How can I efficiently process mixed PDF and DOCX files for a RAG chatbot using file type detection?

Below is a recommended approach. The Langchain‑provided MergeDataLoader works best when each loader can handle the same type of file (or when you intentionally want to aggregate multiple views of the same file). When your files can be either .pdf or .docx, a neat solution is to detect the file type and call the appropriate loader for each file. In other words, perform the type detection first and then merge the resulting documents into one list, which you can then insert into your vector store.

For example, instead of trying to combine loaders (e.g. calling MergeDataLoader with both a PDF loader and a Docx loader for a single file), you can create your own function that delegates to the appropriate loader based on the file extension. This way, each file is processed by the correct loader and you end up with a single collection of documents.

Here’s a concise snippet that follows that approach:

PYTHONCopied
from langchain.document_loaders import PyPDFLoader, Docx2txtLoader
import os

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

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

docs = []
batch_size = 10
total_files = len(all_files)
processed_files = 0

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

Key Points

• Single Responsibility: Each file is handed off to its correct loader rather than forcing a merge of loaders that may not individually support the loaded file.
• Flexibility: Once you have the output documents (a unified list), you can use them in a vector store regardless of their original file format.
• Custom Loader Option: If needed, you could also create a custom loader class that implements the file type check internally and calls the correct loader. This would encapsulate the logic, but the above approach keeps things simple and straightforward.

This method efficiently handles multiple file types and lets you merge documents from different sources into the same vector store without running into issues with MergeDataLoader trying to process unsupported file types.