models/vector_database.py

from pinecone import Pinecone, ServerlessSpec
from llama_index.vector_stores.pinecone import PineconeVectorStore
from dotenv import load_dotenv

import os

load_dotenv()

# Pinecone Vector Database
pc = Pinecone(api_key=os.environ.get("PINECONE_API_KEY"))
pc_index_name = "llama-integration-pinecone"
# pc_index_name = "openai-embeddings"
pc_indexes = pc.list_indexes()

# Check if the index already exists
def index_exists(index_name):
    for index in pc_indexes:
        if index["name"] == index_name:
            return True
    return False

# Create the index if it doesn't exist
if not index_exists(pc_index_name):
    pc.create_index(
        name=pc_index_name,
        dimension=1536,
        spec=ServerlessSpec(cloud="aws", region="us-east-1"),
    )

# Initialize your index
pinecone_index = pc.Index(pc_index_name)

# Define the vector store
pinecone_vector_store = PineconeVectorStore(pinecone_index=pinecone_index)