> ## Documentation Index
> Fetch the complete documentation index at: https://langchain-5e9cc07a-preview-devupd-1765394015-eccef47.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Fireworks

<Warning>
  **You are currently on a page documenting the use of Fireworks models as text completion models. Many popular Fireworks models are [chat completion models](/oss/python/langchain/models).**

  You may be looking for [this page instead](/oss/python/integrations/chat/fireworks/).
</Warning>

> [Fireworks](https://app.fireworks.ai/) 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](https://js.langchain.com/v0.1/docs/integrations/llms/fireworks/) |                                               Downloads                                              |                                              Version                                              |
| :-------------------------------------------------------------------------------------------------------------------------------------------------- | :--------------------------------------------------------------------- | :---: | :----------: | :---------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------------: |
| [`Fireworks`](https://python.langchain.com/api_reference/fireworks/llms/langchain_fireworks.llms.Fireworks.html#langchain_fireworks.llms.Fireworks) | [`langchain-fireworks`](https://pypi.org/project/langchain-fireworks/) |   ❌   |       ❌      |                                       ✅                                       | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain_fireworks?style=flat-square\&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain_fireworks?style=flat-square\&label=%20) |

## Setup

### Credentials

Sign in to [Fireworks AI](http://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](https://app.fireworks.ai/models).

```python theme={null}
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.

```python theme={null}
pip install -qU langchain-fireworks
```

## Instantiation

```python theme={null}
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.

```python theme={null}
output = llm.invoke("Who's the best quarterback in the NFL?")
print(output)
```

```output theme={null}
  That's an easy one. It's Aaron Rodgers. Rodgers has consistently been one
```

### Invoking with multiple prompts

```python theme={null}
# 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)
```

```output theme={null}
[[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

```python theme={null}
# 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?"))
```

```output theme={null}
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.

```python theme={null}
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"}))
```

```output theme={null}
 What do you call a bear with no teeth? A gummy bear!
```

## Streaming

You can stream the output, if you want.

```python theme={null}
for token in chain.stream({"topic": "bears"}):
    print(token, end="", flush=True)
```

```output theme={null}
 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://reference.langchain.com/python/integrations/langchain_fireworks/)

***

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