mirror of
https://github.com/lfnovo/open-notebook.git
synced 2026-05-05 15:24:35 +00:00
b7e656a319
New front-end Launch Chat API Manage Sources Enable re-embedding of all contents Sources can be added without a notebook now Improved settings Enable model selector on all chats Background processing for better experience Dark mode Improved Notes Improved Docs: - Remove all Streamlit references from documentation - Update deployment guides with React frontend setup - Fix Docker environment variables format (SURREAL_URL, SURREAL_PASSWORD) - Update docker image tag from :latest to :v1-latest - Change navigation references (Settings → Models to just Models) - Update development setup to include frontend npm commands - Add MIGRATION.md guide for users upgrading from Streamlit - Update quick-start guide with correct environment variables - Add port 5055 documentation for API access - Update project structure to reflect frontend/ directory - Remove outdated source-chat documentation files
214 lines
8.3 KiB
Python
214 lines
8.3 KiB
Python
import json
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from typing import AsyncGenerator
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from fastapi import APIRouter, HTTPException
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from fastapi.responses import StreamingResponse
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from loguru import logger
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from api.models import AskRequest, AskResponse, SearchRequest, SearchResponse
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from open_notebook.domain.models import Model, model_manager
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from open_notebook.domain.notebook import text_search, vector_search
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from open_notebook.exceptions import DatabaseOperationError, InvalidInputError
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from open_notebook.graphs.ask import graph as ask_graph
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router = APIRouter()
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@router.post("/search", response_model=SearchResponse)
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async def search_knowledge_base(search_request: SearchRequest):
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"""Search the knowledge base using text or vector search."""
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try:
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if search_request.type == "vector":
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# Check if embedding model is available for vector search
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if not await model_manager.get_embedding_model():
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raise HTTPException(
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status_code=400,
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detail="Vector search requires an embedding model. Please configure one in the Models section.",
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)
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results = await vector_search(
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keyword=search_request.query,
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results=search_request.limit,
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source=search_request.search_sources,
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note=search_request.search_notes,
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minimum_score=search_request.minimum_score,
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)
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else:
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# Text search
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results = await text_search(
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keyword=search_request.query,
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results=search_request.limit,
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source=search_request.search_sources,
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note=search_request.search_notes,
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)
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return SearchResponse(
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results=results or [],
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total_count=len(results) if results else 0,
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search_type=search_request.type,
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)
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except InvalidInputError as e:
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raise HTTPException(status_code=400, detail=str(e))
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except DatabaseOperationError as e:
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logger.error(f"Database error during search: {str(e)}")
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raise HTTPException(status_code=500, detail=f"Search failed: {str(e)}")
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except Exception as e:
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logger.error(f"Unexpected error during search: {str(e)}")
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raise HTTPException(status_code=500, detail=f"Search failed: {str(e)}")
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async def stream_ask_response(
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question: str, strategy_model: Model, answer_model: Model, final_answer_model: Model
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) -> AsyncGenerator[str, None]:
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"""Stream the ask response as Server-Sent Events."""
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try:
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final_answer = None
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async for chunk in ask_graph.astream(
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input=dict(question=question), # type: ignore[arg-type]
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config=dict(
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configurable=dict(
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strategy_model=strategy_model.id,
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answer_model=answer_model.id,
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final_answer_model=final_answer_model.id,
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)
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),
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stream_mode="updates",
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):
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if "agent" in chunk:
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strategy_data = {
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"type": "strategy",
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"reasoning": chunk["agent"]["strategy"].reasoning,
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"searches": [
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{"term": search.term, "instructions": search.instructions}
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for search in chunk["agent"]["strategy"].searches
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],
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}
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yield f"data: {json.dumps(strategy_data)}\n\n"
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elif "provide_answer" in chunk:
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for answer in chunk["provide_answer"]["answers"]:
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answer_data = {"type": "answer", "content": answer}
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yield f"data: {json.dumps(answer_data)}\n\n"
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elif "write_final_answer" in chunk:
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final_answer = chunk["write_final_answer"]["final_answer"]
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final_data = {"type": "final_answer", "content": final_answer}
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yield f"data: {json.dumps(final_data)}\n\n"
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# Send completion signal
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completion_data = {"type": "complete", "final_answer": final_answer}
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yield f"data: {json.dumps(completion_data)}\n\n"
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except Exception as e:
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logger.error(f"Error in ask streaming: {str(e)}")
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error_data = {"type": "error", "message": str(e)}
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yield f"data: {json.dumps(error_data)}\n\n"
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@router.post("/search/ask")
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async def ask_knowledge_base(ask_request: AskRequest):
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"""Ask the knowledge base a question using AI models."""
