mirror of
https://github.com/MODSetter/SurfSense.git
synced 2026-04-25 00:36:31 +02:00
feat: message history and PostgreSQL checkpointer integration
This commit is contained in:
DESKTOP-RTLN3BA\$punk
parent
3906ba52e0
commit
73f0f772a8
11 changed files with 434 additions and 115 deletions
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@ -10,6 +10,7 @@ from collections.abc import Sequence
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from deepagents import create_deep_agent
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from langchain_core.tools import BaseTool
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from langchain_litellm import ChatLiteLLM
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from langgraph.types import Checkpointer
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from sqlalchemy.ext.asyncio import AsyncSession
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from app.agents.new_chat.context import SurfSenseContextSchema
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@ -27,6 +28,7 @@ def create_surfsense_deep_agent(
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search_space_id: int,
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db_session: AsyncSession,
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connector_service: ConnectorService,
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checkpointer: Checkpointer,
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user_instructions: str | None = None,
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enable_citations: bool = True,
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additional_tools: Sequence[BaseTool] | None = None,
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@ -39,6 +41,8 @@ def create_surfsense_deep_agent(
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search_space_id: The user's search space ID
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db_session: Database session
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connector_service: Initialized connector service
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checkpointer: LangGraph checkpointer for conversation state persistence.
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Use AsyncPostgresSaver for production or MemorySaver for testing.
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user_instructions: Optional user instructions to inject into the system prompt.
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These will be added to the system prompt to customize agent behavior.
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enable_citations: Whether to include citation instructions in the system prompt (default: True).
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@ -61,7 +65,7 @@ def create_surfsense_deep_agent(
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if additional_tools:
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tools.extend(additional_tools)
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# Create the deep agent with user-configurable system prompt
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# Create the deep agent with user-configurable system prompt and checkpointer
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agent = create_deep_agent(
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model=llm,
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tools=tools,
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@ -70,6 +74,7 @@ def create_surfsense_deep_agent(
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enable_citations=enable_citations,
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),
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context_schema=SurfSenseContextSchema,
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checkpointer=checkpointer, # Enable conversation memory via thread_id
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)
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return agent
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95
surfsense_backend/app/agents/new_chat/checkpointer.py
Normal file
95
surfsense_backend/app/agents/new_chat/checkpointer.py
Normal file
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@ -0,0 +1,95 @@
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"""
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PostgreSQL-based checkpointer for LangGraph agents.
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This module provides a persistent checkpointer using AsyncPostgresSaver
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that stores conversation state in the PostgreSQL database.
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"""
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from langgraph.checkpoint.postgres.aio import AsyncPostgresSaver
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from app.config import config
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# Global checkpointer instance (initialized lazily)
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_checkpointer: AsyncPostgresSaver | None = None
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_checkpointer_context = None # Store the context manager for cleanup
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_checkpointer_initialized: bool = False
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def get_postgres_connection_string() -> str:
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"""
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Convert the async DATABASE_URL to a sync postgres connection string for psycopg3.
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The DATABASE_URL is typically in format:
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postgresql+asyncpg://user:pass@host:port/dbname
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We need to convert it to:
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postgresql://user:pass@host:port/dbname
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"""
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db_url = config.DATABASE_URL
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# Handle asyncpg driver prefix
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if db_url.startswith("postgresql+asyncpg://"):
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return db_url.replace("postgresql+asyncpg://", "postgresql://")
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# Handle other async prefixes
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if "+asyncpg" in db_url:
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return db_url.replace("+asyncpg", "")
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return db_url
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async def get_checkpointer() -> AsyncPostgresSaver:
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"""
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Get or create the global AsyncPostgresSaver instance.
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This function:
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1. Creates the checkpointer if it doesn't exist
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2. Sets up the required database tables on first call
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3. Returns the cached instance on subsequent calls
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Returns:
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AsyncPostgresSaver: The configured checkpointer instance
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"""
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global _checkpointer, _checkpointer_context, _checkpointer_initialized
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if _checkpointer is None:
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conn_string = get_postgres_connection_string()
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# from_conn_string returns an async context manager
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# We need to enter the context to get the actual checkpointer
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_checkpointer_context = AsyncPostgresSaver.from_conn_string(conn_string)
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_checkpointer = await _checkpointer_context.__aenter__()
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# Setup tables on first call (idempotent)
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if not _checkpointer_initialized:
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await _checkpointer.setup()
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_checkpointer_initialized = True
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return _checkpointer
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async def setup_checkpointer_tables() -> None:
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"""
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Explicitly setup the checkpointer tables.
