Documentation Index
Fetch the complete documentation index at: https://docs.langchain.com/llms.txt
Use this file to discover all available pages before exploring further.
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
| Class | Package | Local | Serializable | JS support | Downloads | Version |
|---|
Fireworks | langchain-fireworks | ❌ | ❌ | ✅ |  |  |
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
Instantiation
from langchain_fireworks import Fireworks
# Initialize a Fireworks model
llm = Fireworks(
model="accounts/fireworks/models/llama-v3p1-8b-instruct", # Model library in: https://app.fireworks.ai/models
base_url="https://api.fireworks.ai/inference/v1/completions",
)
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)
That's an easy one. It's Aaron Rodgers. Rodgers has consistently been one
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=' You could choose one of the top performers in 2016, such as Vir')], [Generation(text=' -- Keith Jackson\nA: LeBron James, Chris Paul and Kobe Bryant are the')]]
Invoking with additional parameters
# Setting additional parameters: temperature, max_tokens, top_p
llm = Fireworks(
model="accounts/fireworks/models/llama-v3p1-8b-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/llama-v3p1-8b-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"}))
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