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try:
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# Validate models exist
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strategy_model = await Model.get(ask_request.strategy_model)
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answer_model = await Model.get(ask_request.answer_model)
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final_answer_model = await Model.get(ask_request.final_answer_model)
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if not strategy_model:
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raise HTTPException(
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status_code=400,
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detail=f"Strategy model {ask_request.strategy_model} not found",
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)
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if not answer_model:
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raise HTTPException(
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status_code=400,
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detail=f"Answer model {ask_request.answer_model} not found",
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)
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if not final_answer_model:
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raise HTTPException(
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status_code=400,
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detail=f"Final answer model {ask_request.final_answer_model} not found",
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)
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# Check if embedding model is available
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if not await model_manager.get_embedding_model():
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raise HTTPException(
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status_code=400,
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detail="Ask feature requires an embedding model. Please configure one in the Models section.",
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)
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# For streaming response
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return StreamingResponse(
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stream_ask_response(
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ask_request.question, strategy_model, answer_model, final_answer_model
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),
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media_type="text/plain",
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)
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except HTTPException:
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raise
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except Exception as e:
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logger.error(f"Error in ask endpoint: {str(e)}")
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raise HTTPException(status_code=500, detail=f"Ask operation failed: {str(e)}")
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@router.post("/search/ask/simple", response_model=AskResponse)
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async def ask_knowledge_base_simple(ask_request: AskRequest):
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"""Ask the knowledge base a question and return a simple response (non-streaming)."""
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try:
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# Validate models exist
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strategy_model = await Model.get(ask_request.strategy_model)
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answer_model = await Model.get(ask_request.answer_model)
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final_answer_model = await Model.get(ask_request.final_answer_model)
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if not strategy_model:
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raise HTTPException(
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status_code=400,
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detail=f"Strategy model {ask_request.strategy_model} not found",
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)
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if not answer_model:
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raise HTTPException(
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status_code=400,
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detail=f"Answer model {ask_request.answer_model} not found",
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)
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if not final_answer_model:
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raise HTTPException(
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status_code=400,
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detail=f"Final answer model {ask_request.final_answer_model} not found",
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)
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# Check if embedding model is available
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if not await model_manager.get_embedding_model():
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raise HTTPException(
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status_code=400,
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detail="Ask feature requires an embedding model. Please configure one in the Models section.",
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)
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# Run the ask graph and get final result
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final_answer = None
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async for chunk in ask_graph.astream(
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input=dict(question=ask_request.question), # type: ignore[arg-type]
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config=dict(
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configurable=dict(
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strategy_model=strategy_model.id,
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answer_model=answer_model.id,
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final_answer_model=final_answer_model.id,
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)
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),
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stream_mode="updates",
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):
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if "write_final_answer" in chunk:
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final_answer = chunk["write_final_answer"]["final_answer"]
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if not final_answer:
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raise HTTPException(status_code=500, detail="No answer generated")
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return AskResponse(answer=final_answer, question=ask_request.question)
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except HTTPException:
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raise
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except Exception as e:
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logger.error(f"Error in ask simple endpoint: {str(e)}")
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raise HTTPException(status_code=500, detail=f"Ask operation failed: {str(e)}")
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