import os import click from typing import List from langchain.document_loaders import TextLoader, PDFMinerLoader, CSVLoader from langchain.text_splitter import RecursiveCharacterTextSplitter from langchain.vectorstores import Chroma from langchain.docstore.document import Document from constants import CHROMA_SETTINGS, SOURCE_DIRECTORY, PERSIST_DIRECTORY from langchain.embeddings import HuggingFaceInstructEmbeddings def load_single_document(file_path: str) -> Document: # Loads a single document from a file path if file_path.endswith(".txt"): loader = TextLoader(file_path, encoding="utf8") elif file_path.endswith(".pdf"): loader = PDFMinerLoader(file_path) elif file_path.endswith(".csv"): loader = CSVLoader(file_path) return loader.load()[0] def load_documents(source_dir: str) -> List[Document]: # Loads all documents from source documents directory all_files = os.listdir(source_dir) return [load_single_document(f"{source_dir}/{file_path}") for file_path in all_files if file_path[-4:] in ['.txt', '.pdf', '.csv'] ] @click.command() @click.option('--device_type', default='gpu', help='device to run on, select gpu or cpu') def main(device_type, ): # load the instructorEmbeddings if device_type in ['cpu', 'CPU']: device='cpu' else: device='cuda' # Load documents and split in chunks print(f"Loading documents from {SOURCE_DIRECTORY}") documents = load_documents(SOURCE_DIRECTORY) text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200) texts = text_splitter.split_documents(documents) print(f"Loaded {len(documents)} documents from {SOURCE_DIRECTORY}") print(f"Split into {len(texts)} chunks of text") # Create embeddings embeddings = HuggingFaceInstructEmbeddings(model_name="hkunlp/instructor-xl", model_kwargs={"device": device}) db = Chroma.from_documents(texts, embeddings, persist_directory=PERSIST_DIRECTORY, client_settings=CHROMA_SETTINGS) db.persist() db = None if __name__ == "__main__": main()