diff --git a/docs/encoders/bedrock.ipynb b/docs/encoders/bedrock.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..54cc0a7dfbb3709ce0e6355e9d0a7508804298ec
--- /dev/null
+++ b/docs/encoders/bedrock.ipynb
@@ -0,0 +1,1323 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "[](https://colab.research.google.com/github/aurelio-labs/semantic-router/blob/main/docs/encoders/bedrock.ipynb) [](https://nbviewer.org/github/aurelio-labs/semantic-router/blob/main/docs/encoders/bedrock.ipynb)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Using Bedrock embedding Models"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The 3rd generation embedding models from AWS Bedrock (`amazon.titan-embed-text-v1`, `amazon.titan-embed-text-v2` and `cohere.embed-english-v3`) can both be used with our `BedrockEncoder`."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Getting Started"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We start by installing semantic-router. Support for the new `Bedrock` embedding models was added in `semantic-router==0.0.40`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "!pip install -qU \"semantic-router[bedrock]==0.0.40\""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We start by defining a dictionary mapping routes to example phrases that should trigger those routes."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from semantic_router import Route\n",
+ "\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\",\n",
+ " \"don't you just hate the president\",\n",
+ " \"they're going to destroy this country!\",\n",
+ " \"they will save the country!\",\n",
+ " ],\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Let's define another for good measure:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "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",
+ "routes = [politics, chitchat]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now we initialize our embedding model, we will use the `-3-large` model alongside a `dimensions` value of `256`. This will produce _tiny_ 256-dimensional vectors that — according to OpenAI — outperform the 1536-dimensional vectors produced by `text-embedding-ada-002`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import os\n",
+ "from getpass import getpass\n",
+ "from semantic_router.encoders import BedrockEncoder\n",
+ "\n",
+ "aws_access_key_id = os.getenv(\"AWS_ACCESS_KEY_ID\") or getpass(\n",
+ " \"Enter AWS Access Key ID: \"\n",
+ ")\n",
+ "aws_secret_access_key = os.getenv(\"AWS_SECRET_ACCESS_KEY\") or getpass(\n",
+ " \"Enter AWS Secret Access Key: \"\n",
+ ")\n",
+ "aws_session_token = os.getenv(\"AWS_SESSION_TOKEN\") or getpass(\n",
+ " \"Enter AWS Session Token: \"\n",
+ ")\n",
+ "aws_region = os.getenv(\"AWS_REGION\") or getpass(\"Enter AWS Region: \")\n",
+ "\n",
+ "encoder = BedrockEncoder(\n",
+ " name=\"amazon.titan-embed-image-v1\",\n",
+ " score_threshold=0.5,\n",
+ " access_key_id=aws_access_key_id,\n",
+ " secret_access_key=aws_secret_access_key,\n",
+ " session_token=aws_session_token,\n",
+ " region=aws_region,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
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+ " 0.036132812,\n",
+ " 0.008178711,\n",
+ " ...]]"
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "encoder([\"hey\"])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now we define the `RouteLayer`. When called, the route layer will consume text (a query) and output the category (`Route`) it belongs to — to initialize a `RouteLayer` we need our `encoder` model and a list of `routes`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\u001b[32m2024-05-13 22:26:54 INFO semantic_router.utils.logger local\u001b[0m\n"
+ ]
+ }
+ ],
+ "source": [
+ "from semantic_router.layer import RouteLayer\n",
+ "\n",
+ "rl = RouteLayer(encoder=encoder, routes=routes)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can check the dimensionality of our vectors by looking at the `index` attribute of the `RouteLayer`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(11, 1024)"
+ ]
+ },
+ "execution_count": 18,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "rl.index.index.shape"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We do have 256-dimensional vectors. Now let's test them:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "RouteChoice(name='politics', function_call=None, similarity_score=None)"
