LangChain document loader

LangChain document loader is the adapter class that pulls raw content—PDFs, Word files, HTML pages, cloud buckets, SQL rows—into the LangChain ecosystem as clean Document objects. Each loader handles authentication, decryption, paging, and Unicode fixes, then splits metadata (title, URL, timestamp) from body text so downstream components can filter or score by fields. Popular subclasses include PyPDFLoader, UnstructuredFileLoader, S3DirectoryLoader, and WebBaseLoader. A single line such as loader = PyPDFLoader(“report.pdf”).load() yields a list ready for chunking, embedding, and storage in vector databases like Qdrant or Chroma. Built-in async batching and retry logic tame rate-limited APIs, while streaming loaders support gigabyte-scale ingestion without blowing RAM. Because every loader conforms to the same interface, teams can swap data sources—Slack threads, Gmail, Confluence—without rewriting retrieval or RAG code, making LangChain document loaders the first mile in any production-grade LLM pipeline.