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
240
examples/realtime/realtime-gemini-live-files-api.py
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240
examples/realtime/realtime-gemini-live-files-api.py
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#
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# Copyright (c) 2024-2026, 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 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.frames.frames import LLMRunFrame
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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.llm_context import LLMContext
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from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
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from pipecat.runner.types import RunnerArguments
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from pipecat.runner.utils import create_transport
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from pipecat.services.google.gemini_live.llm import GeminiLiveLLMService
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from pipecat.transports.base_transport import BaseTransport, TransportParams
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from pipecat.transports.daily.transport import DailyParams
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from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
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load_dotenv(override=True)
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# We use lambdas to defer transport parameter creation until the transport
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# type is selected at runtime.
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transport_params = {
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"daily": lambda: DailyParams(
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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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),
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"twilio": lambda: FastAPIWebsocketParams(
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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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),
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"webrtc": lambda: 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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),
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}
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sample_file_path = ""
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async def create_sample_file():
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if sample_file_path:
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return sample_file_path
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else:
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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(transport: BaseTransport, runner_args: RunnerArguments):
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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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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.
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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 spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points.
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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 = GeminiLiveLLMService(
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api_key=os.getenv("GOOGLE_API_KEY"),
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settings=GeminiLiveLLMService.Settings(
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system_instruction=system_instruction,
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voice="Charon", # Aoede, Charon, Fenrir, Kore, Puck
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),
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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, 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 = LLMContext(
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[
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{
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"role": "developer",
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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": {"mime_type": mime_type, "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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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 = LLMContext(
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[
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{
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"role": "developer",
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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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# Server-side VAD is enabled by default; no local VAD is added.
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user_aggregator, assistant_aggregator = LLMContextAggregatorPair(context)
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# Build the pipeline
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pipeline = Pipeline(
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[
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transport.input(),
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user_aggregator,
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llm,
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transport.output(),
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assistant_aggregator,
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]
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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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enable_metrics=True,
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enable_usage_metrics=True,
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),
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idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
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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([LLMRunFrame()])
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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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await task.cancel()
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# Run the pipeline
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runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
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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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async def bot(runner_args: RunnerArguments):
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"""Main bot entry point compatible with Pipecat Cloud."""
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transport = await create_transport(runner_args, transport_params)
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await run_bot(transport, runner_args)
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if __name__ == "__main__":
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from pipecat.runner.run import main
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upload_example_file = input("""
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Please pass in a TEXT filepath to test upload.
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NOTE: Files are stored on Google's servers for 48 hours.
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Press Enter to use a default test file.
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text filepath : """)
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if upload_example_file:
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print(f"Uploading file: {upload_example_file}")
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sample_file_path = upload_example_file.strip()
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else:
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print(f"Using default file")
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main()
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