mirror of
https://github.com/MODSetter/SurfSense.git
synced 2026-06-26 21:39:43 +02:00
Merge remote-tracking branch 'upstream/dev' into feature/podcast-agent
This commit is contained in:
Anish Sarkar
commit
35463eeab4
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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@ -28,6 +29,7 @@ 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_id: str | None = None,
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user_instructions: str | None = None,
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enable_citations: bool = True,
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@ -42,6 +44,8 @@ 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_id: The user's ID (required for podcast generation)
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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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@ -77,7 +81,7 @@ 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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@ -86,6 +90,7 @@ 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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@ -9,13 +9,13 @@ import json
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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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@ -27,13 +27,14 @@ 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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@ -42,6 +43,7 @@ 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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@ -74,20 +76,38 @@ 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 with podcast capability
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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 with podcast capability
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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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user_id=str(user_id),
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enable_podcast=True,
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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
|
||||
from dotenv import load_dotenv
|
||||
|
||||
# Fix for Windows: psycopg requires SelectorEventLoop, not ProactorEventLoop
|
||||
if sys.platform == "win32":
|
||||
asyncio.set_event_loop_policy(asyncio.WindowsSelectorEventLoopPolicy())
|
||||
|
||||
from app.config.uvicorn import load_uvicorn_config
|
||||
|
||||
logging.basicConfig(
|
||||
|
|
|
|||
|
|
@ -54,6 +54,8 @@ dependencies = [
|
|||
"trafilatura>=2.0.0",
|
||||
"fastapi-users[oauth,sqlalchemy]>=15.0.3",
|
||||
"chonkie[all]>=1.5.0",
|
||||
"langgraph-checkpoint-postgres>=3.0.2",
|
||||
"psycopg[binary,pool]>=3.3.2",
|
||||
]
|
||||
|
||||
[dependency-groups]
|
||||
|
|
|
|||
92
surfsense_backend/uv.lock
generated
92
surfsense_backend/uv.lock
generated
|
|
@ -2983,6 +2983,21 @@ wheels = [
|
|||
{ url = "https://files.pythonhosted.org/packages/48/e3/616e3a7ff737d98c1bbb5700dd62278914e2a9ded09a79a1fa93cf24ce12/langgraph_checkpoint-3.0.1-py3-none-any.whl", hash = "sha256:9b04a8d0edc0474ce4eaf30c5d731cee38f11ddff50a6177eead95b5c4e4220b", size = 46249 },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "langgraph-checkpoint-postgres"
|
||||
version = "3.0.2"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
dependencies = [
|
||||
{ name = "langgraph-checkpoint" },
|
||||
{ name = "orjson" },
|
||||
{ name = "psycopg" },
|
||||
{ name = "psycopg-pool" },
|
||||
]
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/68/4e/ffea5b0d667e10d408b3b2d6dd967ea79e208eef73fe6ee5622625496238/langgraph_checkpoint_postgres-3.0.2.tar.gz", hash = "sha256:448cb8ec245b6fe10171a0f90e9aa047e24a9d3febba6a914644b0c1323da158", size = 127766 }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/ac/e4/b4248e10289b6e2c2d33586c87c5eb421e566ef5f336ee45269223cc3b92/langgraph_checkpoint_postgres-3.0.2-py3-none-any.whl", hash = "sha256:15c0fb638edfbc54d496f1758d0327d1a081e0ef94dda8f0c91d4b307d6d8545", size = 42710 },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "langgraph-prebuilt"
|
||||
version = "1.0.5"
|
||||
|
|
@ -4785,6 +4800,79 @@ wheels = [
|
|||
{ 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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]
|
||||
|
||||
[[package]]
|
||||
name = "psycopg"
|
||||
version = "3.3.2"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
dependencies = [
|
||||
{ name = "typing-extensions", marker = "python_full_version < '3.13'" },
|
||||
{ name = "tzdata", marker = "sys_platform == 'win32'" },
|
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]
|
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sdist = { url = "https://files.pythonhosted.org/packages/e0/1a/7d9ef4fdc13ef7f15b934c393edc97a35c281bb7d3c3329fbfcbe915a7c2/psycopg-3.3.2.tar.gz", hash = "sha256:707a67975ee214d200511177a6a80e56e654754c9afca06a7194ea6bbfde9ca7", size = 165630 }
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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 },
|
||||
]
|
||||
|
||||
[package.optional-dependencies]
|
||||
binary = [
|
||||
{ name = "psycopg-binary", marker = "implementation_name != 'pypy'" },
|
||||
]
|
||||
pool = [
|
||||
{ name = "psycopg-pool" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "psycopg-binary"
|
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version = "3.3.2"
|
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source = { registry = "https://pypi.org/simple" }
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wheels = [
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||||
[[package]]
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||||
name = "psycopg-pool"
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||||
version = "3.3.0"
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||||
source = { registry = "https://pypi.org/simple" }
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dependencies = [
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||||
{ name = "typing-extensions" },
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]
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sdist = { url = "https://files.pythonhosted.org/packages/56/9a/9470d013d0d50af0da9c4251614aeb3c1823635cab3edc211e3839db0bcf/psycopg_pool-3.3.0.tar.gz", hash = "sha256:fa115eb2860bd88fce1717d75611f41490dec6135efb619611142b24da3f6db5", size = 31606 }
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wheels = [
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[[package]]
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name = "psycopg2-binary"
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version = "2.9.11"
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|
|
@ -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" },
|
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|
|
@ -6301,6 +6390,7 @@ dependencies = [
|
|||
{ name = "numpy" },
|
||||
{ name = "pgvector" },
|
||||
{ name = "playwright" },
|
||||
{ name = "psycopg", extra = ["binary", "pool"] },
|
||||
{ name = "pypdf" },
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||||
{ 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" },
|
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{ 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" },
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue