diff --git a/docs/.gitignore b/docs/.gitignore new file mode 100644 index 0000000000..075b2542af --- /dev/null +++ b/docs/.gitignore @@ -0,0 +1 @@ +/.quarto/ diff --git a/docs/docs/modules/model_io/chat/function_calling.ipynb b/docs/docs/modules/model_io/chat/function_calling.ipynb deleted file mode 100644 index e807b0001d..0000000000 --- a/docs/docs/modules/model_io/chat/function_calling.ipynb +++ /dev/null @@ -1,492 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "dae8d4ed-9150-45da-b494-7717ab0a2960", - "metadata": {}, - "source": [ - "# Function calling\n", - "\n", - "Certain chat models, like [OpenAI's](https://platform.openai.com/docs/guides/function-calling), have a function-calling API that lets you describe functions and their arguments, and have the model return a JSON object with a function to invoke and the inputs to that function. Function-calling is extremely useful for building [tool-using chains and agents](/docs/use_cases/tool_use/), and for getting structured outputs from models more generally.\n", - "\n", - "LangChain comes with a number of utilities to make function-calling easy. Namely, it comes with\n", - "\n", - "* simple syntax for binding functions to models\n", - "* converters for formatting various types of objects to the expected function schemas\n", - "* output parsers for extracting the function invocations from API responses\n", - "\n", - "We'll focus here on the first two bullets. To see how output parsing works as well check out the [OpenAI Tools output parsers](/docs/modules/model_io/output_parsers/types/openai_tools)." - ] - }, - { - "cell_type": "markdown", - "id": "a177c64b-7c99-495c-b362-5ed3b40aa26a", - "metadata": {}, - "source": [ - "## Defining functions\n", - "\n", - "We'll focus on the [OpenAI function format](https://platform.openai.com/docs/api-reference/chat/create#chat-create-tools) here since as of this writing that is the main model provider that supports function calling. LangChain has a built-in converter that can turn Python functions, Pydantic classes, and LangChain Tools into the OpenAI function format:" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "f6d1dc0c-6170-4977-809f-365099f628ea", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.3.2\u001b[0m\n", - "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n", - "Note: you may need to restart the kernel to use updated packages.\n" - ] - } - ], - "source": [ - "%pip install -qU langchain-core langchain-openai" - ] - }, - { - "cell_type": "markdown", - "id": "6bd290bd-7621-466b-a73e-fc8480f879ec", - "metadata": {}, - "source": [ - "### Python function" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "41ebab5c-0e9f-4b49-86ee-9290ced2fe96", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{\n", - " \"type\": \"function\",\n", - " \"function\": {\n", - " \"name\": \"multiply\",\n", - " \"description\": \"Multiply two integers together.\",\n", - " \"parameters\": {\n", - " \"type\": \"object\",\n", - " \"properties\": {\n", - " \"a\": {\n", - " \"type\": \"integer\",\n", - " \"description\": \"First integer\"\n", - " },\n", - " \"b\": {\n", - " \"type\": \"integer\",\n", - " \"description\": \"Second integer\"\n", - " }\n", - " },\n", - " \"required\": [\n", - " \"a\",\n", - " \"b\"\n", - " ]\n", - " }\n", - " }\n", - "}\n" - ] - } - ], - "source": [ - "import json\n", - "\n", - "from langchain_core.utils.function_calling import convert_to_openai_tool\n", - "\n", - "\n", - "def multiply(a: int, b: int) -> int:\n", - " \"\"\"Multiply two integers together.\n", - "\n", - " Args:\n", - " a: First integer\n", - " b: Second integer\n", - " \"\"\"\n", - " return a * b\n", - "\n", - "\n", - "print(json.dumps(convert_to_openai_tool(multiply), indent=2))" - ] - }, - { - "cell_type": "markdown", - "id": "ecf22577-38ab-48f1-ba0b-371aaba1bacc", - "metadata": {}, - "source": [ - "### Pydantic class" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "ecc8ffd4-aed3-4f47-892d-1896cc1ca4dc", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{\n", - " \"type\": \"function\",\n", - " \"function\": {\n", - " \"name\": \"multiply\",\n", - " \"description\": \"Multiply two integers together.