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Fireworks accelerates product development on generative AI by creating an innovative AI experiment and production platform.

This example goes over how to use LangChain to interact with ChatFireworks models. %pip install langchain-fireworks

from langchain_core.messages import HumanMessage, SystemMessage
from langchain_fireworks import ChatFireworks


  1. Make sure the langchain-fireworks package is installed in your environment.
  2. Sign in to Fireworks AI for the an API Key to access our models, and make sure it is set as the FIREWORKS_API_KEY environment variable.
  3. Set up your model using a model id. If the model is not set, the default model is fireworks-llama-v2-7b-chat. See the full, most up-to-date model list on
import getpass
import os

if "FIREWORKS_API_KEY" not in os.environ:
os.environ["FIREWORKS_API_KEY"] = getpass.getpass("Fireworks API Key:")

# Initialize a Fireworks chat model
chat = ChatFireworks(model="accounts/fireworks/models/mixtral-8x7b-instruct")

Calling the Model Directly

You can call the model directly with a system and human message to get answers.

# ChatFireworks Wrapper
system_message = SystemMessage(content="You are to chat with the user.")
human_message = HumanMessage(content="Who are you?")

chat.invoke([system_message, human_message])
AIMessage(content="Hello! I'm an AI language model, a helpful assistant designed to chat and assist you with any questions or information you might need. I'm here to make your experience as smooth and enjoyable as possible. How can I assist you today?")
# Setting additional parameters: temperature, max_tokens, top_p
chat = ChatFireworks(
system_message = SystemMessage(content="You are to chat with the user.")
human_message = HumanMessage(content="How's the weather today?")
chat.invoke([system_message, human_message])
AIMessage(content="I'm an AI and do not have the ability to experience the weather firsthand. However,")

Tool Calling

Fireworks offers the FireFunction-v1 tool calling model. You can use it for structured output and function calling use cases:

from pprint import pprint

from langchain_core.pydantic_v1 import BaseModel

class ExtractFields(BaseModel):
name: str
age: int

chat = ChatFireworks(

result = chat.invoke("I am a 27 year old named Erick")

{'function': {'arguments': '{"name": "Erick", "age": 27}',
'name': 'ExtractFields'},
'id': 'call_J0WYP2TLenaFw3UeVU0UnWqx',
'index': 0,
'type': 'function'}

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