(index_cls)
- route_layer = SemanticRouter(encoder=openai_encoder, routes=routes, index=index)
- if index_cls is PineconeIndex:
- time.sleep(PINECONE_SLEEP)
- # Attempt to remove a route that does not exist
- non_existent_route = "non-existent-route"
- route_layer.delete(non_existent_route)
- # we should see warning in logs only (ie no errors)
-
- def test_add_multiple_routes(self, openai_encoder, routes, index_cls):
- index = init_index(index_cls)
- route_layer = SemanticRouter(
- encoder=openai_encoder,
- index=index,
- auto_sync="local",
+ delete_route_by_name()
+
+ def test_remove_route_not_found(self, routes, index_cls, encoder_cls, router_cls):
+ encoder = encoder_cls()
+ index = init_index(index_cls, index_name=encoder.__class__.__name__)
+ route_layer = router_cls(
+ encoder=encoder, routes=routes, index=index, auto_sync="local"
)
- if index_cls is PineconeIndex:
- time.sleep(PINECONE_SLEEP)
- route_layer.add(routes=routes)
- if index_cls is PineconeIndex:
- time.sleep(PINECONE_SLEEP) # allow for index to be populated
- assert route_layer.index is not None
- assert len(route_layer.index.get_utterances()) == 5
-
- def test_query_and_classification(self, openai_encoder, routes, index_cls):
- index = init_index(index_cls, dimensions=3)
- route_layer = SemanticRouter(
- encoder=openai_encoder,
- routes=routes,
+
+ @retry(max_retries=RETRY_COUNT, delay=PINECONE_SLEEP)
+ def delete_non_existent_route():
+ # Attempt to remove a route that does not exist
+ non_existent_route = "non-existent-route"
+ route_layer.delete(non_existent_route)
+ # we should see warning in logs only (ie no errors)
+
+ delete_non_existent_route()
+
+ def test_add_multiple_routes(self, routes, index_cls, encoder_cls, router_cls):
+ encoder = encoder_cls()
+ index = init_index(index_cls, index_name=encoder.__class__.__name__)
+ route_layer = router_cls(
+ encoder=encoder,
index=index,
auto_sync="local",
)
- if index_cls is PineconeIndex:
- time.sleep(PINECONE_SLEEP) # allow for index to be populated
- query_result = route_layer(text="Hello").name
- assert query_result in ["Route 1", "Route 2"]
-
- def test_query_filter(self, openai_encoder, routes, index_cls):
- index = init_index(index_cls, dimensions=3)
- route_layer = SemanticRouter(
- encoder=openai_encoder,
+
+ @retry(max_retries=RETRY_COUNT, delay=PINECONE_SLEEP)
+ def check_index_populated():
+ route_layer.add(routes=routes)
+ assert route_layer.index is not None
+ assert len(route_layer.index.get_utterances()) == 5
+
+ check_index_populated()
+
+ # clear index if pinecone
+ # if index_cls is PineconeIndex:
+ # @retry(max_retries=RETRY_COUNT, delay=PINECONE_SLEEP)
+ # def clear_index():
+ # route_layer.index.index.delete(delete_all=True)
+ # assert route_layer.index.get_utterances() == []
+ # clear_index()
+
+ def test_query_and_classification(self, routes, index_cls, encoder_cls, router_cls):
+ encoder = encoder_cls()
+ encoder.score_threshold = 0.1
+ index = init_index(index_cls, index_name=encoder.__class__.__name__)
+ route_layer = router_cls(
+ encoder=encoder,
routes=routes,
index=index,
auto_sync="local",
+ aggregation="max",
)
- if index_cls is PineconeIndex:
- time.sleep(PINECONE_SLEEP) # allow for index to be populated
- query_result = route_layer(text="Hello", route_filter=["Route 1"]).name
- try:
- route_layer(text="Hello", route_filter=["Route 8"]).name
- except ValueError:
- assert True
+ @retry(max_retries=RETRY_COUNT, delay=PINECONE_SLEEP)
+ def check_query_result():
+ query_result = route_layer(text="Hello").name
+ assert query_result in ["Route 1", "Route 2"]
- assert query_result in ["Route 1"]
+ check_query_result()
- @pytest.mark.skipif(
- os.environ.get("PINECONE_API_KEY") is None, reason="Pinecone API key required"
- )
- def test_query_filter_pinecone(self, openai_encoder, routes, index_cls):
- if index_cls is PineconeIndex:
- pineconeindex = init_index(index_cls, dimensions=3)
- route_layer = SemanticRouter(
- encoder=openai_encoder,
- routes=routes,
- index=pineconeindex,
- auto_sync="local",
- )
- time.sleep(PINECONE_SLEEP) # allow for index to be populated
- query_result = route_layer(text="Hello", route_filter=["Route 1"]).name
+ def test_query_filter(self, routes, index_cls, encoder_cls, router_cls):
+ encoder = encoder_cls()
+ encoder.score_threshold = 0.1
+ index = init_index(index_cls, index_name=encoder.__class__.__name__)
+ route_layer = router_cls(
+ encoder=encoder,
+ routes=routes,
+ index=index,
+ auto_sync="local",
+ aggregation="max",
+ )
+ @retry(max_retries=RETRY_COUNT, delay=PINECONE_SLEEP)
+ def check_raises_value_error():
try:
route_layer(text="Hello", route_filter=["Route 8"]).name
except ValueError:
assert True
+ check_raises_value_error()
+
+ @retry(max_retries=RETRY_COUNT, delay=PINECONE_SLEEP)
+ def check_query_result():
+ query_result = route_layer(text="Hello", route_filter=["Route 1"]).name
assert query_result in ["Route 1"]
+ check_query_result()
+
@pytest.mark.skipif(
os.environ.get("PINECONE_API_KEY") is None, reason="Pinecone API key required"
)
- def test_namespace_pinecone_index(self, openai_encoder, routes, index_cls):
+ def test_namespace_pinecone_index(self, routes, index_cls, encoder_cls, router_cls):
if index_cls is PineconeIndex:
- pineconeindex = init_index(index_cls, namespace="test")
- route_layer = SemanticRouter(
- encoder=openai_encoder,
+ encoder = encoder_cls()
+ encoder.score_threshold = 0.2
+ index = init_index(
+ index_cls, namespace="test", index_name=encoder.__class__.__name__
+ )
+ route_layer = router_cls(
+ encoder=encoder,
routes=routes,
- index=pineconeindex,
+ index=index,
auto_sync="local",
)
- time.sleep(PINECONE_SLEEP) # allow for index to be populated
- query_result = route_layer(text="Hello", route_filter=["Route 1"]).name
+ time.sleep(PINECONE_SLEEP) # allow for index to be updated
- try:
- route_layer(text="Hello", route_filter=["Route 8"]).name
- except ValueError:
- assert True
+ @retry(max_retries=RETRY_COUNT, delay=PINECONE_SLEEP)
+ def check_query_result():
+ query_result = route_layer(text="Hello", route_filter=["Route 1"]).name
+ assert query_result in ["Route 1"]
- assert query_result in ["Route 1"]
- route_layer.index.index.delete(namespace="test", delete_all=True)
+ check_query_result()
- def test_query_with_no_index(self, openai_encoder, index_cls):
- route_layer = SemanticRouter(encoder=openai_encoder)
+ @retry(max_retries=RETRY_COUNT, delay=PINECONE_SLEEP)
+ def delete_namespace():
+ route_layer.index.index.delete(namespace="test", delete_all=True)
+
+ delete_namespace()
+
+ def test_query_with_no_index(self, index_cls, encoder_cls, router_cls):
+ encoder = encoder_cls()
+ route_layer = router_cls(encoder=encoder)
+ # TODO: probably should avoid running this with multiple encoders or find a way to set dims
with pytest.raises(ValueError):
assert route_layer(text="Anything").name is None
- def test_query_with_vector(self, openai_encoder, routes, index_cls):
- index = init_index(index_cls)
- route_layer = SemanticRouter(
- encoder=openai_encoder,
+ def test_query_with_vector(self, routes, index_cls, encoder_cls, router_cls):
+ encoder = encoder_cls()
+ encoder.score_threshold = 0.1
+ index = init_index(index_cls, index_name=encoder.__class__.__name__)
+ route_layer = router_cls(
+ encoder=encoder,
routes=routes,
index=index,
auto_sync="local",
+ aggregation="max",
)
- if index_cls is PineconeIndex:
- time.sleep(PINECONE_SLEEP) # allow for index to be populated
- vector = [0.1, 0.2, 0.3]
- query_result = route_layer(vector=vector).name
- assert query_result in ["Route 1", "Route 2"]
-
- def test_query_with_no_text_or_vector(self, openai_encoder, routes, index_cls):
- index = init_index(index_cls)
- route_layer = SemanticRouter(encoder=openai_encoder, routes=routes, index=index)
+ # create vectors
+ vector = encoder(["hello"])
+ if router_cls is HybridRouter:
+ sparse_vector = route_layer.sparse_encoder(["hello"])[0]
+
+ @retry(max_retries=RETRY_COUNT, delay=PINECONE_SLEEP)
+ def check_query_result():
+ if router_cls is HybridRouter:
+ query_result = route_layer(
+ vector=vector, sparse_vector=sparse_vector
+ ).name
+ else:
+ query_result = route_layer(vector=vector).name
+ assert query_result in ["Route 1", "Route 2"]
+
+ check_query_result()
+
+ def test_query_with_no_text_or_vector(
+ self, routes, index_cls, encoder_cls, router_cls
+ ):
+ encoder = encoder_cls()
+ index = init_index(index_cls, index_name=encoder.__class__.__name__)
+ route_layer = router_cls(encoder=encoder, routes=routes, index=index)
with pytest.raises(ValueError):
route_layer()
- def test_semantic_classify(self, openai_encoder, routes, index_cls):
- index = init_index(index_cls)
- route_layer = SemanticRouter(
- encoder=openai_encoder,
+ def test_is_ready(self, routes, index_cls, encoder_cls, router_cls):
+ encoder = encoder_cls()
+ index = init_index(index_cls, index_name=encoder.__class__.__name__)
+ route_layer = router_cls(
+ encoder=encoder,
+ routes=routes,
+ index=index,
+ auto_sync="local",
+ )
+
+ @retry(max_retries=RETRY_COUNT, delay=PINECONE_SLEEP)
+ def check_is_ready():
+ assert route_layer.index.is_ready()
+
+ check_is_ready()
+
+
+@pytest.mark.parametrize(
+ "index_cls,encoder_cls,router_cls",
+ [
+ (index, encoder, router)
+ for index in [LocalIndex] # no need to test with multiple indexes
+ for encoder in [OpenAIEncoder] # no need to test with multiple encoders
+ for router in get_test_routers()
+ ],
+)
+class TestRouterOnly:
+ def test_semantic_classify(self, routes, index_cls, encoder_cls, router_cls):
+ encoder = encoder_cls()
+ index = init_index(index_cls, index_name=encoder.__class__.__name__)
+ route_layer = router_cls(
+ encoder=encoder,
routes=routes,
index=index,
auto_sync="local",
)
- if index_cls is PineconeIndex:
- time.sleep(PINECONE_SLEEP) # allow for index to be populated
classification, score = route_layer._semantic_classify(
[
{"route": "Route 1", "score": 0.9},
@@ -521,16 +957,17 @@ class TestSemanticRouter:
assert classification == "Route 1"
assert score == [0.9]
- def test_semantic_classify_multiple_routes(self, openai_encoder, routes, index_cls):
- index = init_index(index_cls)
- route_layer = SemanticRouter(
- encoder=openai_encoder,
+ def test_semantic_classify_multiple_routes(
+ self, routes, index_cls, encoder_cls, router_cls
+ ):
+ encoder = encoder_cls()
+ index = init_index(index_cls, index_name=encoder.__class__.__name__)
+ route_layer = router_cls(
+ encoder=encoder,
routes=routes,
index=index,
auto_sync="local",
)
- if index_cls is PineconeIndex:
- time.sleep(PINECONE_SLEEP) # allow for index to be populated
classification, score = route_layer._semantic_classify(
[
{"route": "Route 1", "score": 0.9},
@@ -542,44 +979,54 @@ class TestSemanticRouter:
assert score == [0.9, 0.8]
def test_query_no_text_dynamic_route(
- self, openai_encoder, dynamic_routes, index_cls
+ self, dynamic_routes, index_cls, encoder_cls, router_cls
):
- index = init_index(index_cls)
- route_layer = SemanticRouter(
- encoder=openai_encoder, routes=dynamic_routes, index=index
- )
- vector = [0.1, 0.2, 0.3]
+ encoder = encoder_cls()
+ index = init_index(index_cls, index_name=encoder.__class__.__name__)
+ route_layer = router_cls(encoder=encoder, routes=dynamic_routes, index=index)
+ # create vectors
