ChatXAI
xAI is an artificial intelligence company that develops large language models (LLMs). Their flagship model, Grok, is trained on real-time X (formerly Twitter) data and aims to provide witty, personality-rich responses while maintaining high capability on technical tasks.
This guide will help you getting started with ChatXAI
chat
models. For detailed documentation of all
ChatXAI
features and configurations head to the API
reference.
Overview
Integration details
Class | Package | Local | Serializable | PY support | Package downloads | Package latest |
---|---|---|---|---|---|---|
ChatXAI | @langchain/xai | ❌ | ✅ | ❌ |
Model features
See the links in the table headers below for guides on how to use specific features.
Tool calling | Structured output | JSON mode | Image input | Audio input | Video input | Token-level streaming | Token usage | Logprobs |
---|---|---|---|---|---|---|---|---|
✅ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ✅ | ✅ |
Setup
To access ChatXAI
models you’ll need to create an xAI account, get an
API key, and install the @langchain/xai
integration package.
Credentials
Head to the xAI website to sign up to xAI and generate
an API key. Once you’ve done this set the XAI_API_KEY
environment
variable:
export XAI_API_KEY="your-api-key"
If you want to get automated tracing of your model calls you can also set your LangSmith API key by uncommenting below:
# export LANGCHAIN_TRACING_V2="true"
# export LANGCHAIN_API_KEY="your-api-key"
Installation
The LangChain ChatXAI
integration lives in the @langchain/xai
package:
- npm
- yarn
- pnpm
npm i @langchain/xai @langchain/core
yarn add @langchain/xai @langchain/core
pnpm add @langchain/xai @langchain/core
Instantiation
Now we can instantiate our model object and generate chat completions:
import { ChatXAI } from "@langchain/xai";
const llm = new ChatXAI({
model: "grok-beta", // default
temperature: 0,
maxTokens: undefined,
maxRetries: 2,
// other params...
});
Invocation
const aiMsg = await llm.invoke([
[
"system",
"You are a helpful assistant that translates English to French. Translate the user sentence.",
],
["human", "I love programming."],
]);
console.log(aiMsg);
AIMessage {
"id": "71d7e3d8-30dd-472c-8038-b6b283dcee63",
"content": "J'adore programmer.",
"additional_kwargs": {},
"response_metadata": {
"tokenUsage": {
"promptTokens": 30,
"completionTokens": 6,
"totalTokens": 36
},
"finish_reason": "stop",
"usage": {
"prompt_tokens": 30,
"completion_tokens": 6,
"total_tokens": 36
},
"system_fingerprint": "fp_3e3898d4ce"
},
"tool_calls": [],
"invalid_tool_calls": [],
"usage_metadata": {
"output_tokens": 6,
"input_tokens": 30,
"total_tokens": 36,
"input_token_details": {},
"output_token_details": {}
}
}
console.log(aiMsg.content);
J'adore programmer.
Chaining
We can chain our model with a prompt template like so:
import { ChatPromptTemplate } from "@langchain/core/prompts";
const prompt = ChatPromptTemplate.fromMessages([
[
"system",
"You are a helpful assistant that translates {input_language} to {output_language}.",
],
["human", "{input}"],
]);
const chain = prompt.pipe(llm);
await chain.invoke({
input_language: "English",
output_language: "German",
input: "I love programming.",
});
AIMessage {
"id": "b2738008-8247-40e1-81dc-d9bf437a1a0c",
"content": "Ich liebe das Programmieren.",
"additional_kwargs": {},
"response_metadata": {
"tokenUsage": {
"promptTokens": 25,
"completionTokens": 7,
"totalTokens": 32
},
"finish_reason": "stop",
"usage": {
"prompt_tokens": 25,
"completion_tokens": 7,
"total_tokens": 32
},
"system_fingerprint": "fp_3e3898d4ce"
},
"tool_calls": [],
"invalid_tool_calls": [],
"usage_metadata": {
"output_tokens": 7,
"input_tokens": 25,
"total_tokens": 32,
"input_token_details": {},
"output_token_details": {}
}
}
Behind the scenes, xAI uses the OpenAI SDK and OpenAI compatible API.
API reference
For detailed documentation of all ChatXAI features and configurations head to the API reference: https://api.js.langchain.com/classes/\_langchain_xai.ChatXAI.html
Related
- Chat model conceptual guide
- Chat model how-to guides