From 8c79849dc08647f4c5dd53f63a1670650887d58a Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=E2=80=9CDaniel=20Griffiths=E2=80=9D?=
<Danielgriffiths1790@gmail.com>
Date: Thu, 21 Dec 2023 10:25:12 +0000
Subject: [PATCH] schemas in seperate files due to circular import
---
README.md | 7 +-
docs/examples/function_calling.ipynb | 1250 ++++++++---------
docs/examples/hybrid-layer.ipynb | 398 +++---
semantic_router/encoders/tfidf.py | 2 +-
semantic_router/hybrid_layer.py | 2 +-
semantic_router/layer.py | 2 +-
.../{schema.py => schemas/encoder.py} | 21 -
semantic_router/schemas/route.py | 7 +
semantic_router/schemas/semantic_space.py | 17 +
tests/unit/encoders/test_tfidf.py | 2 +-
tests/unit/test_hybrid_layer.py | 2 +-
tests/unit/test_layer.py | 2 +-
tests/unit/test_schema.py | 8 +-
walkthrough.ipynb | 439 +++---
14 files changed, 1096 insertions(+), 1063 deletions(-)
rename semantic_router/{schema.py => schemas/encoder.py} (67%)
create mode 100644 semantic_router/schemas/route.py
create mode 100644 semantic_router/schemas/semantic_space.py
diff --git a/README.md b/README.md
index 2db0e9b7..4ab443ce 100644
--- a/README.md
+++ b/README.md
@@ -1,6 +1,7 @@
[](https://aurelio.ai)
# Semantic Router
+
<p>
<img alt="GitHub Contributors" src="https://img.shields.io/github/contributors/aurelio-labs/semantic-router" />
<img alt="GitHub Last Commit" src="https://img.shields.io/github/last-commit/aurelio-labs/semantic-router" />
@@ -24,7 +25,7 @@ pip install -qU semantic-router
We begin by defining a set of `Decision` objects. These are the decision paths that the semantic router can decide to use, let's try two simple decisions for now — one for talk on _politics_ and another for _chitchat_:
```python
-from semantic_router.schema import Route
+from semantic_router.schemas.route import Route
# we could use this as a guide for our chatbot to avoid political conversations
politics = Route(
@@ -112,6 +113,6 @@ In this case, no decision could be made as we had no matches — so our decision
## 📚 Resources
-| | |
-| --------------------------------------------------------------------------------------------------------------- | -------------------------- |
+| | |
+| ------------------------------------------------------------------------------------------------------------------ | -------------------------- |
| ðŸƒ[Walkthrough](https://colab.research.google.com/github/aurelio-labs/semantic-router/blob/main/walkthrough.ipynb) | Quickstart Python notebook |
diff --git a/docs/examples/function_calling.ipynb b/docs/examples/function_calling.ipynb
index 5d3be2fb..f487f77d 100644
--- a/docs/examples/function_calling.ipynb
+++ b/docs/examples/function_calling.ipynb
@@ -1,647 +1,647 @@
{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Define LLMs"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "metadata": {},
- "outputs": [],
- "source": [
- "# OpenAI\n",
- "import openai\n",
- "from semantic_router.utils.logger import logger\n",
- "\n",
- "\n",
- "# Docs # https://platform.openai.com/docs/guides/function-calling\n",
- "def llm_openai(prompt: str, model: str = \"gpt-4\") -> str:\n",
- " try:\n",
- " logger.info(f\"Calling {model} model\")\n",
- " response = openai.chat.completions.create(\n",
- " model=model,\n",
- " messages=[\n",
- " {\"role\": \"system\", \"content\": f\"{prompt}\"},\n",
- " ],\n",
- " )\n",
- " ai_message = response.choices[0].message.content\n",
- " if not ai_message:\n",
- " raise Exception(\"AI message is empty\", ai_message)\n",
- " logger.info(f\"AI message: {ai_message}\")\n",
- " return ai_message\n",
- " except Exception as e:\n",
- " raise Exception(\"Failed to call OpenAI API\", e)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 13,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Mistral\n",
- "import os\n",
- "import requests\n",
- "\n",
- "# Docs https://huggingface.co/docs/transformers/main_classes/text_generation\n",
- "HF_API_TOKEN = os.getenv(\"HF_API_TOKEN\")\n",
- "\n",
- "\n",
- "def llm_mistral(prompt: str) -> str:\n",
- " api_url = \"https://z5t4cuhg21uxfmc3.us-east-1.aws.endpoints.huggingface.cloud/\"\n",
- " headers = {\n",
- " \"Authorization\": f\"Bearer {HF_API_TOKEN}\",\n",
- " \"Content-Type\": \"application/json\",\n",
- " }\n",
- "\n",
- " logger.info(\"Calling Mistral model\")\n",
- " response = requests.post(\n",
- " api_url,\n",
- " headers=headers,\n",
- " json={\n",
- " \"inputs\": f\"You are a helpful assistant, user query: {prompt}\",\n",
- " \"parameters\": {\n",
- " \"max_new_tokens\": 200,\n",
- " \"temperature\": 0.01,\n",
- " \"num_beams\": 5,\n",
- " \"num_return_sequences\": 1,\n",
- " },\n",
- " },\n",
- " )\n",
- " if response.status_code != 200:\n",
- " raise Exception(\"Failed to call HuggingFace API\", response.text)\n",
- "\n",
- " ai_message = response.json()[0][\"generated_text\"]\n",
- " if not ai_message:\n",
- " raise Exception(\"AI message is empty\", ai_message)\n",
- " logger.info(f\"AI message: {ai_message}\")\n",
- " return ai_message"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Now we need to generate config from function schema using LLM"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 14,
- "metadata": {},
- "outputs": [],
- "source": [
- "import inspect\n",
- "from typing import Any\n",
- "\n",
- "\n",
- "def get_function_schema(function) -> dict[str, Any]:\n",
- " schema = {\n",
- " \"name\": function.__name__,\n",
- " \"description\": str(inspect.getdoc(function)),\n",
- " \"signature\": str(inspect.signature(function)),\n",
- " \"output\": str(\n",
- " inspect.signature(function).return_annotation,\n",
- " ),\n",
- " }\n",
- " return schema"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 15,
- "metadata": {},
- "outputs": [],
- "source": [
- "import json\n",
- "\n",
- "\n",
- "def is_valid_config(route_config_str: str) -> bool:\n",
- " try:\n",
- " output_json = json.loads(route_config_str)\n",
- " return all(key in output_json for key in [\"name\", \"utterances\"])\n",
- " except json.JSONDecodeError:\n",
- " return False"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 16,
- "metadata": {},
- "outputs": [],
- "source": [
- "import json\n",
- "\n",
- "from semantic_router.utils.logger import logger\n",
- "\n",
- "\n",
- "def generate_route(function) -> dict:\n",
- " logger.info(\"Generating config...\")\n",
- "\n",
- " function_schema = get_function_schema(function)\n",
- "\n",
- " prompt = f\"\"\"\n",
- " You are tasked to generate a JSON configuration based on the provided\n",
- " function schema. Please follow the template below:\n",
- "\n",
- " {{\n",
- " \"name\": \"<function_name>\",\n",
- " \"utterances\": [\n",
- " \"<example_utterance_1>\",\n",
- " \"<example_utterance_2>\",\n",
- " \"<example_utterance_3>\",\n",
- " \"<example_utterance_4>\",\n",
- " \"<example_utterance_5>\"]\n",
- " }}\n",
- "\n",
- " Only include the \"name\" and \"utterances\" keys in your answer.\n",
- " The \"name\" should match the function name and the \"utterances\"\n",
- " should comprise a list of 5 example phrases that could be used to invoke\n",
- " the function.\n",
- "\n",
- " Input schema:\n",
- " {function_schema}\n",
- " \"\"\"\n",
- "\n",
- " try:\n",
- " ai_message = llm_mistral(prompt)\n",
- "\n",
- " # Parse the response\n",
- " ai_message = ai_message[ai_message.find(\"{\") :]\n",
- " ai_message = (\n",
- " ai_message.replace(\"'\", '\"')\n",
