Update FireworksLLMService to use OpenAILLMService
This commit is contained in:
Mark Backman
15
CHANGELOG.md
15
CHANGELOG.md
@@ -10,12 +10,11 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
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### Added
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- `GroqLLMService` and `GrokLLMService` for Groq and Grok API integration, with
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OpenAI-compatible interface
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- New examples demonstrating function calling with Groq, Grok, and Azure OpenAI
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OpenAI-compatible interface.
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- `14f-function-calling-groq.py`
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- `14g-function-calling-grok.py`
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- `14h-function-calling-azure.py`
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- New examples demonstrating function calling with Groq, Grok, Azure OpenAI,
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and Fireworks: `14f-function-calling-groq.py`, `14g-function-calling-grok.py`,
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`14h-function-calling-azure.py`, and `14i-function-calling-fireworks.py`.
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- In order to obtain the audio stored by the `AudioBufferProcessor` you can now
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also register an `on_audio_data` event handler. The `on_audio_data` handler
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@@ -47,6 +46,9 @@ async def on_audio_data(processor, audio, sample_rate, num_channels):
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- Updated the `AzureLLMService` to use the `OpenAILLMService`. Updated the
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`api_version` to `2024-09-01-preview`.
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- Updated the `FireworksLLMService` to use the `OpenAILLMService`. Updated the
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default model to `accounts/fireworks/models/firefunction-v2`.
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### Removed
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- Removed `AppFrame`. This was used as a special user custom frame, but there's
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@@ -71,6 +73,9 @@ async def on_audio_data(processor, audio, sample_rate, num_channels):
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- Fixed Google Gemini message handling to properly convert appended messages to
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Gemini's required format.
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- Fixed an issue with `FireworksLLMService` where chat completions were failing
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by removing the `stream_options` from the chat completion options.
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## [0.0.49] - 2024-11-17
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### Added
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140
examples/foundational/14i-function-calling-fireworks.py
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140
examples/foundational/14i-function-calling-fireworks.py
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@@ -0,0 +1,140 @@
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#
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# Copyright (c) 2024, Daily
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#
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# SPDX-License-Identifier: BSD 2-Clause License
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#
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import asyncio
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import os
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import sys
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import aiohttp
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from dotenv import load_dotenv
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from loguru import logger
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from openai.types.chat import ChatCompletionToolParam
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from runner import configure
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from pipecat.audio.vad.silero import SileroVADAnalyzer
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from pipecat.pipeline.pipeline import Pipeline
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from pipecat.pipeline.runner import PipelineRunner
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from pipecat.pipeline.task import PipelineParams, PipelineTask
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from pipecat.services.cartesia import CartesiaTTSService
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from pipecat.services.fireworks import FireworksLLMService
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from pipecat.services.openai import OpenAILLMContext
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from pipecat.transports.services.daily import DailyParams, DailyTransport
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load_dotenv(override=True)
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logger.remove(0)
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logger.add(sys.stderr, level="DEBUG")
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async def start_fetch_weather(function_name, llm, context):
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# note: we can't push a frame to the LLM here. the bot
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# can interrupt itself and/or cause audio overlapping glitches.
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# possible question for Aleix and Chad about what the right way
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# to trigger speech is, now, with the new queues/async/sync refactors.
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# await llm.push_frame(TextFrame("Let me check on that."))
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logger.debug(f"Starting fetch_weather_from_api with function_name: {function_name}")
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async def fetch_weather_from_api(function_name, tool_call_id, args, llm, context, result_callback):
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await result_callback({"conditions": "nice", "temperature": "75"})
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async def main():
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async with aiohttp.ClientSession() as session:
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(room_url, token) = await configure(session)
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transport = DailyTransport(
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room_url,
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token,
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"Respond bot",
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DailyParams(
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audio_out_enabled=True,
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transcription_enabled=True,
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vad_enabled=True,
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vad_analyzer=SileroVADAnalyzer(),
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),
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)
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tts = CartesiaTTSService(
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api_key=os.getenv("CARTESIA_API_KEY"),
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voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22", # British Lady
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)
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llm = FireworksLLMService(
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api_key=os.getenv("FIREWORKS_API_KEY"),
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model="accounts/fireworks/models/firefunction-v2",
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)
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# Register a function_name of None to get all functions
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# sent to the same callback with an additional function_name parameter.