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This can be called during application startup to ensure
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tables exist before any agent calls.
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"""
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await get_checkpointer()
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print("[Checkpointer] PostgreSQL checkpoint tables ready")
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async def close_checkpointer() -> None:
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"""
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Close the checkpointer connection.
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This should be called during application shutdown.
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"""
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global _checkpointer, _checkpointer_context, _checkpointer_initialized
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if _checkpointer_context is not None:
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await _checkpointer_context.__aexit__(None, None, None)
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_checkpointer = None
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_checkpointer_context = None
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_checkpointer_initialized = False
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print("[Checkpointer] PostgreSQL connection closed")
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@ -5,6 +5,7 @@ from fastapi.middleware.cors import CORSMiddleware
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from sqlalchemy.ext.asyncio import AsyncSession
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from uvicorn.middleware.proxy_headers import ProxyHeadersMiddleware
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from app.agents.new_chat.checkpointer import close_checkpointer, setup_checkpointer_tables
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from app.config import config
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from app.db import User, create_db_and_tables, get_async_session
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from app.routes import router as crud_router
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@ -16,7 +17,11 @@ from app.users import SECRET, auth_backend, current_active_user, fastapi_users
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async def lifespan(app: FastAPI):
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# Not needed if you setup a migration system like Alembic
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await create_db_and_tables()
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# Setup LangGraph checkpointer tables for conversation persistence
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await setup_checkpointer_tables()
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yield
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# Cleanup: close checkpointer connection on shutdown
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await close_checkpointer()
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def registration_allowed():
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@ -226,6 +226,7 @@ async def handle_new_chat(
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chat_id=request.chat_id,
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session=session,
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llm_config_id=llm_config_id,
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messages=request.messages, # Pass message history from frontend
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),
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media_type="text/event-stream",
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)
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@ -48,12 +48,20 @@ class AISDKChatRequest(BaseModel):
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data: dict[str, Any] | None = None
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class ChatMessage(BaseModel):
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"""A single message in the chat history."""
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role: str # "user" or "assistant"
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content: str
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class NewChatRequest(BaseModel):
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"""Request schema for the new deep agent chat endpoint."""
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chat_id: int
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user_query: str
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search_space_id: int
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messages: list[ChatMessage] | None = None # Optional chat history from frontend
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class ChatCreate(ChatBase):
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@ -8,13 +8,13 @@ Data Stream Protocol (SSE format).
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from collections.abc import AsyncGenerator
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from uuid import UUID
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from langchain_core.messages import HumanMessage
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from langchain_core.messages import AIMessage, HumanMessage
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from sqlalchemy.ext.asyncio import AsyncSession
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from app.agents.new_chat.chat_deepagent import (
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create_surfsense_deep_agent,
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)
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from app.agents.new_chat.chat_deepagent import create_surfsense_deep_agent
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from app.agents.new_chat.checkpointer import get_checkpointer
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from app.agents.new_chat.llm_config import create_chat_litellm_from_config, load_llm_config_from_yaml
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from app.schemas.chats import ChatMessage
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from app.services.connector_service import ConnectorService
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from app.services.new_streaming_service import VercelStreamingService
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@ -26,13 +26,14 @@ async def stream_new_chat(
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chat_id: int,
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session: AsyncSession,
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llm_config_id: int = -1,
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messages: list[ChatMessage] | None = None,
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) -> AsyncGenerator[str, None]:
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"""
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Stream chat responses from the new SurfSense deep agent.
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This uses the Vercel AI SDK Data Stream Protocol (SSE format) for streaming.
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The chat_id is used as LangGraph's thread_id for memory/checkpointing,
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so chat history is automatically managed by LangGraph.
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The chat_id is used as LangGraph's thread_id for memory/checkpointing.
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Message history can be passed from the frontend for context.
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Args:
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user_query: The user's query
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@ -41,6 +42,7 @@ async def stream_new_chat(
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chat_id: The chat ID (used as LangGraph thread_id for memory)
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session: The database session
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llm_config_id: The LLM configuration ID (default: -1 for first global config)
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messages: Optional chat history from frontend (list of ChatMessage)
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Yields:
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str: SSE formatted response strings
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@ -73,18 +75,36 @@ async def stream_new_chat(
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# Create connector service
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connector_service = ConnectorService(session, search_space_id=search_space_id)
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# Create the deep agent
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# Get the PostgreSQL checkpointer for persistent conversation memory
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checkpointer = await get_checkpointer()
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# Create the deep agent with checkpointer
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agent = create_surfsense_deep_agent(
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llm=llm,
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search_space_id=search_space_id,
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db_session=session,
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connector_service=connector_service,
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checkpointer=checkpointer,
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)
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# Build input with just the current user query
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# Chat history is managed by LangGraph via thread_id
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# Build input with message history from frontend
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langchain_messages = []
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# if messages:
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# # Convert frontend messages to LangChain format
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# for msg in messages:
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# if msg.role == "user":
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# langchain_messages.append(HumanMessage(content=msg.content))
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# elif msg.role == "assistant":
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# langchain_messages.append(AIMessage(content=msg.content))
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# else:
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# Fallback: just use the current user query
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langchain_messages.append(HumanMessage(content=user_query))
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input_state = {
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"messages": [HumanMessage(content=user_query)],
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# Lets not pass this message atm because we are using the checkpointer to manage the conversation history
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# We will use this to simulate group chat functionality in the future
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"messages": langchain_messages,
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"search_space_id": search_space_id,
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}
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@ -1,9 +1,15 @@
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import argparse
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import asyncio
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import logging
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import sys
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import uvicorn
