4a5695ed9c
Расширяем просмотр документа, чтобы оператор видел не только текстовые чанки, но и как они лежат в ChromaDB в виде векторов — по паттерну из work-pcs-dr-cdss. Backend: - services/vectorstore.get_document_chunks теперь запрашивает include=["embeddings"] и отдаёт вектор как list[float]. Chroma возвращает numpy-массивы, поэтому проверка наличия embeddings сделана через len(), без or-шортката. - models.ChunkDetail: поля embedding: list[float] + embedding_dim: int. - routers/documents прокидывает вектор и размерность в ответ. Frontend (static/index.html): - В карточку чанка добавлен блок .chunk-card-actions с кнопкой «вектор (N dim)»; раскрывается в .embedding-box с полным списком координат (округление до 6 знаков, моноширинный шрифт, скролл). - Функция toggleChunkText переписана через .closest + querySelector, чтобы не ломаться от новой обёртки кнопок. - Добавлена toggleEmb(embId). Проверено на загруженных документах — возвращается по 1024 координаты (E5-large), совпадает с ожиданиями embedding-модели. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
158 lines
4.9 KiB
Python
158 lines
4.9 KiB
Python
import logging
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from datetime import datetime, timezone
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from fastapi import APIRouter, File, Form, HTTPException, UploadFile
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from models.responses import (
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ChunkDetail,
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ChunkPreview,
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DocumentChunksResponse,
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DocumentDeleteResponse,
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DocumentInfo,
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DocumentListResponse,
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DocumentUploadResponse,
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)
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from services.document_processor import process_document
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logger = logging.getLogger(__name__)
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router = APIRouter(prefix="/documents", tags=["documents"])
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ALLOWED_EXTENSIONS = {".pdf", ".docx", ".doc", ".txt", ".md"}
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MAX_FILE_SIZE = 50 * 1024 * 1024 # 50 MB
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@router.post("/upload", response_model=DocumentUploadResponse)
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async def upload_document(
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file: UploadFile = File(...),
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document_name: str | None = Form(None),
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):
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from main import vectorstore_service
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if vectorstore_service is None:
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raise HTTPException(status_code=503, detail="Service not ready")
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filename = file.filename or "unknown"
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ext = "." + filename.rsplit(".", 1)[-1].lower() if "." in filename else ""
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if ext not in ALLOWED_EXTENSIONS:
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raise HTTPException(
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status_code=400,
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detail=f"Unsupported file format: {ext}. Allowed: {', '.join(ALLOWED_EXTENSIONS)}",
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)
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file_bytes = await file.read()
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if len(file_bytes) > MAX_FILE_SIZE:
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raise HTTPException(status_code=400, detail="File too large (max 50 MB)")
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if len(file_bytes) == 0:
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raise HTTPException(status_code=400, detail="Empty file")
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display_name = document_name or filename
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try:
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document_id, sections, chunks = process_document(file_bytes, filename)
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except ValueError as e:
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raise HTTPException(status_code=400, detail=str(e))
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except Exception as e:
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logger.exception("Failed to process document: %s", filename)
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raise HTTPException(status_code=500, detail=f"Error processing document: {e}")
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if not chunks:
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raise HTTPException(status_code=400, detail="No content could be extracted from the document")
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file_type = ext.lstrip(".")
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chunks_count = vectorstore_service.add_document(
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document_id=document_id,
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document_name=display_name,
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file_type=file_type,
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chunks=[
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{
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"text": c.text,
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"section": c.section,
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"page_number": c.page_number,
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"chunk_index": c.chunk_index,
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}
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for c in chunks
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],
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)
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chunks_prev = [
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ChunkPreview(
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index=c.chunk_index,
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section=c.section,
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page_number=c.page_number,
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text_preview=c.text[:300],
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char_length=len(c.text),
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)
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for c in chunks[:3]
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]
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return DocumentUploadResponse(
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document_id=document_id,
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name=display_name,
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chunks_count=chunks_count,
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status="indexed",
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created_at=datetime.now(timezone.utc).isoformat(),
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chunks_preview=chunks_prev,
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)
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@router.get("", response_model=DocumentListResponse)
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async def list_documents():
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from main import vectorstore_service
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if vectorstore_service is None:
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raise HTTPException(status_code=503, detail="Service not ready")
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docs = vectorstore_service.list_documents()
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return DocumentListResponse(
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documents=[DocumentInfo(**d) for d in docs],
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total=len(docs),
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)
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@router.get("/{document_id}/chunks", response_model=DocumentChunksResponse)
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async def get_document_chunks(document_id: str):
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from main import vectorstore_service
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if vectorstore_service is None:
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raise HTTPException(status_code=503, detail="Service not ready")
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raw_chunks = vectorstore_service.get_document_chunks(document_id)
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if not raw_chunks:
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raise HTTPException(status_code=404, detail="Document not found")
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meta0 = raw_chunks[0]["metadata"]
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chunks = [
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ChunkDetail(
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index=c["metadata"].get("chunk_index", 0),
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section=c["metadata"].get("section", ""),
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page_number=c["metadata"].get("page_number", 0),
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text=c["text"],
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char_length=len(c["text"]),
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embedding=c.get("embedding", []),
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embedding_dim=len(c.get("embedding", [])),
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)
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for c in raw_chunks
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]
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return DocumentChunksResponse(
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document_id=document_id,
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name=meta0.get("document_name", ""),
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file_type=meta0.get("file_type", ""),
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chunks_count=len(chunks),
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chunks=chunks,
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)
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@router.delete("/{document_id}", response_model=DocumentDeleteResponse)
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async def delete_document(document_id: str):
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from main import vectorstore_service
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if vectorstore_service is None:
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raise HTTPException(status_code=503, detail="Service not ready")
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deleted = vectorstore_service.delete_document(document_id)
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if deleted == 0:
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raise HTTPException(status_code=404, detail="Document not found")
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return DocumentDeleteResponse(ok=True, deleted_chunks=deleted)
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