\",\n", - " \"parameters\": {\n", - " \"type\": \"object\",\n", - " \"properties\": {\n", - " \"a\": {\n", - " \"description\": \"First integer\",\n", - " \"type\": \"integer\"\n", - " },\n", - " \"b\": {\n", - " \"description\": \"Second integer\",\n", - " \"type\": \"integer\"\n", - " }\n", - " },\n", - " \"required\": [\n", - " \"a\",\n", - " \"b\"\n", - " ]\n", - " }\n", - " }\n", - "}\n" - ] - } - ], - "source": [ - "from langchain_core.pydantic_v1 import BaseModel, Field\n", - "\n", - "\n", - "class multiply(BaseModel):\n", - " \"\"\"Multiply two integers together.\"\"\"\n", - "\n", - " a: int = Field(..., description=\"First integer\")\n", - " b: int = Field(..., description=\"Second integer\")\n", - "\n", - "\n", - "print(json.dumps(convert_to_openai_tool(multiply), indent=2))" - ] - }, - { - "cell_type": "markdown", - "id": "b83d5a88-50ed-4ae4-85cf-8b895617496f", - "metadata": {}, - "source": [ - "### LangChain Tool" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "696c7dd6-660c-4797-909f-bf878b3acf93", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{\n", - " \"type\": \"function\",\n", - " \"function\": {\n", - " \"name\": \"multiply\",\n", - " \"description\": \"Multiply two integers together.\",\n", - " \"parameters\": {\n", - " \"type\": \"object\",\n", - " \"properties\": {\n", - " \"a\": {\n", - " \"description\": \"First integer\",\n", - " \"type\": \"integer\"\n", - " },\n", - " \"b\": {\n", - " \"description\": \"Second integer\",\n", - " \"type\": \"integer\"\n", - " }\n", - " },\n", - " \"required\": [\n", - " \"a\",\n", - " \"b\"\n", - " ]\n", - " }\n", - " }\n", - "}\n" - ] - } - ], - "source": [ - "from typing import Any, Type\n", - "\n", - "from langchain_core.tools import BaseTool\n", - "\n", - "\n", - "class MultiplySchema(BaseModel):\n", - " \"\"\"Multiply tool schema.\"\"\"\n", - "\n", - " a: int = Field(..., description=\"First integer\")\n", - " b: int = Field(..., description=\"Second integer\")\n", - "\n", - "\n", - "class Multiply(BaseTool):\n", - " args_schema: Type[BaseModel] = MultiplySchema\n", - " name: str = \"multiply\"\n", - " description: str = \"Multiply two integers together.\"\n", - "\n", - " def _run(self, a: int, b: int, **kwargs: Any) -> Any:\n", - " return a * b\n", - "\n", - "\n", - "# Note: we're passing in a Multiply object not the class itself.\n", - "print(json.dumps(convert_to_openai_tool(Multiply()), indent=2))" - ] - }, - { - "cell_type": "markdown", - "id": "04bda177-202f-4811-bb74-f3fa7094a15b", - "metadata": {}, - "source": [ - "## Binding functions\n", - "\n", - "Now that we've defined a function, we'll want to pass it in to our model." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "a5aa93a7-6859-43e8-be85-619d975b908c", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_JvOu9oUwMrQHiDekZTbpNCHY', 'function': {'arguments': '{\\n \"a\": 5,\\n \"b\": 3\\n}', 'name': 'multiply'}, 'type': 'function'}]})" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from langchain_openai import ChatOpenAI\n", - "\n", - "llm = ChatOpenAI(model=\"gpt-3.5-turbo\")\n", - "llm.invoke(\"what's 5 times three\", tools=[convert_to_openai_tool(multiply)])" - ] - }, - { - "cell_type": "markdown", - "id": "dd0e7365-32d0-46a3-b8f2-caf27d5d9262", - "metadata": {}, - "source": [ - "And if we wanted this function to be passed in every time we call the tool, we could bind it to the tool:" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "87165d64-31a7-4332-965e-18fa939fda50", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_cwRoTnD1ux1SnWXLrTj2KlWH', 'function': {'arguments': '{\\n \"a\": 5,\\n \"b\": 3\\n}', 'name': 'multiply'}, 'type': 'function'}]})" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "llm_with_tool = llm.bind(tools=[convert_to_openai_tool(multiply)])\n", - "llm_with_tool.invoke(\"what's 5 times three\")" - ] - }, - { - "cell_type": "markdown", - "id": "21b4d000-3828-4e32-9226-55119f47ee67", - "metadata": {}, - "source": [ - "We can also enforce that a tool is called using the [tool_choice](https://platform.openai.com/docs/api-reference/chat/create#chat-create-tools) parameter." