Update retriever.py

retriever.py

@@ -1,25 +1,32 @@
-from langchain.vectorstores import Qdrant
-from langchain.embeddings import SentenceTransformerEmbeddings
-from qdrant_client import QdrantClient
-
-embeddings = SentenceTransformerEmbeddings(model_name="NeuML/pubmedbert-base-embeddings")
-
-url = "http://localhost:6333"
-
-client = QdrantClient(
-    url=url, prefer_grpc=False
-)
-
-print(client)
-print("##############")
-
-db = Qdrant(client=client, embeddings=embeddings, collection_name="vector_db")
-
-print(db)
-print("######")
-query = "What is Metastatic disease?"
-
-docs = db.similarity_search_with_score(query=query, k=3)
-for i in docs:
-    doc, score = i
-    print({"score": score, "content": doc.page_content, "metadata": doc.metadata})
+from langchain.vectorstores import Qdrant
+from langchain.embeddings import SentenceTransformerEmbeddings
+from qdrant_client import QdrantClient
+import os  # Added for environment variables
+
+embeddings = SentenceTransformerEmbeddings(model_name="NeuML/pubmedbert-base-embeddings")
+
+# Use environment variables for cloud configuration
+client = QdrantClient(
+    url=os.getenv("QDRANT_URL", "https://YOUR_QDRANT_CLUSTER_URL.aws.cloud.qdrant.io"),
+    api_key=os.getenv("QDRANT_API_KEY"),
+    prefer_grpc=False
+)
+
+print(client)
+print("##############")
+
+db = Qdrant(client=client, embeddings=embeddings, collection_name="vector_db")
+
+print(db)
+print("######")
+
+query = "What is Metastatic disease?"
+
+# Updated similarity search (newer LangChain versions)
+docs = db.similarity_search_with_relevance_scores(query=query, k=3)
+for doc, score in docs:
+    print({
+        "score": score,
+        "content": doc.page_content,
+        "metadata": doc.metadata
+    })