+ ]
+ },
+ "execution_count": 19,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "rl(\"don't you love politics?\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "RouteChoice(name='chitchat', function_call=None, similarity_score=None)"
+ ]
+ },
+ "execution_count": 20,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "rl(\"how's the weather today?\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Both are classified accurately, what if we send a query that is unrelated to our existing `Route` objects?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "RouteChoice(name=None, function_call=None, similarity_score=None)"
+ ]
+ },
+ "execution_count": 22,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "rl(\"How does llama model work?\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In this case, we return `None` because no matches were identified. We always recommend optimizing your `RouteLayer` for optimal performance, you can see how in [this notebook](https://github.com/aurelio-labs/semantic-router/blob/main/docs/06-threshold-optimization.ipynb)."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "---"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "decision-layer",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
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diff --git a/poetry.lock b/poetry.lock
index 53b7b5cd7df3ef496e4c97d39e3beea6cd662bc8..33839cf8dd3449ed66a81666f4658dfe1d03ab38 100644
--- a/poetry.lock
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googleapis-common-protos = ">=1.56.2,<2.0.dev0"
grpcio = [
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socks = ["socksio (==1.*)"]
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[package.extras]
i18n = ["Babel (>=2.7)"]
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- {file = "yarl-1.9.4.tar.gz", hash = "sha256:566db86717cf8080b99b58b083b773a908ae40f06681e87e589a976faf8246bf"},
-]
-
-[package.dependencies]
-idna = ">=2.0"
-multidict = ">=4.0"
-
[[package]]
name = "zipp"
version = "3.18.1"
@@ -4756,6 +4441,7 @@ docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.link
testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy", "pytest-ruff (>=0.2.1)"]
[extras]
+bedrock = ["boto3"]
fastembed = ["fastembed"]
google = ["google-cloud-aiplatform"]
hybrid = ["pinecone-text"]
@@ -4769,4 +4455,4 @@ vision = ["pillow", "torch", "torchvision", "transformers"]
[metadata]
lock-version = "2.0"
python-versions = ">=3.9,<3.13"
-content-hash = "9f308d2dd1c067185f9d84721b25d81e7d1e72a239059863bad1f4439a7a26cc"
+content-hash = "24de8cd2a260ccf1ce5f2edba24a165a36d8712fdc63c14a3be60a796b9533fb"
diff --git a/pyproject.toml b/pyproject.toml
index fba7047b0eb73f7f52a66d2fbfeecfd81536375a..d859f6785a44806e9baa3c00f272cb1200d54710 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
[tool.poetry]
name = "semantic-router"
-version = "0.0.39"
+version = "0.0.40"
description = "Super fast semantic router for AI decision making"
authors = [
"James Briggs <james@aurelio.ai>",
@@ -18,7 +18,7 @@ packages = [{include = "semantic_router"}]
python = ">=3.9,<3.13"
pydantic = "^2.5.3"
openai = "^1.10.0"
-cohere = "^4.32"
+cohere = "^5.00"
mistralai= {version = "^0.0.12", optional = true}
numpy = "^1.25.2"
colorlog = "^6.8.0"
@@ -38,6 +38,7 @@ matplotlib = { version = "^3.8.3", optional = true}
qdrant-client = {version = "^1.8.0", optional = true}
google-cloud-aiplatform = {version = "^1.45.0", optional = true}
requests-mock = "^1.12.1"
+boto3 = { version = "^1.34.98", optional = true }
[tool.poetry.extras]
hybrid = ["pinecone-text"]
@@ -49,6 +50,7 @@ processing = ["matplotlib"]
mistralai = ["mistralai"]
qdrant = ["qdrant-client"]
google = ["google-cloud-aiplatform"]
+bedrock = ["boto3"]
[tool.poetry.group.dev.dependencies]
ipykernel = "^6.25.0"
diff --git a/semantic_router/__init__.py b/semantic_router/__init__.py
index c0828193a8f4565ad1505d25f379d9145b14f505..37380d1a1523f324cf50b077942010562c32068f 100644
--- a/semantic_router/__init__.py
+++ b/semantic_router/__init__.py
@@ -4,4 +4,4 @@ from semantic_router.route import Route
