How can I efficiently load and merge PDF and DOCX files into a single vector store for my RAG chatbot?

Based on the documentation for Langchain's document loaders, the recommended approach for your use case is not to use MergedDataLoader to combine loaders for a single file type. Instead, you should select the appropriate loader based on the file extension (either .pdf or .docx) and then aggregate the loaded documents before storing them in your vector store.

The MergedDataLoader is intended for scenarios where you want to merge the output of multiple loaders for the same source. In your case, since any given file will be either a PDF or a DOCX, it is more efficient and appropriate to use a conditional check to load each file with its corresponding loader. Once you’ve loaded them independently, you can merge the resulting documents into a single dataset which then can be inserted into your vector store.

Below is an example of how you might adjust your code:

PYTHONCopied
import os
from langchain.document_loaders import PyPDFLoader, Docx2txtLoader

# Initialize an empty list to store loaded documents
docs = []

# Function to process a batch of files
def process_batch(all_files):
    batch_docs = []
    for file_path in all_files:
        if file_path.lower().endswith(".pdf"):
            loader = PyPDFLoader(file_path)
        elif file_path.lower().endswith(".docx"):
            loader = Docx2txtLoader(file_path)
        else:
            # Skip unsupported file types
            continue

        batch_docs.extend(loader.load())
    return batch_docs

# Get the list of files to process
root_directory = "path/to/your/documents"
files_to_process = []
for root, dirs, files in os.walk(root_directory):
    files_to_process.extend([
        os.path.join(root, file) 
        for file in files 
        if file.lower().endswith(".pdf") or file.lower().endswith(".docx")
    ])

batch_size = 10  # Adjust your batch size as needed
total_files = len(files_to_process)
processed_files = 0

# Iterate through the files in batches
for i in range(0, total_files, batch_size):
    batch = files_to_process[i:i + batch_size]
    batch_docs = process_batch(batch)
    docs.extend(batch_docs)
    processed_files += len(batch)
    print(f"Processed {processed_files} / {total_files} files")

# At this point, 'docs' contains all the documents loaded from .pdf and .docx files.
# You can now proceed to add these documents into your vector store.

Key Points

• Loader Selection: Use a simple conditional branch to select PyPDFLoader for .pdf files and Docx2txtLoader for .docx files.
• Aggregation: Collect all the documents into a single list (docs) after processing each file.
• Vector Store Merge: Once you have the docs list, you can proceed with the usual steps to insert them into your vector store, ensuring all documents (regardless of type) are merged into the same index.

This approach adheres to the guidance provided by Langchain documentation and avoids the misuse of MergedDataLoader, ensuring an efficient solution for your RAG chatbot.