| """ |
| Document Upload API — /api/v1/documents endpoints. |
| Handles PDF uploading and chunking for custom RAG. |
| """ |
|
|
| import logging |
| import tempfile |
| import os |
|
|
| from fastapi import APIRouter, Depends, HTTPException, UploadFile, File |
| from langchain_community.document_loaders import PyPDFLoader |
| from langchain.text_splitter import RecursiveCharacterTextSplitter |
|
|
| from backend.src.api.middleware.auth import verify_firebase_token |
| from backend.src.services.qdrant_service import QdrantService |
|
|
| logger = logging.getLogger("math_assistant.api.documents") |
| router = APIRouter(prefix="/api/v1/documents", tags=["documents"]) |
|
|
| @router.post("/upload") |
| async def upload_document(file: UploadFile = File(...), uid: str = Depends(verify_firebase_token)): |
| """Upload a PDF file and add it to the knowledge base.""" |
| if not file.filename.lower().endswith('.pdf'): |
| raise HTTPException(status_code=400, detail="Only PDF files are supported.") |
| |
| try: |
| with tempfile.NamedTemporaryFile(delete=False, suffix=".pdf") as tmp: |
| contents = await file.read() |
| tmp.write(contents) |
| tmp_path = tmp.name |
| |
| loader = PyPDFLoader(tmp_path) |
| docs = loader.load() |
| |
| |
| for doc in docs: |
| doc.metadata["source_file"] = file.filename |
| doc.metadata["uploaded_by"] = uid |
| doc.metadata["is_custom"] = True |
| |
| splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=100) |
| chunks = splitter.split_documents(docs) |
| |
| qdrant = QdrantService() |
| qdrant.add_documents(chunks) |
| |
| os.unlink(tmp_path) |
| |
| return { |
| "status": "success", |
| "message": f"Successfully processed {file.filename}", |
| "chunks_added": len(chunks) |
| } |
| except Exception as e: |
| logger.error(f"Error uploading document: {e}") |
| raise HTTPException(status_code=500, detail=str(e)) |
|
|
