ChatFireworks

This will help you getting started with ChatFireworks chat models. For detailed documentation of all ChatFireworks features and configurations head to the API reference.

Overview

Integration details

Class	Package	Local	Serializable	PY support	Package downloads	Package latest
ChatFireworks	@langchain/community	❌	✅	✅

Model features

Tool calling	Structured output	JSON mode	Image input	Audio input	Video input	Token-level streaming	Token usage	Logprobs
✅	✅	✅	❌	❌	❌	✅	✅	✅

Setup

To access ChatFireworks models you’ll need to create a Fireworks account, get an API key, and install the @langchain/community integration package.

Credentials

Head to the Fireworks website to sign up to Fireworks and generate an API key. Once you’ve done this set the FIREWORKS_API_KEY environment variable:

export FIREWORKS_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 ChatFireworks integration lives in the @langchain/community package:

tip

See this section for general instructions on installing integration packages.

npm

npm i @langchain/community

yarn

yarn add @langchain/community

pnpm

pnpm add @langchain/community

Instantiation

Now we can instantiate our model object and generate chat completions:

import { ChatFireworks } from "@langchain/community/chat_models/fireworks";

const llm = new ChatFireworks({
  model: "accounts/fireworks/models/llama-v3p1-70b-instruct",
  temperature: 0,
  maxTokens: undefined,
  timeout: 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."],
]);
aiMsg;

AIMessage {
  "id": "chatcmpl-9rBYHbb6QYRrKyr2tMhO9pH4AYXR4",
  "content": "J'adore la programmation.",
  "additional_kwargs": {},
  "response_metadata": {
    "tokenUsage": {
      "completionTokens": 8,
      "promptTokens": 31,
      "totalTokens": 39
    },
    "finish_reason": "stop"
  },
  "tool_calls": [],
  "invalid_tool_calls": [],
  "usage_metadata": {
    "input_tokens": 31,
    "output_tokens": 8,
    "total_tokens": 39
  }
}

console.log(aiMsg.content);

J'adore la programmation.

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": "chatcmpl-9rBYM3KSIhHOuTXpBvA5oFyk8RSaN",
  "content": "Ich liebe das Programmieren.",
  "additional_kwargs": {},
  "response_metadata": {
    "tokenUsage": {
      "completionTokens": 6,
      "promptTokens": 26,
      "totalTokens": 32
    },
    "finish_reason": "stop"
  },
  "tool_calls": [],
  "invalid_tool_calls": [],
  "usage_metadata": {
    "input_tokens": 26,
    "output_tokens": 6,
    "total_tokens": 32
  }
}

Behind the scenes, Fireworks AI uses the OpenAI SDK and OpenAI compatible API, with some caveats:

• Certain properties are not supported by the Fireworks API, see here.
• Generation using multiple prompts is not supported.

API reference

For detailed documentation of all ChatFireworks features and configurations head to the API reference: https://api.js.langchain.com/classes/langchain_community_chat_models_fireworks.ChatFireworks.html