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langchain-document-loaders

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Load and process LangChain documents efficiently

Data & Analytics#documents#pdf#langchain#rag#loaders#web-scraping#splitter
Authorchristian-bromann
Version1.0.0
Installs0

System Documentation

What problem does it solve?

Documents from diverse sources (PDFs, websites, JSON, CSV, Markdown) often arrive in incompatible formats and require normalization for LangChain-based RAG workflows. This Skill provides a unified loading layer that converts sources into LangChain Document objects with stored metadata, enabling downstream processing and splitting.

Core Features & Use Cases

  • Support multiple document sources (PDF, web, text, CSV, JSON, Notion, YouTube transcripts) and batch loading via DirectoryLoader.
  • Preserve source metadata and chunking compatibility with text splitters like RecursiveCharacterTextSplitter.
  • Integrate with RAG pipelines to feed data into embeddings and retrieval chains; practical use cases include knowledge base construction, research corpora, and content indexing.

Quick Start

Load your first set of documents from a directory or URLs into LangChain Document objects and start splitting for a RAG workflow.

Dependency Matrix

Required Modules

None required

Components

Standard package

💻 Claude Code Installation

Recommended: Let Claude install automatically. Simply copy and paste the text below to Claude Code.

Please help me install this Skill:
Name: langchain-document-loaders
Download link: https://github.com/christian-bromann/langchain-skills/archive/main.zip#langchain-document-loaders

Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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