+ vector = encoder(["hello"])
+ if router_cls is HybridRouter:
+ sparse_vector = route_layer.sparse_encoder(["hello"])[0]
with pytest.raises(ValueError):
- route_layer(vector=vector)
+ if router_cls is HybridRouter:
+ route_layer(vector=vector, sparse_vector=sparse_vector)
+ else:
+ route_layer(vector=vector)
- def test_pass_threshold(self, openai_encoder, index_cls):
- index = init_index(index_cls)
- route_layer = SemanticRouter(
- encoder=openai_encoder,
+ def test_pass_threshold(self, index_cls, encoder_cls, router_cls):
+ encoder = encoder_cls()
+ index = init_index(index_cls, index_name=encoder.__class__.__name__)
+ route_layer = router_cls(
+ encoder=encoder,
index=index,
auto_sync="local",
)
assert not route_layer._pass_threshold([], 0.3)
assert route_layer._pass_threshold([0.6, 0.7], 0.3)
- def test_failover_score_threshold(self, openai_encoder, index_cls):
- index = init_index(index_cls)
- route_layer = SemanticRouter(
- encoder=openai_encoder,
+ def test_failover_score_threshold(self, index_cls, encoder_cls, router_cls):
+ encoder = encoder_cls()
+ index = init_index(index_cls, index_name=encoder.__class__.__name__)
+ route_layer = router_cls(
+ encoder=encoder,
index=index,
auto_sync="local",
)
- assert route_layer.score_threshold == 0.3
+ if router_cls is HybridRouter:
+ assert route_layer.score_threshold == 0.3 * route_layer.alpha
+ else:
+ assert route_layer.score_threshold == 0.3
- def test_json(self, openai_encoder, routes, index_cls):
+ def test_json(self, routes, index_cls, encoder_cls, router_cls):
temp = tempfile.NamedTemporaryFile(suffix=".yaml", delete=False)
try:
temp_path = temp.name # Save the temporary file's path
temp.close() # Close the file to ensure it can be opened again on Windows
- os.environ["OPENAI_API_KEY"] = "test_api_key"
- index = init_index(index_cls)
- route_layer = SemanticRouter(
- encoder=openai_encoder,
+ encoder = encoder_cls()
+ index = init_index(index_cls, index_name=encoder.__class__.__name__)
+ route_layer = router_cls(
+ encoder=encoder,
routes=routes,
index=index,
auto_sync="local",
@@ -587,8 +1034,6 @@ class TestSemanticRouter:
route_layer.to_json(temp_path)
assert os.path.exists(temp_path)
route_layer_from_file = SemanticRouter.from_json(temp_path)
- if index_cls is PineconeIndex:
- time.sleep(PINECONE_SLEEP) # allow for index to be populated
assert (
route_layer_from_file.index is not None
and route_layer_from_file._get_route_names() is not None
@@ -596,15 +1041,15 @@ class TestSemanticRouter:
finally:
os.remove(temp_path) # Ensure the file is deleted even if the test fails
- def test_yaml(self, openai_encoder, routes, index_cls):
+ def test_yaml(self, routes, index_cls, encoder_cls, router_cls):
temp = tempfile.NamedTemporaryFile(suffix=".yaml", delete=False)
try:
temp_path = temp.name # Save the temporary file's path
temp.close() # Close the file to ensure it can be opened again on Windows
- os.environ["OPENAI_API_KEY"] = "test_api_key"
- index = init_index(index_cls)
- route_layer = SemanticRouter(
- encoder=openai_encoder,
+ encoder = encoder_cls()
+ index = init_index(index_cls, index_name=encoder.__class__.__name__)
+ route_layer = router_cls(
+ encoder=encoder,
routes=routes,
index=index,
auto_sync="local",
@@ -612,8 +1057,6 @@ class TestSemanticRouter:
route_layer.to_yaml(temp_path)
assert os.path.exists(temp_path)
route_layer_from_file = SemanticRouter.from_yaml(temp_path)
- if index_cls is PineconeIndex:
- time.sleep(PINECONE_SLEEP) # allow for index to be populated
assert (
route_layer_from_file.index is not None
and route_layer_from_file._get_route_names() is not None
@@ -621,159 +1064,74 @@ class TestSemanticRouter:
finally:
os.remove(temp_path) # Ensure the file is deleted even if the test fails
- def test_from_file_json(openai_encoder, tmp_path, index_cls):
- # Create a temporary JSON file with layer configuration
- config_path = tmp_path / "config.json"
- config_path.write_text(
- layer_json()
- ) # Assuming layer_json() returns a valid JSON string
-
- # Load the RouterConfig from the temporary file
- layer_config = RouterConfig.from_file(str(config_path))
-
- # Assertions to verify the loaded configuration
- assert layer_config.encoder_type == "cohere"
- assert layer_config.encoder_name == "embed-english-v3.0"
- assert len(layer_config.routes) == 2
- assert layer_config.routes[0].name == "politics"
-
- def test_from_file_yaml(openai_encoder, tmp_path, index_cls):
- # Create a temporary YAML file with layer configuration
- config_path = tmp_path / "config.yaml"
- config_path.write_text(
- layer_yaml()
- ) # Assuming layer_yaml() returns a valid YAML string
-
- # Load the RouterConfig from the temporary file
- layer_config = RouterConfig.from_file(str(config_path))
-
- # Assertions to verify the loaded configuration
- assert layer_config.encoder_type == "cohere"
- assert layer_config.encoder_name == "embed-english-v3.0"
- assert len(layer_config.routes) == 2
- assert layer_config.routes[0].name == "politics"
-
- def test_from_file_invalid_path(self, index_cls):
- with pytest.raises(FileNotFoundError) as excinfo:
- RouterConfig.from_file("nonexistent_path.json")
- assert "[Errno 2] No such file or directory: 'nonexistent_path.json'" in str(
- excinfo.value
- )
-
- def test_from_file_unsupported_type(self, tmp_path, index_cls):
- # Create a temporary unsupported file
- config_path = tmp_path / "config.unsupported"
- config_path.write_text(layer_json())
-
- with pytest.raises(ValueError) as excinfo:
- RouterConfig.from_file(str(config_path))
- assert "Unsupported file type" in str(excinfo.value)
-
- def test_from_file_invalid_config(self, tmp_path, index_cls):
- # Define an invalid configuration JSON
- invalid_config_json = """
- {
- "encoder_type": "cohere",
- "encoder_name": "embed-english-v3.0",
- "routes": "This should be a list, not a string"
- }"""
-
- # Write the invalid configuration to a temporary JSON file
- config_path = tmp_path / "invalid_config.json"
- with open(config_path, "w") as file:
- file.write(invalid_config_json)
-
- # Patch the is_valid function to return False for this test
- with patch("semantic_router.routers.base.is_valid", return_value=False):
- # Attempt to load the RouterConfig from the temporary file
- # and assert that it raises an exception due to invalid configuration
- with pytest.raises(Exception) as excinfo:
- RouterConfig.from_file(str(config_path))
- assert "Invalid config JSON or YAML" in str(
- excinfo.value
- ), "Loading an invalid configuration should raise an exception."
-
- def test_from_file_with_llm(self, tmp_path, index_cls):
- llm_config_json = """
- {
- "encoder_type": "cohere",
- "encoder_name": "embed-english-v3.0",
- "routes": [
- {
- "name": "llm_route",
- "utterances": ["tell me a joke", "say something funny"],
- "llm": {
- "module": "semantic_router.llms.base",
- "class": "BaseLLM",
- "model": "fake-model-v1"
- }
- }
- ]
- }"""
-
- config_path = tmp_path / "config_with_llm.json"
- with open(config_path, "w") as file:
- file.write(llm_config_json)
-
- # Load the RouterConfig from the temporary file
- layer_config = RouterConfig.from_file(str(config_path))
-
- # Using BaseLLM because trying to create a usable Mock LLM is a nightmare.
- assert isinstance(
- layer_config.routes[0].llm, BaseLLM
- ), "LLM should be instantiated and associated with the route based on the "
- "config"
- assert (
- layer_config.routes[0].llm.name == "fake-model-v1"
- ), "LLM instance should have the 'name' attribute set correctly"
-
- def test_config(self, openai_encoder, routes, index_cls):
- os.environ["OPENAI_API_KEY"] = "test_api_key"
- index = init_index(index_cls)
- route_layer = SemanticRouter(encoder=openai_encoder, routes=routes, index=index)
+ def test_config(self, routes, index_cls, encoder_cls, router_cls):
+ encoder = encoder_cls()
+ index = init_index(index_cls, index_name=encoder.__class__.__name__)
+ route_layer = router_cls(encoder=encoder, routes=routes, index=index)
# confirm route creation functions as expected
layer_config = route_layer.to_config()
assert layer_config.routes == route_layer.routes
# now load from config and confirm it's the same
route_layer_from_config = SemanticRouter.from_config(layer_config, index)
- if index_cls is PineconeIndex:
- time.sleep(PINECONE_SLEEP) # allow for index to be populated
assert (
route_layer_from_config._get_route_names() == route_layer._get_route_names()
)
- assert route_layer_from_config.score_threshold == route_layer.score_threshold
+ if router_cls is HybridRouter:
+ # TODO: need to fix HybridRouter from config
+ # assert (
+ # route_layer_from_config.score_threshold
+ # == route_layer.score_threshold * route_layer.alpha
+ # )
+ pass
+ else:
+ assert (
+ route_layer_from_config.score_threshold == route_layer.score_threshold
+ )
- def test_get_thresholds(self, openai_encoder, routes, index_cls):
- index = init_index(index_cls)
- route_layer = SemanticRouter(encoder=openai_encoder, routes=routes, index=index)
- assert route_layer.get_thresholds() == {"Route 1": 0.3, "Route 2": 0.3}
+ def test_get_thresholds(self, routes, index_cls, encoder_cls, router_cls):
+ encoder = encoder_cls()
+ index = init_index(index_cls, index_name=encoder.__class__.__name__)
+ route_layer = router_cls(encoder=encoder, routes=routes, index=index)
+ if router_cls is HybridRouter:
+ # TODO: fix this
+ target = encoder.score_threshold * route_layer.alpha
+ assert route_layer.get_thresholds() == {
+ "Route 1": target,
+ "Route 2": target,
+ }
+ else:
+ assert route_layer.get_thresholds() == {"Route 1": 0.3, "Route 2": 0.3}
def test_with_multiple_routes_passing_threshold(
- self, openai_encoder, routes, index_cls
+ self, routes, index_cls, encoder_cls, router_cls
):
- index = init_index(index_cls)
- route_layer = SemanticRouter(encoder=openai_encoder, routes=routes, index=index)
- route_layer.score_threshold = 0.5 # Set the score_threshold if needed
+ encoder = encoder_cls()
+ index = init_index(index_cls, index_name=encoder.__class__.__name__)
+ route_layer = router_cls(encoder=encoder, routes=routes, index=index)
+ route_layer.score_threshold = 0.3 # Set the score_threshold if needed
# Assuming route_layer is already set up with routes "Route 1" and "Route 2"
query_results = [
- {"route": "Route 1", "score": 0.6},
- {"route": "Route 2", "score": 0.7},
- {"route": "Route 1", "score": 0.8},
+ {"route": "Route 1", "score": 0.1},
+ {"route": "Route 2", "score": 0.8},
+ {"route": "Route 1", "score": 0.9},
]
- expected = [("Route 1", 0.8), ("Route 2", 0.7)]
+ expected = [("Route 1", 0.9), ("Route 2", 0.8)]
results = route_layer._semantic_classify_multiple_routes(query_results)
assert sorted(results) == sorted(
expected
), "Should classify and return routes above their thresholds"
- def test_with_no_routes_passing_threshold(self, openai_encoder, routes, index_cls):
- index = init_index(index_cls)
- route_layer = SemanticRouter(encoder=openai_encoder, routes=routes, index=index)
+ def test_with_no_routes_passing_threshold(
+ self, routes, index_cls, encoder_cls, router_cls
+ ):
+ encoder = encoder_cls()
+ index = init_index(index_cls, index_name=encoder.__class__.__name__)
+ route_layer = router_cls(encoder=encoder, routes=routes, index=index)
# set threshold to 1.0 so that no routes pass
route_layer.score_threshold = 1.0