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from dotenv import load_dotenv
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# Fix for Windows: psycopg requires SelectorEventLoop, not ProactorEventLoop
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if sys.platform == "win32":
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asyncio.set_event_loop_policy(asyncio.WindowsSelectorEventLoopPolicy())
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from app.config.uvicorn import load_uvicorn_config
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logging.basicConfig(
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@ -54,6 +54,8 @@ dependencies = [
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"trafilatura>=2.0.0",
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"fastapi-users[oauth,sqlalchemy]>=15.0.3",
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"chonkie[all]>=1.5.0",
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"langgraph-checkpoint-postgres>=3.0.2",
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"psycopg[binary,pool]>=3.3.2",
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]
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[dependency-groups]
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92
surfsense_backend/uv.lock
generated
92
surfsense_backend/uv.lock
generated
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@ -2983,6 +2983,21 @@ wheels = [
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{ url = "https://files.pythonhosted.org/packages/48/e3/616e3a7ff737d98c1bbb5700dd62278914e2a9ded09a79a1fa93cf24ce12/langgraph_checkpoint-3.0.1-py3-none-any.whl", hash = "sha256:9b04a8d0edc0474ce4eaf30c5d731cee38f11ddff50a6177eead95b5c4e4220b", size = 46249 },
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||||
]
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[[package]]
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name = "langgraph-checkpoint-postgres"
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version = "3.0.2"
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source = { registry = "https://pypi.org/simple" }
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dependencies = [
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{ name = "langgraph-checkpoint" },
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{ name = "orjson" },
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{ name = "psycopg" },
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{ name = "psycopg-pool" },
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||||
]
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sdist = { url = "https://files.pythonhosted.org/packages/68/4e/ffea5b0d667e10d408b3b2d6dd967ea79e208eef73fe6ee5622625496238/langgraph_checkpoint_postgres-3.0.2.tar.gz", hash = "sha256:448cb8ec245b6fe10171a0f90e9aa047e24a9d3febba6a914644b0c1323da158", size = 127766 }
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||||
wheels = [
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||||
{ url = "https://files.pythonhosted.org/packages/ac/e4/b4248e10289b6e2c2d33586c87c5eb421e566ef5f336ee45269223cc3b92/langgraph_checkpoint_postgres-3.0.2-py3-none-any.whl", hash = "sha256:15c0fb638edfbc54d496f1758d0327d1a081e0ef94dda8f0c91d4b307d6d8545", size = 42710 },
|
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]
|
||||
|
||||
[[package]]
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||||
name = "langgraph-prebuilt"
|
||||
version = "1.0.5"
|
||||
|
|
@ -4785,6 +4800,79 @@ wheels = [
|
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{ url = "https://files.pythonhosted.org/packages/50/1b/6921afe68c74868b4c9fa424dad3be35b095e16687989ebbb50ce4fceb7c/psutil-7.0.0-cp37-abi3-win_amd64.whl", hash = "sha256:4cf3d4eb1aa9b348dec30105c55cd9b7d4629285735a102beb4441e38db90553", size = 244885 },
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]
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|
||||
[[package]]
|
||||
name = "psycopg"
|
||||
version = "3.3.2"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
dependencies = [
|
||||
{ name = "typing-extensions", marker = "python_full_version < '3.13'" },
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{ name = "tzdata", marker = "sys_platform == 'win32'" },
|
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]
|
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wheels = [
