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>

services/vectorstore.py

@@ -0,0 +1,145 @@
+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(),
+        }