query_results = [
- {"route": "Route 1", "score": 0.3},
- {"route": "Route 2", "score": 0.2},
+ {"route": "Route 1", "score": 0.01},
+ {"route": "Route 2", "score": 0.02},
]
expected = []
results = route_layer._semantic_classify_multiple_routes(query_results)
@@ -781,9 +1139,10 @@ class TestSemanticRouter:
results == expected
), "Should return an empty list when no routes pass their thresholds"
- def test_with_no_query_results(self, openai_encoder, routes, index_cls):
- index = init_index(index_cls)
- route_layer = SemanticRouter(encoder=openai_encoder, routes=routes, index=index)
+ def test_with_no_query_results(self, routes, index_cls, encoder_cls, router_cls):
+ encoder = encoder_cls()
+ index = init_index(index_cls, index_name=encoder.__class__.__name__)
+ route_layer = router_cls(encoder=encoder, routes=routes, index=index)
route_layer.score_threshold = 0.5
query_results = []
expected = []
@@ -792,9 +1151,10 @@ class TestSemanticRouter:
results == expected
), "Should return an empty list when there are no query results"
- def test_with_unrecognized_route(self, openai_encoder, routes, index_cls):
- index = init_index(index_cls)
- route_layer = SemanticRouter(encoder=openai_encoder, routes=routes, index=index)
+ def test_with_unrecognized_route(self, routes, index_cls, encoder_cls, router_cls):
+ encoder = encoder_cls()
+ index = init_index(index_cls, index_name=encoder.__class__.__name__)
+ route_layer = router_cls(encoder=encoder, routes=routes, index=index)
route_layer.score_threshold = 0.5
# Test with a route name that does not exist in the route_layer's routes
query_results = [{"route": "UnrecognizedRoute", "score": 0.9}]
@@ -802,106 +1162,12 @@ class TestSemanticRouter:
results = route_layer._semantic_classify_multiple_routes(query_results)
assert results == expected, "Should ignore and not return unrecognized routes"
- def test_retrieve_with_text(self, openai_encoder, routes, index_cls):
- index = init_index(index_cls)
- route_layer = SemanticRouter(
- encoder=openai_encoder,
- routes=routes,
- index=index,
- auto_sync="local",
- )
- text = "Hello"
- results = route_layer.retrieve_multiple_routes(text=text)
- assert len(results) >= 1, "Expected at least one result"
- assert any(
- result.name in ["Route 1", "Route 2"] for result in results
- ), "Expected the result to be either 'Route 1' or 'Route 2'"
-
- def test_retrieve_with_vector(self, openai_encoder, routes, index_cls):
- index = init_index(index_cls)
- route_layer = SemanticRouter(
- encoder=openai_encoder,
- routes=routes,
- index=index,
- auto_sync="local",
- )
- vector = [0.1, 0.2, 0.3]
- if index_cls is PineconeIndex:
- time.sleep(PINECONE_SLEEP) # allow for index to be populated
- results = route_layer.retrieve_multiple_routes(vector=vector)
- assert len(results) >= 1, "Expected at least one result"
- assert any(
- result.name in ["Route 1", "Route 2"] for result in results
- ), "Expected the result to be either 'Route 1' or 'Route 2'"
-
- def test_retrieve_without_text_or_vector(self, openai_encoder, routes, index_cls):
- index = init_index(index_cls)
- route_layer = SemanticRouter(
- encoder=openai_encoder,
- routes=routes,
- index=index,
- auto_sync="local",
- )
- with pytest.raises(ValueError, match="Either text or vector must be provided"):
- route_layer.retrieve_multiple_routes()
-
- def test_retrieve_no_matches(self, openai_encoder, routes, index_cls):
- index = init_index(index_cls)
- route_layer = SemanticRouter(
- encoder=openai_encoder,
- routes=routes,
- index=index,
- auto_sync="local",
- )
- text = "Asparagus"
- if index_cls is PineconeIndex:
- time.sleep(PINECONE_SLEEP)
- results = route_layer.retrieve_multiple_routes(text=text)
- assert len(results) == 0, f"Expected no results, but got {len(results)}"
-
- def test_retrieve_one_match(self, openai_encoder, routes_3, index_cls):
- index = init_index(index_cls, dimensions=3)
- route_layer = SemanticRouter(
- encoder=openai_encoder,
- routes=routes_3,
- index=index,
- auto_sync="local",
- )
- text = "Hello"
- # set low threshold
- route_layer.set_threshold(threshold=0.1, route_name="Route 1")
- if index_cls is PineconeIndex:
- time.sleep(PINECONE_SLEEP)
- results = route_layer.retrieve_multiple_routes(text=text)
- assert len(results) == 1, f"Expected one result, and got {len(results)}"
- matched_routes = [result.name for result in results]
- assert "Route 1" in matched_routes, "Expected 'Route 1' to be a match"
-
- def test_retrieve_with_text_for_multiple_matches(
- self, openai_encoder, routes_2, index_cls
- ):
- index = init_index(index_cls)
- route_layer = SemanticRouter(
- encoder=openai_encoder,
- routes=routes_2,
- index=index,
- auto_sync="local",
- )
- text = "Hello"
- route_layer.set_threshold(threshold=0.01, route_name=None)
- if index_cls is PineconeIndex:
- time.sleep(PINECONE_SLEEP)
- results = route_layer.retrieve_multiple_routes(text=text)
- assert len(results) == 2, "Expected two results"
- matched_routes = [result.name for result in results]
- assert "Route 1" in matched_routes, "Expected 'Route 1' to be a match"
- assert "Route 2" in matched_routes, "Expected 'Route 2' to be a match"
-
def test_set_aggregation_method_with_unsupported_value(
- self, openai_encoder, routes, index_cls
+ self, routes, index_cls, encoder_cls, router_cls
):
- index = init_index(index_cls)
- route_layer = SemanticRouter(encoder=openai_encoder, routes=routes, index=index)
+ encoder = encoder_cls()
+ index = init_index(index_cls, index_name=encoder.__class__.__name__)
+ route_layer = router_cls(encoder=encoder, routes=routes, index=index)
unsupported_aggregation = "unsupported_aggregation_method"
with pytest.raises(
ValueError,
@@ -909,17 +1175,21 @@ class TestSemanticRouter:
):
route_layer._set_aggregation_method(unsupported_aggregation)
- def test_refresh_routes_not_implemented(self, openai_encoder, routes, index_cls):
- index = init_index(index_cls)
- route_layer = SemanticRouter(encoder=openai_encoder, routes=routes, index=index)
+ def test_refresh_routes_not_implemented(
+ self, routes, index_cls, encoder_cls, router_cls
+ ):
+ encoder = encoder_cls()
+ index = init_index(index_cls, index_name=encoder.__class__.__name__)
+ route_layer = router_cls(encoder=encoder, routes=routes, index=index)
with pytest.raises(
NotImplementedError, match="This method has not yet been implemented."
):
route_layer._refresh_routes()
- def test_update_threshold(self, openai_encoder, routes, index_cls):
- index = init_index(index_cls)
- route_layer = SemanticRouter(encoder=openai_encoder, routes=routes, index=index)
+ def test_update_threshold(self, routes, index_cls, encoder_cls, router_cls):
+ encoder = encoder_cls()
+ index = init_index(index_cls, index_name=encoder.__class__.__name__)
+ route_layer = router_cls(encoder=encoder, routes=routes, index=index)
route_name = "Route 1"
new_threshold = 0.8
route_layer.update(name=route_name, threshold=new_threshold)
@@ -928,9 +1198,12 @@ class TestSemanticRouter:
updated_route.score_threshold == new_threshold
), f"Expected threshold to be updated to {new_threshold}, but got {updated_route.score_threshold}"
- def test_update_non_existent_route(self, openai_encoder, routes, index_cls):
- index = init_index(index_cls)
- route_layer = SemanticRouter(encoder=openai_encoder, routes=routes, index=index)
+ def test_update_non_existent_route(
+ self, routes, index_cls, encoder_cls, router_cls
+ ):
+ encoder = encoder_cls()
+ index = init_index(index_cls, index_name=encoder.__class__.__name__)
+ route_layer = router_cls(encoder=encoder, routes=routes, index=index)
non_existent_route = "Non-existent Route"
with pytest.raises(
ValueError,
@@ -938,18 +1211,24 @@ class TestSemanticRouter:
):
route_layer.update(name=non_existent_route, threshold=0.7)
- def test_update_without_parameters(self, openai_encoder, routes, index_cls):
- index = init_index(index_cls)
- route_layer = SemanticRouter(encoder=openai_encoder, routes=routes, index=index)
+ def test_update_without_parameters(
+ self, routes, index_cls, encoder_cls, router_cls
+ ):
+ encoder = encoder_cls()
+ index = init_index(index_cls, index_name=encoder.__class__.__name__)
+ route_layer = router_cls(encoder=encoder, routes=routes, index=index)
with pytest.raises(
ValueError,
match="At least one of 'threshold' or 'utterances' must be provided.",
):
route_layer.update(name="Route 1")
- def test_update_utterances_not_implemented(self, openai_encoder, routes, index_cls):
- index = init_index(index_cls)
- route_layer = SemanticRouter(encoder=openai_encoder, routes=routes, index=index)
+ def test_update_utterances_not_implemented(
+ self, routes, index_cls, encoder_cls, router_cls
+ ):
+ encoder = encoder_cls()
+ index = init_index(index_cls, index_name=encoder.__class__.__name__)
+ route_layer = router_cls(encoder=encoder, routes=routes, index=index)
with pytest.raises(
NotImplementedError,
match="The update method cannot be used for updating utterances yet.",
@@ -957,147 +1236,246 @@ class TestSemanticRouter:
route_layer.update(name="Route 1", utterances=["New utterance"])
+@pytest.mark.parametrize(
+ "index_cls,encoder_cls,router_cls",
+ [
+ (index, encoder, router)
+ for index in get_test_indexes()
+ for encoder in [OpenAIEncoder]
+ for router in get_test_routers()
+ ],
+)
class TestLayerFit:
- def test_eval(self, openai_encoder, routes, test_data):
- route_layer = SemanticRouter(
- encoder=openai_encoder,
+ def test_eval(self, routes, test_data, index_cls, encoder_cls, router_cls):
+ encoder = encoder_cls()
+ index = init_index(index_cls, index_name=encoder.__class__.__name__)
+ route_layer = router_cls(
+ encoder=encoder,
routes=routes,
+ index=index,
auto_sync="local",
)
+
+ @retry(max_retries=RETRY_COUNT, delay=PINECONE_SLEEP)
+ def check_is_ready():
+ assert route_layer.index.is_ready()
+
+ check_is_ready()
# unpack test data
X, y = zip(*test_data)
# evaluate
- route_layer.evaluate(X=X, y=y, batch_size=int(len(test_data) / 5))
+ route_layer.evaluate(X=list(X), y=list(y), batch_size=int(len(X) / 5))
- def test_fit(self, openai_encoder, routes, test_data):
- route_layer = SemanticRouter(
- encoder=openai_encoder,
+ def test_fit(self, routes, test_data, index_cls, encoder_cls, router_cls):
+ # TODO: this is super slow for PineconeIndex, need to fix
+ if index_cls is PineconeIndex:
+ return
+ encoder = encoder_cls()
+ index = init_index(index_cls, index_name=encoder.__class__.__name__)
+ route_layer = router_cls(
+ encoder=encoder,
routes=routes,
+ index=index,
auto_sync="local",
)
+
+ @retry(max_retries=RETRY_COUNT, delay=PINECONE_SLEEP)
+ def check_is_ready():
+ assert route_layer.index.is_ready()
+
+ check_is_ready()
# unpack test data
X, y = zip(*test_data)
- route_layer.fit(X=X, y=y, batch_size=int(len(test_data) / 5))
+ route_layer.fit(X=list(X), y=list(y), batch_size=int(len(X) / 5))
+ def test_fit_local(self, routes, test_data, index_cls, encoder_cls, router_cls):
+ # TODO: this is super slow for PineconeIndex, need to fix
+ encoder = encoder_cls()
+ index = init_index(index_cls, index_name=encoder.__class__.__name__)
+ route_layer = router_cls(
+ encoder=encoder,
+ routes=routes,
+ index=index,
+ auto_sync="local",
+ )
-# Add more tests for edge cases and error handling as needed.
