feat: add Vision Encoder service + Vision RAG implementation
- Vision Encoder Service (OpenCLIP ViT-L/14, GPU-accelerated)
- FastAPI app with text/image embedding endpoints (768-dim)
- Docker support with NVIDIA GPU runtime
- Port 8001, health checks, model info API
- Qdrant Vector Database integration
- Port 6333/6334 (HTTP/gRPC)
- Image embeddings storage (768-dim, Cosine distance)
- Auto collection creation
- Vision RAG implementation
- VisionEncoderClient (Python client for API)
- Image Search module (text-to-image, image-to-image)
- Vision RAG routing in DAGI Router (mode: image_search)
- VisionEncoderProvider integration
- Documentation (5000+ lines)
- SYSTEM-INVENTORY.md - Complete system inventory
- VISION-ENCODER-STATUS.md - Service status
- VISION-RAG-IMPLEMENTATION.md - Implementation details
- vision_encoder_deployment_task.md - Deployment checklist
- services/vision-encoder/README.md - Deployment guide
- Updated WARP.md, INFRASTRUCTURE.md, Jupyter Notebook
- Testing
- test-vision-encoder.sh - Smoke tests (6 tests)
- Unit tests for client, image search, routing
- Services: 17 total (added Vision Encoder + Qdrant)
- AI Models: 3 (qwen3:8b, OpenCLIP ViT-L/14, BAAI/bge-m3)
- GPU Services: 2 (Vision Encoder, Ollama)
- VRAM Usage: ~10 GB (concurrent)
Status: Production Ready ✅
This commit is contained in:
Apple
260
gateway-bot/http_api_doc.py
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260
gateway-bot/http_api_doc.py
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"""
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Document API Endpoints
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Channel-agnostic HTTP API for document operations.
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Endpoints:
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- POST /api/doc/parse - Parse a document
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- POST /api/doc/ingest - Ingest document to RAG
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- POST /api/doc/ask - Ask question about document
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"""
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import logging
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from typing import Optional, Dict, Any
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from fastapi import APIRouter, HTTPException, UploadFile, File, Form
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from pydantic import BaseModel
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from services.doc_service import (
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doc_service,
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parse_document,
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ingest_document,
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ask_about_document,
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get_doc_context,
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ParsedResult,
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IngestResult,
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QAResult,
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DocContext
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)
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logger = logging.getLogger(__name__)
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router = APIRouter()
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# ========================================
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# Request Models
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# ========================================
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class ParseDocumentRequest(BaseModel):
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"""Request to parse a document"""
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session_id: str
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doc_url: str
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file_name: str
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dao_id: str
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user_id: str
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output_mode: str = "qa_pairs" # qa_pairs, markdown, chunks
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metadata: Optional[Dict[str, Any]] = None
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class IngestDocumentRequest(BaseModel):
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"""Request to ingest a document"""
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session_id: str
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doc_id: Optional[str] = None
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doc_url: Optional[str] = None
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file_name: Optional[str] = None
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dao_id: Optional[str] = None
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user_id: Optional[str] = None
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class AskDocumentRequest(BaseModel):
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"""Request to ask about a document"""
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session_id: str
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question: str
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doc_id: Optional[str] = None
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dao_id: Optional[str] = None
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user_id: Optional[str] = None
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# ========================================
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# Endpoints
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# ========================================
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@router.post("/api/doc/parse")
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async def parse_document_endpoint(request: ParseDocumentRequest):
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"""
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Parse a document through DAGI Router.
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Accepts JSON with doc_url or can accept file upload.
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Returns parsed document data (qa_pairs, markdown, or chunks).
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"""
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try:
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result = await parse_document(
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session_id=request.session_id,
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doc_url=request.doc_url,
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file_name=request.file_name,
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dao_id=request.dao_id,
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user_id=request.user_id,
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output_mode=request.output_mode,
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metadata=request.metadata
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)
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if not result.success:
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raise HTTPException(status_code=400, detail=result.error)
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# Convert QAItem to dict for JSON response
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qa_pairs_dict = None
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if result.qa_pairs:
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qa_pairs_dict = [{"question": qa.question, "answer": qa.answer} for qa in result.qa_pairs]
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return {
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"ok": True,
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"doc_id": result.doc_id,
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"qa_pairs": qa_pairs_dict,
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"markdown": result.markdown,
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"chunks_meta": result.chunks_meta,
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"raw": result.raw
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}
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except Exception as e:
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logger.error(f"Parse document error: {e}", exc_info=True)
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raise HTTPException(status_code=500, detail=str(e))
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@router.post("/api/doc/parse/upload")
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async def parse_document_upload(
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file: UploadFile = File(...),
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session_id: str = Form(...),
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dao_id: str = Form(...),
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user_id: str = Form(...),
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output_mode: str = Form("qa_pairs")
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):
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"""
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Parse a document from file upload.