- " .replace('\"s', \"'s\")\n",
- " .strip()\n",
- " .rstrip(\",\")\n",
- " .replace(\"}\", \"}\")\n",
- " )\n",
- "\n",
- " valid_config = is_valid_config(ai_message)\n",
- "\n",
- " if not valid_config:\n",
- " logger.warning(f\"Mistral failed with error, falling back to OpenAI\")\n",
- " ai_message = llm_openai(prompt)\n",
- " if not is_valid_config(ai_message):\n",
- " raise Exception(\"Invalid config generated\")\n",
- " except Exception as e:\n",
- " logger.error(f\"Fall back to OpenAI failed with error {e}\")\n",
- " ai_message = llm_openai(prompt)\n",
- " if not is_valid_config(ai_message):\n",
- " raise Exception(\"Failed to generate config\")\n",
- "\n",
- " try:\n",
- " route_config = json.loads(ai_message)\n",
- " logger.info(f\"Generated config: {route_config}\")\n",
- " return route_config\n",
- " except json.JSONDecodeError as json_error:\n",
- " logger.error(f\"JSON parsing error {json_error}\")\n",
- " print(f\"AI message: {ai_message}\")\n",
- " return {\"error\": \"Failed to generate config\"}"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Extract function parameters using `Mistral` open-source model"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 17,
- "metadata": {},
- "outputs": [],
- "source": [
- "def validate_parameters(function, parameters):\n",
- " sig = inspect.signature(function)\n",
- " for name, param in sig.parameters.items():\n",
- " if name not in parameters:\n",
- " return False, f\"Parameter {name} missing from query\"\n",
- " if not isinstance(parameters[name], param.annotation):\n",
- " return False, f\"Parameter {name} is not of type {param.annotation}\"\n",
- " return True, \"Parameters are valid\""
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 18,
- "metadata": {},
- "outputs": [],
- "source": [
- "def extract_parameters(query: str, function) -> dict:\n",
- " logger.info(\"Extracting parameters...\")\n",
- " example_query = \"How is the weather in Hawaii right now in International units?\"\n",
- "\n",
- " example_schema = {\n",
- " \"name\": \"get_weather\",\n",
- " \"description\": \"Useful to get the weather in a specific location\",\n",
- " \"signature\": \"(location: str, degree: str) -> str\",\n",
- " \"output\": \"<class 'str'>\",\n",
- " }\n",
- "\n",
- " example_parameters = {\n",
- " \"location\": \"London\",\n",
- " \"degree\": \"Celsius\",\n",
- " }\n",
- "\n",
- " prompt = f\"\"\"\n",
- " You are a helpful assistant designed to output JSON.\n",
- " Given the following function schema\n",
- " << {get_function_schema(function)} >>\n",
- " and query\n",
- " << {query} >>\n",
- " extract the parameters values from the query, in a valid JSON format.\n",
- " Example:\n",
- " Input:\n",
- " query: {example_query}\n",
- " schema: {example_schema}\n",
- "\n",
- " Result: {example_parameters}\n",
- "\n",
- " Input:\n",
- " query: {query}\n",
- " schema: {get_function_schema(function)}\n",
- " Result:\n",
- " \"\"\"\n",
- "\n",
- " try:\n",
- " ai_message = llm_mistral(prompt)\n",
- " ai_message = (\n",
- " ai_message.replace(\"Output:\", \"\").replace(\"'\", '\"').strip().rstrip(\",\")\n",
- " )\n",
- " except Exception as e:\n",
- " logger.error(f\"Mistral failed with error {e}, falling back to OpenAI\")\n",
- " ai_message = llm_openai(prompt)\n",
- "\n",
- " try:\n",
- " parameters = json.loads(ai_message)\n",
- " valid, message = validate_parameters(function, parameters)\n",
- "\n",
- " if not valid:\n",
- " logger.warning(\n",
- " f\"Invalid parameters from Mistral, falling back to OpenAI: {message}\"\n",
- " )\n",
- " # Fall back to OpenAI\n",
- " ai_message = llm_openai(prompt)\n",
- " parameters = json.loads(ai_message)\n",
- " valid, message = validate_parameters(function, parameters)\n",
- " if not valid:\n",
- " raise ValueError(message)\n",
- "\n",
- " logger.info(f\"Extracted parameters: {parameters}\")\n",
- " return parameters\n",
- " except ValueError as e:\n",
- " logger.error(f\"Parameter validation error: {str(e)}\")\n",
- " return {\"error\": \"Failed to validate parameters\"}"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Set up the routing layer"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 19,
- "metadata": {},
- "outputs": [],
- "source": [
- "from semantic_router.schema import Route\n",
- "from semantic_router.encoders import CohereEncoder\n",
- "from semantic_router.layer import RouteLayer\n",
- "from semantic_router.utils.logger import logger\n",
- "\n",
- "\n",
- "def create_router(routes: list[dict]) -> RouteLayer:\n",
- " logger.info(\"Creating route layer...\")\n",
- " encoder = CohereEncoder()\n",
- "\n",
- " route_list: list[Route] = []\n",
- " for route in routes:\n",
- " if \"name\" in route and \"utterances\" in route:\n",
- " print(f\"Route: {route}\")\n",
- " route_list.append(Route(name=route[\"name\"], utterances=route[\"utterances\"]))\n",
- " else:\n",
- " logger.warning(f\"Misconfigured route: {route}\")\n",
- "\n",
- " return RouteLayer(encoder=encoder, routes=route_list)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Set up calling functions"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 24,
- "metadata": {},
- "outputs": [],
- "source": [
- "from typing import Callable\n",
- "from semantic_router.layer import RouteLayer\n",
- "\n",
- "\n",
- "def call_function(function: Callable, parameters: dict[str, str]):\n",
- " try:\n",
- " return function(**parameters)\n",
- " except TypeError as e:\n",
- " logger.error(f\"Error calling function: {e}\")\n",
- "\n",
- "\n",
- "def call_llm(query: str) -> str:\n",
- " try:\n",
- " ai_message = llm_mistral(query)\n",
- " except Exception as e:\n",
- " logger.error(f\"Mistral failed with error {e}, falling back to OpenAI\")\n",
- " ai_message = llm_openai(query)\n",
- "\n",
- " return ai_message\n",
- "\n",
- "\n",
- "def call(query: str, functions: list[Callable], router: RouteLayer):\n",
- " function_name = router(query)\n",
- " if not function_name:\n",
- " logger.warning(\"No function found\")\n",
- " return call_llm(query)\n",
- "\n",
- " for function in functions:\n",
- " if function.__name__ == function_name:\n",
- " parameters = extract_parameters(query, function)\n",
- " print(f\"parameters: {parameters}\")\n",
- " return call_function(function, parameters)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Workflow"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 21,
- "metadata": {},
- "outputs": [
+ "cells": [
{
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "\u001b[32m2023-12-18 12:17:58 INFO semantic_router.utils.logger Generating config...\u001b[0m\n",
- "\u001b[32m2023-12-18 12:17:58 INFO semantic_router.utils.logger Calling Mistral model\u001b[0m\n",
- "\u001b[31m2023-12-18 12:18:00 ERROR semantic_router.utils.logger Fall back to OpenAI failed with error ('Failed to call HuggingFace API', '{\"error\":\"Bad Gateway\"}')\u001b[0m\n",
- "\u001b[32m2023-12-18 12:18:00 INFO semantic_router.utils.logger Calling gpt-4 model\u001b[0m\n",
- "\u001b[32m2023-12-18 12:18:05 INFO semantic_router.utils.logger AI message: {\n",
- " \"name\": \"get_time\",\n",
- " \"utterances\": [\n",
- " \"what is the time in new york\",\n",
- " \"can you tell me the time in london\",\n",
- " \"get me the current time in tokyo\",\n",
- " \"i need to know the time in sydney\",\n",
- " \"please tell me the current time in paris\"\n",
- " ]\n",
- "}\u001b[0m\n",