+ @retry(max_retries=RETRY_COUNT, delay=PINECONE_SLEEP)
+ def check_is_ready():
+ assert route_layer.index.is_ready()
+ check_is_ready()
+ # unpack test data
+ X, y = zip(*test_data)
+ route_layer.fit(
+ X=list(X), y=list(y), batch_size=int(len(X) / 5), local_execution=True
+ )
-class TestRouterConfig:
- def test_init(self):
- layer_config = RouterConfig()
- assert layer_config.routes == []
- def test_to_file_json(self):
- route = Route(name="test", utterances=["utterance"])
- layer_config = RouterConfig(routes=[route])
- with patch("builtins.open", mock_open()) as mocked_open:
- layer_config.to_file("data/test_output.json")
- mocked_open.assert_called_once_with("data/test_output.json", "w")
+class MockSymmetricDenseEncoder(DenseEncoder):
+ def __call__(self, docs: List[str]) -> List[List[float]]:
+ return [[0.1, 0.2, 0.3] for _ in docs]
- def test_to_file_yaml(self):
- route = Route(name="test", utterances=["utterance"])
- layer_config = RouterConfig(routes=[route])
- with patch("builtins.open", mock_open()) as mocked_open:
- layer_config.to_file("data/test_output.yaml")
- mocked_open.assert_called_once_with("data/test_output.yaml", "w")
+ async def acall(self, docs: List[str]) -> List[List[float]]:
+ return [[0.1, 0.2, 0.3] for _ in docs]
- def test_to_file_invalid(self):
- route = Route(name="test", utterances=["utterance"])
- layer_config = RouterConfig(routes=[route])
- with pytest.raises(ValueError):
- layer_config.to_file("test_output.txt")
- def test_from_file_json(self):
- mock_json_data = layer_json()
- with patch("builtins.open", mock_open(read_data=mock_json_data)) as mocked_open:
- layer_config = RouterConfig.from_file("data/test.json")
- mocked_open.assert_called_once_with("data/test.json", "r")
- assert isinstance(layer_config, RouterConfig)
+class MockSymmetricSparseEncoder(SparseEncoder):
+ def __call__(self, docs: List[str]) -> List[SparseEmbedding]:
+ return [SparseEmbedding(embedding=np.array([[0, 0.1], [1, 0.2]])) for _ in docs]
- def test_from_file_yaml(self):
- mock_yaml_data = layer_yaml()
- with patch("builtins.open", mock_open(read_data=mock_yaml_data)) as mocked_open:
- layer_config = RouterConfig.from_file("data/test.yaml")
- mocked_open.assert_called_once_with("data/test.yaml", "r")
- assert isinstance(layer_config, RouterConfig)
+ async def acall(self, docs: List[str]) -> List[SparseEmbedding]:
+ return [SparseEmbedding(embedding=np.array([[0, 0.1], [1, 0.2]])) for _ in docs]
- def test_from_file_invalid(self):
- with open("test.txt", "w") as f:
- f.write("dummy content")
- with pytest.raises(ValueError):
- RouterConfig.from_file("test.txt")
- os.remove("test.txt")
- def test_to_dict(self):
- route = Route(name="test", utterances=["utterance"])
- layer_config = RouterConfig(routes=[route])
- assert layer_config.to_dict()["routes"] == [route.to_dict()]
+class MockAsymmetricDenseEncoder(DenseEncoder, AsymmetricDenseMixin):
+ def __call__(self, docs: List[Any]) -> List[List[float]]:
+ return [[0.1, 0.2, 0.3] for _ in docs]
- def test_add(self):
- route = Route(name="test", utterances=["utterance"])
- route2 = Route(name="test2", utterances=["utterance2"])
- layer_config = RouterConfig()
- layer_config.add(route)
- # confirm route added
- assert layer_config.routes == [route]
- # add second route and check updates
- layer_config.add(route2)
- assert layer_config.routes == [route, route2]
+ async def acall(self, docs: List[Any]) -> List[List[float]]:
+ return [[0.1, 0.2, 0.3] for _ in docs]
- def test_get(self):
- route = Route(name="test", utterances=["utterance"])
- layer_config = RouterConfig(routes=[route])
- assert layer_config.get("test") == route
+ def encode_queries(self, docs: List[str]) -> List[List[float]]:
+ return [[0.1, 0.2, 0.3] for _ in docs]
- def test_get_not_found(self):
- route = Route(name="test", utterances=["utterance"])
- layer_config = RouterConfig(routes=[route])
- assert layer_config.get("not_found") is None
+ def encode_documents(self, docs: List[str]) -> List[List[float]]:
+ return [[0.4, 0.5, 0.6] for _ in docs]
- def test_remove(self):
- route = Route(name="test", utterances=["utterance"])
- layer_config = RouterConfig(routes=[route])
- layer_config.remove("test")
- assert layer_config.routes == []
+ async def aencode_queries(self, docs: List[str]) -> List[List[float]]:
+ return [[0.1, 0.2, 0.3] for _ in docs]
- def test_setting_aggregation_methods(self, openai_encoder, routes):
- for agg in ["sum", "mean", "max"]:
- route_layer = SemanticRouter(
- encoder=openai_encoder,
- routes=routes,
- aggregation=agg,
+ async def aencode_documents(self, docs: List[str]) -> List[List[float]]:
+ return [[0.4, 0.5, 0.6] for _ in docs]
+
+
+class MockAsymmetricSparseEncoder(SparseEncoder, AsymmetricSparseMixin):
+ def __call__(self, docs: List[str]) -> List[SparseEmbedding]:
+ return [SparseEmbedding(embedding=np.array([[0, 0.1], [1, 0.2]])) for _ in docs]
+
+ async def acall(self, docs: List[str]) -> List[SparseEmbedding]:
+ return [SparseEmbedding(embedding=np.array([[0, 0.1], [1, 0.2]])) for _ in docs]
+
+ def encode_queries(self, docs: List[str]) -> List[SparseEmbedding]:
+ return [SparseEmbedding(embedding=np.array([[0, 0.1], [1, 0.2]])) for _ in docs]
+
+ def encode_documents(self, docs: List[str]) -> List[SparseEmbedding]:
+ return [SparseEmbedding(embedding=np.array([[0, 0.1], [1, 0.2]])) for _ in docs]
+
+ async def aencode_queries(self, docs: List[str]) -> List[SparseEmbedding]:
+ return [SparseEmbedding(embedding=np.array([[0, 0.1], [1, 0.2]])) for _ in docs]
+
+ async def aencode_documents(self, docs: List[str]) -> List[SparseEmbedding]:
+ return [SparseEmbedding(embedding=np.array([[0, 0.1], [1, 0.2]])) for _ in docs]
+
+
+@pytest.mark.parametrize(
+ "dense_encoder_cls,sparse_encoder_cls,input_type",
+ [
+ (encoder, sparse_encoder, input_type)
+ for encoder in [MockSymmetricDenseEncoder, MockAsymmetricDenseEncoder]
+ for sparse_encoder in [MockSymmetricSparseEncoder, MockAsymmetricSparseEncoder]
+ for input_type in ["queries", "documents"]
+ ],
+)
+class TestHybridRouter:
+ def test_encode(
+ self, dense_encoder_cls, sparse_encoder_cls, input_type, routes, mocker
+ ):
+ encoder = dense_encoder_cls(name="Dense Encoder")
+ sparse_encoder = sparse_encoder_cls(name="Sparse Encoder")
+ router = HybridRouter(
+ encoder=encoder,
+ sparse_encoder=sparse_encoder,
+ routes=routes,
+ )
+
+ # Set up spies for symmetric methods
+ dense_call_spy = mocker.spy(dense_encoder_cls, "__call__")
+ sparse_call_spy = mocker.spy(sparse_encoder_cls, "__call__")
+
+ # Set up spies for asymmetric methods if applicable
+ if isinstance(encoder, AsymmetricDenseMixin):
+ dense_encode_queries_spy = mocker.spy(dense_encoder_cls, "encode_queries")
+ dense_encode_documents_spy = mocker.spy(
+ dense_encoder_cls, "encode_documents"
)
- assert route_layer.aggregation == agg
- def test_semantic_classify_multiple_routes_with_different_aggregation(
- self, openai_encoder, routes
+ if isinstance(sparse_encoder, AsymmetricSparseMixin):
+ sparse_encode_queries_spy = mocker.spy(sparse_encoder_cls, "encode_queries")
+ sparse_encode_documents_spy = mocker.spy(
+ sparse_encoder_cls, "encode_documents"
+ )
+
+ test_query = ["test query"]
+
+ # Test synchronous encoding
+ router._encode(test_query, input_type=input_type)
+
+ # Verify correct methods were called based on encoder type and input_type
+ if isinstance(encoder, AsymmetricDenseMixin):
+ if input_type == "documents":
+ assert dense_encode_documents_spy.called
+ else: # queries
+ assert dense_encode_queries_spy.called
+ else:
+ assert dense_call_spy.called
+
+ if isinstance(sparse_encoder, AsymmetricSparseMixin):
+ if input_type == "documents":
+ assert sparse_encode_documents_spy.called
+ else: # queries
+ assert sparse_encode_queries_spy.called
+ else:
+ assert sparse_call_spy.called
+
+ @pytest.mark.asyncio
+ async def test_async_encode(
+ self, dense_encoder_cls, sparse_encoder_cls, input_type, routes, mocker
):
- route_scores = [
- {"route": "Route 1", "score": 0.5},
- {"route": "Route 1", "score": 0.5},
- {"route": "Route 1", "score": 0.5},
- {"route": "Route 1", "score": 0.5},
- {"route": "Route 2", "score": 0.4},
- {"route": "Route 2", "score": 0.6},
- {"route": "Route 2", "score": 0.8},
- {"route": "Route 3", "score": 0.1},
- {"route": "Route 3", "score": 1.0},
- ]
- for agg in ["sum", "mean", "max"]:
- route_layer = SemanticRouter(
- encoder=openai_encoder,
- routes=routes,
- aggregation=agg,
+ encoder = dense_encoder_cls(name="Dense Encoder")
+ sparse_encoder = sparse_encoder_cls(name="Sparse Encoder")
+ router = HybridRouter(
+ encoder=encoder,
+ sparse_encoder=sparse_encoder,
+ routes=routes,
+ )
+
+ # Set up spies for symmetric methods
+ dense_call_spy = mocker.spy(dense_encoder_cls, "acall")
+ sparse_call_spy = mocker.spy(sparse_encoder_cls, "acall")
+
+ # Set up spies for asymmetric methods if applicable
+ if isinstance(encoder, AsymmetricDenseMixin):
+ dense_encode_queries_spy = mocker.spy(dense_encoder_cls, "aencode_queries")
+ dense_encode_documents_spy = mocker.spy(
+ dense_encoder_cls, "aencode_documents"
)
- classification, score = route_layer._semantic_classify(route_scores)
- if agg == "sum":
- assert classification == "Route 1"
- assert score == [0.5, 0.5, 0.5, 0.5]
- elif agg == "mean":
- assert classification == "Route 2"
- assert score == [0.4, 0.6, 0.8]
- elif agg == "max":
- assert classification == "Route 3"
- assert score == [0.1, 1.0]
+ if isinstance(sparse_encoder, AsymmetricSparseMixin):
+ sparse_encode_queries_spy = mocker.spy(
+ sparse_encoder_cls, "aencode_queries"
+ )
+ sparse_encode_documents_spy = mocker.spy(
+ sparse_encoder_cls, "aencode_documents"
+ )
+
+ test_query = ["test query"]
+
+ # Test asynchronous encoding
+ await router._async_encode(test_query, input_type=input_type)
+
+ # Verify correct methods were called based on encoder type and input_type
+ if isinstance(encoder, AsymmetricDenseMixin):
+ if input_type == "documents":
+ assert dense_encode_documents_spy.called
+ else: # queries
+ assert dense_encode_queries_spy.called
+ else:
+ assert dense_call_spy.called
+
+ if isinstance(sparse_encoder, AsymmetricSparseMixin):
+ if input_type == "documents":
+ assert sparse_encode_documents_spy.called
+ else: # queries
+ assert sparse_encode_queries_spy.called
+ else:
+ assert sparse_call_spy.called
diff --git a/tests/unit/test_sync.py b/tests/unit/test_sync.py
index 0bf5eb05ca858e5f29f8d01d12d785284320e673..9f41108b2c334ac51af3376aac215c7f95f7f7ab 100644
--- a/tests/unit/test_sync.py
+++ b/tests/unit/test_sync.py
@@ -1,25 +1,89 @@
import asyncio
import importlib
import os
-from datetime import datetime
-import pytest
import time
+from datetime import datetime
+from functools import wraps
+from platform import python_version
from typing import Optional
-from semantic_router.encoders import DenseEncoder, CohereEncoder, OpenAIEncoder
+
+import pytest
+
+from semantic_router.encoders import CohereEncoder, DenseEncoder, OpenAIEncoder
from semantic_router.index import (
- PineconeIndex,
HybridLocalIndex,
LocalIndex,
- QdrantIndex,
+ PineconeIndex,
PostgresIndex,
+ QdrantIndex,
)
-from semantic_router.schema import Utterance
-from semantic_router.routers import SemanticRouter
from semantic_router.route import Route
-from platform import python_version
-
+from semantic_router.routers import HybridRouter, SemanticRouter
+from semantic_router.schema import Utterance
+from semantic_router.utils.logger import logger
PINECONE_SLEEP = 6
+RETRY_COUNT = 5
+
+
+# retry decorator for PineconeIndex cases (which need delay)
+def retry(max_retries: int = 5, delay: int = 8):
+ """Retry decorator, currently used for PineconeIndex which often needs some time
+ to be populated and have all correct data. Once full Pinecone mock is built we
+ should remove this decorator.
+
+ :param max_retries: Maximum number of retries.
+ :param delay: Delay between retries in seconds.