__all__ = ["RouteLayer", "HybridRouteLayer", "Route", "LayerConfig"]
-__version__ = "0.0.39"
+__version__ = "0.0.40"
diff --git a/semantic_router/encoders/__init__.py b/semantic_router/encoders/__init__.py
index 5efc730398a45fc3a9de5f234a6d43a0e37911be..a1026240d37fbfecb1ec8b1445d42fc05f04265f 100644
--- a/semantic_router/encoders/__init__.py
+++ b/semantic_router/encoders/__init__.py
@@ -1,6 +1,7 @@
from typing import List, Optional
from semantic_router.encoders.base import BaseEncoder
+from semantic_router.encoders.bedrock import BedrockEncoder
from semantic_router.encoders.bm25 import BM25Encoder
from semantic_router.encoders.clip import CLIPEncoder
from semantic_router.encoders.cohere import CohereEncoder
@@ -29,6 +30,7 @@ __all__ = [
"VitEncoder",
"CLIPEncoder",
"GoogleEncoder",
+ "BedrockEncoder",
]
@@ -67,6 +69,8 @@ class AutoEncoder:
self.model = CLIPEncoder(name=name)
elif self.type == EncoderType.GOOGLE:
self.model = GoogleEncoder(name=name)
+ elif self.type == EncoderType.BEDROCK:
+ self.model = BedrockEncoder(name=name) # type: ignore
else:
raise ValueError(f"Encoder type '{type}' not supported")
diff --git a/semantic_router/encoders/bedrock.py b/semantic_router/encoders/bedrock.py
new file mode 100644
index 0000000000000000000000000000000000000000..ce04719be7e5fa2117a706938c811803e746b1bc
--- /dev/null
+++ b/semantic_router/encoders/bedrock.py
@@ -0,0 +1,250 @@
+"""
+This module provides the BedrockEncoder class for generating embeddings using Amazon's Bedrock Platform.
+
+The BedrockEncoder class is a subclass of BaseEncoder and utilizes the TextEmbeddingModel from the
+Amazon's Bedrock Platform to generate embeddings for given documents. It requires an AWS Access Key ID
+and AWS Secret Access Key and supports customization of the pre-trained model, score threshold, and region.
+
+Example usage:
+
+ 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"])
+
+Classes:
+ BedrockEncoder: A class for generating embeddings using the Bedrock Platform.
+"""
+
+import json
+from typing import List, Optional, Any
+import os
+import tiktoken
+from semantic_router.encoders import BaseEncoder
+from semantic_router.utils.defaults import EncoderDefault
+
+
+class BedrockEncoder(BaseEncoder):
+ client: Any = None
+ type: str = "bedrock"
+ input_type: Optional[str] = "search_query"
+ name: str
+ access_key_id: Optional[str] = None
+ secret_access_key: Optional[str] = None
+ session_token: Optional[str] = None
+ region: Optional[str] = None
+
+ def __init__(
+ self,
+ name: str = EncoderDefault.BEDROCK.value["embedding_model"],
+ input_type: Optional[str] = "search_query",
+ score_threshold: float = 0.3,
+ access_key_id: Optional[str] = None,
+ secret_access_key: Optional[str] = None,
+ session_token: Optional[str] = None,
+ region: Optional[str] = None,
+ ):
+ """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.
+ """
+
+ super().__init__(name=name, score_threshold=score_threshold)
+ self.access_key_id = self.get_env_variable("access_key_id", access_key_id)
+ self.secret_access_key = self.get_env_variable(
+ "secret_access_key", secret_access_key
+ )
+ self.session_token = self.get_env_variable("AWS_SESSION_TOKEN", session_token)
+ self.region = self.get_env_variable("AWS_REGION", region, default="us-west-1")
+
+ self.input_type = input_type
+
+ try:
+ self.client = self._initialize_client(
+ self.access_key_id,
+ self.secret_access_key,
+ self.session_token,
+ self.region,
+ )
+
+ except Exception as e:
+ raise ValueError(f"Bedrock client failed to initialise. Error: {e}") from e
+
+ def _initialize_client(
+ self, access_key_id, secret_access_key, session_token, region
+ ):
+ """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
+ installed.
+ ValueError: If the Bedrock client fails to initialize.
+ """
+ try:
+ import boto3
+ except ImportError:
+ raise ImportError(
+ "Please install Amazon's Boto3 client library to use the BedrockEncoder. "
+ "You can install them with: "
+ "`pip install boto3`"
+ )
+
+ access_key_id = access_key_id or os.getenv("access_key_id")
+ aws_secret_key = secret_access_key or os.getenv("secret_access_key")
+ region = region or os.getenv("AWS_REGION", "us-west-2")
+
+ if access_key_id is None:
+ raise ValueError("AWS access key ID cannot be 'None'.")
