apunkt/SurfSense
1
0
Fork 0
mirror of https://github.com/MODSetter/SurfSense.git synced 2026-04-25 00:36:31 +02:00
Code Issues Projects Releases Packages Wiki Activity Actions 1

feat: add clone tracking and history bootstrap for cloned chats

Browse source
This commit is contained in:
CREDO23 2026-01-27 13:33:36 +02:00
parent a7145b2c63
commit 3c40c6e365
8 changed files with 225 additions and 70 deletions
Download patch file Download diff file Expand all files Collapse all files

21
surfsense_backend/app/tasks/chat/stream_new_chat.py
View file

@ -18,6 +18,7 @@ from sqlalchemy.ext.asyncio import AsyncSession
from sqlalchemy.future import select
from app.agents.new_chat.chat_deepagent import create_surfsense_deep_agent
from app.utils.content_utils import bootstrap_history_from_db
from app.agents.new_chat.checkpointer import get_checkpointer
from app.agents.new_chat.llm_config import (
AgentConfig,
@ -205,13 +206,13 @@ async def stream_new_chat(
mentioned_document_ids: list[int] | None = None,
mentioned_surfsense_doc_ids: list[int] | None = None,
checkpoint_id: str | None = None,
needs_history_bootstrap: bool = False,
) -> AsyncGenerator[str, None]:
"""
Stream chat responses from the new SurfSense deep agent.
This uses the Vercel AI SDK Data Stream Protocol (SSE format) for streaming.
The chat_id is used as LangGraph's thread_id for memory/checkpointing.
Message history can be passed from the frontend for context.
Args:
user_query: The user's query
@ -221,6 +222,7 @@ async def stream_new_chat(
user_id: The current user's UUID string (for memory tools and session state)
llm_config_id: The LLM configuration ID (default: -1 for first global config)
attachments: Optional attachments with extracted content
needs_history_bootstrap: If True, load message history from DB (for cloned chats)
mentioned_document_ids: Optional list of document IDs mentioned with @ in the chat
mentioned_surfsense_doc_ids: Optional list of SurfSense doc IDs mentioned with @ in the chat
checkpoint_id: Optional checkpoint ID to rewind/fork from (for edit/reload operations)
@ -305,9 +307,24 @@ async def stream_new_chat(
firecrawl_api_key=firecrawl_api_key, # Pass Firecrawl API key if configured
)
# Build input with message history from frontend
# Build input with message history
langchain_messages = []
# Bootstrap history for cloned chats (no LangGraph checkpoint exists yet)
if needs_history_bootstrap:
langchain_messages = await bootstrap_history_from_db(session, chat_id)
# Clear the flag so we don't bootstrap again on next message
from app.db import NewChatThread
thread_result = await session.execute(
select(NewChatThread).filter(NewChatThread.id == chat_id)
)
thread = thread_result.scalars().first()
if thread:
thread.needs_history_bootstrap = False
await session.commit()
# Fetch mentioned documents if any (with chunks for proper citations)
mentioned_documents: list[Document] = []
if mentioned_document_ids: