From 728acba8a56e6ddb01f604d5e213d080d6ec85c1 Mon Sep 17 00:00:00 2001 From: Mark Backman Date: Wed, 4 Dec 2024 10:08:28 -0500 Subject: [PATCH] Add LLMService stubs for Grok and Groq, add examples --- .../14c-function-calling-together.py | 17 +-- .../foundational/14f-function-calling-groq.py | 139 ++++++++++++++++++ .../foundational/14g-function-calling-grok.py | 137 +++++++++++++++++ pyproject.toml | 2 + src/pipecat/services/grok.py | 103 +++++++++++++ src/pipecat/services/groq.py | 39 +++++ src/pipecat/services/together.py | 24 +-- 7 files changed, 439 insertions(+), 22 deletions(-) create mode 100644 examples/foundational/14f-function-calling-groq.py create mode 100644 examples/foundational/14g-function-calling-grok.py create mode 100644 src/pipecat/services/grok.py create mode 100644 src/pipecat/services/groq.py diff --git a/examples/foundational/14c-function-calling-together.py b/examples/foundational/14c-function-calling-together.py index 283b2fe70..a629753e5 100644 --- a/examples/foundational/14c-function-calling-together.py +++ b/examples/foundational/14c-function-calling-together.py @@ -5,10 +5,15 @@ # import asyncio -import aiohttp import os import sys +import aiohttp +from dotenv import load_dotenv +from loguru import logger +from openai.types.chat import ChatCompletionToolParam +from runner import configure + from pipecat.audio.vad.silero import SileroVADAnalyzer from pipecat.pipeline.pipeline import Pipeline from pipecat.pipeline.runner import PipelineRunner @@ -18,14 +23,6 @@ from pipecat.services.openai import OpenAILLMContext from pipecat.services.together import TogetherLLMService from pipecat.transports.services.daily import DailyParams, DailyTransport -from openai.types.chat import ChatCompletionToolParam - -from runner import configure - -from loguru import logger - -from dotenv import load_dotenv - load_dotenv(override=True) logger.remove(0) @@ -125,7 +122,7 @@ async def main(): async def on_first_participant_joined(transport, participant): await transport.capture_participant_transcription(participant["id"]) # Kick off the conversation. - # await tts.say("Hi! Ask me about the weather in San Francisco.") + await task.queue_frames([context_aggregator.user().get_context_frame()]) runner = PipelineRunner() diff --git a/examples/foundational/14f-function-calling-groq.py b/examples/foundational/14f-function-calling-groq.py new file mode 100644 index 000000000..bcd26672f --- /dev/null +++ b/examples/foundational/14f-function-calling-groq.py @@ -0,0 +1,139 @@ +# +# Copyright (c) 2024, Daily +# +# SPDX-License-Identifier: BSD 2-Clause License +# + +import asyncio +import os +import sys + +import aiohttp +from dotenv import load_dotenv +from loguru import logger +from openai.types.chat import ChatCompletionToolParam +from runner import configure + +from pipecat.audio.vad.silero import SileroVADAnalyzer +from pipecat.pipeline.pipeline import Pipeline +from pipecat.pipeline.runner import PipelineRunner +from pipecat.pipeline.task import PipelineParams, PipelineTask +from pipecat.services.cartesia import CartesiaTTSService +from pipecat.services.groq import GroqLLMService +from pipecat.services.openai import OpenAILLMContext +from pipecat.transports.services.daily import DailyParams, DailyTransport + +load_dotenv(override=True) + +logger.remove(0) +logger.add(sys.stderr, level="DEBUG") + + +async def start_fetch_weather(function_name, llm, context): + # note: we can't push a frame to the LLM here. the bot + # can interrupt itself and/or cause audio overlapping glitches. + # possible question for Aleix and Chad about what the right way + # to trigger speech is, now, with the new queues/async/sync refactors. + # await llm.push_frame(TextFrame("Let me check on that.")) + logger.debug(f"Starting fetch_weather_from_api with function_name: {function_name}") + + +async def fetch_weather_from_api(function_name, tool_call_id, args, llm, context, result_callback): + await result_callback({"conditions": "nice", "temperature": "75"}) + + +async def main(): + async with aiohttp.ClientSession() as session: + (room_url, token) = await configure(session) + + transport = DailyTransport( + room_url, + token, + "Respond bot", + DailyParams( + audio_out_enabled=True, + transcription_enabled=True, + vad_enabled=True, + vad_analyzer=SileroVADAnalyzer(), + ), + ) + + tts = CartesiaTTSService( + api_key=os.getenv("CARTESIA_API_KEY"), + voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22", # British Lady + ) + + llm = GroqLLMService( + api_key=os.getenv("GROQ_API_KEY"), model="llama3-groq-70b-8192-tool-use-preview" + ) + # Register a function_name of None to get all functions + # sent to the same callback with an additional function_name parameter. + llm.register_function(None, fetch_weather_from_api, start_callback=start_fetch_weather) + + tools = [ + ChatCompletionToolParam( + type="function", + function={ + "name": "get_current_weather", + "description": "Get the current weather", + "parameters": { + "type": "object", + "properties": { + "location": { + "type": "string", + "description": "The city and state, e.g. San Francisco, CA", + }, + "unit": { + "type": "string", + "enum": ["celsius", "fahrenheit"], + "description": "The temperature unit to use. Infer this from the users location.", + }, + }, + "required": ["location"], + }, + }, + ) + ] + messages = [ + { + "role": "system", + "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.", + }, + ] + + context = OpenAILLMContext(messages, tools) + context_aggregator = llm.create_context_aggregator(context) + + pipeline = Pipeline( + [ + transport.input(), + context_aggregator.user(), + llm, + tts, + transport.output(), + context_aggregator.assistant(), + ] + ) + + task = PipelineTask( + pipeline, + PipelineParams( + allow_interruptions=True, + enable_metrics=True, + enable_usage_metrics=True, + ), + ) + + @transport.event_handler("on_first_participant_joined") + async def on_first_participant_joined(transport, participant): + await transport.capture_participant_transcription(participant["id"]) + # Kick off the conversation. + await task.queue_frames([context_aggregator.user().get_context_frame()]) + + runner = PipelineRunner() + + await runner.run(task) + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/examples/foundational/14g-function-calling-grok.py b/examples/foundational/14g-function-calling-grok.py new file mode 100644 index 000000000..0165f1f7e --- /dev/null +++ b/examples/foundational/14g-function-calling-grok.py @@ -0,0 +1,137 @@ +# +# Copyright (c) 2024, Daily +# +# SPDX-License-Identifier: BSD 2-Clause License +# + +import asyncio +import os +import sys + +import aiohttp +from dotenv import load_dotenv +from loguru import logger +from openai.types.chat import ChatCompletionToolParam +from runner import configure + +from pipecat.audio.vad.silero import SileroVADAnalyzer +from pipecat.pipeline.pipeline import Pipeline +from pipecat.pipeline.runner import PipelineRunner +from pipecat.pipeline.task import PipelineParams, PipelineTask +from pipecat.services.cartesia import CartesiaTTSService +from pipecat.services.grok import GrokLLMService +from pipecat.services.openai import OpenAILLMContext +from pipecat.transports.services.daily import DailyParams, DailyTransport + +load_dotenv(override=True) + +logger.remove(0) +logger.add(sys.stderr, level="DEBUG") + + +async def start_fetch_weather(function_name, llm, context): + # note: we can't push a frame to the LLM here. the bot + # can interrupt itself and/or cause audio overlapping glitches. + # possible question for Aleix and Chad about what the right way + # to trigger speech is, now, with the new queues/async/sync refactors. + # await llm.push_frame(TextFrame("Let me check on that.")) + logger.debug(f"Starting fetch_weather_from_api with function_name: {function_name}") + + +async def fetch_weather_from_api(function_name, tool_call_id, args, llm, context, result_callback): + await result_callback({"conditions": "nice", "temperature": "75"}) + + +async def main(): + async with aiohttp.ClientSession() as session: + (room_url, token) = await configure(session) + + transport = DailyTransport( + room_url, + token, + "Respond bot", + DailyParams( + audio_out_enabled=True, + transcription_enabled=True, + vad_enabled=True, + vad_analyzer=SileroVADAnalyzer(), + ), + ) + + tts = CartesiaTTSService( + api_key=os.getenv("CARTESIA_API_KEY"), + voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22", # British Lady + ) + + llm = GrokLLMService(api_key=os.getenv("GROK_API_KEY")) + # Register a function_name of None to get all functions + # sent to the same callback with an additional function_name parameter. + llm.register_function(None, fetch_weather_from_api, start_callback=start_fetch_weather) + + tools = [ + ChatCompletionToolParam( + type="function", + function={ + "name": "get_current_weather", + "description": "Get the current weather", + "parameters": { + "type": "object", + "properties": { + "location": { + "type": "string", + "description": "The