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Update examples/foundational/26f-gemini-multimodal-live-files-api.py

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vipyne
2025-07-01 16:26:52 -05:00
parent 79e51051c7
commit f1c9f5040b
1 changed files with 95 additions and 63 deletions
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158
examples/foundational/26f-gemini-multimodal-live-files-api.py
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@@ -18,67 +18,87 @@ 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,
GeminiMultimodalLiveLLMService,
)
from pipecat.transports.base_transport import TransportParams
from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
from pipecat.transports.base_transport import BaseTransport, TransportParams
from pipecat.transports.services.daily import DailyParams
load_dotenv(override=True)
# We store functions so objects (e.g. SileroVADAnalyzer) don't get
# instantiated. The function will be called when the desired transport gets
# selected.
transport_params = {
"daily": lambda: DailyParams(
audio_in_enabled=True,
audio_out_enabled=True,
video_in_enabled=False,
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.5)),
),
"twilio": lambda: FastAPIWebsocketParams(
audio_in_enabled=True,
audio_out_enabled=True,
video_in_enabled=False,
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.5)),
),
"webrtc": lambda: TransportParams(
audio_in_enabled=True,
audio_out_enabled=True,
video_in_enabled=False,
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.5)),
),
}
sample_file_path = ""
async def create_sample_file():
"""Create a sample text file for testing the File API."""
content = """# Sample Document for Gemini File API Test
if sample_file_path:
return sample_file_path
else:
"""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.
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"
## 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
## 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
## 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):
async def run_example(transport: BaseTransport, _: argparse.Namespace, handle_sigint: bool):
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:
The document contains test information.
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
@@ -100,15 +120,14 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespac
file_info = None
try:
file_info = await llm.file_api.upload_file(
sample_file_path,
display_name="Sample Test Document"
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(
[
@@ -117,22 +136,19 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespac
"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."
"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
}
}
]
"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
@@ -140,7 +156,7 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespac
[
{
"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."
"content": "Greet the user and explain that there was an issue with file upload, but you're ready to help with other tasks.",
}
]
)
@@ -149,13 +165,15 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespac
context_aggregator = llm.create_context_aggregator(context)
# Build the pipeline
pipeline = Pipeline([
transport.input(),
context_aggregator.user(),
llm,
transport.output(),
context_aggregator.assistant(),
])
pipeline = Pipeline(
[
transport.input(),
context_aggregator.user(),
llm,
transport.output(),
context_aggregator.assistant(),
]
)
# Configure the pipeline task
task = PipelineTask(
@@ -195,7 +213,7 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespac
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)
@@ -205,6 +223,20 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespac
if __name__ == "__main__":
from run import main
from pipecat.examples.run import main
main()
upload_example_file = input("""
Please pass in a TEXT filepath to test upload.
NOTE: Files are stored on Google's servers for 48 hours.
Press Enter to use a default test file.
text filepath : """)
if upload_example_file:
print(f"Uploading file: {upload_example_file}")
sample_file_path = upload_example_file.strip()
else:
print(f"Using default file")
main(run_example, transport_params=transport_params)