Fireworks Function Calling Cookbook

Fireworks.ai supports function calling for its LLMs, similar to OpenAI. This lets users directly describe the set of tools/functions available and have the model dynamically pick the right function calls to invoke, without complex prompting on the user's part.

Since our Fireworks LLM directly subclasses OpenAI, we can use our existing abstractions with Fireworks.

We show this on three levels: directly on the model API, as part of a Pydantic Program (structured output extraction), and as part of an agent.

python

%pip install llama-index-llms-fireworks

python

%pip install llama-index

python

import os

os.environ["FIREWORKS_API_KEY"] = "fw_3ZkvBpQyjRzbicpihhrihaEP"

python

from llama_index.llms.fireworks import Fireworks

## define fireworks model, for a list of function calling models see: https://app.fireworks.ai/models/?filter=LLM&functionCalling=true
llm = Fireworks(
    model="accounts/fireworks/models/deepseek-v3p1-terminus", temperature=0
)

Function Calling on the LLM Module

You can directly input function calls on the LLM module.

python

import os
import json
from openai import OpenAI
from pydantic import BaseModel, Field
from llama_index.llms.openai.utils import to_openai_tool


class Song(BaseModel):
    """A song with name and artist"""

    name: str = Field(description="The name of the song")
    artist: str = Field(description="The artist who performed the song")


song_fn = to_openai_tool(Song)

# Initialize Fireworks client
client = OpenAI(
    api_key=os.environ.get("FIREWORKS_API_KEY"),
    base_url="https://api.fireworks.ai/inference/v1",
)

response = client.chat.completions.create(
    model="accounts/fireworks/models/kimi-k2-instruct-0905",
    messages=[{"role": "user", "content": "Generate a song from Beyonce"}],
    tools=[song_fn],
    temperature=0.1,
)

print(response)

if response.choices[0].message.tool_calls:
    tool_call = response.choices[0].message.tool_calls[0]
    print(f"\nTool called: {tool_call.function.name}")

    # Parse the arguments to get structured output
    arguments = json.loads(tool_call.function.arguments)
    print(f"Arguments: {arguments}")

    # Create Song instance from the structured output
    song = Song(**arguments)
    print(f"\nExtracted Song:")
    print(f"Name: {song.name}")
    print(f"Artist: {song.artist}")