- "\u001b[32m2023-12-18 12:18:05 INFO semantic_router.utils.logger Generated config: {'name': 'get_time', 'utterances': ['what is the time in new york', 'can you tell me the time in london', 'get me the current time in tokyo', 'i need to know the time in sydney', 'please tell me the current time in paris']}\u001b[0m\n",
- "\u001b[32m2023-12-18 12:18:05 INFO semantic_router.utils.logger Generating config...\u001b[0m\n",
- "\u001b[32m2023-12-18 12:18:05 INFO semantic_router.utils.logger Calling Mistral model\u001b[0m\n",
- "\u001b[31m2023-12-18 12:18:07 ERROR semantic_router.utils.logger Fall back to OpenAI failed with error ('Failed to call HuggingFace API', '{\"error\":\"Bad Gateway\"}')\u001b[0m\n",
- "\u001b[32m2023-12-18 12:18:07 INFO semantic_router.utils.logger Calling gpt-4 model\u001b[0m\n",
- "\u001b[32m2023-12-18 12:18:12 INFO semantic_router.utils.logger AI message: {\n",
- " \"name\": \"get_news\",\n",
- " \"utterances\": [\n",
- " \"Can I get the latest news in Canada?\",\n",
- " \"Show me the recent news in the US\",\n",
- " \"I would like to know about the sports news in England\",\n",
- " \"Let's check the technology news in Japan\",\n",
- " \"Show me the health related news in Germany\"\n",
- " ]\n",
- "}\u001b[0m\n",
- "\u001b[32m2023-12-18 12:18:12 INFO semantic_router.utils.logger Generated config: {'name': 'get_news', 'utterances': ['Can I get the latest news in Canada?', 'Show me the recent news in the US', 'I would like to know about the sports news in England', \"Let's check the technology news in Japan\", 'Show me the health related news in Germany']}\u001b[0m\n",
- "\u001b[32m2023-12-18 12:18:12 INFO semantic_router.utils.logger Creating route layer...\u001b[0m\n"
- ]
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Define LLMs"
+ ]
},
{
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Route: {'name': 'get_time', 'utterances': ['what is the time in new york', 'can you tell me the time in london', 'get me the current time in tokyo', 'i need to know the time in sydney', 'please tell me the current time in paris']}\n",
- "Route: {'name': 'get_news', 'utterances': ['Can I get the latest news in Canada?', 'Show me the recent news in the US', 'I would like to know about the sports news in England', \"Let's check the technology news in Japan\", 'Show me the health related news in Germany']}\n"
- ]
- }
- ],
- "source": [
- "def get_time(location: str) -> str:\n",
- " \"\"\"Useful to get the time in a specific location\"\"\"\n",
- " print(f\"Calling `get_time` function with location: {location}\")\n",
- " return \"get_time\"\n",
- "\n",
- "\n",
- "def get_news(category: str, country: str) -> str:\n",
- " \"\"\"Useful to get the news in a specific country\"\"\"\n",
- " print(\n",
- " f\"Calling `get_news` function with category: {category} and country: {country}\"\n",
- " )\n",
- " return \"get_news\"\n",
- "\n",
- "\n",
- "# Registering functions to the router\n",
- "route_get_time = generate_route(get_time)\n",
- "route_get_news = generate_route(get_news)\n",
- "\n",
- "routes = [route_get_time, route_get_news]\n",
- "router = create_router(routes)\n",
- "\n",
- "# Tools\n",
- "tools = [get_time, get_news]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 27,
- "metadata": {},
- "outputs": [
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# OpenAI\n",
+ "import openai\n",
+ "from semantic_router.utils.logger import logger\n",
+ "\n",
+ "\n",
+ "# Docs # https://platform.openai.com/docs/guides/function-calling\n",
+ "def llm_openai(prompt: str, model: str = \"gpt-4\") -> str:\n",
+ " try:\n",
+ " logger.info(f\"Calling {model} model\")\n",
+ " response = openai.chat.completions.create(\n",
+ " model=model,\n",
+ " messages=[\n",
+ " {\"role\": \"system\", \"content\": f\"{prompt}\"},\n",
+ " ],\n",
+ " )\n",
+ " ai_message = response.choices[0].message.content\n",
+ " if not ai_message:\n",
+ " raise Exception(\"AI message is empty\", ai_message)\n",
+ " logger.info(f\"AI message: {ai_message}\")\n",
+ " return ai_message\n",
+ " except Exception as e:\n",
+ " raise Exception(\"Failed to call OpenAI API\", e)"
+ ]
+ },
{
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "\u001b[32m2023-12-18 12:20:12 INFO semantic_router.utils.logger Generating config...\u001b[0m\n",
- "\u001b[32m2023-12-18 12:20:12 INFO semantic_router.utils.logger Calling Mistral model\u001b[0m\n",
- "\u001b[32m2023-12-18 12:20:16 INFO semantic_router.utils.logger AI message: \n",
- " Example output:\n",
- " {\n",
- " \"name\": \"get_time\",\n",
- " \"utterances\": [\n",
- " \"What's the time in New York?\",\n",
- " \"Tell me the time in Tokyo.\",\n",
- " \"Can you give me the time in London?\",\n",
- " \"What's the current time in Sydney?\",\n",
- " \"Can you tell me the time in Berlin?\"\n",
- " ]\n",
- " }\u001b[0m\n",
- "\u001b[32m2023-12-18 12:20:16 INFO semantic_router.utils.logger Generated config: {'name': 'get_time', 'utterances': [\"What's the time in New York?\", 'Tell me the time in Tokyo.', 'Can you give me the time in London?', \"What's the current time in Sydney?\", 'Can you tell me the time in Berlin?']}\u001b[0m\n",
- "\u001b[32m2023-12-18 12:20:16 INFO semantic_router.utils.logger Generating config...\u001b[0m\n",
- "\u001b[32m2023-12-18 12:20:16 INFO semantic_router.utils.logger Calling Mistral model\u001b[0m\n",
- "\u001b[32m2023-12-18 12:20:20 INFO semantic_router.utils.logger AI message: \n",
- " Example output:\n",
- " {\n",
- " \"name\": \"get_news\",\n",
- " \"utterances\": [\n",
- " \"Tell me the latest news from the US\",\n",
- " \"What's happening in India today?\",\n",
- " \"Get me the top stories from Japan\",\n",
- " \"Can you give me the breaking news from Brazil?\",\n",
- " \"What's the latest news from Germany?\"\n",
- " ]\n",
- " }\u001b[0m\n",
- "\u001b[32m2023-12-18 12:20:20 INFO semantic_router.utils.logger Generated config: {'name': 'get_news', 'utterances': ['Tell me the latest news from the US', \"What's happening in India today?\", 'Get me the top stories from Japan', 'Can you give me the breaking news from Brazil?', \"What's the latest news from Germany?\"]}\u001b[0m\n",
- "\u001b[32m2023-12-18 12:20:20 INFO semantic_router.utils.logger Creating route layer...\u001b[0m\n"
- ]
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Mistral\n",
+ "import os\n",
+ "import requests\n",
+ "\n",
+ "# Docs https://huggingface.co/docs/transformers/main_classes/text_generation\n",
+ "HF_API_TOKEN = os.getenv(\"HF_API_TOKEN\")\n",
+ "\n",
+ "\n",
+ "def llm_mistral(prompt: str) -> str:\n",
+ " api_url = \"https://z5t4cuhg21uxfmc3.us-east-1.aws.endpoints.huggingface.cloud/\"\n",
+ " headers = {\n",
+ " \"Authorization\": f\"Bearer {HF_API_TOKEN}\",\n",
+ " \"Content-Type\": \"application/json\",\n",
+ " }\n",
+ "\n",
+ " logger.info(\"Calling Mistral model\")\n",
+ " response = requests.post(\n",
+ " api_url,\n",
+ " headers=headers,\n",
+ " json={\n",
+ " \"inputs\": f\"You are a helpful assistant, user query: {prompt}\",\n",
+ " \"parameters\": {\n",
+ " \"max_new_tokens\": 200,\n",
+ " \"temperature\": 0.01,\n",
+ " \"num_beams\": 5,\n",
+ " \"num_return_sequences\": 1,\n",
+ " },\n",
+ " },\n",
+ " )\n",
+ " if response.status_code != 200:\n",
+ " raise Exception(\"Failed to call HuggingFace API\", response.text)\n",
+ "\n",
+ " ai_message = response.json()[0][\"generated_text\"]\n",
+ " if not ai_message:\n",
