a7f78d71b2
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>
146 lines
4.9 KiB
Python
146 lines
4.9 KiB
Python
import logging
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from datetime import datetime, timezone
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import chromadb
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from services.embeddings import EmbeddingService
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logger = logging.getLogger(__name__)
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COLLECTION_NAME = "operators_wiki"
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class VectorStoreService:
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def __init__(self, persist_dir: str, embedding_service: EmbeddingService):
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self.client = chromadb.PersistentClient(path=persist_dir)
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self.embedding_service = embedding_service
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self.collection = self.client.get_or_create_collection(
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name=COLLECTION_NAME,
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metadata={"hnsw:space": "cosine"},
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)
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logger.info("ChromaDB collection '%s': %d items", COLLECTION_NAME, self.collection.count())
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def add_document(
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self,
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document_id: str,
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document_name: str,
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file_type: str,
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chunks: list[dict],
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) -> int:
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if not chunks:
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return 0
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texts = [c["text"] for c in chunks]
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embeddings = self.embedding_service.embed_documents(texts)
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ids = []
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metadatas = []
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now = datetime.now(timezone.utc).isoformat()
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for i, chunk in enumerate(chunks):
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ids.append(f"{document_id}_chunk_{i}")
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metadatas.append({
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"document_id": document_id,
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"document_name": document_name,
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"file_type": file_type,
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"section": chunk.get("section", ""),
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"page_number": chunk.get("page_number", 0),
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"chunk_index": i,
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"created_at": now,
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})
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self.collection.add(
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ids=ids,
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embeddings=embeddings,
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documents=texts,
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metadatas=metadatas,
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)
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logger.info("Added %d chunks for document '%s'", len(chunks), document_name)
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return len(chunks)
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def query(
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self,
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query_text: str,
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top_k: int = 5,
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document_ids: list[str] | None = None,
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) -> list[dict]:
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query_embedding = self.embedding_service.embed_query(query_text)
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where_filter = None
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if document_ids:
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if len(document_ids) == 1:
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where_filter = {"document_id": document_ids[0]}
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else:
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where_filter = {"document_id": {"$in": document_ids}}
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results = self.collection.query(
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query_embeddings=[query_embedding],
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n_results=top_k,
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where=where_filter,
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include=["documents", "metadatas", "distances"],
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)
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items = []
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if results["ids"] and results["ids"][0]:
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for i, chunk_id in enumerate(results["ids"][0]):
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items.append({
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"chunk_id": chunk_id,
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"text": results["documents"][0][i],
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"metadata": results["metadatas"][0][i],
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"distance": results["distances"][0][i],
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"relevance_score": 1 - results["distances"][0][i],
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})
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return items
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def delete_document(self, document_id: str) -> int:
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existing = self.collection.get(where={"document_id": document_id}, include=[])
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count = len(existing["ids"])
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if count > 0:
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self.collection.delete(ids=existing["ids"])
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logger.info("Deleted %d chunks for document_id=%s", count, document_id)
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return count
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def list_documents(self) -> list[dict]:
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all_items = self.collection.get(include=["metadatas"])
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docs: dict[str, dict] = {}
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for meta in all_items["metadatas"]:
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doc_id = meta["document_id"]
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if doc_id not in docs:
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docs[doc_id] = {
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"document_id": doc_id,
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"name": meta.get("document_name", ""),
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"file_type": meta.get("file_type", ""),
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"created_at": meta.get("created_at", ""),
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"chunks_count": 0,
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"metadata": {},
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}
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docs[doc_id]["chunks_count"] += 1
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return list(docs.values())
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def get_document_chunks(self, document_id: str) -> list[dict]:
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"""Return all chunks for a document, sorted by chunk_index."""
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results = self.collection.get(
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where={"document_id": document_id},
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include=["documents", "metadatas"],
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)
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items = []
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if results["ids"]:
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for i, chunk_id in enumerate(results["ids"]):
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items.append({
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"chunk_id": chunk_id,
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"text": results["documents"][i],
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"metadata": results["metadatas"][i],
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})
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items.sort(key=lambda x: x["metadata"].get("chunk_index", 0))
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return items
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def get_stats(self) -> dict:
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all_items = self.collection.get(include=["metadatas"])
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doc_ids = set()
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for meta in all_items["metadatas"]:
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doc_ids.add(meta.get("document_id", ""))
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return {
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"documents_count": len(doc_ids),
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"chunks_count": self.collection.count(),
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}
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