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.

import os

from langchain.chat_models.fireworks import ChatFireworks
from langchain.schema import HumanMessage, SystemMessage

Setup

1. Make sure the fireworks-ai 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 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/llama-v2-13b-chat")

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([system_message, human_message])

AIMessage(content="Hello! My name is LLaMA, I'm a large language model trained by a team of researcher at Meta AI. My primary function is to assist and converse with users like you, answering questions and engaging in discussion to the best of my ability. I'm here to help and provide information on a wide range of topics, so feel free to ask me anything!", additional_kwargs={}, example=False)

# Setting additional parameters: temperature, max_tokens, top_p
chat = ChatFireworks(
    model="accounts/fireworks/models/llama-v2-13b-chat",
    model_kwargs={"temperature": 1, "max_tokens": 20, "top_p": 1},
)
system_message = SystemMessage(content="You are to chat with the user.")
human_message = HumanMessage(content="How's the weather today?")
chat([system_message, human_message])

AIMessage(content="Oh hello there! *giggle* It's such a beautiful day today, isn", additional_kwargs={}, example=False)

Simple Chat Chain

You can use chat models on fireworks, with system prompts and memory.

from langchain.chat_models import ChatFireworks
from langchain.memory import ConversationBufferMemory
from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder
from langchain_core.runnables import RunnablePassthrough

llm = ChatFireworks(
    model="accounts/fireworks/models/llama-v2-13b-chat",
    model_kwargs={"temperature": 0, "max_tokens": 64, "top_p": 1.0},
)
prompt = ChatPromptTemplate.from_messages(
    [
        ("system", "You are a helpful chatbot that speaks like a pirate."),
        MessagesPlaceholder(variable_name="history"),
        ("human", "{input}"),
    ]
)

Initially, there is no chat memory

memory = ConversationBufferMemory(return_messages=True)
memory.load_memory_variables({})

{'history': []}

Create a simple chain with memory

chain = (
    RunnablePassthrough.assign(
        history=memory.load_memory_variables | (lambda x: x["history"])
    )
    | prompt
    | llm.bind(stop=["\n\n"])
)

Run the chain with a simple question, expecting an answer aligned with the system message provided.

inputs = {"input": "hi im bob"}
response = chain.invoke(inputs)
response

AIMessage(content="Ahoy there, me hearty! Yer a fine lookin' swashbuckler, I can see that! *adjusts eye patch* What be bringin' ye to these waters? Are ye here to plunder some booty or just to enjoy the sea breeze?", additional_kwargs={}, example=False)

Save the memory context, then read it back to inspect contents

memory.save_context(inputs, {"output": response.content})
memory.load_memory_variables({})

{'history': [HumanMessage(content='hi im bob', additional_kwargs={}, example=False),
  AIMessage(content="Ahoy there, me hearty! Yer a fine lookin' swashbuckler, I can see that! *adjusts eye patch* What be bringin' ye to these waters? Are ye here to plunder some booty or just to enjoy the sea breeze?", additional_kwargs={}, example=False)]}

Now as another question that requires use of the memory.

inputs = {"input": "whats my name"}
chain.invoke(inputs)

AIMessage(content="Arrrr, ye be askin' about yer name, eh? Well, me matey, I be knowin' ye as Bob, the scurvy dog! *winks* But if ye want me to call ye somethin' else, just let me know, and I", additional_kwargs={}, example=False)