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https://github.com/hwchase17/langchain
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docs: update func calling doc (#18300)
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"cells": [
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{
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"cell_type": "markdown",
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"id": "dae8d4ed-9150-45da-b494-7717ab0a2960",
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"metadata": {},
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"source": [
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"# Function calling\n",
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"\n",
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"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",
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"\n",
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"LangChain comes with a number of utilities to make function-calling easy. Namely, it comes with\n",
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"\n",
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"* simple syntax for binding functions to models\n",
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"* converters for formatting various types of objects to the expected function schemas\n",
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"* output parsers for extracting the function invocations from API responses\n",
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"\n",
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"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)."
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]
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{
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"cell_type": "markdown",
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"id": "a177c64b-7c99-495c-b362-5ed3b40aa26a",
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"metadata": {},
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"source": [
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"## Defining functions\n",
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"\n",
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"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:"
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"cell_type": "code",
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"execution_count": 1,
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"id": "f6d1dc0c-6170-4977-809f-365099f628ea",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\n",
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"\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",
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"\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",
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"Note: you may need to restart the kernel to use updated packages.\n"
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]
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}
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],
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"source": [
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"%pip install -qU langchain-core langchain-openai"
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]
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},
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{
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"cell_type": "markdown",
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"id": "6bd290bd-7621-466b-a73e-fc8480f879ec",
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"metadata": {},
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"source": [
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"### Python function"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "41ebab5c-0e9f-4b49-86ee-9290ced2fe96",
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"metadata": {},
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"outputs": [
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"{\n",
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" \"type\": \"function\",\n",
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" \"function\": {\n",
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" \"name\": \"multiply\",\n",
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" \"description\": \"Multiply two integers together.\",\n",
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" \"parameters\": {\n",
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" \"type\": \"object\",\n",
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" \"properties\": {\n",
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" \"a\": {\n",
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" \"type\": \"integer\",\n",
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" \"description\": \"First integer\"\n",
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" },\n",
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" \"b\": {\n",
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" \"type\": \"integer\",\n",
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" \"description\": \"Second integer\"\n",
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" }\n",
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" },\n",
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" \"required\": [\n",
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" \"a\",\n",
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" \"b\"\n",
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" ]\n",
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" }\n",
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" }\n",
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"}\n"
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]
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}
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],
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"source": [
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"import json\n",
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"\n",
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"from langchain_core.utils.function_calling import convert_to_openai_tool\n",
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"\n",
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"\n",
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"def multiply(a: int, b: int) -> int:\n",
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" \"\"\"Multiply two integers together.\n",
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"\n",
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" Args:\n",
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" a: First integer\n",
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" b: Second integer\n",
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" \"\"\"\n",
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" return a * b\n",
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"\n",
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"\n",
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"print(json.dumps(convert_to_openai_tool(multiply), indent=2))"
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]
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},
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{
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"cell_type": "markdown",
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"id": "ecf22577-38ab-48f1-ba0b-371aaba1bacc",
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"metadata": {},
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"source": [
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"### Pydantic class"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "ecc8ffd4-aed3-4f47-892d-1896cc1ca4dc",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"{\n",
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" \"type\": \"function\",\n",
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" \"function\": {\n",
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" \"name\": \"multiply\",\n",
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" \"description\": \"Multiply two integers together.\",\n",
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" \"parameters\": {\n",
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" \"type\": \"object\",\n",
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" \"properties\": {\n",
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" \"a\": {\n",
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" \"description\": \"First integer\",\n",
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" \"type\": \"integer\"\n",
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" },\n",
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" \"b\": {\n",
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" \"description\": \"Second integer\",\n",
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" \"type\": \"integer\"\n",
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" }\n",
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" },\n",
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" \"required\": [\n",
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" \"a\",\n",
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" \"b\"\n",
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" ]\n",
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" }\n",
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" }\n",
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"}\n"
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]
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}
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],
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"source": [
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"from langchain_core.pydantic_v1 import BaseModel, Field\n",
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"\n",
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"\n",
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"class multiply(BaseModel):\n",
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" \"\"\"Multiply two integers together.\"\"\"\n",
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"\n",
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" a: int = Field(..., description=\"First integer\")\n",
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" b: int = Field(..., description=\"Second integer\")\n",
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"\n",
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"\n",
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"print(json.dumps(convert_to_openai_tool(multiply), indent=2))"
