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Fireworks

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You are currently on a page documenting the use of Fireworks models as text completion models. Many popular Fireworks models are chat completion models.

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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 Fireworks models.

Overview​

Integration details​

ClassPackageLocalSerializableJS supportPackage downloadsPackage latest
Fireworkslangchain_fireworks❌❌✅PyPI - DownloadsPyPI - Version

Setup​

Credentials​

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 fireworks.ai.

import getpass
import os

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

Installation​

You need to install the langchain_fireworks python package for the rest of the notebook to work.

%pip install -qU langchain-fireworks
Note: you may need to restart the kernel to use updated packages.

Instantiation​

from langchain_fireworks import Fireworks

# Initialize a Fireworks model
llm = Fireworks(
    model="accounts/fireworks/models/mixtral-8x7b-instruct",
    base_url="https://api.fireworks.ai/inference/v1/completions",
)
API Reference:Fireworks

Invocation​

You can call the model directly with string prompts to get completions.

output = llm.invoke("Who's the best quarterback in the NFL?")
print(output)
 If Manningville Station, Lions rookie EJ Manuel's

Invoking with multiple prompts​

# Calling multiple prompts
output = llm.generate(
    [
        "Who's the best cricket player in 2016?",
        "Who's the best basketball player in the league?",
    ]
)
print(output.generations)
[[Generation(text=" We're not just asking, we've done some research. We'")], [Generation(text=' The conversation is dominated by Kobe Bryant, Dwyane Wade,')]]

Invoking with additional parameters​

# Setting additional parameters: temperature, max_tokens, top_p
llm = Fireworks(
    model="accounts/fireworks/models/mixtral-8x7b-instruct",
    temperature=0.7,
    max_tokens=15,
    top_p=1.0,
)
print(llm.invoke("What's the weather like in Kansas City in December?"))

December is a cold month in Kansas City, with temperatures of

Chaining​

You can use the LangChain Expression Language to create a simple chain with non-chat models.

from langchain_core.prompts import PromptTemplate
from langchain_fireworks import Fireworks

llm = Fireworks(
    model="accounts/fireworks/models/mixtral-8x7b-instruct",
    temperature=0.7,
    max_tokens=15,
    top_p=1.0,
)
prompt = PromptTemplate.from_template("Tell me a joke about {topic}?")
chain = prompt | llm

print(chain.invoke({"topic": "bears"}))
API Reference:PromptTemplate | Fireworks
 What do you call a bear with no teeth? A gummy bear!

Streaming​

You can stream the output, if you want.

for token in chain.stream({"topic": "bears"}):
    print(token, end="", flush=True)
 Why do bears hate shoes so much? They like to run around in their

API reference​

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

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