Create vector_handler.py

vector_handler.py

@@ -0,0 +1,38 @@
+import pinecone
+import os
+import time
+import threading
+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"))
+
+embedding_model = GoogleGenerativeAIEmbeddings(model="models/embedding-001")
+
+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)
+    vectorstore = LangchainPinecone.from_texts(texts, embedding_model, index_name=index_name)
+
+
+def query_vector_store(session_id, question):
+    index_name = session_id
+    vectorstore = LangchainPinecone.from_existing_index(index_name, embedding_model)
+    chain = get_chain()
+    docs = vectorstore.similarity_search(question)
+    result = chain({"input_documents": docs, "question": question}, return_only_outputs=True)
+    return result["output_text"]
+
+
+def delete_vector_store(index_name, delay=0):
+    def delayed_delete():
+        if delay:
+            time.sleep(delay)
+        try:
+            pinecone.delete_index(index_name)
+            print(f"Deleted index {index_name}")
+        except Exception as e:
+            print(f"Error deleting index {index_name}: {e}")
+    threading.Thread(target=delayed_delete).start()