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "2daa354c-cc85-4a60-a9b2-b681ec22ca33", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_sWjLyioSZAtYMQRLMTzncz1v', 'function': {'arguments': '{\\n \"a\": 5,\\n \"b\": 4\\n}', 'name': 'multiply'}, 'type': 'function'}]})" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "llm_with_tool = llm.bind(\n", - " tools=[convert_to_openai_tool(multiply)],\n", - " tool_choice={\"type\": \"function\", \"function\": {\"name\": \"multiply\"}},\n", - ")\n", - "llm_with_tool.invoke(\n", - " \"don't answer my question. no do answer my question. no don't. what's five times four\"\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "ce013d11-49ea-4de9-8bbc-bc9ae203002c", - "metadata": {}, - "source": [ - "The [ChatOpenAI](https://api.python.langchain.com/en/latest/chat_models/langchain_openai.chat_models.base.ChatOpenAI.html#langchain_openai.chat_models.base.ChatOpenAI) class even comes with a `bind_tools` helper function that handles converting function-like objects to the OpenAI format and binding them for you:" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "842c9914-ac28-428f-9fcc-556177e8e715", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_LCdBa4cbhMJPRdtkhDzpRh7x', 'function': {'arguments': '{\\n \"a\": 5,\\n \"b\": 3\\n}', 'name': 'multiply'}, 'type': 'function'}]})" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "llm_with_tool = llm.bind_tools([multiply], tool_choice=\"multiply\")\n", - "llm_with_tool.invoke(\"what's 5 times three\")" - ] - }, - { - "cell_type": "markdown", - "id": "7d6e22d8-9f33-4845-9364-0d276df35ff5", - "metadata": {}, - "source": [ - "## Legacy args `functions` and `function_call`\n", - "\n", - "Until Fall of 2023 the OpenAI API expected arguments `functions` and `funtion_call` instead of `tools` and `tool_choice`, and they had a slightly different format than `tools` and `tool_choice`. LangChain maintains utilities for using the old API if you need to use that as well:" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "a317f71e-177e-404b-b09c-8fb365a4d8a2", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'name': 'multiply',\n", - " 'description': 'Multiply two integers together.',\n", - " 'parameters': {'type': 'object',\n", - " 'properties': {'a': {'description': 'First integer', 'type': 'integer'},\n", - " 'b': {'description': 'Second integer', 'type': 'integer'}},\n", - " 'required': ['a', 'b']}}" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from langchain_core.utils.function_calling import convert_to_openai_function\n", - "\n", - "convert_to_openai_function(multiply)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "dd124259-75e2-4704-9f57-824d3e463bfa", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "AIMessage(content='', additional_kwargs={'function_call': {'arguments': '{\\n \"a\": 3,\\n \"b\": 1000000\\n}', 'name': 'multiply'}})" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "llm_with_functions = llm.bind(\n", - " functions=[convert_to_openai_function(multiply)], function_call={\"name\": \"multiply\"}\n", - ")\n", - "llm_with_functions.invoke(\"what's 3 times a million\")" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "d9a90af9-1c81-4ace-b155-1589f7308a1c", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "AIMessage(content='', additional_kwargs={'function_call': {'arguments': '{\\n \"a\": 3,\\n \"b\": 1000000\\n}', 'name': 'multiply'}})" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "llm_with_functions = llm.bind_functions([multiply], function_call=\"multiply\")\n", - "llm_with_functions.invoke(\"what's 3 times a million\")" - ] - }, - { - "cell_type": "markdown", - "id": "7779808d-d75c-4d76-890d-ba8c6c571514", - "metadata": {}, - "source": [ - "## Next steps\n", - "\n", - "* **Output parsing**: See [OpenAI Tools output parsers](/docs/modules/model_io/output_parsers/types/openai_tools) and [OpenAI Functions output parsers](/docs/modules/model_io/output_parsers/types/openai_functions) to learn about extracting the function calling API responses into various formats.\n", - "* **Tool use**: See how to construct chains and agents that actually call the invoked tools in [these guides](/docs/use_cases/tool_use/)." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "poetry-venv", - "language": "python", - "name": "poetry-venv" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.1" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/docs/docs/modules/model_io/chat/function_calling.mdx b/docs/docs/modules/model_io/chat/function_calling.mdx new file mode 100644 index 0000000000..f0cc8b791e --- /dev/null +++ b/docs/docs/modules/model_io/chat/function_calling.mdx @@ -0,0 +1,535 @@ +--- +sidebar_position: 1 +title: Function calling +--- + +# Function calling + +A growing number of chat models, like +[OpenAI](https://platform.openai.com/docs/guides/function-calling), +[Gemini](https://cloud.google.com/vertex-ai/generative-ai/docs/multimodal/function-calling), +etc., have a function-calling API that lets you describe functions and +their arguments, and have the model return a JSON object with a function +to invoke and the inputs to that function. Function-calling is extremely +useful for building [tool-using chains and +agents](../../../../docs/use_cases/tool_use/), and for getting +structured outputs from models more generally. + +LangChain comes with a number of utilities to make function-calling +easy. Namely, it comes with: + +- simple syntax for binding functions to models +- converters for formatting various types of objects to the expected + function schemas +- output parsers for extracting the function invocations from API + responses +- chains for getting structured outputs from a model, built on top of + function calling + +We’ll focus here on the first two points. For a detailed guide on output +parsing check out the [OpenAI Tools output +parsers](../../../../docs/modules/model_io/output_parsers/types/openai_tools) +and to see the structured output chains check out the [Structured output +guide](../../../../docs/guides/structured_output). + +Before getting started make sure you have `langchain-core` installed. + +```python +%pip install -qU langchain-core langchain-openai +``` + + +```python +import getpass +import os +``` + +## Binding functions + +A number of models implement helper methods that will take care of +formatting and binding different function-like objects to the model. +Let’s take a look at how we might take the following Pydantic function +schema and get different models to invoke it: + +```python +from langchain_core.pydantic_v1 import BaseModel, Field + + +# Note that the docstrings here are crucial, as they will be passed along +# to the model along with the class name. +class Multiply(BaseModel): + """Multiply two integers together.""" + + a: int = Field(..., description="First integer") + b: int = Field(..., description="Second integer") +``` + +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + + + + +Set up dependencies and API keys: + +```python +%pip install -qU langchain-openai +``` + + +```python +os.environ["OPENAI_API_KEY"] = getpass.getpass() +``` + +We