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llm.register_function(None, fetch_weather_from_api, start_callback=start_fetch_weather)
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tools = [
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ChatCompletionToolParam(
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type="function",
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function={
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"name": "get_current_weather",
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"description": "Get the current weather",
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"parameters": {
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"type": "object",
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"properties": {
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"location": {
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"type": "string",
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"description": "The city and state, e.g. San Francisco, CA",
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},
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"format": {
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"type": "string",
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"enum": ["celsius", "fahrenheit"],
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"description": "The temperature unit to use. Infer this from the users location.",
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},
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},
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"required": ["location", "format"],
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},
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},
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)
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]
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messages = [
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{
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"role": "system",
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"content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
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},
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]
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context = OpenAILLMContext(messages, tools)
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context_aggregator = llm.create_context_aggregator(context)
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pipeline = Pipeline(
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[
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transport.input(),
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context_aggregator.user(),
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llm,
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tts,
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transport.output(),
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context_aggregator.assistant(),
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]
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)
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task = PipelineTask(
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pipeline,
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PipelineParams(
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allow_interruptions=True,
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enable_metrics=True,
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enable_usage_metrics=True,
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),
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)
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@transport.event_handler("on_first_participant_joined")
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async def on_first_participant_joined(transport, participant):
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await transport.capture_participant_transcription(participant["id"])
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# Kick off the conversation.
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await task.queue_frames([context_aggregator.user().get_context_frame()])
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runner = PipelineRunner()
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await runner.run(task)
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if __name__ == "__main__":
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asyncio.run(main())
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@@ -52,7 +52,7 @@ google = [ "google-generativeai~=0.8.3", "google-cloud-texttospeech~=2.17.2" ]
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grok = [ "openai~=1.50.2" ]
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groq = [ "openai~=1.50.2" ]
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gstreamer = [ "pygobject~=3.48.2" ]
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fireworks = [ "openai~=1.37.2" ]
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fireworks = [ "openai~=1.50.2" ]
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krisp = [ "pipecat-ai-krisp~=0.3.0" ]
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langchain = [ "langchain~=0.2.14", "langchain-community~=0.2.12", "langchain-openai~=0.1.20" ]
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livekit = [ "livekit~=0.17.5", "livekit-api~=0.7.1", "tenacity~=8.5.0" ]
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@@ -4,26 +4,73 @@
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# SPDX-License-Identifier: BSD 2-Clause License
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#
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from pipecat.services.openai import BaseOpenAILLMService
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from typing import List
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from loguru import logger
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from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
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from pipecat.services.openai import OpenAILLMService
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try:
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from openai import AsyncOpenAI
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from openai.types.chat import ChatCompletionMessageParam
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except ModuleNotFoundError as e:
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logger.error(f"Exception: {e}")
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logger.error(
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"In order to use Fireworks, you need to `pip install pipecat-ai[fireworks]`. Also, set the `FIREWORKS_API_KEY` environment variable."
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"In order to use Fireworks, you need to `pip install pipecat-ai[fireworks]`. Also, set `FIREWORKS_API_KEY` environment variable."
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)
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raise Exception(f"Missing module: {e}")
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class FireworksLLMService(BaseOpenAILLMService):
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class FireworksLLMService(OpenAILLMService):
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"""A service for interacting with Fireworks AI using the OpenAI-compatible interface.
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This service extends OpenAILLMService to connect to Fireworks' API endpoint while
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maintaining full compatibility with OpenAI's interface and functionality.
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Args:
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api_key (str): The API key for accessing Fireworks AI
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model (str, optional): The model identifier to use. Defaults to "accounts/fireworks/models/firefunction-v2"
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base_url (str, optional): The base URL for Fireworks API. Defaults to "https://api.fireworks.ai/inference/v1"
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**kwargs: Additional keyword arguments passed to OpenAILLMService
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"""
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def __init__(
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self,
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*,
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api_key: str,
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model: str = "accounts/fireworks/models/firefunction-v1",
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model: str = "accounts/fireworks/models/firefunction-v2",
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base_url: str = "https://api.fireworks.ai/inference/v1",
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**kwargs,
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):
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super().__init__(api_key=api_key, model=model, base_url=base_url)
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super().__init__(api_key=api_key, base_url=base_url, model=model, **kwargs)
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def create_client(self, api_key=None, base_url=None, **kwargs):
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"""Create OpenAI-compatible client for Fireworks API endpoint."""
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logger.debug(f"Creating Fireworks client with api {base_url}")
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return super().create_client(api_key, base_url, **kwargs)
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async def get_chat_completions(
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self, context: OpenAILLMContext, messages: List[ChatCompletionMessageParam]
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):
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"""Get chat completions from Fireworks API.
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Removes OpenAI-specific parameters not supported by Fireworks.
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"""
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params = {
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"model": self.model_name,
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"stream": True,
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"messages": messages,
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"tools": context.tools,
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"tool_choice": context.tool_choice,
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"frequency_penalty": self._settings["frequency_penalty"],
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"presence_penalty": self._settings["presence_penalty"],
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"temperature": self._settings["temperature"],
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"top_p": self._settings["top_p"],
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"max_tokens": self._settings["max_tokens"],
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}
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params.update(self._settings["extra"])
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chunks = await self._client.chat.completions.create(**params)
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return chunks
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