Create 26g-gemini-multimodal-live-groundingMetadata.py
This commit is contained in:
getchannel
@@ -0,0 +1,164 @@
|
||||
|
||||
import argparse
|
||||
import os
|
||||
|
||||
from dotenv import load_dotenv
|
||||
from loguru import logger
|
||||
|
||||
from pipecat.audio.vad.silero import SileroVADAnalyzer
|
||||
from pipecat.audio.vad.vad_analyzer import VADParams
|
||||
from pipecat.adapters.schemas.tools_schema import AdapterType, ToolsSchema
|
||||
from pipecat.frames.frames import Frame
|
||||
from pipecat.pipeline.pipeline import Pipeline
|
||||
from pipecat.pipeline.runner import PipelineRunner
|
||||
from pipecat.pipeline.task import PipelineParams, PipelineTask
|
||||
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
|
||||
from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
|
||||
from pipecat.services.gemini_multimodal_live.gemini import GeminiMultimodalLiveLLMService
|
||||
from pipecat.services.google.frames import LLMSearchResponseFrame
|
||||
from pipecat.transports.base_transport import TransportParams
|
||||
from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
|
||||
from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
|
||||
|
||||
load_dotenv(override=True)
|
||||
|
||||
SYSTEM_INSTRUCTION = """
|
||||
You are a helpful AI assistant that actively uses Google Search to provide up-to-date, accurate information.
|
||||
|
||||
IMPORTANT: For ANY question about current events, news, recent developments, real-time information, or anything that might have changed recently, you MUST use the google_search tool to get the latest information.
|
||||
|
||||
You should use Google Search for:
|
||||
- Current news and events
|
||||
- Recent developments in any field
|
||||
- Today's weather, stock prices, or other real-time data
|
||||
- Any question that starts with "what's happening", "latest", "recent", "current", "today", etc.
|
||||
- When you're not certain about recent information
|
||||
|
||||
Always be proactive about using search when the user asks about anything that could benefit from real-time information.
|
||||
|
||||
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, always using search for current information.
|
||||
"""
|
||||
|
||||
|
||||
class GroundingMetadataProcessor(FrameProcessor):
|
||||
"""Processor to capture and display grounding metadata from Gemini Live API."""
|
||||
|
||||
def __init__(self):
|
||||
super().__init__()
|
||||
self._grounding_count = 0
|
||||
|
||||
async def process_frame(self, frame: Frame, direction: FrameDirection):
|
||||
# Always call super().process_frame first
|
||||
await super().process_frame(frame, direction)
|
||||
|
||||
# Only log important frame types, not every audio frame
|
||||
if hasattr(frame, '__class__'):
|
||||
frame_type = frame.__class__.__name__
|
||||
if frame_type in ['LLMTextFrame', 'TTSTextFrame', 'LLMFullResponseStartFrame', 'LLMFullResponseEndFrame']:
|
||||
logger.debug(f"GroundingProcessor received: {frame_type}")
|
||||
|
||||
if isinstance(frame, LLMSearchResponseFrame):
|
||||
self._grounding_count += 1
|
||||
logger.info(f"\n🔍 GROUNDING METADATA RECEIVED #{self._grounding_count}")
|
||||
logger.info(f"📝 Search Result Text: {frame.search_result[:200]}...")
|
||||
|
||||
if frame.rendered_content:
|
||||
logger.info(f"🔗 Rendered Content: {frame.rendered_content}")
|
||||
|
||||
if frame.origins:
|
||||
logger.info(f"📍 Number of Origins: {len(frame.origins)}")
|
||||
for i, origin in enumerate(frame.origins):
|
||||
logger.info(f" Origin {i+1}: {origin.site_title} - {origin.site_uri}")
|
||||
if origin.results:
|
||||
logger.info(f" Results: {len(origin.results)} items")
|
||||
|
||||
# Always push the frame downstream
|
||||
await self.push_frame(frame, direction)
|
||||
|
||||
|
||||
async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
|
||||
logger.info(f"Starting Gemini Live Grounding Test Bot")
|
||||
|
||||
# Initialize the SmallWebRTCTransport with the connection
|
||||
transport = SmallWebRTCTransport(
|
||||
webrtc_connection=webrtc_connection,
|
||||
params=TransportParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
video_in_enabled=False,
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.5)),
|
||||
),
|
||||
)
|
||||
|
||||
# Create tools using ToolsSchema with custom tools for Gemini
|
||||
tools = ToolsSchema(
|
||||
standard_tools=[], # No standard function declarations needed
|
||||
custom_tools={
|
||||
AdapterType.GEMINI: [
|
||||
{"google_search": {}},
|
||||
{"code_execution": {}}
|
||||
]
|
||||
}
|
||||
)
|
||||
|
||||
llm = GeminiMultimodalLiveLLMService(
|
||||
api_key=os.getenv("GOOGLE_API_KEY"),
|
||||
system_instruction=SYSTEM_INSTRUCTION,
|
||||
voice_id="Charon", # Aoede, Charon, Fenrir, Kore, Puck
|
||||
transcribe_user_audio=True,
|
||||
tools=tools,
|
||||
)
|
||||
|
||||
# Create a processor to capture grounding metadata
|
||||
grounding_processor = GroundingMetadataProcessor()
|
||||
|
||||
messages = [
|
||||
{
|
||||
"role": "user",
|
||||
"content": 'Please introduce yourself and let me know that you can help with current information by searching the web. Ask me what current information I\'d like to know about.',
|
||||
},
|
||||
]
|
||||
|
||||
# Set up conversation context and management
|
||||
context = OpenAILLMContext(messages)
|
||||
context_aggregator = llm.create_context_aggregator(context)
|
||||
|
||||
pipeline = Pipeline(
|
||||
[
|
||||
transport.input(),
|
||||
context_aggregator.user(),
|
||||
llm,
|
||||
grounding_processor, # Add our grounding processor here
|
||||
transport.output(),
|
||||
context_aggregator.assistant(),
|
||||
]
|
||||
)
|
||||
|
||||
task = PipelineTask(pipeline)
|
||||
|
||||
@transport.event_handler("on_client_connected")
|
||||
async def on_client_connected(transport, client):
|
||||
logger.info(f"Client connected")
|
||||
# Kick off the conversation.
|
||||
await task.queue_frames([context_aggregator.user().get_context_frame()])
|
||||
|
||||
@transport.event_handler("on_client_disconnected")
|
||||
async def on_client_disconnected(transport, client):
|
||||
logger.info(f"Client disconnected")
|
||||
|
||||
@transport.event_handler("on_client_closed")
|
||||
async def on_client_closed(transport, client):
|
||||
logger.info(f"Client closed connection")
|
||||
await task.cancel()
|
||||
|
||||
runner = PipelineRunner(handle_sigint=False)
|
||||
|
||||
await runner.run(task)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
from run import main
|
||||
|
||||
main()
|
||||
Reference in New Issue
Block a user