Delete examples/foundational/26f-gemini-multimodal-live-files-api.py
This commit is contained in:
Pete
@@ -1,210 +0,0 @@
|
|||||||
#
|
|
||||||
# Copyright (c) 2024–2025, Daily
|
|
||||||
#
|
|
||||||
# SPDX-License-Identifier: BSD 2-Clause License
|
|
||||||
#
|
|
||||||
|
|
||||||
import argparse
|
|
||||||
import os
|
|
||||||
import tempfile
|
|
||||||
|
|
||||||
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.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.services.gemini_multimodal_live.gemini import (
|
|
||||||
GeminiMultimodalLiveLLMService,
|
|
||||||
GeminiMultimodalLiveContext,
|
|
||||||
)
|
|
||||||
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)
|
|
||||||
|
|
||||||
|
|
||||||
async def create_sample_file():
|
|
||||||
"""Create a sample text file for testing the File API."""
|
|
||||||
content = """# Sample Document for Gemini File API Test
|
|
||||||
|
|
||||||
This is a test document to demonstrate the Gemini File API functionality.
|
|
||||||
|
|
||||||
## Key Information:
|
|
||||||
- This document was created for testing purposes
|
|
||||||
- It contains information about AI assistants
|
|
||||||
- The document should be analyzed by Gemini
|
|
||||||
- The secret phrase for the test is "Pineapple Pizza"
|
|
||||||
|
|
||||||
## AI Assistant Capabilities:
|
|
||||||
1. Natural language processing
|
|
||||||
2. File analysis and understanding
|
|
||||||
3. Context-aware conversations
|
|
||||||
4. Multi-modal interactions
|
|
||||||
|
|
||||||
## Conclusion:
|
|
||||||
This document serves as a test case for the Gemini File API integration with Pipecat.
|
|
||||||
The AI should be able to reference and discuss the contents of this file.
|
|
||||||
"""
|
|
||||||
|
|
||||||
# Create a temporary file
|
|
||||||
with tempfile.NamedTemporaryFile(mode='w', suffix='.txt', delete=False) as f:
|
|
||||||
f.write(content)
|
|
||||||
return f.name
|
|
||||||
|
|
||||||
|
|
||||||
async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
|
|
||||||
logger.info(f"Starting File API bot")
|
|
||||||
|
|
||||||
# Create a sample file to upload
|
|
||||||
sample_file_path = await create_sample_file()
|
|
||||||
logger.info(f"Created sample file: {sample_file_path}")
|
|
||||||
|
|
||||||
# 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)),
|
|
||||||
),
|
|
||||||
)
|
|
||||||
|
|
||||||
system_instruction = """
|
|
||||||
You are a helpful AI assistant with access to a document that has been uploaded for analysis.
|
|
||||||
|
|
||||||
The document contains test information including a secret phrase. You should be able to:
|
|
||||||
- Reference and discuss the contents of the uploaded document
|
|
||||||
- Answer questions about what's in the document
|
|
||||||
- Use the information from the document in our conversation
|
|
||||||
|
|
||||||
Your output will be converted to audio so don't include special characters in your answers.
|
|
||||||
Be friendly and demonstrate your ability to work with the uploaded file.
|
|
||||||
"""
|
|
||||||
|
|
||||||
# Initialize Gemini service with File API support
|
|
||||||
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,
|
|
||||||
)
|
|
||||||
|
|
||||||
# Upload the sample file to Gemini File API
|
|
||||||
logger.info("Uploading file to Gemini File API...")
|
|
||||||
file_info = None
|
|
||||||
try:
|
|
||||||
file_info = await llm.file_api.upload_file(
|
|
||||||
sample_file_path,
|
|
||||||
display_name="Sample Test Document"
|
|
||||||
)
|
|
||||||
logger.info(f"File uploaded successfully: {file_info['file']['name']}")
|
|
||||||
|
|
||||||
# Get file URI and mime type
|
|
||||||
file_uri = file_info["file"]["uri"]
|
|
||||||
mime_type = "text/plain"
|
|
||||||
|
|
||||||
# Create context with file reference
|
|
||||||
context = OpenAILLMContext(
|
|
||||||
[
|
|
||||||
{
|
|
||||||
"role": "user",
|
|
||||||
"content": [
|
|
||||||
{
|
|
||||||
"type": "text",
|
|
||||||
"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."