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{ url = "https://files.pythonhosted.org/packages/8c/51/2779ccdf9305981a06b21a6b27e8547c948d85c41c76ff434192784a4c93/psycopg-3.3.2-py3-none-any.whl", hash = "sha256:3e94bc5f4690247d734599af56e51bae8e0db8e4311ea413f801fef82b14a99b", size = 212774 },
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]
|
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|
||||
[package.optional-dependencies]
|
||||
binary = [
|
||||
{ name = "psycopg-binary", marker = "implementation_name != 'pypy'" },
|
||||
]
|
||||
pool = [
|
||||
{ name = "psycopg-pool" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "psycopg-binary"
|
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|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "psycopg-pool"
|
||||
version = "3.3.0"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
dependencies = [
|
||||
{ name = "typing-extensions" },
|
||||
]
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/56/9a/9470d013d0d50af0da9c4251614aeb3c1823635cab3edc211e3839db0bcf/psycopg_pool-3.3.0.tar.gz", hash = "sha256:fa115eb2860bd88fce1717d75611f41490dec6135efb619611142b24da3f6db5", size = 31606 }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/e7/c3/26b8a0908a9db249de3b4169692e1c7c19048a9bc41a4d3209cee7dbb758/psycopg_pool-3.3.0-py3-none-any.whl", hash = "sha256:2e44329155c410b5e8666372db44276a8b1ebd8c90f1c3026ebba40d4bc81063", size = 39995 },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "psycopg2-binary"
|
||||
version = "2.9.11"
|
||||
|
|
@ -6293,6 +6381,7 @@ dependencies = [
|
|||
{ name = "langchain-litellm" },
|
||||
{ name = "langchain-unstructured" },
|
||||
{ name = "langgraph" },
|
||||
{ name = "langgraph-checkpoint-postgres" },
|
||||
{ name = "linkup-sdk" },
|
||||
{ name = "litellm" },
|
||||
{ name = "llama-cloud-services" },
|
||||
|
|
@ -6301,6 +6390,7 @@ dependencies = [
|
|||
{ name = "numpy" },
|
||||
{ name = "pgvector" },
|
||||
{ name = "playwright" },
|
||||
{ name = "psycopg", extra = ["binary", "pool"] },
|
||||
{ name = "pypdf" },
|
||||
{ name = "python-ffmpeg" },
|
||||
{ name = "redis" },
|
||||
|
|
@ -6351,6 +6441,7 @@ requires-dist = [
|
|||
{ name = "langchain-litellm", specifier = ">=0.3.5" },
|
||||
{ name = "langchain-unstructured", specifier = ">=1.0.0" },
|
||||
{ name = "langgraph", specifier = ">=1.0.5" },
|
||||
{ name = "langgraph-checkpoint-postgres", specifier = ">=3.0.2" },
|
||||
{ name = "linkup-sdk", specifier = ">=0.2.4" },
|
||||
{ name = "litellm", specifier = ">=1.80.10" },
|
||||
{ name = "llama-cloud-services", specifier = ">=0.6.25" },
|
||||
|
|
@ -6359,6 +6450,7 @@ requires-dist = [
|
|||
{ name = "numpy", specifier = ">=1.24.0" },
|
||||
{ name = "pgvector", specifier = ">=0.3.6" },
|
||||
{ name = "playwright", specifier = ">=1.50.0" },
|
||||
{ name = "psycopg", extras = ["binary", "pool"], specifier = ">=3.3.2" },
|
||||
{ name = "pypdf", specifier = ">=5.1.0" },
|
||||
{ name = "python-ffmpeg", specifier = ">=2.0.12" },
|
||||
{ name = "redis", specifier = ">=5.2.1" },
|
||||
|
|
|
|||
|
|
@ -14,7 +14,7 @@ import {
|
|||
Sparkles,
|
||||
} from "lucide-react";
|
||||
import type React from "react";
|
||||
import { type ReactNode, useCallback, useEffect, useRef, useState } from "react";
|
||||
import { type ReactNode, forwardRef, useCallback, useEffect, useRef, useState } from "react";
|
||||
import { createPortal } from "react-dom";
|
||||
import { MarkdownViewer } from "@/components/markdown-viewer";
|
||||
import { Button } from "@/components/ui/button";
|
||||
|
|
@ -44,83 +44,70 @@ const formatDocumentType = (type: string) => {
|
|||
.join(" ");
|
||||
};
|
||||
|
||||
// Chunk card component with enhanced animations
|
||||
const ChunkCard = ({
|
||||
chunk,
|
||||
index,
|
||||
totalChunks,
|
||||
isCited,
|
||||
isActive,
|
||||
}: {
|
||||
// Chunk card component
|
||||
// For large documents (>30 chunks), we disable animation to prevent layout shifts
|
||||
// which break auto-scroll functionality
|
||||
interface ChunkCardProps {
|
||||
chunk: { id: number; content: string };
|
||||
index: number;
|
||||
totalChunks: number;
|
||||
isCited: boolean;
|
||||
isActive: boolean;
|
||||
}) => {
|
||||
const shouldReduceMotion = useReducedMotion();
|
||||
disableLayoutAnimation?: boolean;
|
||||
}
|
||||
|
||||
return (
|
||||
<motion.div
|
||||
data-chunk-index={index}
|
||||
initial={shouldReduceMotion ? { opacity: 1 } : { opacity: 0, y: 20 }}