+
+ if aws_secret_key is None:
+ raise ValueError("AWS secret access key cannot be 'None'.")
+
+ try:
+ bedrock_client = boto3.client(
+ "bedrock-runtime",
+ aws_access_key_id=access_key_id,
+ aws_secret_access_key=secret_access_key,
+ aws_session_token=session_token,
+ region_name=region,
+ )
+ except Exception as err:
+ raise ValueError(
+ f"The Bedrock client failed to initialize. Error: {err}"
+ ) from err
+
+ return bedrock_client
+
+ 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
+ document.
+
+ Raises:
+ ValueError: If the Bedrock Platform client is not initialized or if the
+ API call fails.
+ """
+ if self.client is None:
+ raise ValueError("Bedrock client is not initialised.")
+ try:
+ embeddings = []
+
+ def chunk_strings(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 75.
+
+ Returns:
+ list: A list of lists, where each inner list contains a chunk of strings.
+ """
+ encoding = tiktoken.get_encoding("cl100k_base")
+ chunked_strings = []
+ current_chunk = []
+
+ for text in strings:
+ encoded_text = encoding.encode(text)
+
+ if len(encoded_text) > MAX_WORDS:
+ current_chunk = [
+ encoding.decode(encoded_text[i : i + MAX_WORDS])
+ for i in range(0, len(encoded_text), MAX_WORDS)
+ ]
+ else:
+ current_chunk = [encoding.decode(encoded_text)]
+
+ chunked_strings.append(current_chunk)
+ return chunked_strings
+
+ if self.name and "amazon" in self.name:
+ for doc in docs:
+ embedding_body = json.dumps(
+ {
+ "inputText": doc,
+ }
+ )
+ response = self.client.invoke_model(
+ body=embedding_body,
+ modelId=self.name,
+ accept="application/json",
+ contentType="application/json",
+ )
+
+ response_body = json.loads(response.get("body").read())
+ embeddings.append(response_body.get("embedding"))
+ elif self.name and "cohere" in self.name:
+ chunked_docs = chunk_strings(docs)
+ for chunk in chunked_docs:
+ chunk = json.dumps({"texts": chunk, "input_type": self.input_type})
+
+ response = self.client.invoke_model(
+ body=chunk,
+ modelId=self.name,
+ accept="*/*",
+ contentType="application/json",
+ )
+
+ response_body = json.loads(response.get("body").read())
+
+ chunk_embeddings = response_body.get("embeddings")
+ embeddings.extend(chunk_embeddings)
+ else:
+ raise ValueError("Unknown model name")
+ return embeddings
+ except Exception as e:
+ raise ValueError(f"Bedrock call failed. Error: {e}") from e
+
+ @staticmethod
+ 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.
+
+ Raises:
+ ValueError: If no value is provided and the environment variable is not set.
+ """
+ if provided_value is not None:
+ return provided_value
+ value = os.getenv(var_name, default)
+ if value is None:
+ raise ValueError(f"No {var_name} provided")
+ return value
diff --git a/semantic_router/encoders/cohere.py b/semantic_router/encoders/cohere.py
index b49769eb4c1b32c36467d299f4905477b522bb1a..3fcac85c1cb6f9cb9165a4dfa5fcb19be1336afa 100644
--- a/semantic_router/encoders/cohere.py
+++ b/semantic_router/encoders/cohere.py
@@ -2,6 +2,7 @@ import os
from typing import List, Optional
import cohere
+from cohere.types.embed_response import EmbedResponse_EmbeddingsByType
from semantic_router.encoders import BaseEncoder
from semantic_router.utils.defaults import EncoderDefault
@@ -42,8 +43,14 @@ class CohereEncoder(BaseEncoder):
raise ValueError("Cohere client is not initialized.")
try:
embeds = self.client.embed(
- docs, input_type=self.input_type, model=self.name
+ texts=docs, input_type=self.input_type, model=self.name
)
- return embeds.embeddings
+ # Check for unsupported type.