+ " raise Exception(\"AI message is empty\", ai_message)\n",
+ " logger.info(f\"AI message: {ai_message}\")\n",
+ " return ai_message"
+ ]
},
{
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Route: {'name': 'get_time', 'utterances': [\"What's the time in New York?\", 'Tell me the time in Tokyo.', 'Can you give me the time in London?', \"What's the current time in Sydney?\", 'Can you tell me the time in Berlin?']}\n",
- "Route: {'name': 'get_news', 'utterances': ['Tell me the latest news from the US', \"What's happening in India today?\", 'Get me the top stories from Japan', 'Can you give me the breaking news from Brazil?', \"What's the latest news from Germany?\"]}\n"
- ]
- }
- ],
- "source": [
- "def get_time(location: str) -> str:\n",
- " \"\"\"Useful to get the time in a specific location\"\"\"\n",
- " print(f\"Calling `get_time` function with location: {location}\")\n",
- " return \"get_time\"\n",
- "\n",
- "\n",
- "def get_news(category: str, country: str) -> str:\n",
- " \"\"\"Useful to get the news in a specific country\"\"\"\n",
- " print(\n",
- " f\"Calling `get_news` function with category: {category} and country: {country}\"\n",
- " )\n",
- " return \"get_news\"\n",
- "\n",
- "\n",
- "# Registering functions to the router\n",
- "route_get_time = generate_route(get_time)\n",
- "route_get_news = generate_route(get_news)\n",
- "\n",
- "routes = [route_get_time, route_get_news]\n",
- "router = create_router(routes)\n",
- "\n",
- "# Tools\n",
- "tools = [get_time, get_news]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 26,
- "metadata": {},
- "outputs": [
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Now we need to generate config from function schema using LLM"
+ ]
+ },
{
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "\u001b[32m2023-12-18 12:20:02 INFO semantic_router.utils.logger Extracting parameters...\u001b[0m\n",
- "\u001b[32m2023-12-18 12:20:02 INFO semantic_router.utils.logger Calling Mistral model\u001b[0m\n",
- "\u001b[32m2023-12-18 12:20:04 INFO semantic_router.utils.logger AI message: \n",
- " {\n",
- " \"location\": \"Stockholm\"\n",
- " }\u001b[0m\n",
- "\u001b[32m2023-12-18 12:20:04 INFO semantic_router.utils.logger Extracted parameters: {'location': 'Stockholm'}\u001b[0m\n"
- ]
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import inspect\n",
+ "from typing import Any\n",
+ "\n",
+ "\n",
+ "def get_function_schema(function) -> dict[str, Any]:\n",
+ " schema = {\n",
+ " \"name\": function.__name__,\n",
+ " \"description\": str(inspect.getdoc(function)),\n",
+ " \"signature\": str(inspect.signature(function)),\n",
+ " \"output\": str(\n",
+ " inspect.signature(function).return_annotation,\n",
+ " ),\n",
+ " }\n",
+ " return schema"
+ ]
},
{
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "parameters: {'location': 'Stockholm'}\n",
- "Calling `get_time` function with location: Stockholm\n"
- ]
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import json\n",
+ "\n",
+ "\n",
+ "def is_valid_config(route_config_str: str) -> bool:\n",
+ " try:\n",
+ " output_json = json.loads(route_config_str)\n",
+ " return all(key in output_json for key in [\"name\", \"utterances\"])\n",
+ " except json.JSONDecodeError:\n",
+ " return False"
+ ]
},
{
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "\u001b[32m2023-12-18 12:20:04 INFO semantic_router.utils.logger Extracting parameters...\u001b[0m\n",
- "\u001b[32m2023-12-18 12:20:04 INFO semantic_router.utils.logger Calling Mistral model\u001b[0m\n",
- "\u001b[32m2023-12-18 12:20:05 INFO semantic_router.utils.logger AI message: \n",
- " {\n",
- " \"category\": \"tech\",\n",
- " \"country\": \"Lithuania\"\n",
- " }\u001b[0m\n",
- "\u001b[32m2023-12-18 12:20:05 INFO semantic_router.utils.logger Extracted parameters: {'category': 'tech', 'country': 'Lithuania'}\u001b[0m\n"
- ]
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import json\n",
+ "\n",
+ "from semantic_router.utils.logger import logger\n",
+ "\n",
+ "\n",
+ "def generate_route(function) -> dict:\n",
+ " logger.info(\"Generating config...\")\n",
+ "\n",
+ " function_schema = get_function_schema(function)\n",
+ "\n",
+ " prompt = f\"\"\"\n",
+ " You are tasked to generate a JSON configuration based on the provided\n",
+ " function schema. Please follow the template below:\n",
+ "\n",
+ " {{\n",
+ " \"name\": \"<function_name>\",\n",
+ " \"utterances\": [\n",
+ " \"<example_utterance_1>\",\n",
+ " \"<example_utterance_2>\",\n",
+ " \"<example_utterance_3>\",\n",
+ " \"<example_utterance_4>\",\n",
+ " \"<example_utterance_5>\"]\n",
+ " }}\n",
+ "\n",
+ " Only include the \"name\" and \"utterances\" keys in your answer.\n",
+ " The \"name\" should match the function name and the \"utterances\"\n",
+ " should comprise a list of 5 example phrases that could be used to invoke\n",
+ " the function.\n",
+ "\n",
+ " Input schema:\n",
+ " {function_schema}\n",
+ " \"\"\"\n",
+ "\n",
+ " try:\n",
+ " ai_message = llm_mistral(prompt)\n",
+ "\n",
+ " # Parse the response\n",
+ " ai_message = ai_message[ai_message.find(\"{\") :]\n",
+ " ai_message = (\n",
+ " ai_message.replace(\"'\", '\"')\n",
+ " .replace('\"s', \"'s\")\n",
+ " .strip()\n",
+ " .rstrip(\",\")\n",
+ " .replace(\"}\", \"}\")\n",
+ " )\n",
+ "\n",
+ " valid_config = is_valid_config(ai_message)\n",
+ "\n",
+ " if not valid_config:\n",
+ " logger.warning(f\"Mistral failed with error, falling back to OpenAI\")\n",
+ " ai_message = llm_openai(prompt)\n",
+ " if not is_valid_config(ai_message):\n",
+ " raise Exception(\"Invalid config generated\")\n",
+ " except Exception as e:\n",
+ " logger.error(f\"Fall back to OpenAI failed with error {e}\")\n",
+ " ai_message = llm_openai(prompt)\n",
+ " if not is_valid_config(ai_message):\n",
+ " raise Exception(\"Failed to generate config\")\n",
+ "\n",
+ " try:\n",
+ " route_config = json.loads(ai_message)\n",
+ " logger.info(f\"Generated config: {route_config}\")\n",
+ " return route_config\n",
+ " except json.JSONDecodeError as json_error:\n",
+ " logger.error(f\"JSON parsing error {json_error}\")\n",
+ " print(f\"AI message: {ai_message}\")\n",
+ " return {\"error\": \"Failed to generate config\"}"
+ ]
},
{
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "parameters: {'category': 'tech', 'country': 'Lithuania'}\n",
- "Calling `get_news` function with category: tech and country: Lithuania\n"
- ]
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Extract function parameters using `Mistral` open-source model"
+ ]
},
{
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "\u001b[33m2023-12-18 12:20:05 WARNING semantic_router.utils.logger No function found\u001b[0m\n",
- "\u001b[32m2023-12-18 12:20:05 INFO semantic_router.utils.logger Calling Mistral model\u001b[0m\n",
- "\u001b[32m2023-12-18 12:20:06 INFO semantic_router.utils.logger AI message: How can I help you today?\u001b[0m\n"
- ]
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def validate_parameters(function, parameters):\n",
+ " sig = inspect.signature(function)\n",
+ " for name, param in sig.parameters.items():\n",
+ " if name not in parameters:\n",
+ " return False, f\"Parameter {name} missing from query\"\n",
+ " if not isinstance(parameters[name], param.annotation):\n",
+ " return False, f\"Parameter {name} is not of type {param.annotation}\"\n",
+ " return True, \"Parameters are valid\""
+ ]
},
{
- "data": {
- "text/plain": [
- "' How can I help you today?'"
