RAG_helper/services/vectorstore.py

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]:
        # Различаем None (фильтра нет — вся коллекция) и [] (ветка без подписок —
        # сознательный возврат 0 чанков, см. Спринт 7 в SPRINTS.md).
        if document_ids is not None and len(document_ids) == 0:
            return []

        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. Includes embeddings."""
        results = self.collection.get(
            where={"document_id": document_id},
            include=["documents", "metadatas", "embeddings"],
        )
        items = []
        if results["ids"]:
            embeddings = results.get("embeddings")
            has_emb = embeddings is not None and len(embeddings) > 0
            for i, chunk_id in enumerate(results["ids"]):
                emb = embeddings[i] if has_emb and i < len(embeddings) else None
                items.append({
                    "chunk_id": chunk_id,
                    "text": results["documents"][i],
                    "metadata": results["metadatas"][i],
                    "embedding": [float(x) for x in emb] if emb is not None else [],
                })
        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(),
        }