|
||||
animate={{ opacity: 1, y: 0 }}
|
||||
transition={{
|
||||
type: "spring",
|
||||
stiffness: 100,
|
||||
damping: 15,
|
||||
delay: shouldReduceMotion ? 0 : Math.min(index * 0.05, 0.3),
|
||||
}}
|
||||
className={cn(
|
||||
"group relative rounded-2xl border-2 transition-all duration-300",
|
||||
isCited
|
||||
? "bg-linear-to-br from-primary/5 via-primary/10 to-primary/5 border-primary shadow-lg shadow-primary/10"
|
||||
: "bg-card border-border/50 hover:border-border hover:shadow-md"
|
||||
)}
|
||||
>
|
||||
{/* Cited indicator glow effect */}
|
||||
{isCited && (
|
||||
<div className="absolute inset-0 rounded-2xl bg-primary/5 blur-xl -z-10" />
|
||||
)}
|
||||
|
||||
{/* Header */}
|
||||
<div className="flex items-center justify-between px-5 py-4 border-b border-border/50">
|
||||
<div className="flex items-center gap-3">
|
||||
<div
|
||||
className={cn(
|
||||
"flex items-center justify-center w-8 h-8 rounded-full text-sm font-semibold transition-colors",
|
||||
isCited
|
||||
? "bg-primary text-primary-foreground"
|
||||
: "bg-muted text-muted-foreground group-hover:bg-muted/80"
|
||||
)}
|
||||
>
|
||||
{index + 1}
|
||||
</div>
|
||||
<span className="text-sm text-muted-foreground">
|
||||
of {totalChunks} chunks
|
||||
</span>
|
||||
</div>
|
||||
const ChunkCard = forwardRef<HTMLDivElement, ChunkCardProps>(
|
||||
({ chunk, index, totalChunks, isCited, isActive, disableLayoutAnimation }, ref) => {
|
||||
return (
|
||||
<div
|
||||
ref={ref}
|
||||
data-chunk-index={index}
|
||||
className={cn(
|
||||
"group relative rounded-2xl border-2 transition-all duration-300",
|
||||
isCited
|
||||
? "bg-linear-to-br from-primary/5 via-primary/10 to-primary/5 border-primary shadow-lg shadow-primary/10"
|
||||
: "bg-card border-border/50 hover:border-border hover:shadow-md"
|
||||
)}
|
||||
>
|
||||
{/* Cited indicator glow effect */}
|
||||
{isCited && (
|
||||
<motion.div
|
||||
initial={{ scale: 0, opacity: 0 }}
|
||||
animate={{ scale: 1, opacity: 1 }}
|
||||
transition={{ type: "spring", stiffness: 200, damping: 15, delay: 0.2 }}
|
||||
>
|
||||
<div className="absolute inset-0 rounded-2xl bg-primary/5 blur-xl -z-10" />
|
||||
)}
|
||||
|
||||
{/* Header */}
|
||||
<div className="flex items-center justify-between px-5 py-4 border-b border-border/50">
|
||||
<div className="flex items-center gap-3">
|
||||
<div
|
||||
className={cn(
|
||||
"flex items-center justify-center w-8 h-8 rounded-full text-sm font-semibold transition-colors",
|
||||
isCited
|
||||
? "bg-primary text-primary-foreground"
|
||||
: "bg-muted text-muted-foreground group-hover:bg-muted/80"
|
||||
)}
|
||||
>
|
||||
{index + 1}
|
||||
</div>
|
||||
<span className="text-sm text-muted-foreground">
|
||||
of {totalChunks} chunks
|
||||
</span>
|
||||
</div>
|
||||
{isCited && (
|
||||
<Badge variant="default" className="gap-1.5 px-3 py-1">
|
||||
<Sparkles className="h-3 w-3" />
|
||||
Cited Source
|
||||
</Badge>
|
||||
</motion.div>
|
||||
)}
|
||||
</div>
|
||||
)}
|
||||
</div>
|
||||
|
||||
{/* Content */}
|
||||
<div className="p-5 overflow-hidden">
|
||||
<MarkdownViewer content={chunk.content} />
|
||||
{/* Content */}
|
||||
<div className="p-5 overflow-hidden">
|
||||
<MarkdownViewer content={chunk.content} />
|
||||
</div>
|
||||
</div>
|
||||
</motion.div>
|
||||
);
|
||||
};
|
||||
);
|
||||
}
|
||||
);
|
||||
ChunkCard.displayName = "ChunkCard";
|
||||
|
||||
export function SourceDetailPanel({
|
||||
open,
|
||||
|
|
@ -133,6 +120,7 @@ export function SourceDetailPanel({
|
|||
children,
|
||||
}: SourceDetailPanelProps) {
|
||||
const scrollAreaRef = useRef<HTMLDivElement>(null);
|
||||
const hasScrolledRef = useRef(false); // Use ref to avoid stale closures
|
||||
const [summaryOpen, setSummaryOpen] = useState(false);
|
||||
const [activeChunkIndex, setActiveChunkIndex] = useState<number | null>(null);
|
||||
const [mounted, setMounted] = useState(false);
|
||||
|
|
@ -163,30 +151,92 @@ export function SourceDetailPanel({
|
|||
// Find cited chunk index
|
||||
const citedChunkIndex = documentData?.chunks?.findIndex((chunk) => chunk.id === chunkId) ?? -1;
|
||||
|
||||
// Auto-scroll to cited chunk when data loads
|
||||
useEffect(() => {
|
||||
if (documentData?.chunks && citedChunkIndex !== -1 && !hasScrolledToCited && open) {
|
||||
// Wait for animations to complete then scroll
|
||||
const timer = setTimeout(() => {
|
||||
const chunkElement = scrollAreaRef.current?.querySelector(
