Files
RAG_helper/services/vectorstore.py
T
AR 15 M4 a7f78d71b2 feat: Спринт 1 — RAG-ядро, загрузка wiki и Debug UI
FastAPI + ChromaDB + E5-large + DeepSeek по паттерну work-pcs-dr-cdss,
адаптированному под пациентский контекст:

- services: embeddings (E5-large с префиксами), vectorstore (коллекция
  operators_wiki), document_processor (PDF/DOCX/TXT/MD + чанкер с FAQ-
  паттерном под wiki), llm_client (системный промпт ассистента клиники),
  rag_pipeline (одиночный вопрос → retrieval → ответ).
- routers: /health, /documents (upload, list, chunks, delete), /query.
- static/index.html: шапка со статусом, блок базы знаний с раскрытием
  чанков по клику, блок тест-вопроса с 3-колоночным ответом
  (чанки со score / собранный промпт / ответ LLM).
- Порт 8003 (8001 занят CDSS, 8002 — voicenote).

E2E проверен: загрузка wiki_test.md → 2 чанка, вопрос «как записать
ребёнка к лору?» → top score 84.8%, корректный ответ DeepSeek.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-04-22 14:57:34 +05:00

146 lines
4.9 KiB
Python

import logging
from datetime import datetime, timezone
import chromadb
from services.embeddings import EmbeddingService
logger = logging.getLogger(__name__)
COLLECTION_NAME = "operators_wiki"
class VectorStoreService:
def __init__(self, persist_dir: str, embedding_service: EmbeddingService):
self.client = chromadb.PersistentClient(path=persist_dir)
self.embedding_service = embedding_service
self.collection = self.client.get_or_create_collection(
name=COLLECTION_NAME,
metadata={"hnsw:space": "cosine"},
)
logger.info("ChromaDB collection '%s': %d items", COLLECTION_NAME, self.collection.count())
def add_document(
self,
document_id: str,
document_name: str,
file_type: str,
chunks: list[dict],
) -> int:
if not chunks:
return 0
texts = [c["text"] for c in chunks]
embeddings = self.embedding_service.embed_documents(texts)
ids = []
metadatas = []
now = datetime.now(timezone.utc).isoformat()
for i, chunk in enumerate(chunks):
ids.append(f"{document_id}_chunk_{i}")
metadatas.append({
"document_id": document_id,
"document_name": document_name,
"file_type": file_type,
"section": chunk.get("section", ""),
"page_number": chunk.get("page_number", 0),
"chunk_index": i,
"created_at": now,
})
self.collection.add(
ids=ids,
embeddings=embeddings,
documents=texts,
metadatas=metadatas,
)
logger.info("Added %d chunks for document '%s'", len(chunks), document_name)
return len(chunks)
def query(
self,
query_text: str,
top_k: int = 5,
document_ids: list[str] | None = None,
) -> list[dict]:
query_embedding = self.embedding_service.embed_query(query_text)
where_filter = None
if document_ids:
if len(document_ids) == 1:
where_filter = {"document_id": document_ids[0]}
else:
where_filter = {"document_id": {"$in": document_ids}}
results = self.collection.query(
query_embeddings=[query_embedding],
n_results=top_k,
where=where_filter,
include=["documents", "metadatas", "distances"],
)
items = []
if results["ids"] and results["ids"][0]:
for i, chunk_id in enumerate(results["ids"][0]):
items.append({
"chunk_id": chunk_id,
"text": results["documents"][0][i],
"metadata": results["metadatas"][0][i],
"distance": results["distances"][0][i],
"relevance_score": 1 - results["distances"][0][i],
})
return items
def delete_document(self, document_id: str) -> int:
existing = self.collection.get(where={"document_id": document_id}, include=[])
count = len(existing["ids"])
if count > 0:
self.collection.delete(ids=existing["ids"])
logger.info("Deleted %d chunks for document_id=%s", count, document_id)
return count
def list_documents(self) -> list[dict]:
all_items = self.collection.get(include=["metadatas"])
docs: dict[str, dict] = {}
for meta in all_items["metadatas"]:
doc_id = meta["document_id"]
if doc_id not in docs:
docs[doc_id] = {
"document_id": doc_id,
"name": meta.get("document_name", ""),
"file_type": meta.get("file_type", ""),
"created_at": meta.get("created_at", ""),
"chunks_count": 0,
"metadata": {},
}
docs[doc_id]["chunks_count"] += 1
return list(docs.values())
def get_document_chunks(self, document_id: str) -> list[dict]:
"""Return all chunks for a document, sorted by chunk_index."""
results = self.collection.get(
where={"document_id": document_id},
include=["documents", "metadatas"],
)
items = []
if results["ids"]:
for i, chunk_id in enumerate(results["ids"]):
items.append({
"chunk_id": chunk_id,
"text": results["documents"][i],
"metadata": results["metadatas"][i],
})
items.sort(key=lambda x: x["metadata"].get("chunk_index", 0))
return items
def get_stats(self) -> dict:
all_items = self.collection.get(include=["metadatas"])
doc_ids = set()
for meta in all_items["metadatas"]:
doc_ids.add(meta.get("document_id", ""))
return {
"documents_count": len(doc_ids),
"chunks_count": self.collection.count(),
}