import pinecone
from langchain_google_genai import GoogleGenerativeAIEmbeddings
from variables import variables  # Import the list of variable names
import uuid

# Initialize Google Embeddings
google_embeddings = GoogleGenerativeAIEmbeddings(
    model="models/embedding-001",  # Correct model name
    google_api_key="AIzaSyANNRKfEb-YnVIBaSAq6hQ38XpxxGwvaws"  # Your API key
)

# Initialize Pinecone instance
pc = pinecone.Pinecone(
    api_key="4a80f293-ae6d-489a-a7d8-33ea3fcdd26b"  # Your Pinecone API key
)

# Define the Pinecone index name (make sure it exists in your Pinecone dashboard)
index_name = "iocl2"
index = pc.Index(index_name)


def create_embedding(variable):
    try:
        content = variable.get("description", None)
        url = variable.get("url", "")
        tag = variable.get("tag", "")
        updated_url = ""

        if isinstance(url, list):
            updated_url = ",".join(url)
        else:
            updated_url = url

        embedding = google_embeddings.embed_query(content)
        vectors = []
        vectors.append({
            'id': str(uuid.uuid4()),
            'values': embedding,
            'metadata': {
                'chunk': content,
                "url": updated_url,
                "tag": tag
            }
        })
        index.upsert(vectors)
        print(f"Inserted the chunk: {updated_url}")
    except Exception as e:
        print(f"Error occurred: {e}")


# Iterate over the variable names and create embeddings
for variable_name in variables:
    # Dynamically import the variable from paragraphs2
    variable = __import__('paragraphs2', fromlist=[variable_name])
    variable_data = getattr(variable, variable_name)
    print(f"trying to create embedding for {variable}")
    # Call the create_embedding function with the variable data
    create_embedding(variable_data)