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def extract_parameters(query: str, function) -> dict:\n",
+ " logger.info(\"Extracting parameters...\")\n",
+ " example_query = \"How is the weather in Hawaii right now in International units?\"\n",
+ "\n",
+ " example_schema = {\n",
+ " \"name\": \"get_weather\",\n",
+ " \"description\": \"Useful to get the weather in a specific location\",\n",
+ " \"signature\": \"(location: str, degree: str) -> str\",\n",
+ " \"output\": \"<class 'str'>\",\n",
+ " }\n",
+ "\n",
+ " example_parameters = {\n",
+ " \"location\": \"London\",\n",
+ " \"degree\": \"Celsius\",\n",
+ " }\n",
+ "\n",
+ " prompt = f\"\"\"\n",
+ " You are a helpful assistant designed to output JSON.\n",
+ " Given the following function schema\n",
+ " << {get_function_schema(function)} >>\n",
+ " and query\n",
+ " << {query} >>\n",
+ " extract the parameters values from the query, in a valid JSON format.\n",
+ " Example:\n",
+ " Input:\n",
+ " query: {example_query}\n",
+ " schema: {example_schema}\n",
+ "\n",
+ " Result: {example_parameters}\n",
+ "\n",
+ " Input:\n",
+ " query: {query}\n",
+ " schema: {get_function_schema(function)}\n",
+ " Result:\n",
+ " \"\"\"\n",
+ "\n",
+ " try:\n",
+ " ai_message = llm_mistral(prompt)\n",
+ " ai_message = (\n",
+ " ai_message.replace(\"Output:\", \"\").replace(\"'\", '\"').strip().rstrip(\",\")\n",
+ " )\n",
+ " except Exception as e:\n",
+ " logger.error(f\"Mistral failed with error {e}, falling back to OpenAI\")\n",
+ " ai_message = llm_openai(prompt)\n",
+ "\n",
+ " try:\n",
+ " parameters = json.loads(ai_message)\n",
+ " valid, message = validate_parameters(function, parameters)\n",
+ "\n",
+ " if not valid:\n",
+ " logger.warning(\n",
+ " f\"Invalid parameters from Mistral, falling back to OpenAI: {message}\"\n",
+ " )\n",
+ " # Fall back to OpenAI\n",
+ " ai_message = llm_openai(prompt)\n",
+ " parameters = json.loads(ai_message)\n",
+ " valid, message = validate_parameters(function, parameters)\n",
+ " if not valid:\n",
+ " raise ValueError(message)\n",
+ "\n",
+ " logger.info(f\"Extracted parameters: {parameters}\")\n",
+ " return parameters\n",
+ " except ValueError as e:\n",
+ " logger.error(f\"Parameter validation error: {str(e)}\")\n",
+ " return {\"error\": \"Failed to validate parameters\"}"
]
- },
- "execution_count": 26,
- "metadata": {},
- "output_type": "execute_result"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Set up the routing layer"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from semantic_router.schemas.route import Route\n",
+ "from semantic_router.encoders import CohereEncoder\n",
+ "from semantic_router.layer import RouteLayer\n",
+ "from semantic_router.utils.logger import logger\n",
+ "\n",
+ "\n",
+ "def create_router(routes: list[dict]) -> RouteLayer:\n",
+ " logger.info(\"Creating route layer...\")\n",
+ " encoder = CohereEncoder()\n",
+ "\n",
+ " route_list: list[Route] = []\n",
+ " for route in routes:\n",
+ " if \"name\" in route and \"utterances\" in route:\n",
+ " print(f\"Route: {route}\")\n",
+ " route_list.append(Route(name=route[\"name\"], utterances=route[\"utterances\"]))\n",
+ " else:\n",
+ " logger.warning(f\"Misconfigured route: {route}\")\n",
+ "\n",
+ " return RouteLayer(encoder=encoder, routes=route_list)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Set up calling functions"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from typing import Callable\n",
+ "from semantic_router.layer import RouteLayer\n",
+ "\n",
+ "\n",
+ "def call_function(function: Callable, parameters: dict[str, str]):\n",
+ " try:\n",
+ " return function(**parameters)\n",
+ " except TypeError as e:\n",
+ " logger.error(f\"Error calling function: {e}\")\n",
+ "\n",
+ "\n",
+ "def call_llm(query: str) -> str:\n",
+ " try:\n",
+ " ai_message = llm_mistral(query)\n",
+ " except Exception as e:\n",
+ " logger.error(f\"Mistral failed with error {e}, falling back to OpenAI\")\n",
+ " ai_message = llm_openai(query)\n",
+ "\n",
+ " return ai_message\n",
+ "\n",
+ "\n",
+ "def call(query: str, functions: list[Callable], router: RouteLayer):\n",
+ " function_name = router(query)\n",
+ " if not function_name:\n",
+ " logger.warning(\"No function found\")\n",
+ " return call_llm(query)\n",
+ "\n",
+ " for function in functions:\n",
+ " if function.__name__ == function_name:\n",
+ " parameters = extract_parameters(query, function)\n",
+ " print(f\"parameters: {parameters}\")\n",
+ " return call_function(function, parameters)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Workflow"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\u001b[32m2023-12-18 12:17:58 INFO semantic_router.utils.logger Generating config...\u001b[0m\n",
+ "\u001b[32m2023-12-18 12:17:58 INFO semantic_router.utils.logger Calling Mistral model\u001b[0m\n",
+ "\u001b[31m2023-12-18 12:18:00 ERROR semantic_router.utils.logger Fall back to OpenAI failed with error ('Failed to call HuggingFace API', '{\"error\":\"Bad Gateway\"}')\u001b[0m\n",
+ "\u001b[32m2023-12-18 12:18:00 INFO semantic_router.utils.logger Calling gpt-4 model\u001b[0m\n",
+ "\u001b[32m2023-12-18 12:18:05 INFO semantic_router.utils.logger AI message: {\n",
+ " \"name\": \"get_time\",\n",
+ " \"utterances\": [\n",
+ " \"what is the time in new york\",\n",
+ " \"can you tell me the time in london\",\n",
+ " \"get me the current time in tokyo\",\n",
+ " \"i need to know the time in sydney\",\n",
+ " \"please tell me the current time in paris\"\n",
+ " ]\n",
+ "}\u001b[0m\n",
+ "\u001b[32m2023-12-18 12:18:05 INFO semantic_router.utils.logger Generated config: {'name': 'get_time', 'utterances': ['what is the time in new york', 'can you tell me the time in london', 'get me the current time in tokyo', 'i need to know the time in sydney', 'please tell me the current time in paris']}\u001b[0m\n",
+ "\u001b[32m2023-12-18 12:18:05 INFO semantic_router.utils.logger Generating config...\u001b[0m\n",
+ "\u001b[32m2023-12-18 12:18:05 INFO semantic_router.utils.logger Calling Mistral model\u001b[0m\n",
+ "\u001b[31m2023-12-18 12:18:07 ERROR semantic_router.utils.logger Fall back to OpenAI failed with error ('Failed to call HuggingFace API', '{\"error\":\"Bad Gateway\"}')\u001b[0m\n",
+ "\u001b[32m2023-12-18 12:18:07 INFO semantic_router.utils.logger Calling gpt-4 model\u001b[0m\n",
+ "\u001b[32m2023-12-18 12:18:12 INFO semantic_router.utils.logger AI message: {\n",
+ " \"name\": \"get_news\",\n",
+ " \"utterances\": [\n",
+ " \"Can I get the latest news in Canada?\",\n",
+ " \"Show me the recent news in the US\",\n",
+ " \"I would like to know about the sports news in England\",\n",
+ " \"Let's check the technology news in Japan\",\n",
+ " \"Show me the health related news in Germany\"\n",
+ " ]\n",
+ "}\u001b[0m\n",
+ "\u001b[32m2023-12-18 12:18:12 INFO semantic_router.utils.logger Generated config: {'name': 'get_news', 'utterances': ['Can I get the latest news in Canada?', 'Show me the recent news in the US', 'I would like to know about the sports news in England', \"Let's check the technology news in Japan\", 'Show me the health related news in Germany']}\u001b[0m\n",
