Delete examples/foundational/26f-gemini-multimodal-live-files-api.py
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Pete
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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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import argparse
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import os
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import tempfile
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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.audio.vad.vad_analyzer import VADParams
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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.services.gemini_multimodal_live.gemini import (
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GeminiMultimodalLiveLLMService,
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GeminiMultimodalLiveContext,
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)
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from pipecat.transports.base_transport import TransportParams
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from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
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from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
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load_dotenv(override=True)
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async def create_sample_file():
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"""Create a sample text file for testing the File API."""
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content = """# Sample Document for Gemini File API Test
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This is a test document to demonstrate the Gemini File API functionality.
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## Key Information:
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- This document was created for testing purposes
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- It contains information about AI assistants
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- The document should be analyzed by Gemini
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- The secret phrase for the test is "Pineapple Pizza"
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## AI Assistant Capabilities:
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1. Natural language processing
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2. File analysis and understanding
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3. Context-aware conversations
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4. Multi-modal interactions
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## Conclusion:
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This document serves as a test case for the Gemini File API integration with Pipecat.
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The AI should be able to reference and discuss the contents of this file.
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"""
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# Create a temporary file
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with tempfile.NamedTemporaryFile(mode='w', suffix='.txt', delete=False) as f:
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f.write(content)
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return f.name
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async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
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logger.info(f"Starting File API bot")
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# Create a sample file to upload
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sample_file_path = await create_sample_file()
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logger.info(f"Created sample file: {sample_file_path}")
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# Initialize the SmallWebRTCTransport with the connection
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transport = SmallWebRTCTransport(
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webrtc_connection=webrtc_connection,
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params=TransportParams(
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audio_in_enabled=True,
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audio_out_enabled=True,
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video_in_enabled=False,
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vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.5)),
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),
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)
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system_instruction = """
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You are a helpful AI assistant with access to a document that has been uploaded for analysis.
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The document contains test information including a secret phrase. You should be able to:
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- Reference and discuss the contents of the uploaded document
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- Answer questions about what's in the document
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- Use the information from the document in our conversation
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Your output will be converted to audio so don't include special characters in your answers.
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Be friendly and demonstrate your ability to work with the uploaded file.
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"""
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# Initialize Gemini service with File API support
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llm = GeminiMultimodalLiveLLMService(
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api_key=os.getenv("GOOGLE_API_KEY"),
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system_instruction=system_instruction,
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voice_id="Charon", # Aoede, Charon, Fenrir, Kore, Puck
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transcribe_user_audio=True,
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)
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# Upload the sample file to Gemini File API
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logger.info("Uploading file to Gemini File API...")
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file_info = None
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try:
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file_info = await llm.file_api.upload_file(
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sample_file_path,
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display_name="Sample Test Document"
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)
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logger.info(f"File uploaded successfully: {file_info['file']['name']}")
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# Get file URI and mime type
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file_uri = file_info["file"]["uri"]
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mime_type = "text/plain"
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# Create context with file reference
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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": [
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{
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"type": "text",
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"text": "Greet the user and let them know you have access to a document they can ask you about. Mention that you can discuss its contents."
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},
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{
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"type": "file_data",
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"file_data": {
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"mime_type": mime_type,
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"file_uri": file_uri
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}
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}
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]
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}
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]
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)
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logger.info("File reference added to conversation context")
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except Exception as e:
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logger.error(f"Error uploading file: {e}")
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# Continue with a basic context if file upload fails
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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": "Greet the user and explain that there was an issue with file upload, but you're ready to help with other tasks."
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}
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]
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)
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# Create context aggregator
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context_aggregator = llm.create_context_aggregator(context)
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# Build the pipeline
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pipeline = Pipeline([
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transport.input(),
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context_aggregator.user(),
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llm,
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transport.output(),
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context_aggregator.assistant(),
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])
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# Configure the pipeline task
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task = PipelineTask(
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pipeline,
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params=PipelineParams(
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allow_interruptions=True,
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enable_metrics=True,
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enable_usage_metrics=True,
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),
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)
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# Handle client connection event
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@transport.event_handler("on_client_connected")
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async def on_client_connected(transport, client):
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logger.info(f"Client connected")
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# Kick off the conversation using standard context frame
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await task.queue_frames([context_aggregator.user().get_context_frame()])
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# Handle client disconnection events
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@transport.event_handler("on_client_disconnected")
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async def on_client_disconnected(transport, client):
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logger.info(f"Client disconnected")
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@transport.event_handler("on_client_closed")
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async def on_client_closed(transport, client):
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logger.info(f"Client closed connection")
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await task.cancel()
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# Run the pipeline
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runner = PipelineRunner(handle_sigint=False)
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await runner.run(task)
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# Clean up: delete the uploaded file and temporary file
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if file_info:
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try:
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await llm.file_api.delete_file(file_info["file"]["name"])
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logger.info("Cleaned up uploaded file from Gemini")
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except Exception as e:
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logger.error(f"Error cleaning up file: {e}")
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# Remove temporary file
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try:
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os.unlink(sample_file_path)
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logger.info("Cleaned up temporary file")
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except Exception as e:
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logger.error(f"Error removing temporary file: {e}")
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if __name__ == "__main__":
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from run import main
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main()
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