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"type": "file_data",
|
|
||||||
"file_data": {
|
|
||||||
"mime_type": mime_type,
|
|
||||||
"file_uri": file_uri
|
|
||||||
}
|
|
||||||
}
|
|
||||||
]
|
|
||||||
}
|
|
||||||
]
|
|
||||||
)
|
|
||||||
|
|
||||||
logger.info("File reference added to conversation context")
|
|
||||||
|
|
||||||
except Exception as e:
|
|
||||||
logger.error(f"Error uploading file: {e}")
|
|
||||||
# Continue with a basic context if file upload fails
|
|
||||||
context = OpenAILLMContext(
|
|
||||||
[
|
|
||||||
{
|
|
||||||
"role": "user",
|
|
||||||
"content": "Greet the user and explain that there was an issue with file upload, but you're ready to help with other tasks."
|
|
||||||
}
|
|
||||||
]
|
|
||||||
)
|
|
||||||
|
|
||||||
# Create context aggregator
|
|
||||||
context_aggregator = llm.create_context_aggregator(context)
|
|
||||||
|
|
||||||
# Build the pipeline
|
|
||||||
pipeline = Pipeline([
|
|
||||||
transport.input(),
|
|
||||||
context_aggregator.user(),
|
|
||||||
llm,
|
|
||||||
transport.output(),
|
|
||||||
context_aggregator.assistant(),
|
|
||||||
])
|
|
||||||
|
|
||||||
# Configure the pipeline task
|
|
||||||
task = PipelineTask(
|
|
||||||
pipeline,
|
|
||||||
params=PipelineParams(
|
|
||||||
allow_interruptions=True,
|
|
||||||
enable_metrics=True,
|
|
||||||
enable_usage_metrics=True,
|
|
||||||
),
|
|
||||||
)
|
|
||||||
|
|
||||||
# Handle client connection event
|
|
||||||
@transport.event_handler("on_client_connected")
|
|
||||||
async def on_client_connected(transport, client):
|
|
||||||
logger.info(f"Client connected")
|
|
||||||
# Kick off the conversation using standard context frame
|
|
||||||
await task.queue_frames([context_aggregator.user().get_context_frame()])
|
|
||||||
|
|
||||||
# Handle client disconnection events
|
|
||||||
@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()
|
|
||||||
|
|
||||||
# Run the pipeline
|
|
||||||
runner = PipelineRunner(handle_sigint=False)
|
|
||||||
await runner.run(task)
|
|
||||||
|
|
||||||
# Clean up: delete the uploaded file and temporary file
|
|
||||||
if file_info:
|
|
||||||
try:
|
|
||||||
await llm.file_api.delete_file(file_info["file"]["name"])
|
|
||||||
logger.info("Cleaned up uploaded file from Gemini")
|
|
||||||
except Exception as e:
|
|
||||||
logger.error(f"Error cleaning up file: {e}")
|
|
||||||
|
|
||||||
# Remove temporary file
|
|
||||||
try:
|
|
||||||
os.unlink(sample_file_path)
|
|
||||||
logger.info("Cleaned up temporary file")
|
|
||||||
except Exception as e:
|
|
||||||
logger.error(f"Error removing temporary file: {e}")
|
|
||||||
|
|
||||||
|
|
||||||
if __name__ == "__main__":
|
|
||||||
from run import main
|
|
||||||
|
|
||||||
main()
|
|
||||||
Reference in New Issue
Block a user