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]
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},
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{
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"cell_type": "markdown",
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"id": "b83d5a88-50ed-4ae4-85cf-8b895617496f",
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"metadata": {},
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"source": [
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"### LangChain Tool"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"id": "696c7dd6-660c-4797-909f-bf878b3acf93",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"{\n",
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" \"type\": \"function\",\n",
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" \"function\": {\n",
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" \"name\": \"multiply\",\n",
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" \"description\": \"Multiply two integers together.\",\n",
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" \"parameters\": {\n",
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" \"type\": \"object\",\n",
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" \"properties\": {\n",
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" \"a\": {\n",
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" \"description\": \"First integer\",\n",
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" \"type\": \"integer\"\n",
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" },\n",
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" \"b\": {\n",
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" \"description\": \"Second integer\",\n",
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" \"type\": \"integer\"\n",
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" }\n",
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" },\n",
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" \"required\": [\n",
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" \"a\",\n",
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" \"b\"\n",
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" ]\n",
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" }\n",
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" }\n",
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"}\n"
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]
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}
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],
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"source": [
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"from typing import Any, Type\n",
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"\n",
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"from langchain_core.tools import BaseTool\n",
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"\n",
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"\n",
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"class MultiplySchema(BaseModel):\n",
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" \"\"\"Multiply tool schema.\"\"\"\n",
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"\n",
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" a: int = Field(..., description=\"First integer\")\n",
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" b: int = Field(..., description=\"Second integer\")\n",
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"\n",
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"\n",
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"class Multiply(BaseTool):\n",
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" args_schema: Type[BaseModel] = MultiplySchema\n",
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" name: str = \"multiply\"\n",
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" description: str = \"Multiply two integers together.\"\n",
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"\n",
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" def _run(self, a: int, b: int, **kwargs: Any) -> Any:\n",
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" return a * b\n",
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"\n",
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"\n",
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"# Note: we're passing in a Multiply object not the class itself.\n",
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"print(json.dumps(convert_to_openai_tool(Multiply()), indent=2))"
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]
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},
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{
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"cell_type": "markdown",
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"id": "04bda177-202f-4811-bb74-f3fa7094a15b",
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"metadata": {},
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"source": [
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"## Binding functions\n",
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"\n",
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"Now that we've defined a function, we'll want to pass it in to our model."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"id": "a5aa93a7-6859-43e8-be85-619d975b908c",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_JvOu9oUwMrQHiDekZTbpNCHY', 'function': {'arguments': '{\\n \"a\": 5,\\n \"b\": 3\\n}', 'name': 'multiply'}, 'type': 'function'}]})"
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]
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},
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"execution_count": 7,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"from langchain_openai import ChatOpenAI\n",
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"\n",
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"llm = ChatOpenAI(model=\"gpt-3.5-turbo\")\n",
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"llm.invoke(\"what's 5 times three\", tools=[convert_to_openai_tool(multiply)])"
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]
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},
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{
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"cell_type": "markdown",
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"id": "dd0e7365-32d0-46a3-b8f2-caf27d5d9262",
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"metadata": {},
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"source": [
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"And if we wanted this function to be passed in every time we call the tool, we could bind it to the tool:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"id": "87165d64-31a7-4332-965e-18fa939fda50",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_cwRoTnD1ux1SnWXLrTj2KlWH', 'function': {'arguments': '{\\n \"a\": 5,\\n \"b\": 3\\n}', 'name': 'multiply'}, 'type': 'function'}]})"
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]
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},
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"execution_count": 8,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"llm_with_tool = llm.bind(tools=[convert_to_openai_tool(multiply)])\n",
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"llm_with_tool.invoke(\"what's 5 times three\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "21b4d000-3828-4e32-9226-55119f47ee67",
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"metadata": {},
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"source": [
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"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."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"id": "2daa354c-cc85-4a60-a9b2-b681ec22ca33",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
|
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"AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_sWjLyioSZAtYMQRLMTzncz1v', 'function': {'arguments': '{\\n \"a\": 5,\\n \"b\": 4\\n}', 'name': 'multiply'}, 'type': 'function'}]})"
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]
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},
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"execution_count": 10,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"llm_with_tool = llm.bind(\n",
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" tools=[convert_to_openai_tool(multiply)],\n",
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" tool_choice={\"type\": \"function\", \"function\": {\"name\": \"multiply\"}},\n",
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")\n",
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"llm_with_tool.invoke(\n",
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" \"don't answer my question. no do answer my question. no don't. what's five times four\"\n",
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")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "ce013d11-49ea-4de9-8bbc-bc9ae203002c",
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"metadata": {},
|
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"source": [
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"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:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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|
||||||
"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
|
|
||||||
}
|
|
||||||
535
docs/docs/modules/model_io/chat/function_calling.mdx
Normal file
535
docs/docs/modules/model_io/chat/function_calling.mdx
Normal file
@ -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';
|
||||||
|
|
||||||
|
<Tabs>
|
||||||
|
<TabItem value="openai" label="OpenAI" default>
|
||||||
|
|
||||||
|
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).
|
||||||
|
|
||||||
|
</TabItem>
|
||||||
|
<TabItem value="fireworks" label="Fireworks">
|
||||||
|
|
||||||
|
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.
|
||||||
|
|
||||||
|
</TabItem>
|
||||||
|
<TabItem value="mistral" label="Mistral">
|
||||||
|
|
||||||
|
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': <ToolType.function: '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)]
|
||||||
|
```
|
||||||
|
|
||||||
|
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': <ToolType.function: 'function'>, '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).
|
||||||
|
|
||||||
|
</TabItem>
|
||||||
|
<TabItem value="together" label="Together">
|
||||||
|
|
||||||
|
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).
|
||||||
|
|
||||||
|
</TabItem>
|
||||||
|
</Tabs>
|
||||||
|
|
||||||
|
## 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/).
|
||||||
Loading…
Reference in New Issue
Block a user