Merge pull request #1030 from pipecat-ai/gemini_grounding_metadata

Introduce support for extracting and processing grounding metadata from GoogleLLMService.
2025-01-24 15:41:54 -03:00
parent 48ce751602 9b61633aa0
commit 7c52736ff6
4 changed files with 235 additions and 0 deletions
--- a/examples/foundational/31-gemini-grounding-metadata.py
+++ b/examples/foundational/31-gemini-grounding-metadata.py
@@ -0,0 +1,130 @@
+#
+# Copyright (c) 2024, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+import asyncio
+import os
+import sys
+from pathlib import Path
+
+import aiohttp
+from dotenv import load_dotenv
+from loguru import logger
+
+from pipecat.audio.vad.silero import SileroVADAnalyzer
+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.cartesia import CartesiaTTSService
+from pipecat.services.deepgram import DeepgramSTTService
+from pipecat.services.google import GoogleLLMService, LLMSearchResponseFrame
+from pipecat.transports.services.daily import DailyParams, DailyTransport
+
+sys.path.append(str(Path(__file__).parent.parent))
+from runner import configure
+
+load_dotenv(override=True)
+
+logger.remove(0)
+logger.add(sys.stderr, level="DEBUG")
+
+# Function handlers for the LLM
+search_tool = {"google_search_retrieval": {}}
+tools = [search_tool]
+
+system_instruction = """
+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. 
+
+Always use the google search API to retrieve the latest news. You must also use it to check which day is today.
+
+You can:
+- Use the Google search API to check the current date.
+- Provide the most recent and relevant news from any place by using the google search API.
+- Answer any questions the user may have, ensuring your responses are accurate and concise.
+
+Start each interaction by asking the user about which place they would like to know the information.
+"""
+
+
+class LLMSearchLoggerProcessor(FrameProcessor):
+    async def process_frame(self, frame: Frame, direction: FrameDirection):
+        await super().process_frame(frame, direction)
+
+        if isinstance(frame, LLMSearchResponseFrame):
+            print(f"LLMSearchLoggerProcessor: {frame}")
+
+        await self.push_frame(frame)
+
+
+async def main():
+    async with aiohttp.ClientSession() as session:
+        (room_url, token) = await configure(session)
+
+        transport = DailyTransport(
+            room_url,
+            token,
+            "Latest news!",
+            DailyParams(
+                audio_out_enabled=True,
+                vad_enabled=True,
+                vad_analyzer=SileroVADAnalyzer(),
+                vad_audio_passthrough=True,
+            ),
+        )
+
+        stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
+
+        tts = CartesiaTTSService(
+            api_key=os.getenv("CARTESIA_API_KEY"),
+            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
+        )
+
+        # Initialize the Gemini Multimodal Live model
+        llm = GoogleLLMService(
+            api_key=os.getenv("GOOGLE_API_KEY"),
+            system_instruction=system_instruction,
+            tools=tools,
+        )
+
+        context = OpenAILLMContext(
+            [
+                {
+                    "role": "user",
+                    "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.",
+                }
+            ],
+        )
+        context_aggregator = llm.create_context_aggregator(context)
+
+        llm_search_logger = LLMSearchLoggerProcessor()
+
+        pipeline = Pipeline(
+            [
+                transport.input(),
+                stt,
+                context_aggregator.user(),
+                llm,
+                llm_search_logger,
+                tts,
+                transport.output(),
+                context_aggregator.assistant(),
+            ]
+        )
+
+        task = PipelineTask(pipeline, PipelineParams(allow_interruptions=True))
+
+        @transport.event_handler("on_first_participant_joined")
+        async def on_first_participant_joined(transport, participant):
+            await task.queue_frames([context_aggregator.user().get_context_frame()])
+
+        runner = PipelineRunner()
+        await runner.run(task)
+
+
+if __name__ == "__main__":
+    asyncio.run(main())