diff --git a/coverage.xml b/coverage.xml
index 52f00b34c6122ea9b73d3f3cf386ee7b09a1c64a..9bdbfa3735a680c28c2ffcced9bfe29ca71f5977 100644
--- a/coverage.xml
+++ b/coverage.xml
@@ -1,5 +1,5 @@
<?xml version="1.0" ?>
-<coverage version="7.3.2" timestamp="1702538160019" lines-valid="344" lines-covered="344" line-rate="1" branches-covered="0" branches-valid="0" branch-rate="0" complexity="0">
+<coverage version="7.3.2" timestamp="1702633916069" lines-valid="344" lines-covered="344" line-rate="1" branches-covered="0" branches-valid="0" branch-rate="0" complexity="0">
<!-- Generated by coverage.py: https://coverage.readthedocs.io/en/7.3.2 -->
<!-- Based on https://raw.githubusercontent.com/cobertura/web/master/htdocs/xml/coverage-04.dtd -->
<sources>
diff --git a/docs/examples/function_calling.ipynb b/docs/examples/function_calling.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..650b5b9426fd9af7733cae3584540cb52a6d64b3
--- /dev/null
+++ b/docs/examples/function_calling.ipynb
@@ -0,0 +1,446 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Define LLMs"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 213,
+ "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": 214,
+ "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.environ[\"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\": 0.1,\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": null,
+ "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": null,
+ "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",
+ " example_schema = {\n",
+ " \"name\": \"get_weather\",\n",
+ " \"description\": \"Useful to get the weather in a specific location\",\n",
+ " \"signature\": \"(location: str) -> str\",\n",
+ " \"output\": \"<class 'str'>\",\n",
+ " }\n",
+ "\n",
+ " example_config = {\n",
+ " \"name\": \"get_weather\",\n",
+ " \"utterances\": [\n",
+ " \"What is the weather like in SF?\",\n",
+ " \"What is the weather in Cyprus?\",\n",
+ " \"weather in London?\",\n",
+ " \"Tell me the weather in New York\",\n",
+ " \"what is the current weather in Paris?\",\n",
+ " ],\n",
+ " }\n",
+ "\n",
+ " function_schema = get_function_schema(function)\n",
+ "\n",
+ " prompt = f\"\"\"\n",
+ " You are a helpful assistant designed to output JSON.\n",
+ " Given the following function schema\n",
+ " {function_schema}\n",
+ " generate a routing config with the format:\n",
+ " {example_config}\n",
+ "\n",
+ " For example:\n",
+ " Input: {example_schema}\n",
+ " Output: {example_config}\n",
+ "\n",
+ " Input: {function_schema}\n",
+ " Output:\n",
+ " \"\"\"\n",
+ "\n",
+ " ai_message = llm_openai(prompt)\n",
+ "\n",
+ " ai_message = ai_message.replace(\"CONFIG:\", \"\").replace(\"'\", '\"').strip().rstrip(\",\")\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": 217,
+ "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",
+ " Output:\n",
+ " parameters: {example_parameters}\n",
+ "\n",
+ " Input:\n",
+ " query: {query}\n",
+ " schema: {get_function_schema(function)}\n",
+ " Output:\n",
+ " parameters:\n",
+ " \"\"\"\n",
+ "\n",
+ " ai_message = llm_mistral(prompt)\n",
+ "\n",
+ " ai_message = ai_message.replace(\"CONFIG:\", \"\").replace(\"'\", '\"').strip().rstrip(\",\")\n",
+ "\n",
+ " try:\n",
+ " parameters = json.loads(ai_message)\n",
+ " logger.info(f\"Extracted parameters: {parameters}\")\n",
+ " return parameters\n",
+ " except json.JSONDecodeError as json_error:\n",
+ " logger.error(f\"JSON parsing error {json_error}\")\n",
+ " return {\"error\": \"Failed to extract parameters\"}"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Set up the routing layer"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "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": 219,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from typing import Callable\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):\n",
+ " return llm_mistral(query)\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": null,
+ "metadata": {},
+ "outputs": [],
+ "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": 220,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\u001b[32m2023-12-15 11:41:54 INFO semantic_router.utils.logger Extracting parameters...\u001b[0m\n",
+ "\u001b[32m2023-12-15 11:41:54 INFO semantic_router.utils.logger Calling Mistral model\u001b[0m\n",
+ "\u001b[32m2023-12-15 11:41:55 INFO semantic_router.utils.logger AI message: \n",
+ " {\n",
+ " 'location': 'Stockholm'\n",
+ " }\u001b[0m\n",
+ "\u001b[32m2023-12-15 11:41:55 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[32m2023-12-15 11:41:55 INFO semantic_router.utils.logger Extracting parameters...\u001b[0m\n",
+ "\u001b[32m2023-12-15 11:41:55 INFO semantic_router.utils.logger Calling Mistral model\u001b[0m\n",
+ "\u001b[32m2023-12-15 11:41:56 INFO semantic_router.utils.logger AI message: \n",
+ " {\n",
+ " 'category': 'tech',\n",
+ " 'country': 'Lithuania'\n",
+ " }\u001b[0m\n",
+ "\u001b[32m2023-12-15 11:41:56 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[33m2023-12-15 11:41:57 WARNING semantic_router.utils.logger No function found\u001b[0m\n",
+ "\u001b[32m2023-12-15 11:41:57 INFO semantic_router.utils.logger Calling Mistral model\u001b[0m\n",
+ "\u001b[32m2023-12-15 11:41:57 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": 220,
+ "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)"
+ ]
+ }
+ ],
+ "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": "3.11.3"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/pyproject.toml b/pyproject.toml
index 5152d8937c55949b6540178ca262757db8ef6f3e..8a92ec383d86701f6aca5e9908e5306d016e1bbf 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -36,7 +36,7 @@ requires = ["poetry-core"]
build-backend = "poetry.core.masonry.api"
[tool.ruff.per-file-ignores]
-"*.ipynb" = ["E402"]
+"*.ipynb" = ["ALL"]
[tool.mypy]
ignore_missing_imports = true