can use the `ChatOpenAI.bind_tools()` method to handle converting +`Multiply` to an OpenAI function and binding it to the model (i.e., +passing it in each time the model is invoked). + + + +```python +from langchain_openai import ChatOpenAI + +llm = ChatOpenAI(model="gpt-3.5-turbo-0125", temperature=0) +llm_with_tools = llm.bind_tools([Multiply]) +llm_with_tools.invoke("what's 3 * 12") +``` + +``` text +AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_Q8ZQ97Qrj5zalugSkYMGV1Uo', 'function': {'arguments': '{"a":3,"b":12}', 'name': 'Multiply'}, 'type': 'function'}]}) +``` + +We can add a tool parser to extract the tool calls from the generated +message to JSON: + +```python +from langchain_core.output_parsers.openai_tools import JsonOutputToolsParser + +tool_chain = llm_with_tools | JsonOutputToolsParser() +tool_chain.invoke("what's 3 * 12") +``` + +``` text +[{'type': 'Multiply', 'args': {'a': 3, 'b': 12}}] +``` + +Or back to the original Pydantic class: + +```python +from langchain_core.output_parsers.openai_tools import PydanticToolsParser + +tool_chain = llm_with_tools | PydanticToolsParser(tools=[Multiply]) +tool_chain.invoke("what's 3 * 12") +``` + +``` text +[Multiply(a=3, b=12)] +``` + +If we wanted to force that a tool is used (and that it is used only +once), we can set the `tool_choice` argument: + +```python +llm_with_multiply = llm.bind_tools([Multiply], tool_choice="Multiply") +llm_with_multiply.invoke( + "make up some numbers if you really want but I'm not forcing you" +) +``` + +``` text +AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_f3DApOzb60iYjTfOhVFhDRMI', 'function': {'arguments': '{"a":5,"b":10}', 'name': 'Multiply'}, 'type': 'function'}]}) +``` + +For more see the [ChatOpenAI API +reference](https://api.python.langchain.com/en/latest/chat_models/langchain_openai.chat_models.base.ChatOpenAI.html#langchain_openai.chat_models.base.ChatOpenAI.bind_tools). + + + + +Install dependencies and set API keys: + +```python +%pip install -qU langchain-fireworks +``` + + +```python +os.environ["FIREWORKS_API_KEY"] = getpass.getpass() +``` + +We can use the `ChatFireworks.bind_tools()` method to handle converting +`Multiply` to a valid function schema and binding it to the model (i.e., +passing it in each time the model is invoked). + +```python +from langchain_fireworks import ChatFireworks + +llm = ChatFireworks(model="accounts/fireworks/models/firefunction-v1", temperature=0) +llm_with_tools = llm.bind_tools([Multiply]) +llm_with_tools.invoke("what's 3 * 12") +``` + +``` text +AIMessage(content='Three multiplied by twelve is 36.') +``` + +If our model isn’t using the tool, as is the case here, we can force +tool usage by specifying `tool_choice="any"` or by specifying the name +of the specific tool we want used: + +```python +llm_with_tools = llm.bind_tools([Multiply], tool_choice="Multiply") +llm_with_tools.invoke("what's 3 * 12") +``` + +``` text +AIMessage(content='', additional_kwargs={'tool_calls': [{'index': 0, 'id': 'call_qIP2bJugb67LGvc6Zhwkvfqc', 'type': 'function', 'function': {'name': 'Multiply', 'arguments': '{"a": 3, "b": 12}'}}]}) +``` + +We can add a tool parser to extract the tool calls from the generated +message to JSON: + +```python +from langchain_core.output_parsers.openai_tools import JsonOutputToolsParser + +tool_chain = llm_with_tools | JsonOutputToolsParser() +tool_chain.invoke("what's 3 * 12") +``` + +``` text +[{'type': 'Multiply', 'args': {'a': 3, 'b': 12}}] +``` + +Or back to the original Pydantic class: + +```python +from langchain_core.output_parsers.openai_tools import PydanticToolsParser + +tool_chain = llm_with_tools | PydanticToolsParser(tools=[Multiply]) +tool_chain.invoke("what's 3 * 12") +``` + +``` text +[Multiply(a=3, b=12)] +``` + +For more see the [ChatFireworks](https://api.python.langchain.com/en/latest/chat_models/langchain_fireworks.chat_models.ChatFireworks.html#langchain_fireworks.chat_models.ChatFireworks.bind_tools) reference. + + + + +Install dependencies and set API keys: + +```python +%pip install -qU langchain-mistralai +``` + + +```python +os.environ["MISTRAL_API_KEY"] = getpass.getpass() +``` + +We can use the `ChatMistralAI.bind_tools()` method to handle converting +`Multiply` to a valid function schema and binding it to the model (i.e., +passing it in each time the model is invoked). + +```python +from langchain_mistralai import ChatMistralAI + +llm = ChatMistralAI(model="mistral-large-latest", temperature=0) +llm_with_tools = llm.bind_tools([Multiply]) +llm_with_tools.invoke("what's 3 * 12") +``` + +``` text +AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'null', 'type': , 'function': {'name': 'Multiply', 'arguments': '{"a": 3, "b": 12}'}}]}) +``` + +We can add a tool parser to extract the tool calls from the generated +message to JSON: + +```python +from langchain_core.output_parsers.openai_tools import JsonOutputToolsParser + +tool_chain = llm_with_tools | JsonOutputToolsParser() +tool_chain.invoke("what's 3 * 12") +``` + +``` text +[{'type': 'Multiply', 'args': {'a': 3, 'b': 12}}] +``` + +Or back to the original Pydantic class: + +```python +from langchain_core.output_parsers.openai_tools import PydanticToolsParser + +tool_chain = llm_with_tools | PydanticToolsParser(tools=[Multiply]) +tool_chain.invoke("what's 3 * 12") +``` + +``` text +[Multiply(a=3, b=12)] +``` + +We can force tool usage by specifying `tool_choice="any"`: + +```python +llm_with_tools = llm.bind_tools([Multiply], tool_choice="any") +llm_with_tools.invoke("I don't even want you to use the tool") +``` + +``` text +AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'null', 'type': , 'function': {'name': 'Multiply', 'arguments': '{"a": 5, "b": 7}'}}]}) +``` + +For more see the [ChatMistralAI API reference](https://api.python.langchain.com/en/latest/chat_models/langchain_mistralai.chat_models.ChatMistralAI.html#langchain_mistralai.chat_models.ChatMistralAI). + + + + +Since TogetherAI is a drop-in replacement for OpenAI, we can just use +the OpenAI integration. + +Install dependencies and set API keys: + +```python +%pip install -qU langchain-openai +``` + + +```python +os.environ["TOGETHER_API_KEY"] = getpass.getpass() +``` + +We can use the `ChatOpenAI.bind_tools()` method to handle converting +`Multiply` to a valid function schema and binding it to the model (i.e., +passing it in each time the model is invoked). + +```python +from langchain_openai import ChatOpenAI + +llm = ChatOpenAI( + base_url="https://api.together.xyz/v1", + api_key=os.environ["TOGETHER_API_KEY"], + model="mistralai/Mixtral-8x7B-Instruct-v0.1", +) +llm_with_tools = llm.bind_tools([Multiply]) +llm_with_tools.invoke("what's 3 * 12") +``` + +``` text +AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_4tc61dp0478zafqe33hfriee', 'function': {'arguments': '{"a":3,"b":12}', 'name': 'Multiply'}, 'type': 'function'}]}) +``` + +We can add a tool parser to extract the tool calls from the generated +message to JSON: + +```python +from langchain_core.output_parsers.openai_tools import JsonOutputToolsParser + +tool_chain = llm_with_tools | JsonOutputToolsParser() +tool_chain.invoke("what's 3 * 12") +``` + +``` text +[{'type': 'Multiply', 'args': {'a': 3, 'b': 12}}] +``` + +Or back to the original Pydantic class: + +```python +from langchain_core.output_parsers.openai_tools import PydanticToolsParser + +tool_chain = llm_with_tools | PydanticToolsParser(tools=[Multiply]) +tool_chain.invoke("what's 3 * 12") +``` + +``` text +[Multiply(a=3, b=12)] +``` + +If we wanted to force that a tool is used (and that it is used only +once), we can set the `tool_choice` argument: + +```python +llm_with_multiply = llm.bind_tools([Multiply], tool_choice="Multiply") +llm_with_multiply.invoke( + "make