fix index

Author: mostafa6765

.env.example

@@ -1,4 +1,4 @@
 OPENAI_API_KEY=""
 PINECONE_API_KEY=""
-PINECONE_INDEX="movie-history"
-FEATURE_NAME="chat"
+PINECONE_INDEX_NAME="movie-history"
+FEATURE_NAME="chat" # "qa"

README.md

@@ -38,7 +38,7 @@ This is a Retrieval-Augmented Generation (RAG) application using Langchain, Open
     ```plaintext
     OPENAI_API_KEY=""
     PINECONE_API_KEY=""
-    PINECONE_INDEX="movie-history"
+    PINECONE_INDEX_NAME="movie-history"
     FEATURE_NAME="chat" # "qa"
     ```
 

src/feature/chat/chat_utils.py

@@ -17,6 +17,7 @@ def __init__(self):
         # Set up API keys
         self.openai_api_key = os.environ.get("OPENAI_API_KEY")
         self.pinecone_api_key = os.environ.get("PINECONE_API_KEY")
+        self.pinecone_index_name = os.environ.get("PINECONE_INDEX_NAME")
 
 
         # Initialize LLM
@@ -26,7 +27,7 @@ def __init__(self):
         embeddings = OpenAIEmbeddings(openai_api_key=self.openai_api_key)
 
         # Initialize Pinecone Vector Store
-        self.pincone_index = os.environ.get("PINECONE_INDEX")
+        self.index_name = self.pinecone_index_name
         self.vectorstore = PineconeVectorStore(index_name=self.index_name, embedding=embeddings)
 
         # Contextualize question

src/feature/qa/qa_utils.py

@@ -11,12 +11,12 @@
 # Set up API keys
 openai_api_key = os.environ.get("OPENAI_API_KEY")
 pinecone_api_key = os.environ.get("PINECONE_API_KEY")
-pincone_index = os.environ.get("PINECONE_INDEX")
+pincone_index_name = os.environ.get("PINECONE_INDEX_NAME")
 
 class MovieQASystem:
     def __init__(self):
         embeddings = OpenAIEmbeddings(openai_api_key=openai_api_key)
-        self.index_name = pincone_index
+        self.index_name = pincone_index_name
         self.vectorstore = PineconeVectorStore(index_name=self.index_name, embedding=embeddings)
 
         # Using VectorStoreRetriever

src/vector_store/load_data.py

@@ -38,16 +38,17 @@ def create_vectorstore():
     # Retrieve API keys and index name from environment variables
     openai_api_key = os.environ.get("OPENAI_API_KEY")
     pinecone_api_key = os.environ.get("PINECONE_API_KEY")
-    pinecone_index = os.environ.get("PINECONE_INDEX")
+    pinecone_index_name = os.environ.get("PINECONE_INDEX_NAME")
 
     # Initialize OpenAI embeddings and Pinecone
     embeddings = OpenAIEmbeddings(openai_api_key=openai_api_key)
     pc = Pinecone(api_key=pinecone_api_key)
 
     # Create or connect to the Pinecone index
-    index_name = pinecone_index
+    index_name = pinecone_index_name
     if index_name not in pc.list_indexes().names():  
-        pc.create_index(name=index_name, dimension=1536, metric="cosine")
+        print(f"pinecone {index_name} is not exist, create index and try again")
+        return
 
     print("Vector is storing, wait...")
     # Create the vector store using the split documents and embeddings