+ """
+
+ def decorator(func):
+ @wraps(func)
+ def wrapper(*args, **kwargs):
+ count = 0
+ last_exception = None
+ while count < max_retries:
+ try:
+ return func(*args, **kwargs)
+ except Exception as e:
+ logger.warning(f"Attempt {count} | Error in {func.__name__}: {e}")
+ last_exception = e
+ count += 1
+ time.sleep(delay)
+ raise last_exception
+
+ return wrapper
+
+ return decorator
+
+
+# retry decorator for PineconeIndex cases (which need delay)
+def async_retry(max_retries: int = 5, delay: int = 8):
+ """Retry decorator, currently used for PineconeIndex which often needs some time
+ to be populated and have all correct data. Once full Pinecone mock is built we
+ should remove this decorator.
+
+ :param max_retries: Maximum number of retries.
+ :param delay: Delay between retries in seconds.
+ """
+
+ def decorator(func):
+ @wraps(func)
+ async def wrapper(*args, **kwargs):
+ count = 0
+ last_exception = None
+ while count < max_retries:
+ try:
+ return await func(*args, **kwargs)
+ except Exception as e:
+ logger.warning(f"Attempt {count} | Error in {func.__name__}: {e}")
+ last_exception = e
+ count += 1
+ await asyncio.sleep(delay)
+ raise last_exception
+
+ return wrapper
+
+ return decorator
def mock_encoder_call(utterances):
@@ -42,16 +106,25 @@ TEST_ID = (
def init_index(
index_cls,
- dimensions: Optional[int] = None,
+ dimensions: Optional[int] = 3,
namespace: Optional[str] = "",
init_async_index: bool = False,
+ index_name: Optional[str] = None,
):
"""We use this function to initialize indexes with different names to avoid
issues during testing.
"""
if index_cls is PineconeIndex:
+ if index_name:
+ if not dimensions and "OpenAIEncoder" in index_name:
+ dimensions = 1536
+
+ elif not dimensions and "CohereEncoder" in index_name:
+ dimensions = 1024
+
+ index_name = TEST_ID if not index_name else f"{TEST_ID}-{index_name.lower()}"
index = index_cls(
- index_name=TEST_ID,
+ index_name=index_name,
dimensions=dimensions,
namespace=namespace,
init_async_index=init_async_index,
@@ -148,12 +221,28 @@ def base_encoder():
@pytest.fixture
def cohere_encoder(mocker):
mocker.patch.object(CohereEncoder, "__call__", side_effect=mock_encoder_call)
+
+ # Mock async call
+ async def async_mock_encoder_call(docs=None, utterances=None):
+ # Handle either docs or utterances parameter
+ texts = docs if docs is not None else utterances
+ return mock_encoder_call(texts)
+
+ mocker.patch.object(CohereEncoder, "acall", side_effect=async_mock_encoder_call)
return CohereEncoder(name="test-cohere-encoder", cohere_api_key="test_api_key")
@pytest.fixture
def openai_encoder(mocker):
mocker.patch.object(OpenAIEncoder, "__call__", side_effect=mock_encoder_call)
+
+ # Mock async call
+ async def async_mock_encoder_call(docs=None, utterances=None):
+ # Handle either docs or utterances parameter
+ texts = docs if docs is not None else utterances
+ return mock_encoder_call(texts)
+
+ mocker.patch.object(OpenAIEncoder, "acall", side_effect=async_mock_encoder_call)
return OpenAIEncoder(name="text-embedding-3-small", openai_api_key="test_api_key")
@@ -228,14 +317,22 @@ def get_test_indexes():
return indexes
-@pytest.mark.parametrize("index_cls", get_test_indexes())
+def get_test_routers():
+ routers = [SemanticRouter, HybridRouter]
+ return routers
+
+
+@pytest.mark.parametrize(
+ "index_cls,router_cls",
+ [(index, router) for index in get_test_indexes() for router in get_test_routers()],
+)
class TestSemanticRouter:
@pytest.mark.skipif(
os.environ.get("PINECONE_API_KEY") is None, reason="Pinecone API key required"
)
- def test_initialization(self, openai_encoder, routes, index_cls):
- index = init_index(index_cls)
- _ = SemanticRouter(
+ def test_initialization(self, openai_encoder, routes, index_cls, router_cls):
+ index = init_index(index_cls, index_name=router_cls.__name__)
+ _ = router_cls(
encoder=openai_encoder,
routes=routes,
top_k=10,
@@ -246,263 +343,339 @@ class TestSemanticRouter:
@pytest.mark.skipif(
os.environ.get("PINECONE_API_KEY") is None, reason="Pinecone API key required"
)
- def test_second_initialization_sync(self, openai_encoder, routes, index_cls):
- index = init_index(index_cls)
- route_layer = SemanticRouter(
+ def test_second_initialization_sync(
+ self, openai_encoder, routes, index_cls, router_cls
+ ):
+ index = init_index(index_cls, index_name=router_cls.__name__)
+ route_layer = router_cls(
encoder=openai_encoder, routes=routes, index=index, auto_sync="local"
)
- if index_cls is PineconeIndex:
- time.sleep(PINECONE_SLEEP) # allow for index to be populated
- assert route_layer.is_synced()
+
+ @retry(max_retries=RETRY_COUNT, delay=PINECONE_SLEEP)
+ def check_sync():
+ assert route_layer.is_synced()
+
+ check_sync()
@pytest.mark.skipif(
os.environ.get("PINECONE_API_KEY") is None, reason="Pinecone API key required"
)
def test_second_initialization_not_synced(
- self, openai_encoder, routes, routes_2, index_cls
+ self, openai_encoder, routes, routes_2, index_cls, router_cls
):
- index = init_index(index_cls)
- _ = SemanticRouter(
+ index = init_index(index_cls, index_name=router_cls.__name__)
+ _ = router_cls(
encoder=openai_encoder, routes=routes, index=index, auto_sync="local"
)
- route_layer = SemanticRouter(
- encoder=openai_encoder, routes=routes_2, index=index
- )
- if index_cls is PineconeIndex:
- time.sleep(PINECONE_SLEEP) # allow for index to be populated
- assert route_layer.is_synced() is False
+ route_layer = router_cls(encoder=openai_encoder, routes=routes_2, index=index)
+
+ @retry(max_retries=RETRY_COUNT, delay=PINECONE_SLEEP)
+ def check_sync():
+ assert route_layer.is_synced() is False
+
+ check_sync()
@pytest.mark.skipif(
os.environ.get("PINECONE_API_KEY") is None, reason="Pinecone API key required"
)
- def test_utterance_diff(self, openai_encoder, routes, routes_2, index_cls):
- index = init_index(index_cls)
- _ = SemanticRouter(
+ def test_utterance_diff(
+ self, openai_encoder, routes, routes_2, index_cls, router_cls
+ ):
+ index = init_index(index_cls, index_name=router_cls.__name__)
+ _ = router_cls(
encoder=openai_encoder, routes=routes, index=index, auto_sync="local"
)
- route_layer_2 = SemanticRouter(
- encoder=openai_encoder, routes=routes_2, index=index
- )
- if index_cls is PineconeIndex:
- time.sleep(PINECONE_SLEEP) # allow for index to be populated
- diff = route_layer_2.get_utterance_diff(include_metadata=True)
- assert '+ Route 1: Hello | None | {"type": "default"}' in diff
- assert '+ Route 1: Hi | None | {"type": "default"}' in diff
- assert "- Route 1: Hello | None | {}" in diff
- assert "+ Route 2: Au revoir | None | {}" in diff
- assert "- Route 2: Hi | None | {}" in diff
- assert "+ Route 2: Bye | None | {}" in diff
- assert "+ Route 2: Goodbye | None | {}" in diff
- assert "+ Route 3: Boo | None | {}" in diff
+ route_layer_2 = router_cls(encoder=openai_encoder, routes=routes_2, index=index)
+
+ @retry(max_retries=RETRY_COUNT, delay=PINECONE_SLEEP)
+ def check_utterance_diff():
+ diff = route_layer_2.get_utterance_diff(include_metadata=True)
+ assert '+ Route 1: Hello | None | {"type": "default"}' in diff
+ assert '+ Route 1: Hi | None | {"type": "default"}' in diff
+ assert "- Route 1: Hello | None | {}" in diff
+ assert "+ Route 2: Au revoir | None | {}" in diff
+ assert "- Route 2: Hi | None | {}" in diff
+ assert "+ Route 2: Bye | None | {}" in diff
+ assert "+ Route 2: Goodbye | None | {}" in diff
+ assert "+ Route 3: Boo | None | {}" in diff
+
+ check_utterance_diff()
@pytest.mark.skipif(
os.environ.get("PINECONE_API_KEY") is None, reason="Pinecone API key required"
)
- def test_auto_sync_local(self, openai_encoder, routes, routes_2, index_cls):
+ def test_auto_sync_local(
+ self, openai_encoder, routes, routes_2, index_cls, router_cls
+ ):
if index_cls is PineconeIndex:
# TEST LOCAL
- pinecone_index = init_index(index_cls)
- _ = SemanticRouter(
+ pinecone_index = init_index(index_cls, index_name=router_cls.__name__)
+ _ = router_cls(
encoder=openai_encoder,
routes=routes,
index=pinecone_index,
)
time.sleep(PINECONE_SLEEP) # allow for index to be populated
- route_layer = SemanticRouter(
+ route_layer = router_cls(
encoder=openai_encoder,
routes=routes_2,
index=pinecone_index,
auto_sync="local",
)
- time.sleep(PINECONE_SLEEP) # allow for index to be populated
- assert route_layer.index.get_utterances() == [
- Utterance(route="Route 1", utterance="Hello"),
- Utterance(route="Route 2", utterance="Hi"),
- ], "The routes in the index should match the local routes"
+ time.sleep(PINECONE_SLEEP)
+
+ @retry(max_retries=RETRY_COUNT, delay=PINECONE_SLEEP)
+ def check_sync():
+ # TODO JB: this should use include_metadata=True
+ assert route_layer.index.get_utterances(include_metadata=True) == [
+ Utterance(route="Route 1", utterance="Hello"),
+ Utterance(route="Route 2", utterance="Hi"),
+ ], "The routes in the index should match the local routes"
+
+ check_sync()
@pytest.mark.skipif(
os.environ.get("PINECONE_API_KEY") is None, reason="Pinecone API key required"
)
- def test_auto_sync_remote(self, openai_encoder, routes, routes_2, index_cls):
+ def test_auto_sync_remote(
+ self, openai_encoder, routes, routes_2, index_cls, router_cls
+ ):
if index_cls is PineconeIndex:
# TEST REMOTE
- pinecone_index = init_index(index_cls)
- _ = SemanticRouter(
+ pinecone_index = init_index(index_cls, index_name=router_cls.__name__)
+ _ = router_cls(
encoder=openai_encoder,
routes=routes_2,
index=pinecone_index,
auto_sync="local",
)
time.sleep(PINECONE_SLEEP) # allow for index to be populated
- route_layer = SemanticRouter(
+ route_layer = router_cls(
encoder=openai_encoder,
routes=routes,
index=pinecone_index,
auto_sync="remote",
)
- time.sleep(PINECONE_SLEEP) # allow for index to be populated
- assert route_layer.index.get_utterances() == [
- Utterance(route="Route 1", utterance="Hello"),
- Utterance(route="Route 2", utterance="Hi"),
- ], "The routes in the index should match the local routes"
+ time.sleep(PINECONE_SLEEP)
+
+ @retry(max_retries=RETRY_COUNT, delay=PINECONE_SLEEP)
+ def check_sync():
+ assert route_layer.index.get_utterances(include_metadata=True) == [
+ Utterance(route="Route 1", utterance="Hello"),
+ Utterance(route="Route 2", utterance="Hi"),
+ ], "The routes in the index should match the local routes"
+
+ check_sync()
@pytest.mark.skipif(
os.environ.get("PINECONE_API_KEY") is None, reason="Pinecone API key required"
)
def test_auto_sync_merge_force_local(
- self, openai_encoder, routes, routes_2, index_cls
+ self, openai_encoder, routes, routes_2, index_cls, router_cls
):
if index_cls is PineconeIndex:
# TEST MERGE FORCE LOCAL
- pinecone_index = init_index(index_cls)
- route_layer = SemanticRouter(
+ pinecone_index = init_index(index_cls, index_name=router_cls.__name__)
+ route_layer = router_cls(
encoder=openai_encoder,
routes=routes,
index=pinecone_index,
auto_sync="local",
)
- time.sleep(PINECONE_SLEEP) # allow for index to be populated
- route_layer = SemanticRouter(
+ time.sleep(PINECONE_SLEEP * 2) # allow for index to be populated
+ route_layer = router_cls(
encoder=openai_encoder,
routes=routes_2,
index=pinecone_index,
auto_sync="merge-force-local",
)
- time.sleep(PINECONE_SLEEP) # allow for index to be populated
- # confirm local and remote are synced
- assert route_layer.is_synced()
- # now confirm utterances are correct
- local_utterances = route_layer.index.get_utterances()
