added RAG, Multiuser, TG bot

backend/app/rag/store.py

@@ -0,0 +1,64 @@
+from __future__ import annotations
+
+import logging
+from typing import Any
+
+from qdrant_client import QdrantClient
+from qdrant_client.http import models as qm
+
+from app.config import get_settings
+
+logger = logging.getLogger(__name__)
+
+COLLECTION_FACTS = "memory_facts"
+COLLECTION_SUMMARIES = "session_summaries"
+COLLECTION_DOC_CHUNKS = "document_chunks"
+VECTOR_SIZE = 1536
+
+
+def _client() -> QdrantClient:
+    settings = get_settings()
+    return QdrantClient(url=settings.qdrant_url)
+
+
+def ensure_collections() -> None:
+    settings = get_settings()
+    if not settings.rag_enabled:
+        return
+    client = _client()
+    for name in (COLLECTION_FACTS, COLLECTION_SUMMARIES, COLLECTION_DOC_CHUNKS):
+        if client.collection_exists(name):
+            continue
+        client.create_collection(
+            collection_name=name,
+            vectors_config=qm.VectorParams(size=VECTOR_SIZE, distance=qm.Distance.COSINE),
+        )
+        logger.info("Created Qdrant collection %s", name)
+
+
+def upsert_points(collection: str, points: list[qm.PointStruct]) -> None:
+    if not points:
+        return
+    _client().upsert(collection_name=collection, points=points)
+
+
+def delete_by_filter(collection: str, must: list[qm.FieldCondition]) -> None:
+    _client().delete(
+        collection_name=collection,
+        points_selector=qm.FilterSelector(filter=qm.Filter(must=must)),
+    )
+
+
+def search(
+    collection: str,
+    vector: list[float],
+    *,
+    limit: int,
+    query_filter: qm.Filter | None = None,
+) -> list[qm.ScoredPoint]:
+    return _client().search(
+        collection_name=collection,
+        query_vector=vector,
+        limit=limit,
+        query_filter=query_filter,
+    )