|
||||
`[data-chunk-index="${citedChunkIndex}"]`
|
||||
);
|
||||
if (chunkElement) {
|
||||
chunkElement.scrollIntoView({
|
||||
behavior: shouldReduceMotion ? "auto" : "smooth",
|
||||
block: "center",
|
||||
});
|
||||
setHasScrolledToCited(true);
|
||||
setActiveChunkIndex(citedChunkIndex);
|
||||
}
|
||||
}, 400);
|
||||
return () => clearTimeout(timer);
|
||||
// Simple scroll function that scrolls to a chunk by index
|
||||
const scrollToChunkByIndex = useCallback((chunkIndex: number, smooth = true) => {
|
||||
const scrollContainer = scrollAreaRef.current;
|
||||
if (!scrollContainer) return;
|
||||
|
||||
const viewport = scrollContainer.querySelector(
|
||||
'[data-radix-scroll-area-viewport]'
|
||||
) as HTMLElement | null;
|
||||
if (!viewport) return;
|
||||
|
||||
const chunkElement = scrollContainer.querySelector(
|
||||
`[data-chunk-index="${chunkIndex}"]`
|
||||
) as HTMLElement | null;
|
||||
if (!chunkElement) return;
|
||||
|
||||
// Get positions using getBoundingClientRect for accuracy
|
||||
const viewportRect = viewport.getBoundingClientRect();
|
||||
const chunkRect = chunkElement.getBoundingClientRect();
|
||||
|
||||
// Calculate where to scroll to center the chunk
|
||||
const currentScrollTop = viewport.scrollTop;
|
||||
const chunkTopRelativeToViewport = chunkRect.top - viewportRect.top + currentScrollTop;
|
||||
const scrollTarget = chunkTopRelativeToViewport - (viewportRect.height / 2) + (chunkRect.height / 2);
|
||||
|
||||
viewport.scrollTo({
|
||||
top: Math.max(0, scrollTarget),
|
||||
behavior: smooth && !shouldReduceMotion ? "smooth" : "auto",
|
||||
});
|
||||
|
||||
setActiveChunkIndex(chunkIndex);
|
||||
}, [shouldReduceMotion]);
|
||||
|
||||
// Callback ref for the cited chunk - scrolls when the element mounts
|
||||
const citedChunkRefCallback = useCallback((node: HTMLDivElement | null) => {
|
||||
if (node && !hasScrolledRef.current && open) {
|
||||
hasScrolledRef.current = true; // Mark immediately to prevent duplicate scrolls
|
||||
|
||||
// Store the node reference for the delayed scroll
|
||||
const scrollToCitedChunk = () => {
|
||||
const scrollContainer = scrollAreaRef.current;
|
||||
if (!scrollContainer || !node.isConnected) return false;
|
||||
|
||||
const viewport = scrollContainer.querySelector(
|
||||
'[data-radix-scroll-area-viewport]'
|
||||
) as HTMLElement | null;
|
||||
if (!viewport) return false;
|
||||
|
||||
// Get positions
|
||||
const viewportRect = viewport.getBoundingClientRect();
|
||||
const chunkRect = node.getBoundingClientRect();
|
||||
|
||||
// Calculate scroll position to center the chunk
|
||||
const currentScrollTop = viewport.scrollTop;
|
||||
const chunkTopRelativeToViewport = chunkRect.top - viewportRect.top + currentScrollTop;
|
||||
const scrollTarget = chunkTopRelativeToViewport - (viewportRect.height / 2) + (chunkRect.height / 2);
|
||||
|
||||
viewport.scrollTo({
|
||||
top: Math.max(0, scrollTarget),
|
||||
behavior: "auto", // Instant scroll for initial positioning
|
||||
});
|
||||
|
||||
return true;
|
||||
};
|
||||
|
||||
// Scroll multiple times with delays to handle progressive content rendering
|
||||
// Each subsequent scroll will correct for any layout shifts
|
||||
const scrollAttempts = [50, 150, 300, 600, 1000];
|
||||
|
||||
scrollAttempts.forEach((delay) => {
|
||||
setTimeout(() => {
|
||||
scrollToCitedChunk();
|
||||
}, delay);
|
||||
});
|
||||
|
||||
// After final attempt, mark state as scrolled
|
||||
setTimeout(() => {
|
||||
setHasScrolledToCited(true);
|
||||
setActiveChunkIndex(citedChunkIndex);
|
||||
}, scrollAttempts[scrollAttempts.length - 1] + 50);
|
||||
}
|
||||
}, [documentData, citedChunkIndex, hasScrolledToCited, open, shouldReduceMotion]);
|
||||
}, [open, citedChunkIndex]);
|
||||
|
||||
// Reset scroll state when panel closes
|
||||
useEffect(() => {
|
||||
if (!open) {
|
||||
hasScrolledRef.current = false;
|
||||
setHasScrolledToCited(false);
|
||||
setActiveChunkIndex(null);
|
||||
}
|
||||
|
|
@ -222,12 +272,8 @@ export function SourceDetailPanel({
|
|||
};
|
||||
|
||||
const scrollToChunk = useCallback((index: number) => {
|
||||
setActiveChunkIndex(index);
|
||||
const chunkElement = scrollAreaRef.current?.querySelector(
|
||||
`[data-chunk-index="${index}"]`
|
||||
);
|
||||
chunkElement?.scrollIntoView({ behavior: "smooth", block: "center" });
|
||||
}, []);
|
||||
scrollToChunkByIndex(index, true);