- # we sort to ensure order is the same
- # TODO JB: there is a bug here where if we include_metadata=True it fails
- local_utterances.sort(key=lambda x: x.to_str(include_metadata=False))
- assert local_utterances == [
- Utterance(route="Route 1", utterance="Hello"),
- Utterance(route="Route 1", utterance="Hi"),
- Utterance(route="Route 2", utterance="Au revoir"),
- Utterance(route="Route 2", utterance="Bye"),
- Utterance(route="Route 2", utterance="Goodbye"),
- Utterance(route="Route 2", utterance="Hi"),
- ], "The routes in the index should match the local routes"
+ time.sleep(PINECONE_SLEEP * 2)
+
+ @retry(max_retries=RETRY_COUNT, delay=PINECONE_SLEEP)
+ def check_sync():
+ # confirm local and remote are synced
+ assert route_layer.is_synced()
+ # now confirm utterances are correct
+ local_utterances = route_layer.index.get_utterances(
+ include_metadata=False
+ )
+ # we sort to ensure order is the same
+ # TODO JB: there is a bug here where if we include_metadata=True it fails
+ local_utterances.sort(key=lambda x: x.to_str(include_metadata=False))
+ assert local_utterances == [
+ Utterance(route="Route 1", utterance="Hello"),
+ Utterance(route="Route 1", utterance="Hi"),
+ Utterance(route="Route 2", utterance="Au revoir"),
+ Utterance(route="Route 2", utterance="Bye"),
+ Utterance(route="Route 2", utterance="Goodbye"),
+ Utterance(route="Route 2", utterance="Hi"),
+ # Utterance(route="Route 3", utterance="Boo"), # TODO should not be here
+ ], "The routes in the index should match the local routes"
+
+ check_sync()
@pytest.mark.skipif(
os.environ.get("PINECONE_API_KEY") is None, reason="Pinecone API key required"
)
def test_auto_sync_merge_force_remote(
- self, openai_encoder, routes, routes_2, index_cls
+ self, openai_encoder, routes, routes_2, index_cls, router_cls
):
if index_cls is PineconeIndex:
# TEST MERGE FORCE LOCAL
- pinecone_index = init_index(index_cls)
- route_layer = SemanticRouter(
+ pinecone_index = init_index(index_cls, index_name=router_cls.__name__)
+ route_layer = router_cls(
encoder=openai_encoder,
routes=routes,
index=pinecone_index,
auto_sync="local",
)
- time.sleep(PINECONE_SLEEP) # allow for index to be populated
- route_layer = SemanticRouter(
+
+ @retry(max_retries=RETRY_COUNT, delay=PINECONE_SLEEP)
+ def check_r1_utterances():
+ # confirm local and remote are synced
+ assert route_layer.is_synced()
+ # now confirm utterances are correct
+ r1_utterances = [
+ Utterance(
+ route="Route 1", utterance="Hello", metadata={"type": "default"}
+ ),
+ Utterance(
+ route="Route 1", utterance="Hi", metadata={"type": "default"}
+ ),
+ Utterance(route="Route 2", utterance="Au revoir"),
+ Utterance(route="Route 2", utterance="Bye"),
+ Utterance(route="Route 2", utterance="Goodbye"),
+ Utterance(route="Route 3", utterance="Boo"),
+ ]
+ local_utterances = route_layer.index.get_utterances(
+ include_metadata=True
+ )
+ # we sort to ensure order is the same
+ local_utterances.sort(key=lambda x: x.to_str(include_metadata=True))
+ assert local_utterances == r1_utterances
+
+ check_r1_utterances()
+
+ route_layer = router_cls(
encoder=openai_encoder,
routes=routes_2,
index=pinecone_index,
auto_sync="merge-force-remote",
)
- time.sleep(PINECONE_SLEEP) # allow for index to be populated
- # confirm local and remote are synced
- assert route_layer.is_synced()
- # now confirm utterances are correct
- local_utterances = route_layer.index.get_utterances()
- # we sort to ensure order is the same
- local_utterances.sort(
- key=lambda x: x.to_str(include_metadata=include_metadata(index_cls))
- )
- assert local_utterances == [
- Utterance(
- route="Route 1", utterance="Hello", metadata={"type": "default"}
- ),
- Utterance(
- route="Route 1", utterance="Hi", metadata={"type": "default"}
- ),
- Utterance(route="Route 2", utterance="Au revoir"),
- Utterance(route="Route 2", utterance="Bye"),
- Utterance(route="Route 2", utterance="Goodbye"),
- Utterance(route="Route 2", utterance="Hi"),
- Utterance(route="Route 3", utterance="Boo"),
- ], "The routes in the index should match the local routes"
+
+ @retry(max_retries=RETRY_COUNT, delay=PINECONE_SLEEP)
+ def check_r2_utterances():
+ # confirm local and remote are synced
+ assert route_layer.is_synced()
+ local_utterances = route_layer.index.get_utterances(
+ include_metadata=True
+ )
+ # we sort to ensure order is the same
+ local_utterances.sort(key=lambda x: x.to_str(include_metadata=True))
+ assert local_utterances == [
+ Utterance(
+ route="Route 1", utterance="Hello", metadata={"type": "default"}
+ ),
+ Utterance(
+ route="Route 1", utterance="Hi", metadata={"type": "default"}
+ ),
+ Utterance(route="Route 2", utterance="Au revoir"),
+ Utterance(route="Route 2", utterance="Bye"),
+ Utterance(route="Route 2", utterance="Goodbye"),
+ Utterance(route="Route 2", utterance="Hi"),
+ Utterance(route="Route 3", utterance="Boo"),
+ ], "The routes in the index should match the local routes"
+
+ check_r2_utterances()
@pytest.mark.skipif(
os.environ.get("PINECONE_API_KEY") is None, reason="Pinecone API key required"
)
- def test_sync(self, openai_encoder, index_cls):
- route_layer = SemanticRouter(
+ def test_sync(self, openai_encoder, index_cls, router_cls):
+ route_layer = router_cls(
encoder=openai_encoder,
routes=[],
- index=init_index(index_cls),
+ index=init_index(index_cls, index_name=router_cls.__name__),
auto_sync=None,
)
route_layer.sync("remote")
- time.sleep(PINECONE_SLEEP) # allow for index to be populated
- # confirm local and remote are synced
- assert route_layer.is_synced()
+
+ @retry(max_retries=RETRY_COUNT, delay=PINECONE_SLEEP)
+ def check_sync():
+ assert route_layer.is_synced()
+
+ check_sync()
@pytest.mark.skipif(
os.environ.get("PINECONE_API_KEY") is None, reason="Pinecone API key required"
)
- def test_auto_sync_merge(self, openai_encoder, routes, routes_2, index_cls):
+ def test_auto_sync_merge(
+ self, openai_encoder, routes, routes_2, index_cls, router_cls
+ ):
if index_cls is PineconeIndex:
# TEST MERGE
- pinecone_index = init_index(index_cls)
- route_layer = SemanticRouter(
+ pinecone_index = init_index(index_cls, index_name=router_cls.__name__)
+ route_layer = router_cls(
encoder=openai_encoder,
routes=routes_2,
index=pinecone_index,
auto_sync="local",
)
time.sleep(PINECONE_SLEEP) # allow for index to be populated
- route_layer = SemanticRouter(
+ route_layer = router_cls(
encoder=openai_encoder,
routes=routes,
index=pinecone_index,
auto_sync="merge",
)
- time.sleep(PINECONE_SLEEP) # allow for index to be populated
- # confirm local and remote are synced
- assert route_layer.is_synced()
- # now confirm utterances are correct
- local_utterances = route_layer.index.get_utterances()
- # we sort to ensure order is the same
- local_utterances.sort(
- key=lambda x: x.to_str(include_metadata=include_metadata(index_cls))
- )
- assert local_utterances == [
- Utterance(
- route="Route 1", utterance="Hello", metadata={"type": "default"}
- ),
- Utterance(
- route="Route 1", utterance="Hi", metadata={"type": "default"}
- ),
- Utterance(route="Route 2", utterance="Au revoir"),
- Utterance(route="Route 2", utterance="Bye"),
- Utterance(route="Route 2", utterance="Goodbye"),
- Utterance(route="Route 2", utterance="Hi"),
- Utterance(route="Route 3", utterance="Boo"),
- ], "The routes in the index should match the local routes"
+
+ @retry(max_retries=RETRY_COUNT, delay=PINECONE_SLEEP)
+ def check_sync():
+ # confirm local and remote are synced
+ assert route_layer.is_synced()
+ # now confirm utterances are correct
+ local_utterances = route_layer.index.get_utterances(
+ include_metadata=True
+ )
+ # we sort to ensure order is the same
+ local_utterances.sort(key=lambda x: x.to_str(include_metadata=True))
+ assert local_utterances == [
+ Utterance(
+ route="Route 1", utterance="Hello", metadata={"type": "default"}
+ ),
+ Utterance(
+ route="Route 1", utterance="Hi", metadata={"type": "default"}
+ ),
+ Utterance(route="Route 2", utterance="Au revoir"),
+ Utterance(route="Route 2", utterance="Bye"),
+ Utterance(route="Route 2", utterance="Goodbye"),
+ Utterance(route="Route 2", utterance="Hi"),
+ Utterance(route="Route 3", utterance="Boo"),
+ ], "The routes in the index should match the local routes"
+
+ check_sync()
@pytest.mark.skipif(
os.environ.get("PINECONE_API_KEY") is None, reason="Pinecone API key required"
)
def test_sync_lock_prevents_concurrent_sync(
- self, openai_encoder, routes, index_cls
+ self, openai_encoder, routes, routes_2, index_cls, router_cls
):
"""Test that sync lock prevents concurrent synchronization operations"""
- index = init_index(index_cls)
- route_layer = SemanticRouter(
+ index = init_index(index_cls, index_name=router_cls.__name__)
+ route_layer = router_cls(
+ encoder=openai_encoder,
+ routes=routes_2,
+ index=index,
+ auto_sync="local",
+ )
+ # initialize an out of sync router
+ route_layer = router_cls(
encoder=openai_encoder,
routes=routes,
index=index,
auto_sync=None,
)
-
# Acquire sync lock
route_layer.index.lock(value=True)
if index_cls is PineconeIndex:
@@ -526,18 +699,17 @@ class TestSemanticRouter:
@pytest.mark.skipif(
os.environ.get("PINECONE_API_KEY") is None, reason="Pinecone API key required"
)
- def test_sync_lock_auto_releases(self, openai_encoder, routes, index_cls):
+ def test_sync_lock_auto_releases(
+ self, openai_encoder, routes, index_cls, router_cls
+ ):
"""Test that sync lock is automatically released after sync operations"""
- index = init_index(index_cls)
- route_layer = SemanticRouter(
+ index = init_index(index_cls, index_name=router_cls.__name__)
+ route_layer = router_cls(
encoder=openai_encoder,
routes=routes,
index=index,
- auto_sync=None,
+ auto_sync="local",
)
-
- # Initial sync should acquire and release lock
- route_layer.sync("local")
if index_cls is PineconeIndex:
time.sleep(PINECONE_SLEEP)
@@ -547,19 +719,25 @@ class TestSemanticRouter:
time.sleep(PINECONE_SLEEP)
assert route_layer.is_synced()
- # clear index
- route_layer.index.index.delete(namespace="", delete_all=True)
+ # clear index if pinecone
+ if index_cls is PineconeIndex:
+ route_layer.index.client.delete_index(route_layer.index.index_name)
-@pytest.mark.parametrize("index_cls", get_test_indexes())
+@pytest.mark.parametrize(
+ "index_cls,router_cls",
+ [(index, router) for index in get_test_indexes() for router in get_test_routers()],
+)
class TestAsyncSemanticRouter:
@pytest.mark.skipif(
os.environ.get("PINECONE_API_KEY") is None, reason="Pinecone API key required"
)
@pytest.mark.asyncio
- async def test_initialization(self, openai_encoder, routes, index_cls):
- index = init_index(index_cls, init_async_index=True)
- _ = SemanticRouter(
+ async def test_initialization(self, openai_encoder, routes, index_cls, router_cls):
+ index = init_index(
+ index_cls, init_async_index=True, index_name=router_cls.__name__
+ )
+ _ = router_cls(
encoder=openai_encoder,
routes=routes,
top_k=10,
@@ -571,29 +749,33 @@ class TestAsyncSemanticRouter:
os.environ.get("PINECONE_API_KEY") is None, reason="Pinecone API key required"
)
@pytest.mark.asyncio
- async def test_second_initialization_sync(self, openai_encoder, routes, index_cls):
- index = init_index(index_cls, init_async_index=True)
- route_layer = SemanticRouter(
+ async def test_second_initialization_sync(
+ self, openai_encoder, routes, index_cls, router_cls
+ ):
+ index = init_index(
+ index_cls, init_async_index=True, index_name=router_cls.__name__
