mirror of
https://github.com/MODSetter/SurfSense.git
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94 lines
2.9 KiB
Python
94 lines
2.9 KiB
Python
from sqlalchemy.ext.asyncio import AsyncSession
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from app.schemas import PodcastGenerateRequest
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from typing import List
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from sqlalchemy import select
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from app.db import Chat, Podcast
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from app.agents.podcaster.graph import graph as podcaster_graph
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from surfsense_backend.app.agents.podcaster.state import State
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async def generate_document_podcast(
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session: AsyncSession,
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document_id: int,
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search_space_id: int,
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user_id: int
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):
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# TODO: Need to fetch the document chunks, then concatenate them and pass them to the podcast generation model
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pass
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async def generate_chat_podcast(
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session: AsyncSession,
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chat_id: int,
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search_space_id: int,
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podcast_title: str
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):
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# Fetch the chat with the specified ID
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query = select(Chat).filter(
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Chat.id == chat_id,
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Chat.search_space_id == search_space_id
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)
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result = await session.execute(query)
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chat = result.scalars().first()
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if not chat:
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raise ValueError(f"Chat with id {chat_id} not found in search space {search_space_id}")
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# Create chat history structure
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chat_history_str = "<chat_history>"
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for message in chat.messages:
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if message["role"] == "user":
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chat_history_str += f"<user_message>{message['content']}</user_message>"
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elif message["role"] == "assistant":
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# Last annotation type will always be "ANSWER" here
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answer_annotation = message["annotations"][-1]
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answer_text = ""
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if answer_annotation["type"] == "ANSWER":
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answer_text = answer_annotation["content"]
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# If content is a list, join it into a single string
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if isinstance(answer_text, list):
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answer_text = "\n".join(answer_text)
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chat_history_str += f"<assistant_message>{answer_text}</assistant_message>"
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chat_history_str += "</chat_history>"
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# Pass it to the SurfSense Podcaster
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config = {
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"configurable": {
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"podcast_title" : "Surfsense",
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}
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}
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# Initialize state with database session and streaming service
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initial_state = State(
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source_content=chat_history_str,
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)
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# Run the graph directly
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result = await podcaster_graph.ainvoke(initial_state, config=config)
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# Convert podcast transcript entries to serializable format
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serializable_transcript = []
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for entry in result["podcast_transcript"]:
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serializable_transcript.append({
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"speaker_id": entry.speaker_id,
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"dialog": entry.dialog
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})
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# Create a new podcast entry
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podcast = Podcast(
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title=f"{podcast_title}",
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podcast_transcript=serializable_transcript,
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file_location=result["final_podcast_file_path"],
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search_space_id=search_space_id
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)
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# Add to session and commit
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session.add(podcast)
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await session.commit()
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await session.refresh(podcast)
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return podcast
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