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ChatFireworks

This doc help you get started with Fireworks AI chat models. For detailed documentation of all ChatFireworks features and configurations head to the API reference.

Fireworks AI is an AI inference platform to run and customize models. For a list of all models served by Fireworks see the Fireworks docs.

Overview

Integration details

ClassPackageLocalSerializableJS supportPackage downloadsPackage latest
ChatFireworkslangchain-fireworksbetaPyPI - DownloadsPyPI - Version

Model features

Tool callingStructured outputJSON modeImage inputAudio inputVideo inputToken-level streamingNative asyncToken usageLogprobs

Setup

To access Fireworks models you'll need to create a Fireworks account, get an API key, and install the langchain-fireworks integration package.

Credentials

Head to (ttps://fireworks.ai/login to sign up to Fireworks and generate an API key. Once you've done this set the FIREWORKS_API_KEY environment variable:

import getpass
import os

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

If you want to get automated tracing of your model calls you can also set your LangSmith API key by uncommenting below:

# os.environ["LANGSMITH_API_KEY"] = getpass.getpass("Enter your LangSmith API key: ")
# os.environ["LANGSMITH_TRACING"] = "true"

Installation

The LangChain Fireworks integration lives in the langchain-fireworks package:

%pip install -qU langchain-fireworks

Instantiation

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

  • TODO: Update model instantiation with relevant params.
from langchain_fireworks import ChatFireworks

llm = ChatFireworks(
model="accounts/fireworks/models/llama-v3-70b-instruct",
temperature=0,
max_tokens=None,
timeout=None,
max_retries=2,
# other params...
)
API Reference:ChatFireworks

Invocation

messages = [
(
"system",
"You are a helpful assistant that translates English to French. Translate the user sentence.",
),
("human", "I love programming."),
]
ai_msg = llm.invoke(messages)
ai_msg
AIMessage(content="J'adore la programmation.", response_metadata={'token_usage': {'prompt_tokens': 35, 'total_tokens': 44, 'completion_tokens': 9}, 'model_name': 'accounts/fireworks/models/llama-v3-70b-instruct', 'system_fingerprint': '', 'finish_reason': 'stop', 'logprobs': None}, id='run-df28e69a-ff30-457e-a743-06eb14d01cb0-0', usage_metadata={'input_tokens': 35, 'output_tokens': 9, 'total_tokens': 44})
print(ai_msg.content)
J'adore la programmation.

Chaining

We can chain our model with a prompt template like so:

from langchain_core.prompts import ChatPromptTemplate

prompt = ChatPromptTemplate.from_messages(
[
(
"system",
"You are a helpful assistant that translates {input_language} to {output_language}.",
),
("human", "{input}"),
]
)

chain = prompt | llm
chain.invoke(
{
"input_language": "English",
"output_language": "German",
"input": "I love programming.",
}
)
API Reference:ChatPromptTemplate
AIMessage(content='Ich liebe das Programmieren.', response_metadata={'token_usage': {'prompt_tokens': 30, 'total_tokens': 37, 'completion_tokens': 7}, 'model_name': 'accounts/fireworks/models/llama-v3-70b-instruct', 'system_fingerprint': '', 'finish_reason': 'stop', 'logprobs': None}, id='run-ff3f91ad-ed81-4acf-9f59-7490dc8d8f48-0', usage_metadata={'input_tokens': 30, 'output_tokens': 7, 'total_tokens': 37})

API reference

For detailed documentation of all ChatFireworks features and configurations head to the API reference: https://python.langchain.com/api_reference/fireworks/chat_models/langchain_fireworks.chat_models.ChatFireworks.html


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