Update vectordb_utils.py

vectordb_utils.py

@@ -3,20 +3,27 @@
 from qdrant_client import QdrantClient
 from qdrant_client.models import VectorParams, Distance, PointStruct
 from sentence_transformers import SentenceTransformer
+from datasets import load_dataset
 import uuid
 import os
-cache_dir = os.environ.get("MODEL_CACHE_DIR", "/app/cache")  # Fallback to /app/cache
+
+# Setup cache dir
+cache_dir = os.environ.get("MODEL_CACHE_DIR", "/app/cache")  # Fallback
 os.makedirs(cache_dir, exist_ok=True)
-encoder =SentenceTransformer("all-MiniLM-L6-v2", cache_folder=cache_dir)
+
+# Encoder and Qdrant config
+encoder = SentenceTransformer("all-MiniLM-L6-v2", cache_folder=cache_dir)
 qdrant = QdrantClient(":memory:")
 collection_name = "customer_support_docsv1"
 
+# Initialize collection
 def init_qdrant_collection():
     qdrant.recreate_collection(
         collection_name=collection_name,
         vectors_config=VectorParams(size=384, distance=Distance.COSINE)
     )
 
+# Add a query/response to DB
 def add_to_vectordb(query, response):
     vector = encoder.encode(query).tolist()
     qdrant.upload_points(
@@ -28,6 +35,21 @@ def add_to_vectordb(query, response):
         )]
     )
 
+# Search DB
 def search_vectordb(query, limit=3):
     vector = encoder.encode(query).tolist()
     return qdrant.search(collection_name=collection_name, query_vector=vector, limit=limit)
+
+# 🆕 Load and populate from Hugging Face dataset
+def populate_vectordb_from_hf():
+    print("Loading dataset from Hugging Face...")
+    dataset = load_dataset("Talhat/Customer_IT_Support", split="train")
+
+    print("Populating vector DB...")
+    for item in dataset:
+        query = item.get("input", "").strip()
+        response = item.get("output", "").strip()
+        if query and response:
+            add_to_vectordb(query, response)
+
+    print("Vector DB population complete.")