Update vector_handler.py

vector_handler.py

@@ -1,37 +1,43 @@
-import pinecone
-import os
 import time
 import threading
+from pinecone import Pinecone, ServerlessSpec
 from langchain_google_genai import GoogleGenerativeAIEmbeddings
 from langchain.vectorstores.pinecone import Pinecone as LangchainPinecone
 
-pinecone.init(api_key=os.getenv("PINECONE_API_KEY"), environment=os.getenv("PINECONE_ENV"))
+# Initialize Pinecone client with API key
+pc = Pinecone(api_key="YOUR_API_KEY")  # Replace with your actual key or use os.getenv if needed
 
+# Initialize embedding model
 embedding_model = GoogleGenerativeAIEmbeddings(model="models/embedding-001")
 
+# Create vector store (index) using session_id
 def create_vector_store(session_id, texts):
     index_name = session_id
-    if index_name not in pinecone.list_indexes():
-        pinecone.create_index(index_name, dimension=768)
-    index = pinecone.Index(index_name)
+    if not pc.list_indexes().names or index_name not in pc.list_indexes().names:
+        pc.create_index(
+            name=index_name,
+            dimension=768,  # Match your embedding model dimension
+            spec=ServerlessSpec(cloud="aws", region="us-east-1")
+        )
+        time.sleep(5)  # Wait for index to be ready
     vectorstore = LangchainPinecone.from_texts(texts, embedding_model, index_name=index_name)
 
-
+# Query vector store
 def query_vector_store(session_id, question):
     index_name = session_id
     vectorstore = LangchainPinecone.from_existing_index(index_name, embedding_model)
-    chain = get_chain()
+    chain = get_chain()  # Make sure `get_chain` is defined elsewhere
     docs = vectorstore.similarity_search(question)
     result = chain({"input_documents": docs, "question": question}, return_only_outputs=True)
     return result["output_text"]
 
-
+# Delete vector store with optional delay
 def delete_vector_store(index_name, delay=0):
     def delayed_delete():
         if delay:
             time.sleep(delay)
         try:
-            pinecone.delete_index(index_name)
+            pc.delete_index(index_name)
             print(f"Deleted index {index_name}")
         except Exception as e:
             print(f"Error deleting index {index_name}: {e}")