create_database.py

from langchain_community.document_loaders import DirectoryLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.schema import Document

from langchain_community.embeddings import HuggingFaceEmbeddings

from langchain_community.vectorstores import Chroma
import openai 
from dotenv import load_dotenv
import os
import shutil





CHROMA_PATH = "chroma"
DATA_PATH = "data/books"


def main():
    generate_data_store()


def generate_data_store():
    documents = load_documents()
    chunks = split_text(documents)
    save_to_chroma(chunks)


def load_documents():
    loader = DirectoryLoader(DATA_PATH, glob="*.md")
    documents = loader.load()
    return documents


def split_text(documents: list[Document]):
    text_splitter = RecursiveCharacterTextSplitter(
        chunk_size=300,
        chunk_overlap=100,
        length_function=len,
        add_start_index=True,
    )
    chunks = text_splitter.split_documents(documents)
    print(f"Split {len(documents)} documents into {len(chunks)} chunks.")

    if len(chunks) > 10:
        document = chunks[10]
        print(document.page_content)
        print(document.metadata)

    return chunks


def save_to_chroma(chunks: list[Document]):
   
    if os.path.exists(CHROMA_PATH):
        shutil.rmtree(CHROMA_PATH)

    
    db = Chroma.from_documents(
        chunks, 
        HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2"), 
        persist_directory=CHROMA_PATH
    )
    # --------------------------------------------------------
    db.persist()
    print(f"Saved {len(chunks)} chunks to {CHROMA_PATH}.")


if __name__ == "__main__":
    main()