ChatFireworks
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
API Reference:
Setup
- Make sure the
langchain-fireworks
package is installed in your environment. - 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. - 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 app.fireworks.ai.
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(
model="accounts/fireworks/models/mixtral-8x7b-instruct",
temperature=1,
max_tokens=20,
)
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(
model="accounts/fireworks/models/firefunction-v1",
).bind_tools([ExtractFields])
result = chat.invoke("I am a 27 year old named Erick")
pprint(result.additional_kwargs["tool_calls"][0])
{'function': {'arguments': '{"name": "Erick", "age": 27}',
'name': 'ExtractFields'},
'id': 'call_J0WYP2TLenaFw3UeVU0UnWqx',
'index': 0,
'type': 'function'}