Rename example files to prepend parent folder name, preventing package shadowing
Example files like openai.py shadow installed packages when Python adds the script directory to sys.path. Prepend the parent folder name to each example file (e.g. openai.py -> function-calling-openai.py). Also split thinking-and-mcp/ into separate mcp/ and thinking/ directories.
This commit is contained in:
Mark Backman
175
examples/rag/rag-gemini-grounding-metadata.py
Normal file
175
examples/rag/rag-gemini-grounding-metadata.py
Normal file
@@ -0,0 +1,175 @@
|
||||
#
|
||||
# Copyright (c) 2024-2026, Daily
|
||||
#
|
||||
# SPDX-License-Identifier: BSD 2-Clause License
|
||||
#
|
||||
|
||||
|
||||
import os
|
||||
import sys
|
||||
from pathlib import Path
|
||||
|
||||
from dotenv import load_dotenv
|
||||
from loguru import logger
|
||||
|
||||
from pipecat.audio.vad.silero import SileroVADAnalyzer
|
||||
from pipecat.frames.frames import LLMRunFrame
|
||||
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.llm_context import LLMContext
|
||||
from pipecat.processors.aggregators.llm_response_universal import (
|
||||
LLMContextAggregatorPair,
|
||||
LLMUserAggregatorParams,
|
||||
)
|
||||
from pipecat.runner.types import RunnerArguments
|
||||
from pipecat.runner.utils import create_transport
|
||||
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 BaseTransport, TransportParams
|
||||
from pipecat.transports.daily.transport import DailyParams
|
||||
from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
|
||||
|
||||
sys.path.append(str(Path(__file__).parent.parent))
|
||||
|
||||
load_dotenv(override=True)
|
||||
|
||||
|
||||
# Function handlers for the LLM
|
||||
search_tool = {"google_search": {}}
|
||||
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 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):
|
||||
logger.debug(f"🧠 {arrow} {dst} LLM SEARCH RESPONSE FRAME: {frame} at {time_sec:.2f}s")
|
||||
|
||||
|
||||
# We use lambdas to defer transport parameter creation until the transport
|
||||
# type is selected at runtime.
|
||||
transport_params = {
|
||||
"daily": lambda: DailyParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
),
|
||||
"twilio": lambda: FastAPIWebsocketParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
),
|
||||
"webrtc": lambda: TransportParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
),
|
||||
}
|
||||
|
||||
|
||||
async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
logger.info(f"Starting bot")
|
||||
|
||||
stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
|
||||
|
||||
tts = CartesiaTTSService(
|
||||
api_key=os.getenv("CARTESIA_API_KEY"),
|
||||
settings=CartesiaTTSService.Settings(
|
||||
voice="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
|
||||
),
|
||||
)
|
||||
|
||||
# Initialize the Gemini Multimodal Live model
|
||||
llm = GoogleLLMService(
|
||||
api_key=os.getenv("GOOGLE_API_KEY"),
|
||||
settings=GoogleLLMService.Settings(
|
||||
system_instruction=system_instruction,
|
||||
),
|
||||
tools=tools,
|
||||
)
|
||||
|
||||
context = LLMContext(
|
||||
[
|
||||
{
|
||||
"role": "developer",
|
||||
"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.",
|
||||
}
|
||||
],
|
||||
)
|
||||
user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
|
||||
context,
|
||||
user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
|
||||
)
|
||||
|
||||
pipeline = Pipeline(
|
||||
[
|
||||
transport.input(),
|
||||
stt,
|
||||
user_aggregator,
|
||||
llm,
|
||||
tts,
|
||||
transport.output(),
|
||||
assistant_aggregator,
|
||||
]
|
||||
)
|
||||
|
||||
task = PipelineTask(
|
||||
pipeline,
|
||||
params=PipelineParams(
|
||||
enable_metrics=True,
|
||||
enable_usage_metrics=True,
|
||||
),
|
||||
idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
|
||||
observers=[LLMSearchLoggerObserver()],
|
||||
)
|
||||
|
||||
@transport.event_handler("on_client_connected")
|
||||
async def on_client_connected(transport, client):
|
||||
logger.info(f"Client connected")
|
||||
# Start conversation - empty prompt to let LLM follow system instructions
|
||||
await task.queue_frames([LLMRunFrame()])
|
||||
|
||||
@transport.event_handler("on_client_disconnected")
|
||||
async def on_client_disconnected(transport, client):
|
||||
logger.info(f"Client disconnected")
|
||||
await task.cancel()
|
||||
|
||||
runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
|
||||
await runner.run(task)
|
||||
|
||||
|
||||
async def bot(runner_args: RunnerArguments):
|
||||
"""Main bot entry point compatible with Pipecat Cloud."""
|
||||
transport = await create_transport(runner_args, transport_params)
|
||||
await run_bot(transport, runner_args)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
from pipecat.runner.run import main
|
||||
|
||||
main()
|
||||
Reference in New Issue
Block a user