city and state, e.g. San Francisco, CA", + }, + "format": { + "type": "string", + "enum": ["celsius", "fahrenheit"], + "description": "The temperature unit to use. Infer this from the users location.", + }, + }, + "required": ["location", "format"], + }, + }, + ) + ] + messages = [ + { + "role": "system", + "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.", + }, + ] + + context = OpenAILLMContext(messages, tools) + context_aggregator = llm.create_context_aggregator(context) + + pipeline = Pipeline( + [ + transport.input(), + context_aggregator.user(), + llm, + tts, + transport.output(), + context_aggregator.assistant(), + ] + ) + + task = PipelineTask( + pipeline, + PipelineParams( + allow_interruptions=True, + enable_metrics=True, + enable_usage_metrics=True, + ), + ) + + @transport.event_handler("on_first_participant_joined") + async def on_first_participant_joined(transport, participant): + await transport.capture_participant_transcription(participant["id"]) + # Kick off the conversation. + await task.queue_frames([context_aggregator.user().get_context_frame()]) + + runner = PipelineRunner() + + await runner.run(task) + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/pyproject.toml b/pyproject.toml index d4ffee4ef..bf61d5c23 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -49,6 +49,8 @@ examples = [ "python-dotenv~=1.0.1", "flask~=3.0.3", "flask_cors~=4.0.1" ] fal = [ "fal-client~=0.4.1" ] gladia = [ "websockets~=13.1" ] google = [ "google-generativeai~=0.8.3", "google-cloud-texttospeech~=2.17.2" ] +grok = [ "openai~=1.50.2" ] +groq = [ "openai~=1.50.2" ] gstreamer = [ "pygobject~=3.48.2" ] fireworks = [ "openai~=1.37.2" ] krisp = [ "pipecat-ai-krisp~=0.3.0" ] diff --git a/src/pipecat/services/grok.py b/src/pipecat/services/grok.py new file mode 100644 index 000000000..505dfcca5 --- /dev/null +++ b/src/pipecat/services/grok.py @@ -0,0 +1,103 @@ +# +# Copyright (c) 2024, Daily +# +# SPDX-License-Identifier: BSD 2-Clause License +# + + +from loguru import logger + +from pipecat.metrics.metrics import LLMTokenUsage +from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext +from pipecat.services.openai import OpenAILLMService + + +class GrokLLMService(OpenAILLMService): + """A service for interacting with Grok's API using the OpenAI-compatible interface. + + This service extends OpenAILLMService to connect to Grok's API endpoint while + maintaining full compatibility with OpenAI's interface and functionality. + + Args: + api_key (str): The API key for accessing Grok's API + base_url (str, optional): The base URL for Grok API. Defaults to "https://api.x.ai/v1" + model (str, optional): The model identifier to use. Defaults to "grok-beta" + **kwargs: Additional keyword arguments passed to OpenAILLMService + """ + + def __init__( + self, + *, + api_key: str, + base_url: str = "https://api.x.ai/v1", + model: str = "grok-beta", + **kwargs, + ): + super().__init__(api_key=api_key, base_url=base_url, model=model, **kwargs) + # Initialize counters for token usage metrics + self._prompt_tokens = 0 + self._completion_tokens = 0 + self._total_tokens = 0 + self._has_reported_prompt_tokens = False + self._is_processing = False + + def create_client(self, api_key=None, base_url=None, **kwargs): + """Create OpenAI-compatible client for Grok API endpoint.""" + logger.debug(f"Creating Grok client with api {base_url}") + return super().create_client(api_key, base_url, **kwargs) + + async def _process_context(self, context: OpenAILLMContext): + """Process a context through the LLM and accumulate token usage metrics. + + This method overrides the parent class implementation to handle Grok's + incremental token reporting style, accumulating the counts and reporting + them once at the end of processing. + + Args: + context (OpenAILLMContext): The context to process, containing messages + and other information needed for the LLM interaction. + """ + # Reset all counters and flags at the start of processing + self._prompt_tokens = 0 + self._completion_tokens = 0 + self._total_tokens = 0 + self._has_reported_prompt_tokens = False + self._is_processing = True + + try: + await super()._process_context(context) + finally: + self._is_processing = False + # Report final accumulated token usage at the end of processing + if self._prompt_tokens > 0 or self._completion_tokens > 0: + self._total_tokens = self._prompt_tokens + self._completion_tokens + tokens = LLMTokenUsage( + prompt_tokens=self._prompt_tokens, + completion_tokens=self._completion_tokens, + total_tokens=self._total_tokens, + ) + await super().start_llm_usage_metrics(tokens) + + async def start_llm_usage_metrics(self, tokens: LLMTokenUsage): + """Accumulate token usage metrics during processing. + + This method intercepts the incremental token updates from Grok's API + and accumulates them instead of passing each update to the metrics system. + The final accumulated totals are reported at the end of processing. + + Args: + tokens (LLMTokenUsage): The token usage metrics for the current chunk + of processing, containing prompt_tokens and completion_tokens counts. + """ + # Only accumulate metrics during active processing + if not self._is_processing: + return + + # Record prompt tokens the first time we see them + if not self._has_reported_prompt_tokens and tokens.prompt_tokens > 0: + self._prompt_tokens = tokens.prompt_tokens + self._has_reported_prompt_tokens = True + + # Update completion tokens count if it has increased + if tokens.completion_tokens > self._completion_tokens: + self._completion_tokens = tokens.completion_tokens diff --git a/src/pipecat/services/groq.py b/src/pipecat/services/groq.py new file mode 100644 index 000000000..c9b49d636 --- /dev/null +++ b/src/pipecat/services/groq.py @@ -0,0 +1,39 @@ +# +# Copyright (c) 2024, Daily +# +# SPDX-License-Identifier: BSD 2-Clause License +# + + +from loguru import logger + +from pipecat.services.openai import OpenAILLMService + + +class GroqLLMService(OpenAILLMService): + """A service for interacting with Groq's API using the OpenAI-compatible interface. + + This service extends OpenAILLMService to connect to Groq's API endpoint while + maintaining full compatibility with OpenAI's interface and functionality. + + Args: + api_key (str): The API key for accessing Groq's API + base_url (str, optional): The base URL for Groq API. Defaults to "https://api.groq.com/openai/v1" + model (str, optional): The model identifier to use. Defaults to "llama-3.1-70b-versatile" + **kwargs: Additional keyword arguments passed to OpenAILLMService + """ + + def __init__( + self, + *, + api_key: str, + base_url: str = "https://api.groq.com/openai/v1", + model: str = "llama-3.1-70b-versatile", + **kwargs, + ): + super().__init__(api_key=api_key, base_url=base_url, model=model, **kwargs) + + def create_client(self, api_key=None, base_url=None, **kwargs): + """Create OpenAI-compatible client for Groq API endpoint.""" + logger.debug(f"Creating Groq client with api {base_url}") + return super().create_client(api_key, base_url, **kwargs) diff --git a/src/pipecat/services/together.py b/src/pipecat/services/together.py index e2aed4da1..e18e9650d 100644 --- a/src/pipecat/services/together.py +++ b/src/pipecat/services/together.py @@ -9,20 +9,19 @@ from loguru import logger from pipecat.services.openai import OpenAILLMService -try: - # Together.ai is recommending OpenAI-compatible function calling, so we've switched over - # to using the OpenAI client library here rather than the Together Python client library. - from openai import AsyncOpenAI, DefaultAsyncHttpxClient -except ModuleNotFoundError as e: - logger.error(f"Exception: {e}") - logger.error( - "In order to use Together.ai, you need to `pip install pipecat-ai[together]`. Also, set `TOGETHER_API_KEY` environment variable." - ) - raise Exception(f"Missing module: {e}") - class TogetherLLMService(OpenAILLMService): - """This class implements inference with Together's Llama 3.1 models""" + """A service for interacting with Together.ai's API using the OpenAI-compatible interface. + + This service extends OpenAILLMService to connect to Together.ai's API endpoint while + maintaining full compatibility with OpenAI's interface and functionality. + + Args: + api_key (str): The API key for accessing Together.ai's API + base_url (str, optional): The base URL for Together.ai API. Defaults to "https://api.together.xyz/v1" + model (str, optional): The model identifier to use. Defaults to "meta-llama/Meta-Llama-3.1-8B-Instruct-Turbo" + **kwargs: Additional keyword arguments passed to OpenAILLMService + """ def __init__( self, @@ -35,5 +34,6 @@ class TogetherLLMService(OpenAILLMService): super().__init__(api_key=api_key, base_url=base_url, model=model, **kwargs) def create_client(self, api_key=None, base_url=None, **kwargs): + """Create OpenAI-compatible client for Together.ai API endpoint.""" logger.debug(f"Creating Together.ai client with api {base_url}") return super().create_client(api_key, base_url, **kwargs)