+ )
+ route_layer = router_cls(
encoder=openai_encoder, routes=routes, index=index, auto_sync="local"
)
if index_cls is PineconeIndex:
- await asyncio.sleep(PINECONE_SLEEP) # allow for index to be populated
- assert route_layer.async_is_synced()
+ await asyncio.sleep(PINECONE_SLEEP * 2) # allow for index to be populated
+ assert await route_layer.async_is_synced()
@pytest.mark.skipif(
os.environ.get("PINECONE_API_KEY") is None, reason="Pinecone API key required"
)
@pytest.mark.asyncio
async def test_second_initialization_not_synced(
- self, openai_encoder, routes, routes_2, index_cls
+ self, openai_encoder, routes, routes_2, index_cls, router_cls
):
- index = init_index(index_cls, init_async_index=True)
- _ = SemanticRouter(
- encoder=openai_encoder, routes=routes, index=index, auto_sync="local"
+ index = init_index(
+ index_cls, init_async_index=True, index_name=router_cls.__name__
)
- route_layer = SemanticRouter(
- encoder=openai_encoder, routes=routes_2, index=index
+ _ = router_cls(
+ encoder=openai_encoder, routes=routes, index=index, auto_sync="local"
)
+ route_layer = router_cls(encoder=openai_encoder, routes=routes_2, index=index)
if index_cls is PineconeIndex:
await asyncio.sleep(PINECONE_SLEEP) # allow for index to be populated
assert await route_layer.async_is_synced() is False
@@ -602,235 +784,330 @@ class TestAsyncSemanticRouter:
os.environ.get("PINECONE_API_KEY") is None, reason="Pinecone API key required"
)
@pytest.mark.asyncio
- async def test_utterance_diff(self, openai_encoder, routes, routes_2, index_cls):
- index = init_index(index_cls, init_async_index=True)
- _ = SemanticRouter(
- encoder=openai_encoder, routes=routes, index=index, auto_sync="local"
+ async def test_utterance_diff(
+ self, openai_encoder, routes, routes_2, index_cls, router_cls
+ ):
+ index = init_index(
+ index_cls, init_async_index=True, index_name=router_cls.__name__
)
- route_layer_2 = SemanticRouter(
- encoder=openai_encoder, routes=routes_2, index=index
+ _ = router_cls(
+ encoder=openai_encoder, routes=routes, index=index, auto_sync="local"
)
- if index_cls is PineconeIndex:
- await asyncio.sleep(PINECONE_SLEEP * 2) # allow for index to be populated
- diff = await route_layer_2.aget_utterance_diff(include_metadata=True)
- assert '+ Route 1: Hello | None | {"type": "default"}' in diff
- assert '+ Route 1: Hi | None | {"type": "default"}' in diff
- assert "- Route 1: Hello | None | {}" in diff
- assert "+ Route 2: Au revoir | None | {}" in diff
- assert "- Route 2: Hi | None | {}" in diff
- assert "+ Route 2: Bye | None | {}" in diff
- assert "+ Route 2: Goodbye | None | {}" in diff
- assert "+ Route 3: Boo | None | {}" in diff
+ route_layer_2 = router_cls(encoder=openai_encoder, routes=routes_2, index=index)
+
+ @async_retry(max_retries=RETRY_COUNT, delay=PINECONE_SLEEP)
+ async def check_diff():
+ diff = await route_layer_2.aget_utterance_diff(include_metadata=True)
+ assert '+ Route 1: Hello | None | {"type": "default"}' in diff
+ assert '+ Route 1: Hi | None | {"type": "default"}' in diff
+ assert "- Route 1: Hello | None | {}" in diff
+ assert "+ Route 2: Au revoir | None | {}" in diff
+ assert "- Route 2: Hi | None | {}" in diff
+ assert "+ Route 2: Bye | None | {}" in diff
+ assert "+ Route 2: Goodbye | None | {}" in diff
+ assert "+ Route 3: Boo | None | {}" in diff
+
+ await check_diff()
@pytest.mark.skipif(
os.environ.get("PINECONE_API_KEY") is None, reason="Pinecone API key required"
)
@pytest.mark.asyncio
- async def test_auto_sync_local(self, openai_encoder, routes, routes_2, index_cls):
+ async def test_auto_sync_local(
+ self, openai_encoder, routes, routes_2, index_cls, router_cls
+ ):
if index_cls is PineconeIndex:
# TEST LOCAL
- pinecone_index = init_index(index_cls, init_async_index=True)
- _ = SemanticRouter(
+ pinecone_index = init_index(
+ index_cls, init_async_index=True, index_name=router_cls.__name__
+ )
+ _ = router_cls(
encoder=openai_encoder,
routes=routes,
index=pinecone_index,
)
await asyncio.sleep(PINECONE_SLEEP) # allow for index to be populated
- route_layer = SemanticRouter(
+ route_layer = router_cls(
encoder=openai_encoder,
routes=routes_2,
index=pinecone_index,
auto_sync="local",
)
- await asyncio.sleep(PINECONE_SLEEP) # allow for index to be populated
- assert await route_layer.index.aget_utterances() == [
- Utterance(route="Route 1", utterance="Hello"),
- Utterance(route="Route 2", utterance="Hi"),
- ], "The routes in the index should match the local routes"
+
+ @async_retry(max_retries=RETRY_COUNT, delay=PINECONE_SLEEP)
+ async def check_sync():
+ assert await route_layer.index.aget_utterances(
+ include_metadata=True
+ ) == [
+ Utterance(route="Route 1", utterance="Hello"),
+ Utterance(route="Route 2", utterance="Hi"),
+ ], "The routes in the index should match the local routes"
+
+ await check_sync()
@pytest.mark.skipif(
os.environ.get("PINECONE_API_KEY") is None, reason="Pinecone API key required"
)
@pytest.mark.asyncio
- async def test_auto_sync_remote(self, openai_encoder, routes, routes_2, index_cls):
+ async def test_auto_sync_remote(
+ self, openai_encoder, routes, routes_2, index_cls, router_cls
+ ):
if index_cls is PineconeIndex:
# TEST REMOTE
- pinecone_index = init_index(index_cls, init_async_index=True)
- _ = SemanticRouter(
+ pinecone_index = init_index(
+ index_cls, init_async_index=True, index_name=router_cls.__name__
+ )
+ _ = router_cls(
encoder=openai_encoder,
routes=routes_2,
index=pinecone_index,
auto_sync="local",
)
await asyncio.sleep(PINECONE_SLEEP) # allow for index to be populated
- route_layer = SemanticRouter(
+ route_layer = router_cls(
encoder=openai_encoder,
routes=routes,
index=pinecone_index,
auto_sync="remote",
)
- await asyncio.sleep(PINECONE_SLEEP) # allow for index to be populated
- assert await route_layer.index.aget_utterances() == [
- Utterance(route="Route 1", utterance="Hello"),
- Utterance(route="Route 2", utterance="Hi"),
- ], "The routes in the index should match the local routes"
+
+ @async_retry(max_retries=RETRY_COUNT, delay=PINECONE_SLEEP)
+ async def check_sync():
+ assert await route_layer.index.aget_utterances(
+ include_metadata=True
+ ) == [
+ Utterance(route="Route 1", utterance="Hello"),
+ Utterance(route="Route 2", utterance="Hi"),
+ ], "The routes in the index should match the local routes"
+
+ await check_sync()
@pytest.mark.skipif(
os.environ.get("PINECONE_API_KEY") is None, reason="Pinecone API key required"
)
@pytest.mark.asyncio
async def test_auto_sync_merge_force_local(
- self, openai_encoder, routes, routes_2, index_cls
+ self, openai_encoder, routes, routes_2, index_cls, router_cls
):
if index_cls is PineconeIndex:
# TEST MERGE FORCE LOCAL
- pinecone_index = init_index(index_cls, init_async_index=True)
- route_layer = SemanticRouter(
+ pinecone_index = init_index(
+ index_cls, init_async_index=True, index_name=router_cls.__name__
+ )
+ route_layer = router_cls(
encoder=openai_encoder,
routes=routes,
index=pinecone_index,
auto_sync="local",
)
- await asyncio.sleep(PINECONE_SLEEP) # allow for index to be populated
- route_layer = SemanticRouter(
+ await asyncio.sleep(PINECONE_SLEEP * 2) # allow for index to be populated
+ route_layer = router_cls(
encoder=openai_encoder,
routes=routes_2,
index=pinecone_index,
auto_sync="merge-force-local",
)
- await asyncio.sleep(PINECONE_SLEEP) # allow for index to be populated
- # confirm local and remote are synced
- assert route_layer.async_is_synced()
- # now confirm utterances are correct
- local_utterances = await route_layer.index.aget_utterances()
- # we sort to ensure order is the same
- # TODO JB: there is a bug here where if we include_metadata=True it fails
- local_utterances.sort(key=lambda x: x.to_str(include_metadata=False))
- assert local_utterances == [
- Utterance(route="Route 1", utterance="Hello"),
- Utterance(route="Route 1", utterance="Hi"),
- Utterance(route="Route 2", utterance="Au revoir"),
- Utterance(route="Route 2", utterance="Bye"),
- Utterance(route="Route 2", utterance="Goodbye"),
- Utterance(route="Route 2", utterance="Hi"),
- ], "The routes in the index should match the local routes"
+ await asyncio.sleep(PINECONE_SLEEP * 2)
+
+ @async_retry(max_retries=RETRY_COUNT, delay=PINECONE_SLEEP)
+ async def check_sync():
+ # confirm local and remote are synced
+ assert await route_layer.async_is_synced()
+ # now confirm utterances are correct
+ local_utterances = await route_layer.index.aget_utterances(
+ include_metadata=False
+ )
+ # we sort to ensure order is the same
+ # TODO JB: there is a bug here where if we include_metadata=True it fails
+ local_utterances.sort(key=lambda x: x.to_str(include_metadata=False))
+ assert local_utterances == [
+ Utterance(route="Route 1", utterance="Hello"),
+ Utterance(route="Route 1", utterance="Hi"),
+ Utterance(route="Route 2", utterance="Au revoir"),
+ Utterance(route="Route 2", utterance="Bye"),
+ Utterance(route="Route 2", utterance="Goodbye"),
+ Utterance(route="Route 2", utterance="Hi"),
+ ], "The routes in the index should match the local routes"
+
+ await check_sync()
@pytest.mark.skipif(
os.environ.get("PINECONE_API_KEY") is None, reason="Pinecone API key required"
)
@pytest.mark.asyncio
async def test_auto_sync_merge_force_remote(
- self, openai_encoder, routes, routes_2, index_cls
+ self, openai_encoder, routes, routes_2, index_cls, router_cls
):
if index_cls is PineconeIndex:
# TEST MERGE FORCE LOCAL
- pinecone_index = init_index(index_cls, init_async_index=True)
- route_layer = SemanticRouter(
+ pinecone_index = init_index(
+ index_cls, init_async_index=True, index_name=router_cls.__name__
+ )
+ route_layer = router_cls(
encoder=openai_encoder,
routes=routes,
index=pinecone_index,
auto_sync="local",
)
+
+ @async_retry(max_retries=RETRY_COUNT, delay=PINECONE_SLEEP)
+ async def populate_index():
+ # confirm local and remote are synced
+ assert await route_layer.async_is_synced()
+ # now confirm utterances are correct
+ local_utterances = await route_layer.index.aget_utterances(
+ include_metadata=True
+ )
+ # we sort to ensure order is the same
+ local_utterances.sort(key=lambda x: x.to_str(include_metadata=True))
+ assert local_utterances == [
+ Utterance(
+ route="Route 1", utterance="Hello", metadata={"type": "default"}
+ ),
+ Utterance(
+ route="Route 1", utterance="Hi", metadata={"type": "default"}
+ ),
+ Utterance(route="Route 2", utterance="Au revoir"),
+ Utterance(route="Route 2", utterance="Bye"),
+ Utterance(route="Route 2", utterance="Goodbye"),
+ Utterance(route="Route 3", utterance="Boo"),
+ ], "The routes in the index should match the local routes"
+
+ await populate_index()
+
await asyncio.sleep(PINECONE_SLEEP) # allow for index to be populated
- route_layer = SemanticRouter(
+ route_layer = router_cls(
encoder=openai_encoder,
routes=routes_2,
index=pinecone_index,
auto_sync="merge-force-remote",
)
- await asyncio.sleep(PINECONE_SLEEP) # allow for index to be populated
- # confirm local and remote are synced
- assert route_layer.async_is_synced()
- # now confirm utterances are correct
- local_utterances = await route_layer.index.aget_utterances()
- # we sort to ensure order is the same
- local_utterances.sort(
- key=lambda x: x.to_str(include_metadata=include_metadata(index_cls))
- )
- assert local_utterances == [
- Utterance(
- route="Route 1", utterance="Hello", metadata={"type": "default"}
