Skip to main content

ChatGroq

Setup​

In order to use the Groq API you'll need an API key. You can sign up for a Groq account and create an API key here.

You'll first need to install the @langchain/groq package:

tip

See this section for general instructions on installing integration packages.

npm install @langchain/groq
tip

We're unifying model params across all packages. We now suggest using model instead of modelName, and apiKey for API keys.

Usage​

import { ChatGroq } from "@langchain/groq";
import { ChatPromptTemplate } from "@langchain/core/prompts";

const model = new ChatGroq({
  apiKey: process.env.GROQ_API_KEY,
});
const prompt = ChatPromptTemplate.fromMessages([
  ["system", "You are a helpful assistant"],
  ["human", "{input}"],
]);
const chain = prompt.pipe(model);
const response = await chain.invoke({
  input: "Hello",
});
console.log("response", response);
/**
response AIMessage {
  content: "Hello! I'm happy to assist you in any way I can. Is there something specific you need help with or a question you have?",
}
 */

API Reference:

info

You can see a LangSmith trace of this example here

Tool calling​

Groq chat models support calling multiple functions to get all required data to answer a question. Here's an example:

import { ChatGroq } from "@langchain/groq";

// Mocked out function, could be a database/API call in production
function getCurrentWeather(location: string, _unit?: string) {
  if (location.toLowerCase().includes("tokyo")) {
    return JSON.stringify({ location, temperature: "10", unit: "celsius" });
  } else if (location.toLowerCase().includes("san francisco")) {
    return JSON.stringify({
      location,
      temperature: "72",
      unit: "fahrenheit",
    });
  } else {
    return JSON.stringify({ location, temperature: "22", unit: "celsius" });
  }
}

// Bind function to the model as a tool
const chat = new ChatGroq({
  model: "mixtral-8x7b-32768",
  maxTokens: 128,
}).bind({
  tools: [
    {
      type: "function",
      function: {
        name: "get_current_weather",
        description: "Get the current weather in a given location",
        parameters: {
          type: "object",
          properties: {
            location: {
              type: "string",
              description: "The city and state, e.g. San Francisco, CA",
            },
            unit: { type: "string", enum: ["celsius", "fahrenheit"] },
          },
          required: ["location"],
        },
      },
    },
  ],
  tool_choice: "auto",
});

const res = await chat.invoke([
  ["human", "What's the weather like in San Francisco?"],
]);
console.log(res.additional_kwargs.tool_calls);
/*
  [
    {
      id: 'call_01htk055jpftwbb9tvphyf9bnf',
      type: 'function',
      function: {
        name: 'get_current_weather',
        arguments: '{"location":"San Francisco, CA"}'
      }
    }
  ]
*/

API Reference:

.withStructuredOutput({ ... })​

info

The .withStructuredOutput method is in beta. It is actively being worked on, so the API may change.

You can also use the .withStructuredOutput({ ... }) method to coerce ChatGroq into returning a structured output.

The method allows for passing in either a Zod object, or a valid JSON schema (like what is returned from zodToJsonSchema).

Using the method is simple. Just define your LLM and call .withStructuredOutput({ ... }) on it, passing the desired schema.

Here is an example using a Zod schema and the functionCalling mode (default mode):

import { ChatPromptTemplate } from "@langchain/core/prompts";
import { ChatGroq } from "@langchain/groq";
import { z } from "zod";

const model = new ChatGroq({
  temperature: 0,
  model: "mixtral-8x7b-32768",
});

const calculatorSchema = z.object({
  operation: z.enum(["add", "subtract", "multiply", "divide"]),
  number1: z.number(),
  number2: z.number(),
});

const modelWithStructuredOutput = model.withStructuredOutput(calculatorSchema);

const prompt = ChatPromptTemplate.fromMessages([
  ["system", "You are VERY bad at math and must always use a calculator."],
  ["human", "Please help me!! What is 2 + 2?"],
]);
const chain = prompt.pipe(modelWithStructuredOutput);
const result = await chain.invoke({});
console.log(result);
/*
  { operation: 'add', number1: 2, number2: 2 }
*/

