Files
RAG_helper/routers/documents.py
T
AR 15 M4 4a5695ed9c feat(sprint1): показ эмбеддингов чанков на странице отладки
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>
2026-04-23 09:59:24 +05:00

158 lines
4.9 KiB
Python

import logging
from datetime import datetime, timezone
from fastapi import APIRouter, File, Form, HTTPException, UploadFile
from models.responses import (
ChunkDetail,
ChunkPreview,
DocumentChunksResponse,
DocumentDeleteResponse,
DocumentInfo,
DocumentListResponse,
DocumentUploadResponse,
)
from services.document_processor import process_document
logger = logging.getLogger(__name__)
router = APIRouter(prefix="/documents", tags=["documents"])
ALLOWED_EXTENSIONS = {".pdf", ".docx", ".doc", ".txt", ".md"}
MAX_FILE_SIZE = 50 * 1024 * 1024 # 50 MB
@router.post("/upload", response_model=DocumentUploadResponse)
async def upload_document(
file: UploadFile = File(...),
document_name: str | None = Form(None),
):
from main import vectorstore_service
if vectorstore_service is None:
raise HTTPException(status_code=503, detail="Service not ready")
filename = file.filename or "unknown"
ext = "." + filename.rsplit(".", 1)[-1].lower() if "." in filename else ""
if ext not in ALLOWED_EXTENSIONS:
raise HTTPException(
status_code=400,
detail=f"Unsupported file format: {ext}. Allowed: {', '.join(ALLOWED_EXTENSIONS)}",
)
file_bytes = await file.read()
if len(file_bytes) > MAX_FILE_SIZE:
raise HTTPException(status_code=400, detail="File too large (max 50 MB)")
if len(file_bytes) == 0:
raise HTTPException(status_code=400, detail="Empty file")
display_name = document_name or filename
try:
document_id, sections, chunks = process_document(file_bytes, filename)
except ValueError as e:
raise HTTPException(status_code=400, detail=str(e))
except Exception as e:
logger.exception("Failed to process document: %s", filename)
raise HTTPException(status_code=500, detail=f"Error processing document: {e}")
if not chunks:
raise HTTPException(status_code=400, detail="No content could be extracted from the document")
file_type = ext.lstrip(".")
chunks_count = vectorstore_service.add_document(
document_id=document_id,
document_name=display_name,
file_type=file_type,
chunks=[
{
"text": c.text,
"section": c.section,
"page_number": c.page_number,
"chunk_index": c.chunk_index,
}
for c in chunks
],
)
chunks_prev = [
ChunkPreview(
index=c.chunk_index,
section=c.section,
page_number=c.page_number,
text_preview=c.text[:300],
char_length=len(c.text),
)
for c in chunks[:3]
]
return DocumentUploadResponse(
document_id=document_id,
name=display_name,
chunks_count=chunks_count,
status="indexed",
created_at=datetime.now(timezone.utc).isoformat(),
chunks_preview=chunks_prev,
)
@router.get("", response_model=DocumentListResponse)
async def list_documents():
from main import vectorstore_service
if vectorstore_service is None:
raise HTTPException(status_code=503, detail="Service not ready")
docs = vectorstore_service.list_documents()
return DocumentListResponse(
documents=[DocumentInfo(**d) for d in docs],
total=len(docs),
)
@router.get("/{document_id}/chunks", response_model=DocumentChunksResponse)
async def get_document_chunks(document_id: str):
from main import vectorstore_service
if vectorstore_service is None:
raise HTTPException(status_code=503, detail="Service not ready")
raw_chunks = vectorstore_service.get_document_chunks(document_id)
if not raw_chunks:
raise HTTPException(status_code=404, detail="Document not found")
meta0 = raw_chunks[0]["metadata"]
chunks = [
ChunkDetail(
index=c["metadata"].get("chunk_index", 0),
section=c["metadata"].get("section", ""),
page_number=c["metadata"].get("page_number", 0),
text=c["text"],
char_length=len(c["text"]),
embedding=c.get("embedding", []),
embedding_dim=len(c.get("embedding", [])),
)
for c in raw_chunks
]
return DocumentChunksResponse(
document_id=document_id,
name=meta0.get("document_name", ""),
file_type=meta0.get("file_type", ""),
chunks_count=len(chunks),
chunks=chunks,
)
@router.delete("/{document_id}", response_model=DocumentDeleteResponse)
async def delete_document(document_id: str):
from main import vectorstore_service
if vectorstore_service is None:
raise HTTPException(status_code=503, detail="Service not ready")
deleted = vectorstore_service.delete_document(document_id)
if deleted == 0:
raise HTTPException(status_code=404, detail="Document not found")
return DocumentDeleteResponse(ok=True, deleted_chunks=deleted)