create_vector_stores.py

import os
import shutil
from langchain_community.document_loaders import TextLoader, PyPDFLoader
from langchain_text_splitters import RecursiveCharacterTextSplitter
from langchain_openai import OpenAIEmbeddings
from langchain_community.vectorstores import FAISS
from langchain_core.documents import Document
from getpass import getpass

# Set OpenAI API Key
os.environ["OPENAI_API_KEY"] = getpass("Provide OpenAI API Key:")

# Function to create and save a combined vector store from all summary documents
def create_combined_summary_vector_store():
    # Directory containing the Markdown summaries
    directory_path = "CAPS_Summaries"

    # List all Markdown files in the directory
    md_files = [f for f in os.listdir(directory_path) if f.endswith('.md')]

    # Load the Markdown documents
    documents = []
    for file_name in md_files:
        file_path = os.path.join(directory_path, file_name)
        with open(file_path, 'r', encoding='utf-8') as file:
            content = file.read()
            # Wrap the content in a Document object
            documents.append(Document(page_content=content))
        print(f"Successfully added {file_name} to the combined vector store.")

    # Split the documents into chunks
    text_splitter = RecursiveCharacterTextSplitter(chunk_size=3000, chunk_overlap=500)
    splits = text_splitter.split_documents(documents)

    # Create embeddings and vector store
    embeddings = OpenAIEmbeddings(model="text-embedding-3-large")
    vector_store = FAISS.from_documents(documents=splits, embedding=embeddings)

    # Save the vector store locally
    vector_store.save_local("Combined_Summary_Vectorstore")
    print("Combined summary vector store creation complete and saved as 'Combined_Summary_Vectorstore'.")

# Function to create and save individual vector stores for summary documents
def create_individual_summary_vector_stores():
    # Directory containing the Markdown summaries
    directory_path = "CAPS_Summaries"
    # Directory to save individual vector stores
    save_directory = "Individual_Summary_Vectorstores"

    # Ensure the save directory exists
    os.makedirs(save_directory, exist_ok=True)

    # List all Markdown files in the directory
    md_files = [f for f in os.listdir(directory_path) if f.endswith('.md')]

    # Process each file individually
    for file_name in md_files:
        file_path = os.path.join(directory_path, file_name)
        with open(file_path, 'r', encoding='utf-8') as file:
            content = file.read()
            # Wrap the content in a Document object
            document = Document(page_content=content)
            print(f"Successfully loaded {file_name}.")

        # Split the document into chunks
        text_splitter = RecursiveCharacterTextSplitter(chunk_size=3000, chunk_overlap=500)
        splits = text_splitter.split_documents([document])

        # Create embeddings and vector store for each document
        embeddings = OpenAIEmbeddings(model="text-embedding-3-large")
        vector_store = FAISS.from_documents(documents=splits, embedding=embeddings)

        # Save the vector store locally with a unique name in the specified directory
        vector_store_name = os.path.join(save_directory, f"{os.path.splitext(file_name)[0]}_vectorstore")
        vector_store.save_local(vector_store_name)
        print(f"Vector store for {file_name} created and saved as '{vector_store_name}'.")
    print(f"All Individual Summary Vectorstores created.")

# Function to create and save individual vector stores for all documents in CAPS_Summaries and CAPS
def create_individual_vector_stores_for_all_documents():
    # Directories containing the documents
    summary_directory = "CAPS_Summaries"
    caps_directory = "CAPS"
    # Directory to save individual vector stores
    save_directory = "Individual_All_Vectorstores"

    # Ensure the save directory exists
    os.makedirs(save_directory, exist_ok=True)

    # List all Markdown files in the summary directory
    summary_files = [f for f in os.listdir(summary_directory) if f.endswith('.md')]
    # List all PDF files in the CAPS directory
    caps_files = [f for f in os.listdir(caps_directory) if f.endswith('.pdf')]

    # Process each summary file individually by copying existing vector stores
    for file_name in summary_files:
        # Source vector store path in Individual_Summary_Vectorstores
        source_vector_store_name = os.path.join("Individual_Summary_Vectorstores", f"{os.path.splitext(file_name)[0]}_vectorstore")
        # Destination vector store path in Individual_All_Vectorstores
        destination_vector_store_name = os.path.join(save_directory, f"{os.path.splitext(file_name)[0]}_vectorstore")
        # Copy the vector store
        shutil.copytree(source_vector_store_name, destination_vector_store_name, dirs_exist_ok=True)
        print(f"Copied vector store for {file_name} to '{destination_vector_store_name}'.")

    # Process each CAPS file individually
    for file_name in caps_files:
        file_path = os.path.join(caps_directory, file_name)
        loader = PyPDFLoader(file_path)
        documents = loader.load()
        print(f"Successfully loaded {file_name} from CAPS.")

        # Split the document into chunks
        text_splitter = RecursiveCharacterTextSplitter(chunk_size=3000, chunk_overlap=500)
        splits = text_splitter.split_documents(documents)

        # Create embeddings and vector store for each document
        embeddings = OpenAIEmbeddings(model="text-embedding-3-large")
        vector_store = FAISS.from_documents(documents=splits, embedding=embeddings)

        # Save the vector store locally with a unique name in the specified directory
        vector_store_name = os.path.join(save_directory, f"{os.path.splitext(file_name)[0]}_vectorstore")
        vector_store.save_local(vector_store_name)
        print(f"Vector store for {file_name} created and saved as '{vector_store_name}'.")
    print(f"All Individual Vectorstores for complete and summary plans created.")

# Run the functions to create and save the vector stores
if __name__ == "__main__":
    create_combined_summary_vector_store()
    create_individual_summary_vector_stores()
    create_individual_vector_stores_for_all_documents()