+ if isinstance(embeds, EmbedResponse_EmbeddingsByType):
+ 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
diff --git a/semantic_router/index/pinecone.py b/semantic_router/index/pinecone.py
index 136422d1fa0bc678db2fe1118a0bd4171598f0c6..de2885243b8e8b17a7eeeee1f23e84a34e9cb964 100644
--- a/semantic_router/index/pinecone.py
+++ b/semantic_router/index/pinecone.py
@@ -38,6 +38,7 @@ class PineconeRecord(BaseModel):
class PineconeIndex(BaseIndex):
index_prefix: str = "semantic-router--"
+ api_key: Optional[str] = None
index_name: str = "index"
dimensions: Union[int, None] = None
metric: str = "cosine"
@@ -69,7 +70,12 @@ class PineconeIndex(BaseIndex):
self.host = host
self.namespace = namespace
self.type = "pinecone"
- self.client = self._initialize_client(api_key=api_key)
+ self.api_key = api_key or os.getenv("PINECONE_API_KEY")
+
+ if self.api_key is None:
+ raise ValueError("Pinecone API key is required.")
+
+ self.client = self._initialize_client(api_key=self.api_key)
def _initialize_client(self, api_key: Optional[str] = None):
try:
@@ -82,9 +88,6 @@ class PineconeIndex(BaseIndex):
"You can install it with: "
"`pip install 'semantic-router[pinecone]'`"
)
- api_key = api_key or os.getenv("PINECONE_API_KEY")
- if api_key is None:
- raise ValueError("Pinecone API key is required.")
pinecone_args = {"api_key": api_key, "source_tag": "semantic-router"}
if self.namespace:
pinecone_args["namespace"] = self.namespace
@@ -190,7 +193,7 @@ class PineconeIndex(BaseIndex):
params: Dict = {}
if self.namespace:
params["namespace"] = self.namespace
- headers = {"Api-Key": os.environ["PINECONE_API_KEY"]}
+ headers = {"Api-Key": self.api_key}
metadata = []
while True:
diff --git a/semantic_router/schema.py b/semantic_router/schema.py
index 63f1e440a1a816a79a3b37b974d191eac48a24af..86ab123318812dbc3c18a8a6f98a7e4bb186669c 100644
--- a/semantic_router/schema.py
+++ b/semantic_router/schema.py
@@ -15,6 +15,7 @@ class EncoderType(Enum):
VIT = "vit"
CLIP = "clip"
GOOGLE = "google"
+ BEDROCK = "bedrock"
class EncoderInfo(BaseModel):
diff --git a/semantic_router/utils/defaults.py b/semantic_router/utils/defaults.py
index 3c9cbb2dd1010f5b861c49fcafad389c591fe9cb..75331c06581ad4692bc24f1633ba5a609ba28e47 100644
--- a/semantic_router/utils/defaults.py
+++ b/semantic_router/utils/defaults.py
@@ -31,3 +31,8 @@ class EncoderDefault(Enum):
"GOOGLE_EMBEDDING_MODEL", "textembedding-gecko@003"
),
}
+ BEDROCK = {
+ "embedding_model": os.environ.get(
+ "BEDROCK_EMBEDDING_MODEL", "amazon.titan-embed-image-v1"
+ )
+ }
diff --git a/tests/unit/encoders/test_bedrock.py b/tests/unit/encoders/test_bedrock.py
new file mode 100644
index 0000000000000000000000000000000000000000..43955d453c704bc95fcce598b94e0a46479947a0
--- /dev/null
+++ b/tests/unit/encoders/test_bedrock.py
@@ -0,0 +1,116 @@
+import pytest
+import json
+from io import BytesIO
+from semantic_router.encoders import BedrockEncoder
+
+
+@pytest.fixture
+def bedrock_encoder(mocker):
+ mocker.patch("semantic_router.encoders.bedrock.BedrockEncoder._initialize_client")
+ return BedrockEncoder(
+ access_key_id="fake_id",
+ secret_access_key="fake_secret",
+ session_token="fake_token",
+ region="us-west-2",
+ )
+
+
+class TestBedrockEncoder:
+ def test_initialisation_with_default_values(self, bedrock_encoder):
+ assert (
+ bedrock_encoder.input_type == "search_query"
+ ), "Default input type not set correctly"
+ assert bedrock_encoder.region == "us-west-2", "Region should be initialised"
+
+ def test_initialisation_with_custom_values(self, mocker):