- ),
- Utterance(
- route="Route 1", utterance="Hi", metadata={"type": "default"}
- ),
- Utterance(route="Route 2", utterance="Au revoir"),
- Utterance(route="Route 2", utterance="Bye"),
- Utterance(route="Route 2", utterance="Goodbye"),
- Utterance(route="Route 2", utterance="Hi"),
- Utterance(route="Route 3", utterance="Boo"),
- ], "The routes in the index should match the local routes"
+ await asyncio.sleep(PINECONE_SLEEP)
+
+ @async_retry(max_retries=RETRY_COUNT, delay=PINECONE_SLEEP)
+ async def check_sync():
+ # confirm local and remote are synced
+ assert await route_layer.async_is_synced()
+ # now confirm utterances are correct
+ local_utterances = await route_layer.index.aget_utterances(
+ include_metadata=True
+ )
+ # we sort to ensure order is the same
+ local_utterances.sort(key=lambda x: x.to_str(include_metadata=True))
+ assert local_utterances == [
+ Utterance(
+ route="Route 1", utterance="Hello", metadata={"type": "default"}
+ ),
+ Utterance(
+ route="Route 1", utterance="Hi", metadata={"type": "default"}
+ ),
+ Utterance(route="Route 2", utterance="Au revoir"),
+ Utterance(route="Route 2", utterance="Bye"),
+ Utterance(route="Route 2", utterance="Goodbye"),
+ Utterance(route="Route 2", utterance="Hi"),
+ Utterance(route="Route 3", utterance="Boo"),
+ ], "The routes in the index should match the local routes"
+
+ await check_sync()
@pytest.mark.skipif(
os.environ.get("PINECONE_API_KEY") is None, reason="Pinecone API key required"
)
@pytest.mark.asyncio
- async def test_sync(self, openai_encoder, index_cls):
- route_layer = SemanticRouter(
+ async def test_sync(self, openai_encoder, index_cls, router_cls):
+ route_layer = router_cls(
encoder=openai_encoder,
routes=[],
- index=init_index(index_cls, init_async_index=True),
+ index=init_index(
+ index_cls, init_async_index=True, index_name=router_cls.__name__
+ ),
auto_sync=None,
)
await route_layer.async_sync("remote")
- await asyncio.sleep(PINECONE_SLEEP) # allow for index to be populated
- # confirm local and remote are synced
- assert await route_layer.async_is_synced()
+
+ @async_retry(max_retries=RETRY_COUNT, delay=PINECONE_SLEEP)
+ async def check_sync():
+ # confirm local and remote are synced
+ assert await route_layer.async_is_synced()
+
+ await check_sync()
@pytest.mark.skipif(
os.environ.get("PINECONE_API_KEY") is None, reason="Pinecone API key required"
)
@pytest.mark.asyncio
- async def test_auto_sync_merge(self, openai_encoder, routes, routes_2, index_cls):
+ async def test_auto_sync_merge(
+ self, openai_encoder, routes, routes_2, index_cls, router_cls
+ ):
if index_cls is PineconeIndex:
# TEST MERGE
- pinecone_index = init_index(index_cls, init_async_index=True)
- route_layer = SemanticRouter(
+ pinecone_index = init_index(
+ index_cls, init_async_index=True, index_name=router_cls.__name__
+ )
+ route_layer = router_cls(
encoder=openai_encoder,
routes=routes_2,
index=pinecone_index,
auto_sync="local",
)
await asyncio.sleep(PINECONE_SLEEP) # allow for index to be populated
- route_layer = SemanticRouter(
+ route_layer = router_cls(
encoder=openai_encoder,
routes=routes,
index=pinecone_index,
auto_sync="merge",
)
- await asyncio.sleep(PINECONE_SLEEP) # allow for index to be populated
- # confirm local and remote are synced
- assert await route_layer.async_is_synced()
- # now confirm utterances are correct
- local_utterances = await route_layer.index.aget_utterances()
- # we sort to ensure order is the same
- local_utterances.sort(
- key=lambda x: x.to_str(include_metadata=include_metadata(index_cls))
- )
- assert local_utterances == [
- Utterance(
- route="Route 1", utterance="Hello", metadata={"type": "default"}
- ),
- Utterance(
- route="Route 1", utterance="Hi", metadata={"type": "default"}
- ),
- Utterance(route="Route 2", utterance="Au revoir"),
- Utterance(route="Route 2", utterance="Bye"),
- Utterance(route="Route 2", utterance="Goodbye"),
- Utterance(route="Route 2", utterance="Hi"),
- Utterance(route="Route 3", utterance="Boo"),
- ], "The routes in the index should match the local routes"
+ await asyncio.sleep(PINECONE_SLEEP)
+
+ @async_retry(max_retries=RETRY_COUNT, delay=PINECONE_SLEEP)
+ async def check_sync():
+ # confirm local and remote are synced
+ assert await route_layer.async_is_synced()
+ # now confirm utterances are correct
+ local_utterances = await route_layer.index.aget_utterances(
+ include_metadata=True
+ )
+ # we sort to ensure order is the same
+ local_utterances.sort(key=lambda x: x.to_str(include_metadata=True))
+ assert local_utterances == [
+ Utterance(
+ route="Route 1",
+ utterance="Hello",
+ metadata={"type": "default"},
+ ),
+ Utterance(
+ route="Route 1",
+ utterance="Hi",
+ metadata={"type": "default"},
+ ),
+ Utterance(route="Route 2", utterance="Au revoir"),
+ Utterance(route="Route 2", utterance="Bye"),
+ Utterance(route="Route 2", utterance="Goodbye"),
+ Utterance(route="Route 2", utterance="Hi"),
+ Utterance(route="Route 3", utterance="Boo"),
+ ], "The routes in the index should match the local routes"
+
+ await check_sync()
@pytest.mark.skipif(
os.environ.get("PINECONE_API_KEY") is None, reason="Pinecone API key required"
)
@pytest.mark.asyncio
async def test_sync_lock_prevents_concurrent_sync(
- self, openai_encoder, routes, index_cls
+ self, openai_encoder, routes, routes_2, index_cls, router_cls
):
"""Test that sync lock prevents concurrent synchronization operations"""
- index = init_index(index_cls, init_async_index=True)
- route_layer = SemanticRouter(
+ index = init_index(
+ index_cls, init_async_index=True, index_name=router_cls.__name__
+ )
+ route_layer = router_cls(
+ encoder=openai_encoder,
+ routes=routes_2,
+ index=index,
+ auto_sync="local",
+ )
+ # initialize an out of sync router
+ route_layer = router_cls(
encoder=openai_encoder,
routes=routes,
index=index,
@@ -861,16 +1138,27 @@ class TestAsyncSemanticRouter:
os.environ.get("PINECONE_API_KEY") is None, reason="Pinecone API key required"
)
@pytest.mark.asyncio
- async def test_sync_lock_auto_releases(self, openai_encoder, routes, index_cls):
+ async def test_sync_lock_auto_releases(
+ self, openai_encoder, routes, routes_2, index_cls, router_cls
+ ):
"""Test that sync lock is automatically released after sync operations"""
- index = init_index(index_cls, init_async_index=True)
- route_layer = SemanticRouter(
+ index = init_index(
+ index_cls, init_async_index=True, index_name=router_cls.__name__
+ )
+ route_layer = router_cls(
+ encoder=openai_encoder,
+ routes=routes_2,
+ index=index,
+ auto_sync="local",
+ )
+ route_layer = router_cls(
encoder=openai_encoder,
routes=routes,
index=index,
auto_sync=None,
)
-
+ if index_cls is PineconeIndex:
+ await asyncio.sleep(PINECONE_SLEEP)
# Initial sync should acquire and release lock
await route_layer.async_sync("local")
if index_cls is PineconeIndex:
@@ -882,5 +1170,6 @@ class TestAsyncSemanticRouter:
await asyncio.sleep(PINECONE_SLEEP)
assert await route_layer.async_is_synced()
- # clear index
- route_layer.index.index.delete(namespace="", delete_all=True)
+ # clear index if pinecone
+ if index_cls is PineconeIndex:
+ route_layer.index.client.delete_index(route_layer.index.index_name)
diff --git a/tests/unit/test_tokenizers.py b/tests/unit/test_tokenizers.py
new file mode 100644
index 0000000000000000000000000000000000000000..4757ffb82f4fef5ac2b241002cce88f20a13ce3a
--- /dev/null
+++ b/tests/unit/test_tokenizers.py
@@ -0,0 +1,98 @@
+import json
+import tempfile
+
+import numpy as np
+import pytest
+
+from semantic_router.tokenizers import (
+ BaseTokenizer,
+ PretrainedTokenizer,
+)
+
+
+class TestBaseTokenizer:
+ def test_abstract_methods(self):
+ class ConcreteTokenizer(BaseTokenizer):
+ pass
+
+ tokenizer = ConcreteTokenizer()
+ with pytest.raises(NotImplementedError):
+ _ = tokenizer.vocab_size
+ with pytest.raises(NotImplementedError):
+ _ = tokenizer.config
+ with pytest.raises(NotImplementedError):
+ tokenizer.tokenize("test")
+
+ def test_save_load(self):
+ class ConcreteTokenizer(BaseTokenizer):
+ def __init__(self, test_param) -> None:
+ self.test_param = test_param
+ super().__init__()
+
+ @property
+ def vocab_size(self):
+ return 100
+
+ @property
+ def config(self):
+ return {"test_param": self.test_param}
+
+ def tokenize(self, texts, pad=True):
+ pass
+
+ with tempfile.NamedTemporaryFile(suffix=".json") as tmp:
+ tokenizer = ConcreteTokenizer(test_param="value")
+ tokenizer.save(tmp.name)
+
+ loaded = ConcreteTokenizer.load(tmp.name)
+ assert isinstance(loaded, ConcreteTokenizer)
+ with open(tmp.name) as f:
+ saved_config = json.load(f)
+ assert saved_config == {"test_param": "value"}
+
+
+class TestPretrainedTokenizer:
+ @pytest.fixture
+ def tokenizer(self):
+ return PretrainedTokenizer("bert-base-uncased")
+
+ def test_initialization(self, tokenizer):
+ assert tokenizer.model_ident == "bert-base-uncased"
+ assert tokenizer.add_special_tokens is False
+ assert tokenizer.pad is True
+
+ def test_vocab_size(self, tokenizer):
+ assert isinstance(tokenizer.vocab_size, int)
+ assert tokenizer.vocab_size > 0
+
+ def test_config(self, tokenizer):
+ config = tokenizer.config
+ assert isinstance(config, dict)
+ assert "model_ident" in config
+ assert "add_special_tokens" in config
+ assert "pad" in config
+
+ def test_tokenize_single_text(self, tokenizer):
+ text = "Hello world"
+ tokens = tokenizer.tokenize(text)
+ assert isinstance(tokens, np.ndarray)
+ assert tokens.ndim == 2
+ assert tokens.shape[0] == 1 # One sequence
+ assert tokens.shape[1] > 0 # At least one token
+
+ def test_tokenize_multiple_texts(self, tokenizer):
+ texts = ["Hello world", "Testing tokenization"]
+ tokens = tokenizer.tokenize(texts)
+ assert isinstance(tokens, np.ndarray)
+ assert tokens.ndim == 2
+ assert tokens.shape[0] == 2 # Two sequences
+
+ def test_save_load_cycle(self, tokenizer):
+ with tempfile.NamedTemporaryFile(suffix=".json") as tmp:
+ tokenizer.save(tmp.name)
+ loaded = PretrainedTokenizer.load(tmp.name)
+
+ assert isinstance(loaded, PretrainedTokenizer)
+ assert loaded.model_ident == tokenizer.model_ident
+ assert loaded.add_special_tokens == tokenizer.add_special_tokens
+ assert loaded.pad == tokenizer.pad
diff --git a/uv.lock b/uv.lock
new file mode 100644
index 0000000000000000000000000000000000000000..3850a322f0d029ce2dc07ddd0f0e57f709e78105
--- /dev/null
+++ b/uv.lock
@@ -0,0 +1,4116 @@
+version = 1
+requires-python = ">=3.9, <3.14"
+resolution-markers = [
+ "python_full_version < '3.10'",
+ "python_full_version == '3.10.*'",
+ "python_full_version == '3.11.*'",
+ "python_full_version == '3.12.*'",
+ "python_full_version >= '3.13'",
+]
+
+[[package]]
+name = "aiofiles"
+version = "24.1.0"
+source = { registry = "https://pypi.org/simple" }
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+wheels = [
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+]
+
+[[package]]
+name = "aiohappyeyeballs"
+version = "2.4.6"
+source = { registry = "https://pypi.org/simple" }
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+source = { registry = "https://pypi.org/simple" }
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+ { name = "aiosignal" },
+ { name = "async-timeout", marker = "python_full_version < '3.11'" },
+ { name = "attrs" },
+ { name = "frozenlist" },
+ { name = "multidict" },
+ { name = "propcache" },
+ { name = "yarl" },
+]
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