+ "\u001b[32m2023-12-18 12:18:12 INFO semantic_router.utils.logger Creating route layer...\u001b[0m\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Route: {'name': 'get_time', 'utterances': ['what is the time in new york', 'can you tell me the time in london', 'get me the current time in tokyo', 'i need to know the time in sydney', 'please tell me the current time in paris']}\n",
+ "Route: {'name': 'get_news', 'utterances': ['Can I get the latest news in Canada?', 'Show me the recent news in the US', 'I would like to know about the sports news in England', \"Let's check the technology news in Japan\", 'Show me the health related news in Germany']}\n"
+ ]
+ }
+ ],
+ "source": [
+ "def get_time(location: str) -> str:\n",
+ " \"\"\"Useful to get the time in a specific location\"\"\"\n",
+ " print(f\"Calling `get_time` function with location: {location}\")\n",
+ " return \"get_time\"\n",
+ "\n",
+ "\n",
+ "def get_news(category: str, country: str) -> str:\n",
+ " \"\"\"Useful to get the news in a specific country\"\"\"\n",
+ " print(\n",
+ " f\"Calling `get_news` function with category: {category} and country: {country}\"\n",
+ " )\n",
+ " return \"get_news\"\n",
+ "\n",
+ "\n",
+ "# Registering functions to the router\n",
+ "route_get_time = generate_route(get_time)\n",
+ "route_get_news = generate_route(get_news)\n",
+ "\n",
+ "routes = [route_get_time, route_get_news]\n",
+ "router = create_router(routes)\n",
+ "\n",
+ "# Tools\n",
+ "tools = [get_time, get_news]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\u001b[32m2023-12-18 12:20:12 INFO semantic_router.utils.logger Generating config...\u001b[0m\n",
+ "\u001b[32m2023-12-18 12:20:12 INFO semantic_router.utils.logger Calling Mistral model\u001b[0m\n",
+ "\u001b[32m2023-12-18 12:20:16 INFO semantic_router.utils.logger AI message: \n",
+ " Example output:\n",
+ " {\n",
+ " \"name\": \"get_time\",\n",
+ " \"utterances\": [\n",
+ " \"What's the time in New York?\",\n",
+ " \"Tell me the time in Tokyo.\",\n",
+ " \"Can you give me the time in London?\",\n",
+ " \"What's the current time in Sydney?\",\n",
+ " \"Can you tell me the time in Berlin?\"\n",
+ " ]\n",
+ " }\u001b[0m\n",
+ "\u001b[32m2023-12-18 12:20:16 INFO semantic_router.utils.logger Generated config: {'name': 'get_time', 'utterances': [\"What's the time in New York?\", 'Tell me the time in Tokyo.', 'Can you give me the time in London?', \"What's the current time in Sydney?\", 'Can you tell me the time in Berlin?']}\u001b[0m\n",
+ "\u001b[32m2023-12-18 12:20:16 INFO semantic_router.utils.logger Generating config...\u001b[0m\n",
+ "\u001b[32m2023-12-18 12:20:16 INFO semantic_router.utils.logger Calling Mistral model\u001b[0m\n",
+ "\u001b[32m2023-12-18 12:20:20 INFO semantic_router.utils.logger AI message: \n",
+ " Example output:\n",
+ " {\n",
+ " \"name\": \"get_news\",\n",
+ " \"utterances\": [\n",
+ " \"Tell me the latest news from the US\",\n",
+ " \"What's happening in India today?\",\n",
+ " \"Get me the top stories from Japan\",\n",
+ " \"Can you give me the breaking news from Brazil?\",\n",
+ " \"What's the latest news from Germany?\"\n",
+ " ]\n",
+ " }\u001b[0m\n",
+ "\u001b[32m2023-12-18 12:20:20 INFO semantic_router.utils.logger Generated config: {'name': 'get_news', 'utterances': ['Tell me the latest news from the US', \"What's happening in India today?\", 'Get me the top stories from Japan', 'Can you give me the breaking news from Brazil?', \"What's the latest news from Germany?\"]}\u001b[0m\n",
+ "\u001b[32m2023-12-18 12:20:20 INFO semantic_router.utils.logger Creating route layer...\u001b[0m\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Route: {'name': 'get_time', 'utterances': [\"What's the time in New York?\", 'Tell me the time in Tokyo.', 'Can you give me the time in London?', \"What's the current time in Sydney?\", 'Can you tell me the time in Berlin?']}\n",
+ "Route: {'name': 'get_news', 'utterances': ['Tell me the latest news from the US', \"What's happening in India today?\", 'Get me the top stories from Japan', 'Can you give me the breaking news from Brazil?', \"What's the latest news from Germany?\"]}\n"
+ ]
+ }
+ ],
+ "source": [
+ "def get_time(location: str) -> str:\n",
+ " \"\"\"Useful to get the time in a specific location\"\"\"\n",
+ " print(f\"Calling `get_time` function with location: {location}\")\n",
+ " return \"get_time\"\n",
+ "\n",
+ "\n",
+ "def get_news(category: str, country: str) -> str:\n",
+ " \"\"\"Useful to get the news in a specific country\"\"\"\n",
+ " print(\n",
+ " f\"Calling `get_news` function with category: {category} and country: {country}\"\n",
+ " )\n",
+ " return \"get_news\"\n",
+ "\n",
+ "\n",
+ "# Registering functions to the router\n",
+ "route_get_time = generate_route(get_time)\n",
+ "route_get_news = generate_route(get_news)\n",
+ "\n",
+ "routes = [route_get_time, route_get_news]\n",
+ "router = create_router(routes)\n",
+ "\n",
+ "# Tools\n",
+ "tools = [get_time, get_news]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\u001b[32m2023-12-18 12:20:02 INFO semantic_router.utils.logger Extracting parameters...\u001b[0m\n",
+ "\u001b[32m2023-12-18 12:20:02 INFO semantic_router.utils.logger Calling Mistral model\u001b[0m\n",
+ "\u001b[32m2023-12-18 12:20:04 INFO semantic_router.utils.logger AI message: \n",
+ " {\n",
+ " \"location\": \"Stockholm\"\n",
+ " }\u001b[0m\n",
+ "\u001b[32m2023-12-18 12:20:04 INFO semantic_router.utils.logger Extracted parameters: {'location': 'Stockholm'}\u001b[0m\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "parameters: {'location': 'Stockholm'}\n",
+ "Calling `get_time` function with location: Stockholm\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\u001b[32m2023-12-18 12:20:04 INFO semantic_router.utils.logger Extracting parameters...\u001b[0m\n",
+ "\u001b[32m2023-12-18 12:20:04 INFO semantic_router.utils.logger Calling Mistral model\u001b[0m\n",
+ "\u001b[32m2023-12-18 12:20:05 INFO semantic_router.utils.logger AI message: \n",
+ " {\n",
+ " \"category\": \"tech\",\n",
+ " \"country\": \"Lithuania\"\n",
+ " }\u001b[0m\n",
+ "\u001b[32m2023-12-18 12:20:05 INFO semantic_router.utils.logger Extracted parameters: {'category': 'tech', 'country': 'Lithuania'}\u001b[0m\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "parameters: {'category': 'tech', 'country': 'Lithuania'}\n",
+ "Calling `get_news` function with category: tech and country: Lithuania\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\u001b[33m2023-12-18 12:20:05 WARNING semantic_router.utils.logger No function found\u001b[0m\n",
+ "\u001b[32m2023-12-18 12:20:05 INFO semantic_router.utils.logger Calling Mistral model\u001b[0m\n",
+ "\u001b[32m2023-12-18 12:20:06 INFO semantic_router.utils.logger AI message: How can I help you today?\u001b[0m\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "' How can I help you today?'"