+ # mocker.patch(
+ # "semantic_router.encoders.bedrock.BedrockEncoder._initialize_client"
+ # )
+ name = "custom_model"
+ score_threshold = 0.5
+ input_type = "custom_input"
+ bedrock_encoder = BedrockEncoder(
+ name=name,
+ score_threshold=score_threshold,
+ input_type=input_type,
+ access_key_id="fake_id",
+ secret_access_key="fake_secret",
+ session_token="fake_token",
+ region="us-west-2",
+ )
+ assert bedrock_encoder.name == name, "Custom name not set correctly"
+ assert bedrock_encoder.region == "us-west-2", "Custom region not set correctly"
+ assert (
+ bedrock_encoder.score_threshold == score_threshold
+ ), "Custom score threshold not set correctly"
+ assert (
+ bedrock_encoder.input_type == input_type
+ ), "Custom input type not set correctly"
+
+ def test_call_method(self, bedrock_encoder):
+ response_content = json.dumps({"embedding": [0.1, 0.2, 0.3]})
+ response_body = BytesIO(response_content.encode("utf-8"))
+ mock_response = {"body": response_body}
+ bedrock_encoder.client.invoke_model.return_value = mock_response
+ result = bedrock_encoder(["test"])
+ assert isinstance(result, list), "Result should be a list"
+ assert all(
+ isinstance(item, list) for item in result
+ ), "Each item in result should be a list"
+ assert result == [[0.1, 0.2, 0.3]], "Embedding should be [0.1, 0.2, 0.3]"
+
+ def test_raises_value_error_if_client_is_not_initialised(self, mocker):
+ mocker.patch(
+ "semantic_router.encoders.bedrock.BedrockEncoder._initialize_client",
+ side_effect=Exception("Client initialisation failed"),
+ )
+ with pytest.raises(ValueError):
+ BedrockEncoder(
+ access_key_id="fake_id",
+ secret_access_key="fake_secret",
+ session_token="fake_token",
+ region="us-west-2",
+ )
+
+ def test_raises_value_error_if_call_to_bedrock_fails(self, bedrock_encoder):
+ bedrock_encoder.client.invoke_model.side_effect = Exception(
+ "Bedrock call failed."
+ )
+ with pytest.raises(ValueError):
+ bedrock_encoder(["test"])
+
+
+@pytest.fixture
+def bedrock_encoder_with_cohere(mocker):
+ mocker.patch("semantic_router.encoders.bedrock.BedrockEncoder._initialize_client")
+ return BedrockEncoder(
+ name="cohere_model",
+ access_key_id="fake_id",
+ secret_access_key="fake_secret",
+ session_token="fake_token",
+ region="us-west-2",
+ )
+
+
+class TestBedrockEncoderWithCohere:
+ def test_cohere_embedding_single_chunk(self, bedrock_encoder_with_cohere):
+ response_content = json.dumps({"embeddings": [[0.1, 0.2, 0.3]]})
+ response_body = BytesIO(response_content.encode("utf-8"))
+ mock_response = {"body": response_body}
+ bedrock_encoder_with_cohere.client.invoke_model.return_value = mock_response
+ result = bedrock_encoder_with_cohere(["short test"])
+ assert isinstance(result, list), "Result should be a list"
+ assert all(
+ isinstance(item, list) for item in result
+ ), "Each item should be a list"
+ assert result == [[0.1, 0.2, 0.3]], "Expected embedding [0.1, 0.2, 0.3]"
+
+ def test_cohere_input_type(self, bedrock_encoder_with_cohere):
+ bedrock_encoder_with_cohere.input_type = "different_type"
+ response_content = json.dumps({"embeddings": [[0.1, 0.2, 0.3]]})
+ response_body = BytesIO(response_content.encode("utf-8"))
+ mock_response = {"body": response_body}
+ bedrock_encoder_with_cohere.client.invoke_model.return_value = mock_response
+ result = bedrock_encoder_with_cohere(["test with different input type"])
+ assert isinstance(result, list), "Result should be a list"
+ assert result == [[0.1, 0.2, 0.3]], "Expected specific embeddings"