|
||||
}, [scrollToChunkByIndex]);
|
||||
|
||||
const panelContent = (
|
||||
<AnimatePresence mode="wait">
|
||||
|
|
@ -388,9 +434,9 @@ export function SourceDetailPanel({
|
|||
initial={{ opacity: 0, x: -20 }}
|
||||
animate={{ opacity: 1, x: 0 }}
|
||||
transition={{ delay: 0.2 }}
|
||||
className="hidden lg:flex flex-col w-16 border-r bg-muted/10"
|
||||
className="hidden lg:flex flex-col w-16 border-r bg-muted/10 overflow-hidden"
|
||||
>
|
||||
<ScrollArea className="flex-1">
|
||||
<ScrollArea className="flex-1 h-full">
|
||||
<div className="p-2 pt-3 flex flex-col gap-1.5">
|
||||
{documentData.chunks.map((chunk, idx) => {
|
||||
const isCited = chunk.id === chunkId;
|
||||
|
|
@ -514,16 +560,21 @@ export function SourceDetailPanel({
|
|||
|
||||
{/* Chunks */}
|
||||
<div className="space-y-4">
|
||||
{documentData.chunks.map((chunk, idx) => (
|
||||
<ChunkCard
|
||||
key={chunk.id}
|
||||
chunk={chunk}
|
||||
index={idx}
|
||||
totalChunks={documentData.chunks.length}
|
||||
isCited={chunk.id === chunkId}
|
||||
isActive={activeChunkIndex === idx}
|
||||
/>
|
||||
))}
|
||||
{documentData.chunks.map((chunk, idx) => {
|
||||
const isCited = chunk.id === chunkId;
|
||||
return (
|
||||
<ChunkCard
|
||||
key={chunk.id}
|
||||
ref={isCited ? citedChunkRefCallback : undefined}
|
||||
chunk={chunk}
|
||||
index={idx}
|
||||
totalChunks={documentData.chunks.length}
|
||||
isCited={isCited}
|
||||
isActive={activeChunkIndex === idx}
|
||||
disableLayoutAnimation={documentData.chunks.length > 30}
|
||||
/>
|
||||
);
|
||||
})}
|
||||
</div>
|
||||
</div>
|
||||
</ScrollArea>
|
||||
|
|
|
|||
|
|
@ -11,12 +11,41 @@ interface NewChatAdapterConfig {
|
|||
chatId: number;
|
||||
}
|
||||
|
||||
interface ChatMessageForBackend {
|
||||
role: "user" | "assistant";
|
||||
content: string;
|
||||
}
|
||||
|
||||
/**
|
||||
* Converts assistant-ui messages to a simple format for the backend
|
||||
*/
|
||||
function convertMessagesToBackendFormat(
|
||||
messages: ChatModelRunOptions["messages"]
|
||||
): ChatMessageForBackend[] {
|
||||
return messages
|
||||
.filter((m) => m.role === "user" || m.role === "assistant")
|
||||
.map((m) => {
|
||||
// Extract text content from the message parts
|
||||
let content = "";
|
||||
for (const part of m.content) {
|
||||
if (part.type === "text") {
|
||||
content += part.text;
|
||||
}
|
||||
}
|
||||
return {
|
||||
role: m.role as "user" | "assistant",
|
||||
content: content.trim(),
|
||||
};
|
||||
})
|
||||
.filter((m) => m.content.length > 0); // Filter out empty messages
|
||||
}
|
||||
|
||||
/**
|
||||
* Creates a ChatModelAdapter that connects to the FastAPI new_chat endpoint.
|
||||
*
|
||||
* The backend expects:
|
||||
* - POST /api/v1/new_chat
|
||||
* - Body: { chat_id: number, user_query: string, search_space_id: number }
|
||||
* - Body: { chat_id: number, user_query: string, search_space_id: number, messages: [...] }
|
||||
* - Returns: SSE stream with Vercel AI SDK Data Stream Protocol
|
||||
*/
|
||||
export function createNewChatAdapter(config: NewChatAdapterConfig): ChatModelAdapter {
|
||||
|
|
@ -31,7 +60,7 @@ export function createNewChatAdapter(config: NewChatAdapterConfig): ChatModelAda
|
|||
throw new Error("No user message found");
|
||||
}
|
||||
|
||||
// Extract text content from the message
|
||||
// Extract text content from the last user message
|
||||
let userQuery = "";
|
||||
for (const part of lastUserMessage.content) {
|
||||
if (part.type === "text") {
|
||||
|
|
@ -48,6 +77,9 @@ export function createNewChatAdapter(config: NewChatAdapterConfig): ChatModelAda
|
|||
throw new Error("Not authenticated. Please log in again.");
|
||||
}
|
||||
|
||||
// Convert all messages to backend format for chat history
|
||||
const messageHistory = convertMessagesToBackendFormat(messages);
|
||||
|
||||
const response = await fetch(`${backendUrl}/api/v1/new_chat`, {
|
||||
method: "POST",
|
||||
headers: {
|
||||
|
|
@ -58,6 +90,7 @@ export function createNewChatAdapter(config: NewChatAdapterConfig): ChatModelAda
|
|||
chat_id: config.chatId,
|
||||
user_query: userQuery.trim(),
|
||||
search_space_id: config.searchSpaceId,
|
||||
messages: messageHistory,
|
||||
}),
|
||||
signal: abortSignal,
|
||||
});
|
||||
|
|
@ -165,3 +198,4 @@ export function createNewChatAdapter(config: NewChatAdapterConfig): ChatModelAda
|
|||
},
|
||||
};
|
||||
}
|
||||
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue