GoogleLLMService: deprecate google-generativeai

examples/foundational/32-gemini-grounding-metadata.py

@@ -11,18 +11,17 @@ from pathlib import Path
 
 from dotenv import load_dotenv
 from loguru import logger
-from openai import audio
 
 from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.frames.frames import Frame
+from pipecat.observers.base_observer import BaseObserver, FramePushed
 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.tts import CartesiaTTSService
 from pipecat.services.deepgram.stt import DeepgramSTTService
 from pipecat.services.google.llm import GoogleLLMService, LLMSearchResponseFrame
+from pipecat.services.llm_service import LLMService
 from pipecat.transports.base_transport import TransportParams
 from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
 from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
@@ -33,7 +32,7 @@ load_dotenv(override=True)
 
 
 # Function handlers for the LLM
-search_tool = {"google_search_retrieval": {}}
+search_tool = {"google_search": {}}
 tools = [search_tool]
 
 system_instruction = """
@@ -50,14 +49,22 @@ Start each interaction by asking the user about which place they would like to k
 """
 
 
-class LLMSearchLoggerProcessor(FrameProcessor):
-    async def process_frame(self, frame: Frame, direction: FrameDirection):
-        await super().process_frame(frame, direction)
+class LLMSearchLoggerObserver(BaseObserver):
+    async def on_push_frame(self, data: FramePushed):
+        src = data.source
+        dst = data.destination
+        frame = data.frame
+        timestamp = data.timestamp
+
+        if not isinstance(src, LLMService) and not isinstance(dst, LLMService):
+            return
+
+        time_sec = timestamp / 1_000_000_000
+
+        arrow = "→"
 
         if isinstance(frame, LLMSearchResponseFrame):
-            print(f"LLMSearchLoggerProcessor: {frame}")
-
-        await self.push_frame(frame)
+            logger.debug(f"🧠 {arrow} {dst} LLM SEARCH RESPONSE FRAME: {frame} at {time_sec:.2f}s")
 
 
 async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
@@ -84,7 +91,6 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespac
         api_key=os.getenv("GOOGLE_API_KEY"),
         system_instruction=system_instruction,
         tools=tools,
-        model="gemini-1.5-flash-002",
     )
 
     context = OpenAILLMContext(
@@ -97,22 +103,23 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespac
     )
     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, params=PipelineParams(allow_interruptions=True))
+    task = PipelineTask(
+        pipeline,
+        params=PipelineParams(allow_interruptions=True),
+        observers=[LLMSearchLoggerObserver()],
+    )
 
     @transport.event_handler("on_client_connected")
     async def on_client_connected(transport, client):

examples/news-chatbot/server/news_bot.py

@@ -102,9 +102,9 @@ async def main():
 
         llm = GoogleLLMService(
             api_key=os.getenv("GOOGLE_API_KEY"),
-            model="gemini-1.5-flash-002",
             system_instruction=system_instruction,
             tools=tools,
+            model="gemini-1.5-flash",
         )
 
         context = OpenAILLMContext(