import os
from langchain.document_loaders import PyPDFLoader
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_community.vectorstores import FAISS

def create_faiss_index():
    try:
        # Ensure the 'docs' directory exists and contains files
        docs_directory = 'docs'
        if not os.path.exists(docs_directory) or not os.listdir(docs_directory):
            raise ValueError(f"Directory '{docs_directory}' is empty or does not exist.")

        # Load all documents from the 'docs' directory
        documents = []
        for file in os.listdir(docs_directory):
            if file.endswith('.pdf'):
                loader = PyPDFLoader(os.path.join(docs_directory, file))
                documents.extend(loader.load())
        
        if not documents:
            raise ValueError("No valid documents found in the 'docs' directory.")

        # Create embeddings using HuggingFace's 'sentence-transformers/all-MiniLM-L6-v2' model
        embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
        
        # Create the FAISS vector store index
        faiss_index = FAISS.from_documents(documents, embeddings)

        # Save the FAISS index locally
        index_path = "faiss_index"
        os.makedirs(index_path, exist_ok=True)
        faiss_index.save_local(index_path)
        
        print("FAISS index created and saved successfully.")
    except Exception as e:
        print(f"An error occurred during FAISS index creation: {e}")

if __name__ == "__main__":
    create_faiss_index()