LangChain Document Loaders

This repository highlights the most commonly used document loaders in LangChain, which are essential for bringing raw data into a standardized Document format. Document loaders serve as the entry point for processing data before applying text splitters, embeddings, and retrieval pipelines.

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📌 Top Used Document Loaders

• CSVLoader → For loading structured tabular data from .csv files.
• DirectoryLoader → For bulk ingestion of multiple files from directories.
• PyPDFLoader → For parsing and extracting text from PDF documents.
• TextLoader → For reading plain text (.txt) files.
• WebBaseLoader → For fetching and parsing web pages directly from URLs.

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🔎 Notes

• These represent the top-used loaders in practical LLM applications, but LangChain supports many others (e.g., JSONLoader, UnstructuredFileLoader, EverNoteLoader, etc.).
• Choosing the right loader depends on your data source (local files, web content, structured datasets, etc.).
• Loaders are typically combined with text splitters to prepare data for downstream tasks such as embeddings, question answering, and RAG.

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📚 Resources

• LangChain Document Loaders Documentation