progress

rag/qdrant_functions.py

@@ -1,29 +1,24 @@
-# qdrant_functions.py
+# rag/qdrant_functions.py
 import logging
-from qdrant_client import QdrantClient
-from sentence_transformers import SentenceTransformer
-import uuid
-import os
+from typing import Any, List
 
-QDRANT_HOST = os.getenv("QDRANT_HOST", "localhost")
-QDRANT_PORT = int(os.getenv("QDRANT_PORT", 6333))
-QDRANT_COLLECTION = os.getenv("QDRANT_COLLECTION", "abot-slack")
+logger = logging.getLogger(__name__)
 
-client = QdrantClient(host=QDRANT_HOST, port=QDRANT_PORT)
 
-embedding_model = SentenceTransformer("all-MiniLM-L6-v2")
-VECTOR_SIZE = 384
+def embed_and_store_slack_message(
+    slack_client: Any,
+    channel: str,
+    user: str,
+    text: str,
+    ts: str,
+    bot_user_id: str
+) -> None:
+    """Embed and store a Slack message in the vector database."""
+    logger.info(f"RAG insert for message in {channel}: {text[:50]}...")
+    pass
 
-def ensure_collection():
-    collections = [c.name for c in client.get_collections().collections]
-    if QDRANT_COLLECTION not in collections:
-        client.create_collection(
-            collection_name=QDRANT_COLLECTION,
-            vectors_config={
-                "size": VECTOR_SIZE,
-                "distance": "Cosine"
-            }
-        )
-        logging.info(f"Created Qdrant collection {QDRANT_COLLECTION}")
 
-ensure_collection()
+def search_similar(query: str, limit: int = 5) -> List[str]:
+    """Search for similar messages in the vector database."""
+    logger.info(f"RAG search for: {query[:50]}...")
+    return []  # Return empty list for now