Merge pull request #1030 from pipecat-ai/gemini_grounding_metadata
Introduce support for extracting and processing grounding metadata from GoogleLLMService.
This commit is contained in:
Filipi da Silva Fuchter
130
examples/foundational/31-gemini-grounding-metadata.py
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130
examples/foundational/31-gemini-grounding-metadata.py
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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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from pathlib import Path
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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 pipecat.audio.vad.silero import SileroVADAnalyzer
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from pipecat.frames.frames import Frame
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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.processors.aggregators.openai_llm_context import OpenAILLMContext
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from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
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from pipecat.services.cartesia import CartesiaTTSService
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from pipecat.services.deepgram import DeepgramSTTService
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from pipecat.services.google import GoogleLLMService, LLMSearchResponseFrame
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from pipecat.transports.services.daily import DailyParams, DailyTransport
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sys.path.append(str(Path(__file__).parent.parent))
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from runner import configure
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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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# Function handlers for the LLM
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search_tool = {"google_search_retrieval": {}}
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tools = [search_tool]
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system_instruction = """
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You are an expert at providing the most recent news from any place. Your responses will be converted to audio, so avoid using special characters or overly complex formatting.
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Always use the google search API to retrieve the latest news. You must also use it to check which day is today.
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You can:
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- Use the Google search API to check the current date.
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- Provide the most recent and relevant news from any place by using the google search API.
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- Answer any questions the user may have, ensuring your responses are accurate and concise.
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Start each interaction by asking the user about which place they would like to know the information.
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"""
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class LLMSearchLoggerProcessor(FrameProcessor):
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async def process_frame(self, frame: Frame, direction: FrameDirection):
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await super().process_frame(frame, direction)
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if isinstance(frame, LLMSearchResponseFrame):
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print(f"LLMSearchLoggerProcessor: {frame}")
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await self.push_frame(frame)
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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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"Latest news!",
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DailyParams(
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audio_out_enabled=True,
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vad_enabled=True,
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vad_analyzer=SileroVADAnalyzer(),
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vad_audio_passthrough=True,
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),
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)
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stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
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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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# Initialize the Gemini Multimodal Live model
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llm = GoogleLLMService(
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api_key=os.getenv("GOOGLE_API_KEY"),
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system_instruction=system_instruction,
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tools=tools,
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)
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context = OpenAILLMContext(
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[
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{
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"role": "user",
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"content": "Start by greeting the user warmly, introducing yourself, and mentioning the current day. Be friendly and engaging to set a positive tone for the interaction.",
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}
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],
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)
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context_aggregator = llm.create_context_aggregator(context)
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llm_search_logger = LLMSearchLoggerProcessor()
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pipeline = Pipeline(
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[
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transport.input(),
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stt,
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context_aggregator.user(),
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llm,
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llm_search_logger,
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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(pipeline, PipelineParams(allow_interruptions=True))
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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 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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2
src/pipecat/services/google/__init__.py
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2
src/pipecat/services/google/__init__.py
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from .frames import LLMSearchResponseFrame
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from .google import *
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33
src/pipecat/services/google/frames.py
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33
src/pipecat/services/google/frames.py
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#
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# Copyright (c) 2024–2025, Daily
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#
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# SPDX-License-Identifier: BSD 2-Clause License
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#
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from dataclasses import dataclass, field
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from typing import List, Optional
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from pipecat.frames.frames import DataFrame
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@dataclass
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class LLMSearchResult:
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text: str
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confidence: Optional[float] = None
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@dataclass
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class LLMSearchOrigin:
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site_uri: Optional[str] = None
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site_title: Optional[str] = None
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results: List[LLMSearchResult] = field(default_factory=list)
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@dataclass
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class LLMSearchResponseFrame(DataFrame):
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search_result: Optional[str] = None
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rendered_content: Optional[str] = None
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origins: List[LLMSearchOrigin] = field(default_factory=list)
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def __str__(self):
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return f"LLMSearchResponseFrame(search_result={self.search_result}, origins={self.origins})"
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@@ -38,6 +38,7 @@ from pipecat.processors.aggregators.openai_llm_context import (
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)
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from pipecat.processors.frame_processor import FrameDirection
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from pipecat.services.ai_services import LLMService, TTSService
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from pipecat.services.google.frames import LLMSearchResponseFrame
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from pipecat.services.openai import (
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OpenAIAssistantContextAggregator,
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OpenAIUserContextAggregator,
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@@ -639,6 +640,9 @@ class GoogleLLMService(LLMService):
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completion_tokens = 0
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total_tokens = 0
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grounding_metadata = None
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search_result = ""
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try:
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logger.debug(
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# f"Generating chat: {self._system_instruction} | {context.get_messages_for_logging()}"
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@@ -698,6 +702,7 @@ class GoogleLLMService(LLMService):
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try:
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for c in chunk.parts:
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if c.text:
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search_result += c.text
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await self.push_frame(LLMTextFrame(c.text))
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elif c.function_call:
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logger.debug(f"!!! Function call: {c.function_call}")
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@@ -708,6 +713,63 @@ class GoogleLLMService(LLMService):
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function_name=c.function_call.name,
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arguments=args,
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)
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# Handle grounding metadata
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# It seems only the last chunk that we receive may contain this information
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# If the response doesn't include groundingMetadata, this means the response wasn't grounded.
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if chunk.candidates:
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for candidate in chunk.candidates:
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# logger.debug(f"candidate received: {candidate}")
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# Extract grounding metadata
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grounding_metadata = (
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{
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"rendered_content": getattr(
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getattr(candidate, "grounding_metadata", None),
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"search_entry_point",
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None,
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).rendered_content
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if hasattr(
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getattr(candidate, "grounding_metadata", None),
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"search_entry_point",
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)
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else None,
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"origins": [
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{
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"site_uri": getattr(grounding_chunk.web, "uri", None),
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"site_title": getattr(
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grounding_chunk.web, "title", None
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),
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"results": [
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{
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"text": getattr(
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grounding_support.segment, "text", ""
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),
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"confidence": getattr(
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grounding_support, "confidence_scores", None
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),
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}
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for grounding_support in getattr(
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getattr(candidate, "grounding_metadata", None),
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"grounding_supports",
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[],
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)
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if index
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in getattr(
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grounding_support, "grounding_chunk_indices", []
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)
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],
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}
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for index, grounding_chunk in enumerate(
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getattr(
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getattr(candidate, "grounding_metadata", None),
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"grounding_chunks",
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[],
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)
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)
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],
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}
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if getattr(candidate, "grounding_metadata", None)
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else None
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)
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except Exception as e:
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# Google LLMs seem to flag safety issues a lot!
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if chunk.candidates[0].finish_reason == 3:
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@@ -720,6 +782,14 @@ class GoogleLLMService(LLMService):
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except Exception as e:
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logger.exception(f"{self} exception: {e}")
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finally:
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if grounding_metadata is not None and isinstance(grounding_metadata, dict):
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llm_search_frame = LLMSearchResponseFrame(
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search_result=search_result,
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origins=grounding_metadata["origins"],
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rendered_content=grounding_metadata["rendered_content"],
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)
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await self.push_frame(llm_search_frame)
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await self.start_llm_usage_metrics(
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LLMTokenUsage(
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prompt_tokens=prompt_tokens,
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