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Accepts multipart/form-data with file and metadata.
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"""
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try:
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# Check file type
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if not file.filename or not file.filename.lower().endswith(".pdf"):
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raise HTTPException(status_code=400, detail="Only PDF files are supported")
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# For now, we need to upload file somewhere accessible
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# TODO: Implement file storage (S3, local storage, etc.)
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# For now, return error suggesting to use doc_url instead
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raise HTTPException(
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status_code=501,
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detail="File upload not yet implemented. Please use /api/doc/parse with doc_url instead."
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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"Parse document upload error: {e}", exc_info=True)
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raise HTTPException(status_code=500, detail=str(e))
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@router.post("/api/doc/ingest")
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async def ingest_document_endpoint(request: IngestDocumentRequest):
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"""
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Ingest document chunks into RAG/Memory.
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Can use doc_id from previous parse, or doc_url to parse and ingest.
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"""
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try:
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# If doc_id not provided, try to get from context
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doc_id = request.doc_id
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if not doc_id:
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doc_context = await get_doc_context(request.session_id)
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if doc_context:
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doc_id = doc_context.doc_id
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if not request.dao_id:
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request.dao_id = doc_context.dao_id
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if not request.user_id:
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request.user_id = doc_context.user_id
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result = await ingest_document(
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session_id=request.session_id,
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doc_id=doc_id,
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doc_url=request.doc_url,
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file_name=request.file_name,
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dao_id=request.dao_id,
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user_id=request.user_id
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)
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if not result.success:
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raise HTTPException(status_code=400, detail=result.error)
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return {
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"ok": True,
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"doc_id": result.doc_id,
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"ingested_chunks": result.ingested_chunks,
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"status": result.status
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}
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except Exception as e:
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logger.error(f"Ingest document error: {e}", exc_info=True)
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raise HTTPException(status_code=500, detail=str(e))
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@router.post("/api/doc/ask")
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async def ask_about_document_endpoint(request: AskDocumentRequest):
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"""
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Ask a question about a document using RAG query.
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Uses doc_id from session context if not provided.
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"""
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try:
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# If doc_id not provided, try to get from context
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doc_id = request.doc_id
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if not doc_id:
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doc_context = await get_doc_context(request.session_id)
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if doc_context:
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doc_id = doc_context.doc_id
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if not request.dao_id:
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request.dao_id = doc_context.dao_id
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if not request.user_id:
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request.user_id = doc_context.user_id
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result = await ask_about_document(
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session_id=request.session_id,
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question=request.question,
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doc_id=doc_id,
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dao_id=request.dao_id,
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user_id=request.user_id
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)
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if not result.success:
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raise HTTPException(status_code=400, detail=result.error)
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return {
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"ok": True,
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"answer": result.answer,
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"doc_id": result.doc_id,
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"sources": result.sources
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}
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except Exception as e:
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logger.error(f"Ask document error: {e}", exc_info=True)
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raise HTTPException(status_code=500, detail=str(e))
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@router.get("/api/doc/context/{session_id}")
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async def get_document_context(session_id: str):
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"""
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Get document context for a session.
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Returns the last parsed document ID and metadata for the session.
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"""
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try:
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context = await get_doc_context(session_id)
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if not context:
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raise HTTPException(status_code=404, detail="No document context found")
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return {
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"ok": True,
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"context": {
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"doc_id": context.doc_id,
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"dao_id": context.dao_id,
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"user_id": context.user_id,
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"doc_url": context.doc_url,
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"file_name": context.file_name,
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"saved_at": context.saved_at
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}
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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"Get document context error: {e}", exc_info=True)
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raise HTTPException(status_code=500, detail=str(e))
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