+ ]
+ },
+ "execution_count": 26,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "call(query=\"What is the time in Stockholm?\", functions=tools, router=router)\n",
+ "call(query=\"What is the tech news in the Lithuania?\", functions=tools, router=router)\n",
+ "call(query=\"Hi!\", functions=tools, router=router)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": ".venv",
+ "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": "3.11.5"
}
- ],
- "source": [
- "call(query=\"What is the time in Stockholm?\", functions=tools, router=router)\n",
- "call(query=\"What is the tech news in the Lithuania?\", functions=tools, router=router)\n",
- "call(query=\"Hi!\", functions=tools, router=router)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": ".venv",
- "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": "3.11.5"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat": 4,
+ "nbformat_minor": 2
}
diff --git a/docs/examples/hybrid-layer.ipynb b/docs/examples/hybrid-layer.ipynb
index 8b1da5ae..9e5eca66 100644
--- a/docs/examples/hybrid-layer.ipynb
+++ b/docs/examples/hybrid-layer.ipynb
@@ -1,207 +1,199 @@
{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Semantic Router: Hybrid Layer"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "The Hybrid Layer in the Semantic Router library can improve making performance particularly for niche use-cases that contain specific terminology, such as finance or medical. It helps us provide more importance to making based on the keywords contained in our utterances and user queries."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Getting Started"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "We start by installing the library:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "!pip install -qU semantic-router==0.0.6"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "We start by defining a dictionary mapping s to example phrases that should trigger those s."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {},
- "outputs": [
- {
- "ename": "ImportError",
- "evalue": "cannot import name 'Route' from 'semantic_router.schema' (/Users/jakit/customers/aurelio/semantic-router/.venv/lib/python3.11/site-packages/semantic_router/schema.py)",
- "output_type": "error",
- "traceback": [
- "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
- "\u001b[0;31mImportError\u001b[0m Traceback (most recent call last)",
- "\u001b[1;32m/Users/jakit/customers/aurelio/semantic-router/docs/examples/hybrid-layer.ipynb Cell 7\u001b[0m line \u001b[0;36m1\n\u001b[0;32m----> <a href='vscode-notebook-cell:/Users/jakit/customers/aurelio/semantic-router/docs/examples/hybrid-layer.ipynb#X10sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39msemantic_router\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mschema\u001b[39;00m \u001b[39mimport\u001b[39;00m Route\n\u001b[1;32m <a href='vscode-notebook-cell:/Users/jakit/customers/aurelio/semantic-router/docs/examples/hybrid-layer.ipynb#X10sZmlsZQ%3D%3D?line=2'>3</a>\u001b[0m politics \u001b[39m=\u001b[39m Route(\n\u001b[1;32m <a href='vscode-notebook-cell:/Users/jakit/customers/aurelio/semantic-router/docs/examples/hybrid-layer.ipynb#X10sZmlsZQ%3D%3D?line=3'>4</a>\u001b[0m name\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mpolitics\u001b[39m\u001b[39m\"\u001b[39m,\n\u001b[1;32m <a href='vscode-notebook-cell:/Users/jakit/customers/aurelio/semantic-router/docs/examples/hybrid-layer.ipynb#X10sZmlsZQ%3D%3D?line=4'>5</a>\u001b[0m utterances\u001b[39m=\u001b[39m[\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m <a href='vscode-notebook-cell:/Users/jakit/customers/aurelio/semantic-router/docs/examples/hybrid-layer.ipynb#X10sZmlsZQ%3D%3D?line=11'>12</a>\u001b[0m ],\n\u001b[1;32m <a href='vscode-notebook-cell:/Users/jakit/customers/aurelio/semantic-router/docs/examples/hybrid-layer.ipynb#X10sZmlsZQ%3D%3D?line=12'>13</a>\u001b[0m )\n",
- "\u001b[0;31mImportError\u001b[0m: cannot import name 'Route' from 'semantic_router.schema' (/Users/jakit/customers/aurelio/semantic-router/.venv/lib/python3.11/site-packages/semantic_router/schema.py)"
- ]
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Semantic Router: Hybrid Layer\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The Hybrid Layer in the Semantic Router library can improve making performance particularly for niche use-cases that contain specific terminology, such as finance or medical. It helps us provide more importance to making based on the keywords contained in our utterances and user queries.\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Getting Started\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We start by installing the library:\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#!pip install -qU semantic-router==0.0.6"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We start by defining a dictionary mapping s to example phrases that should trigger those s.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from semantic_router.schemas.route 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:\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "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",
+ "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:\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import os\n",
+ "from semantic_router.encoders import CohereEncoder, BM25Encoder, TfidfEncoder\n",
+ "from getpass import getpass\n",
+ "\n",
+ "os.environ[\"COHERE_API_KEY\"] = os.environ[\"COHERE_API_KEY\"] or getpass(\n",
+ " \"Enter Cohere API Key: \"\n",
+ ")\n",
+ "\n",
+ "dense_encoder = CohereEncoder()\n",
+ "# sparse_encoder = BM25Encoder()\n",
+ "sparse_encoder = TfidfEncoder()"
+ ]
+ },
+ {
+ "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`.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from semantic_router.hybrid_layer import HybridRouteLayer\n",
+ "\n",
+ "dl = HybridRouteLayer(\n",
+ " dense_encoder=dense_encoder, sparse_encoder=sparse_encoder, routes=routes\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "dl(\"don't you love politics?\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "dl(\"how's the weather today?\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "---\n"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "decision-layer",
+ "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": "3.11.5"
}
- ],
- "source": [
- "from semantic_router.schema 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": null,
- "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",
- "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:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "import os\n",
- "from semantic_router.encoders import CohereEncoder\n",
- "from getpass import getpass\n",
- "\n",
- "os.environ[\"COHERE_API_KEY\"] = os.environ[\"COHERE_API_KEY\"] or getpass(\n",
- " \"Enter Cohere API Key: \"\n",
- ")\n",
- "\n",
- "encoder = CohereEncoder()"
- ]
- },
- {
- "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": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "from semantic_router.hybrid_layer import HybridRouteLayer\n",
- "\n",
- "dl = HybridRouteLayer(encoder=encoder, routes=routes)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "dl(\"don't you love politics?\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "dl(\"how's the weather today?\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "---"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "decision-layer",
- "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": "3.11.3"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat": 4,
+ "nbformat_minor": 2
}
diff --git a/semantic_router/encoders/tfidf.py b/semantic_router/encoders/tfidf.py
index d6be6da5..6fc420eb 100644
--- a/semantic_router/encoders/tfidf.py
+++ b/semantic_router/encoders/tfidf.py
@@ -1,7 +1,7 @@
import numpy as np
from collections import Counter
from semantic_router.encoders import BaseEncoder
-from semantic_router.schema import Route
+from semantic_router.schemas.route import Route
from numpy.linalg import norm
import string
diff --git a/semantic_router/hybrid_layer.py b/semantic_router/hybrid_layer.py
index 3993ca45..2901871a 100644
--- a/semantic_router/hybrid_layer.py
+++ b/semantic_router/hybrid_layer.py
@@ -8,7 +8,7 @@ from semantic_router.encoders import (
OpenAIEncoder,
TfidfEncoder,
)
-from semantic_router.schema import Route
+from semantic_router.schemas.route import Route
from semantic_router.utils.logger import logger
diff --git a/semantic_router/layer.py b/semantic_router/layer.py
index cb408c5c..af08a9c1 100644
--- a/semantic_router/layer.py
+++ b/semantic_router/layer.py
@@ -6,7 +6,7 @@ from semantic_router.encoders import (
OpenAIEncoder,
)
from semantic_router.linear import similarity_matrix, top_scores
-from semantic_router.schema import Route
+from semantic_router.schemas.route import Route
from semantic_router.utils.logger import logger
diff --git a/semantic_router/schema.py b/semantic_router/schemas/encoder.py
similarity index 67%
rename from semantic_router/schema.py
rename to semantic_router/schemas/encoder.py
index 007cddcb..1b2ad74c 100644
--- a/semantic_router/schema.py
+++ b/semantic_router/schemas/encoder.py
@@ -1,6 +1,5 @@
from enum import Enum
-from pydantic import BaseModel
from pydantic.dataclasses import dataclass
from semantic_router.encoders import (
@@ -10,12 +9,6 @@ from semantic_router.encoders import (
)
-class Route(BaseModel):
- name: str
- utterances: list[str]
- description: str | None = None
-
-
class EncoderType(Enum):
HUGGINGFACE = "huggingface"
OPENAI = "openai"
@@ -40,17 +33,3 @@ class Encoder:
def __call__(self, texts: list[str]) -> list[list[float]]:
return self.model(texts)
-
-
-@dataclass
-class SemanticSpace:
- id: str
- routes: list[Route]
- encoder: str = ""
-
- def __init__(self, routes: list[Route] = []):
- self.id = ""
- self.routes = routes
-
- def add(self, route: Route):
- self.routes.append(route)
diff --git a/semantic_router/schemas/route.py b/semantic_router/schemas/route.py
new file mode 100644
index 00000000..b70bc60f
--- /dev/null
+++ b/semantic_router/schemas/route.py
@@ -0,0 +1,7 @@
+from pydantic import BaseModel
+
+
+class Route(BaseModel):
+ name: str
+ utterances: list[str]
+ description: str | None = None
diff --git a/semantic_router/schemas/semantic_space.py b/semantic_router/schemas/semantic_space.py
new file mode 100644
index 00000000..92e7adaf
--- /dev/null
+++ b/semantic_router/schemas/semantic_space.py
@@ -0,0 +1,17 @@
+from pydantic.dataclasses import dataclass
+
+from semantic_router.schemas.route import Route
+
+
+@dataclass
+class SemanticSpace:
+ id: str
+ routes: list[Route]
+ encoder: str = ""
+
+ def __init__(self, routes: list[Route] = []):
+ self.id = ""
+ self.routes = routes
+
+ def add(self, route: Route):
+ self.routes.append(route)
diff --git a/tests/unit/encoders/test_tfidf.py b/tests/unit/encoders/test_tfidf.py
index 93a96639..68e37d9e 100644
--- a/tests/unit/encoders/test_tfidf.py
+++ b/tests/unit/encoders/test_tfidf.py
@@ -1,6 +1,6 @@
import pytest
from semantic_router.encoders import TfidfEncoder
-from semantic_router.schema import Route
+from semantic_router.schemas.route import Route
@pytest.fixture
diff --git a/tests/unit/test_hybrid_layer.py b/tests/unit/test_hybrid_layer.py
index ee7d8f6b..2506c199 100644
--- a/tests/unit/test_hybrid_layer.py
+++ b/tests/unit/test_hybrid_layer.py
@@ -8,7 +8,7 @@ from semantic_router.encoders import (
TfidfEncoder,
)
from semantic_router.hybrid_layer import HybridRouteLayer
-from semantic_router.schema import Route
+from semantic_router.schemas.route import Route
def mock_encoder_call(utterances):
diff --git a/tests/unit/test_layer.py b/tests/unit/test_layer.py
index 66e0d53b..d049243f 100644
--- a/tests/unit/test_layer.py
+++ b/tests/unit/test_layer.py
@@ -2,7 +2,7 @@ import pytest
from semantic_router.encoders import BaseEncoder, CohereEncoder, OpenAIEncoder
from semantic_router.layer import RouteLayer
-from semantic_router.schema import Route
+from semantic_router.schemas.route import Route
def mock_encoder_call(utterances):
diff --git a/tests/unit/test_schema.py b/tests/unit/test_schema.py
index f471755c..f47643c9 100644
--- a/tests/unit/test_schema.py
+++ b/tests/unit/test_schema.py
@@ -1,11 +1,17 @@
import pytest
-from semantic_router.schema import (
+from semantic_router.schemas.encoder import (
CohereEncoder,
Encoder,
EncoderType,
OpenAIEncoder,
+)
+
+from semantic_router.schemas.route import (
Route,
+)
+
+from semantic_router.schemas.semantic_space import (
SemanticSpace,
)
diff --git a/walkthrough.ipynb b/walkthrough.ipynb
index d008739c..346b576c 100644
--- a/walkthrough.ipynb
+++ b/walkthrough.ipynb
@@ -1,206 +1,237 @@
{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Semantic Router Walkthrough"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "The Semantic Router library can be used as a super fast route making layer on top of LLMs. That means rather than waiting on a slow agent to decide what to do, we can use the magic of semantic vector space to make routes. Cutting route making time down from seconds to milliseconds."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Getting Started"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "We start by installing the library:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "!pip install -qU semantic-router==0.0.8"
- ]
- },
- {
- "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": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "from semantic_router.schema 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\" \"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": null,
- "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:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "import os\n",
- "from getpass import getpass\n",
- "from semantic_router.encoders import CohereEncoder\n",
- "\n",
- "os.environ[\"COHERE_API_KEY\"] = os.getenv(\"COHERE_API_KEY\") or getpass(\n",
- " \"Enter Cohere API Key: \"\n",
- ")\n",
- "\n",
- "encoder = CohereEncoder()"
- ]
- },
- {
- "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": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "from semantic_router.layer import RouteLayer\n",
- "\n",
- "dl = RouteLayer(encoder=encoder, routes=routes)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Now we can test it:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "dl(\"don't you love politics?\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "dl(\"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": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "dl(\"I'm interested in learning about llama 2\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "In this case, we return `None` because no matches were identified."
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "decision-layer",
- "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": "3.11.3"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 2
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Semantic Router Walkthrough\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The Semantic Router library can be used as a super fast route making layer on top of LLMs. That means rather than waiting on a slow agent to decide what to do, we can use the magic of semantic vector space to make routes. Cutting route making time down from seconds to milliseconds.\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Getting Started\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We start by installing the library:\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "!pip install -qU semantic-router==0.0.8"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We start by defining a dictionary mapping routes to example phrases that should trigger those routes.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/Users/danielgriffiths/Coding_files/Aurelio_local/semantic-router/.venv/lib/python3.11/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.schemas.route 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\" \"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:\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "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:\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import os\n",
+ "from getpass import getpass\n",
+ "from semantic_router.encoders import CohereEncoder\n",
+ "\n",
+ "os.environ[\"COHERE_API_KEY\"] = os.getenv(\"COHERE_API_KEY\") or getpass(\n",
+ " \"Enter Cohere API Key: \"\n",
+ ")\n",
+ "\n",
+ "encoder = CohereEncoder()"
+ ]
+ },
+ {
+ "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`.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from semantic_router.layer import RouteLayer\n",
+ "\n",
+ "dl = RouteLayer(encoder=encoder, routes=routes)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now we can test it:\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "'politics'"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "dl(\"don't you love politics?\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "'chitchat'"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "dl(\"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?\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "dl(\"I'm interested in learning about llama 2\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In this case, we return `None` because no matches were identified.\n"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "decision-layer",
+ "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": "3.11.5"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
}
--
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