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feat: add podcast generation capabilities to SurfSense deep agent and UI integration

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This commit is contained in:
Anish Sarkar 2025-12-21 19:07:46 +05:30
parent 3906ba52e0
commit 4c4e4b3c4c
9 changed files with 985 additions and 22 deletions
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75
surfsense_backend/app/tasks/chat/stream_new_chat.py
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@ -5,6 +5,7 @@ This module streams responses from the deep agent using the Vercel AI SDK
Data Stream Protocol (SSE format).
"""
import json
from collections.abc import AsyncGenerator
from uuid import UUID
@ -73,12 +74,14 @@ async def stream_new_chat(
# Create connector service
connector_service = ConnectorService(session, search_space_id=search_space_id)
# Create the deep agent
# Create the deep agent with podcast capability
agent = create_surfsense_deep_agent(
llm=llm,
search_space_id=search_space_id,
db_session=session,
connector_service=connector_service,
user_id=str(user_id),
enable_podcast=True,
)
# Build input with just the current user query
@ -162,22 +165,72 @@ async def stream_new_chat(
f"Searching knowledge base: {query[:100]}{'...' if len(query) > 100 else ''}",
"info",
)
elif tool_name == "generate_podcast":
title = (
tool_input.get("podcast_title", "SurfSense Podcast")
if isinstance(tool_input, dict)
else "SurfSense Podcast"
)
yield streaming_service.format_terminal_info(
f"Generating podcast: {title}",
"info",
)
elif event_type == "on_tool_end":
run_id = event.get("run_id", "")
tool_output = event.get("data", {}).get("output", "")
tool_name = event.get("name", "unknown_tool")
raw_output = event.get("data", {}).get("output", "")
# Extract content from ToolMessage if needed
# LangGraph may return a ToolMessage object instead of raw dict
if hasattr(raw_output, "content"):
# It's a ToolMessage object - extract the content
content = raw_output.content
# If content is a string that looks like JSON, try to parse it
if isinstance(content, str):
try:
tool_output = json.loads(content)
except (json.JSONDecodeError, TypeError):
tool_output = {"result": content}
elif isinstance(content, dict):
tool_output = content
else:
tool_output = {"result": str(content)}
elif isinstance(raw_output, dict):
tool_output = raw_output
else:
tool_output = {"result": str(raw_output) if raw_output else "completed"}
tool_call_id = f"call_{run_id[:32]}" if run_id else "call_unknown"
# Don't stream the full output (can be very large), just acknowledge
yield streaming_service.format_tool_output_available(
tool_call_id,
{"status": "completed", "result_length": len(str(tool_output))},
)
yield streaming_service.format_terminal_info(
"Knowledge base search completed", "success"
)
# Handle different tool outputs
if tool_name == "generate_podcast":
# Stream the full podcast result so frontend can render the audio player
yield streaming_service.format_tool_output_available(
tool_call_id,
tool_output if isinstance(tool_output, dict) else {"result": tool_output},
)
# Send appropriate terminal message based on status
if isinstance(tool_output, dict) and tool_output.get("status") == "success":
yield streaming_service.format_terminal_info(
f"Podcast generated successfully: {tool_output.get('title', 'Podcast')}",
"success",
)
else:
error_msg = tool_output.get("error", "Unknown error") if isinstance(tool_output, dict) else "Unknown error"
yield streaming_service.format_terminal_info(
f"Podcast generation failed: {error_msg}",
"error",
)
else:
# Don't stream the full output for other tools (can be very large), just acknowledge
yield streaming_service.format_tool_output_available(
tool_call_id,
{"status": "completed", "result_length": len(str(tool_output))},
)
yield streaming_service.format_terminal_info(
"Knowledge base search completed", "success"
)
# Handle chain/agent end to close any open text blocks
elif event_type in ("on_chain_end", "on_agent_end"):