Skip to main content

Fireworks

caution

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

You may be looking for this page instead.

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.

Overviewโ€‹

Integration detailsโ€‹

ClassPackageLocalSerializableJS supportPackage downloadsPackage latest
Fireworkslangchain_fireworksโŒโŒโœ…PyPI - DownloadsPyPI - Version

Setupโ€‹

Credentialsโ€‹

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:")

Installationโ€‹

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.

Instantiationโ€‹

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

Invocationโ€‹

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 promptsโ€‹

# 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 parametersโ€‹

# 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

Chainingโ€‹

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!

Streamingโ€‹

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 referenceโ€‹

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


Was this page helpful?


You can also leave detailed feedback on GitHub.