/**
 * You can also specify 'includeRaw' to return the parsed
 * and raw output in the result.
 */
const includeRawModel = model.withStructuredOutput(calculatorSchema, {
  name: "calculator",
  includeRaw: true,
});

const includeRawChain = prompt.pipe(includeRawModel);
const includeRawResult = await includeRawChain.invoke({});
console.log(includeRawResult);
/*
  {
    raw: AIMessage {
      content: '',
      additional_kwargs: {
        tool_calls: [
          {
            "id": "call_01htk094ktfgxtkwj40n0ehg61",
            "type": "function",
            "function": {
              "name": "calculator",
              "arguments": "{\"operation\": \"add\", \"number1\": 2, \"number2\": 2}"
            }
          }
        ]
      },
      response_metadata: {
        "tokenUsage": {
          "completionTokens": 197,
          "promptTokens": 1214,
          "totalTokens": 1411
        },
        "finish_reason": "tool_calls"
      }
    },
    parsed: { operation: 'add', number1: 2, number2: 2 }
  }
*/

API Reference:

Streaming​

Groq's API also supports streaming token responses. The example below demonstrates how to use this feature.

import { ChatGroq } from "@langchain/groq";
import { ChatPromptTemplate } from "@langchain/core/prompts";
import { StringOutputParser } from "@langchain/core/output_parsers";

const model = new ChatGroq({
  apiKey: process.env.GROQ_API_KEY,
});
const prompt = ChatPromptTemplate.fromMessages([
  ["system", "You are a helpful assistant"],
  ["human", "{input}"],
]);
const outputParser = new StringOutputParser();
const chain = prompt.pipe(model).pipe(outputParser);
const response = await chain.stream({
  input: "Hello",
});
let res = "";
for await (const item of response) {
  res += item;
  console.log("stream:", res);
}
/**
stream: Hello
stream: Hello!
stream: Hello! I
stream: Hello! I'
stream: Hello! I'm
stream: Hello! I'm happy
stream: Hello! I'm happy to
stream: Hello! I'm happy to assist
stream: Hello! I'm happy to assist you
stream: Hello! I'm happy to assist you in
stream: Hello! I'm happy to assist you in any
stream: Hello! I'm happy to assist you in any way
stream: Hello! I'm happy to assist you in any way I
stream: Hello! I'm happy to assist you in any way I can
stream: Hello! I'm happy to assist you in any way I can.
stream: Hello! I'm happy to assist you in any way I can. Is
stream: Hello! I'm happy to assist you in any way I can. Is there
stream: Hello! I'm happy to assist you in any way I can. Is there something
stream: Hello! I'm happy to assist you in any way I can. Is there something specific
stream: Hello! I'm happy to assist you in any way I can. Is there something specific you
stream: Hello! I'm happy to assist you in any way I can. Is there something specific you need
stream: Hello! I'm happy to assist you in any way I can. Is there something specific you need help
stream: Hello! I'm happy to assist you in any way I can. Is there something specific you need help with
stream: Hello! I'm happy to assist you in any way I can. Is there something specific you need help with or
stream: Hello! I'm happy to assist you in any way I can. Is there something specific you need help with or a
stream: Hello! I'm happy to assist you in any way I can. Is there something specific you need help with or a question
stream: Hello! I'm happy to assist you in any way I can. Is there something specific you need help with or a question you
stream: Hello! I'm happy to assist you in any way I can. Is there something specific you need help with or a question you have
stream: Hello! I'm happy to assist you in any way I can. Is there something specific you need help with or a question you have?
 */

API Reference:

info

You can see a LangSmith trace of this example here

Was this page helpful?


You can also leave detailed feedback on GitHub.