> ## Documentation Index > Fetch the complete documentation index at: https://nvd-54.mintlify.site/llms.txt > Use this file to discover all available pages before exploring further. # ChatXAI 集成 > 使用 LangChain Python 集成 ChatXAI 聊天模型。 This page makes reference to Grok models provided by [xAI](https://docs.x.ai/docs/overview) - not to be confused with [Groq](https://console.groq.com/docs/overview), a separate AI hardware and software company. 请参阅 [Groq provider page](/oss/python/integrations/providers/groq). [xAI](https://console.x.ai/) offers an API to interact with Grok models. **API 参考** 有关所有 features and configuration options, 请前往 [`ChatXAI`](https://reference.langchain.com/python/langchain-xai/chat_models/ChatXAI) API reference. ## 概述 ### 集成详情 | 类 | 包 | 可序列化 | [JS 支持](https://js.langchain.com/docs/integrations/chat/xai) | 下载量 | 版本 | | :------------------------------------------------------------------------------------ | :----------------------------------------------------------------------- | :--: | :----------------------------------------------------------: | :--------------------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------: | | [`ChatXAI`](https://reference.langchain.com/python/langchain-xai/chat_models/ChatXAI) | [`langchain-xai`](https://reference.langchain.com/python/langchain-xai/) | beta | ✅ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-xai?style=flat-square\&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-xai?style=flat-square\&label=%20) | ### 模型功能 | [Tool calling](/oss/python/langchain/tools) | [Structured output](/oss/python/langchain/structured-output) | [Image input](/oss/python/langchain/messages#multimodal) | 音频输入 | 视频输入 | [Token-level streaming](/oss/python/langchain/streaming#llm-tokens) | 原生异步 | [Token usage](/oss/python/langchain/models#token-usage) | [Logprobs](/oss/python/langchain/models#log-probabilities) | | :-----------------------------------------: | :----------------------------------------------------------: | :------------------------------------------------------: | :--: | :--: | :-----------------------------------------------------------------: | :--: | :-----------------------------------------------------: | :--------------------------------------------------------: | | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ | ✅ | ✅ | ## 设置 要访问 xAI models, you'll need to create an xAI 账户，获取 API 密钥，并安装 `langchain-xai` 集成包。 ### 凭证 前往 [this page](https://console.x.ai/) 注册 xAI 并生成 API 密钥。 完成后，设置 `XAI_API_KEY` 环境变量： ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} import getpass import os if "XAI_API_KEY" not in os.environ: os.environ["XAI_API_KEY"] = getpass.getpass("Enter your xAI API key: ") ``` 要启用模型调用的自动追踪，请设置您的 [LangSmith](/langsmith/home) API key: ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} os.environ["LANGSMITH_API_KEY"] = getpass.getpass("请输入您的 LangSmith API 密钥: ") os.environ["LANGSMITH_TRACING"] = "true" ``` ### 安装 LangChain 的 xAI 集成位于 `langchain-xai` 包中： ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} pip install -qU langchain-xai ``` ## 实例化 现在我们可以实例化模型对象并生成聊天补全： ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} from langchain_xai import ChatXAI llm = ChatXAI( model="grok-beta", temperature=0, max_tokens=None, timeout=None, max_retries=2, # 其他参数... ) ``` ## 调用 ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} messages = [ ( "system", "You are a helpful assistant that translates English to French. Translate the user sentence.", ), ("human", "I love programming."), ] ai_msg = llm.invoke(messages) ai_msg ``` ```text theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} AIMessage(content="J'adore programmer.", additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 6, 'prompt_tokens': 30, 'total_tokens': 36, 'completion_tokens_details': None, 'prompt_tokens_details': None}, 'model_name': 'grok-beta', 'system_fingerprint': 'fp_14b89b2dfc', 'finish_reason': 'stop', 'logprobs': None}, id='run-adffb7a3-e48a-4f52-b694-340d85abe5c3-0', usage_metadata={'input_tokens': 30, 'output_tokens': 6, 'total_tokens': 36, 'input_token_details': {}, 'output_token_details': {}}) ``` ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} print(ai_msg.content) ``` ```text theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} J'adore programmer. ``` ## 工具调用 ChatXAI has a [tool calling](https://docs.x.ai/docs#capabilities) (we use "tool calling" and "function calling" interchangeably here) API that lets you describe tools and their arguments, and have the model return a JSON object with a tool to invoke and the inputs to that tool. Tool-calling is extremely useful for building tool-using chains and agents, and for getting structured outputs from models more generally. ### ChatXAI.bind\_tools() With `ChatXAI.bind_tools`, we can easily pass in Pydantic classes, dict schemas, LangChain tools, or even functions as tools to the model. Under the hood, these are converted to an OpenAI tool schema, which looks like: ``` { "name": "...", "description": "...", "parameters": {...} # JSONSchema } ``` and passed in every model invocation. ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} from pydantic import BaseModel, Field class GetWeather(BaseModel): """Get the current weather in a given location""" location: str = Field(description="The city and state, e.g. San Francisco, CA") llm_with_tools = llm.bind_tools([GetWeather]) ``` ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} ai_msg = llm_with_tools.invoke( "what is the weather like in San Francisco", ) ai_msg ``` ```text theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} AIMessage(content='I am retrieving the current weather for San Francisco.', additional_kwargs={'tool_calls': [{'id': '0', 'function': {'arguments': '{"location":"San Francisco, CA"}', 'name': 'GetWeather'}, 'type': 'function'}], 'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 11, 'prompt_tokens': 151, 'total_tokens': 162, 'completion_tokens_details': None, 'prompt_tokens_details': None}, 'model_name': 'grok-beta', 'system_fingerprint': 'fp_14b89b2dfc', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-73707da7-afec-4a52-bee1-a176b0ab8585-0', tool_calls=[{'name': 'GetWeather', 'args': {'location': 'San Francisco, CA'}, 'id': '0', 'type': 'tool_call'}], usage_metadata={'input_tokens': 151, 'output_tokens': 11, 'total_tokens': 162, 'input_token_details': {}, 'output_token_details': {}}) ``` ## Live search xAI supports a [Live Search](https://docs.x.ai/docs/guides/live-search) feature that enables Grok to ground its answers using results from web searches: ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} from langchain_xai import ChatXAI llm = ChatXAI( model="grok-3-latest", search_parameters={ "mode": "auto", # Example optional parameters below: "max_search_results": 3, "from_date": "2025-05-26", "to_date": "2025-05-27", }, ) llm.invoke("Provide me a digest of world news in the last 24 hours.") ``` See [xAI docs](https://docs.x.ai/docs/guides/live-search) for the full set of web search options. *** ## API 参考 有关所有 `ChatXAI` 功能和配置的详细文档，请前往 [API reference](https://reference.langchain.com/python/integrations/langchain_xai/). ***
[Connect these docs](/use-these-docs) to Claude, VSCode, and more via MCP for real-time answers. [Edit this page on GitHub](https://github.com/langchain-ai/docs/edit/main/src/oss/python/integrations/chat/xai.mdx) or [file an issue](https://github.com/langchain-ai/docs/issues/new/choose).