create_database.py

from langchain_community.document_loaders import DirectoryLoader, UnstructuredHTMLLoader, PyPDFLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.schema import Document
from langchain.embeddings import OpenAIEmbeddings
from langchain_openai import OpenAIEmbeddings
from langchain_community.vectorstores import Chroma
import openai 
from dotenv import load_dotenv
import os
import shutil
import argparse
from langchain.document_loaders.pdf import PyPDFDirectoryLoader
from get_embedding_function import get_embedding_function

# Load environment variables. Assumes that project contains .env file with API keys
load_dotenv()
#---- Set OpenAI API key 
openai.api_key = os.environ['OPENAI_API_KEY']

CHROMA_PATH = "chroma"
DATA_PATH = "data/"


def main():

    # Check if the database should be cleared (using the --clear flag).
    parser = argparse.ArgumentParser()
    parser.add_argument("--reset", action="store_true", help="Reset the database.")
    args = parser.parse_args()
    if args.reset:
        print("✨ Clearing Database")
        clear_database()

    # Create (or update) the data store.
    documents = load_documents()
    chunks = split_text(documents)
    add_to_chroma(chunks)


def load_documents():
    # Load .pdf files
    pdf_loader = DirectoryLoader(DATA_PATH, glob="*.pdf", loader_cls=PyPDFLoader)
    pdf_documents = pdf_loader.load()
    
    # Load .html files
    html_loader = DirectoryLoader(DATA_PATH, glob="*.html", loader_cls=UnstructuredHTMLLoader)
    html_documents = html_loader.load()

    # Combine the documents
    documents = pdf_documents + html_documents
    
    return documents


def split_text(documents: list[Document]):
    text_splitter = RecursiveCharacterTextSplitter(
        chunk_size=1200,
        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.")

    document = chunks[10]
    print(document.page_content)
    print(document.metadata)

    return chunks


def add_to_chroma(chunks: list[Document]):
    # Load the existing database.
    db = Chroma(
        persist_directory=CHROMA_PATH, embedding_function=get_embedding_function()
    )

    # Calculate Page IDs.
    chunks_with_ids = calculate_chunk_ids(chunks)

    # Add or Update the documents.
    existing_items = db.get(include=[])  # IDs are always included by default
    existing_ids = set(existing_items["ids"])
    print(f"Number of existing documents in DB: {len(existing_ids)}")

    # Only add documents that don't exist in the DB.
    new_chunks = []
    for chunk in chunks_with_ids:
        if chunk.metadata["id"] not in existing_ids:
            new_chunks.append(chunk)

    if len(new_chunks):
        print(f"👉 Adding new documents: {len(new_chunks)}")
        new_chunk_ids = [chunk.metadata["id"] for chunk in new_chunks]
        db.add_documents(new_chunks, ids=new_chunk_ids)
        db.persist()
    else:
        print("✅ No new documents to add")

def calculate_chunk_ids(chunks):

    # This will create IDs like "data/O1 Qualification.pdf:6:2"
    # Page Source : Page Number : Chunk Index

    last_page_id = None
    current_chunk_index = 0

    for chunk in chunks:
        source = chunk.metadata.get("source")
        page = chunk.metadata.get("page")
        current_page_id = f"{source}:{page}"

        # If the page ID is the same as the last one, increment the index.
        if current_page_id == last_page_id:
            current_chunk_index += 1
        else:
            current_chunk_index = 0

        # Calculate the chunk ID.
        chunk_id = f"{current_page_id}:{current_chunk_index}"
        last_page_id = current_page_id

        # Add it to the page meta-data.
        chunk.metadata["id"] = chunk_id

    return chunks

def clear_database():
    if os.path.exists(CHROMA_PATH):
        shutil.rmtree(CHROMA_PATH)

if __name__ == "__main__":
    main()