What is Document Loader?
A document loader is a component or utility that extracts text content and metadata from various file formats and data sources, converting them into a structured format suitable for ingestion into AI systems. Document loaders handle the format-specific complexities of reading PDFs, Word documents, web pages, databases, APIs, and other sources, providing a consistent interface for downstream processing regardless of the original data format.
Different file formats require different extraction approaches. PDFs may need layout analysis and text extraction, HTML requires parsing and content selection to separate main content from navigation and boilerplate, spreadsheets need table interpretation, and structured data sources require conversion to natural language representations. Document loaders abstract these complexities, often preserving important metadata like titles, authors, timestamps, and document structure that can be valuable for retrieval and context.
In frameworks like LangChain and LlamaIndex, document loaders are the first step in the data ingestion pipeline. They output standardized document objects that can be passed to text splitters, embedding generators, and vector stores. The ecosystem includes specialized loaders for common sources (Google Docs, Notion, Confluence, GitHub, etc.) and general-purpose loaders for standard formats. Choosing appropriate document loaders and configuring them correctly is essential for maintaining data quality throughout the ingestion process.