up some numbers if you really want but I'm not forcing you" +) +``` + +``` text +AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_6k6d0gr3jhqil2kqf7sgeusl', 'function': {'arguments': '{"a":5,"b":7}', 'name': 'Multiply'}, 'type': 'function'}]}) +``` + +For more see the [ChatOpenAI API +reference](https://api.python.langchain.com/en/latest/chat_models/langchain_openai.chat_models.base.ChatOpenAI.html#langchain_openai.chat_models.base.ChatOpenAI.bind_tools). + + + + +## Defining functions schemas + +In case you need to access function schemas directly, LangChain has a built-in converter that can turn +Python functions, Pydantic classes, and LangChain Tools into the OpenAI format JSON schema: + +### Python function + +```python +import json + +from langchain_core.utils.function_calling import convert_to_openai_tool + + +def multiply(a: int, b: int) -> int: + """Multiply two integers together. + + Args: + a: First integer + b: Second integer + """ + return a * b + + +print(json.dumps(convert_to_openai_tool(multiply), indent=2)) +``` + +``` text +{ + "type": "function", + "function": { + "name": "multiply", + "description": "Multiply two integers together.", + "parameters": { + "type": "object", + "properties": { + "a": { + "type": "integer", + "description": "First integer" + }, + "b": { + "type": "integer", + "description": "Second integer" + } + }, + "required": [ + "a", + "b" + ] + } + } +} +``` + +### Pydantic class + +```python +from langchain_core.pydantic_v1 import BaseModel, Field + + +class multiply(BaseModel): + """Multiply two integers together.""" + + a: int = Field(..., description="First integer") + b: int = Field(..., description="Second integer") + + +print(json.dumps(convert_to_openai_tool(multiply), indent=2)) +``` + +``` text +{ + "type": "function", + "function": { + "name": "multiply", + "description": "Multiply two integers together.", + "parameters": { + "type": "object", + "properties": { + "a": { + "description": "First integer", + "type": "integer" + }, + "b": { + "description": "Second integer", + "type": "integer" + } + }, + "required": [ + "a", + "b" + ] + } + } +} +``` + +### LangChain Tool + +```python +from typing import Any, Type + +from langchain_core.tools import BaseTool + + +class MultiplySchema(BaseModel): + """Multiply tool schema.""" + + a: int = Field(..., description="First integer") + b: int = Field(..., description="Second integer") + + +class Multiply(BaseTool): + args_schema: Type[BaseModel] = MultiplySchema + name: str = "multiply" + description: str = "Multiply two integers together." + + def _run(self, a: int, b: int, **kwargs: Any) -> Any: + return a * b + + +# Note: we're passing in a Multiply object not the class itself. +print(json.dumps(convert_to_openai_tool(Multiply()), indent=2)) +``` + +``` text +{ + "type": "function", + "function": { + "name": "multiply", + "description": "Multiply two integers together.", + "parameters": { + "type": "object", + "properties": { + "a": { + "description": "First integer", + "type": "integer" + }, + "b": { + "description": "Second integer", + "type": "integer" + } + }, + "required": [ + "a", + "b" + ] + } + } +} +``` + +## Next steps + +- **Output parsing**: See [OpenAI Tools output + parsers](../../../../docs/modules/model_io/output_parsers/types/openai_tools) + and [OpenAI Functions output + parsers](../../../../docs/modules/model_io/output_parsers/types/openai_functions) + to learn about extracting the function calling API responses into + various formats. +- **Structured output chains**: [Some models have constructors](../../../../docs/guides/structured_output) that + handle creating a structured output chain for you. +- **Tool use**: See how to construct chains and agents that actually + call the invoked tools in [these + guides](../../../../docs/use_cases/tool_use/).