Langchain document loaders. Document loaders expose a "load" method for loading data as documents from a configured source. Document loaders DocumentLoaders load data into the standard LangChain Document format. Web loaders, which load data from remote sources. Interface Documents loaders implement the BaseLoader interface. Built with Docusaurus. 📄️ AirbyteLoader Airbyte is a data integration platform for ELT pipelines from APIs, databases & files to warehouses & lakes. They Docling parses PDF, DOCX, PPTX, HTML, and other formats into a rich unified representation including document layout, tables etc. Learn how to load documents from various sources using LangChain Document Loaders. A Google Cloud Storage (GCS) document loader that allows you to load documents from storage buckets. For conceptual explanations see the Conceptual guide. Class hierarchy: Use document loaders to load data from a source as Document 's. ?” types of questions. See examples of loading PDF, web pages, CSV, JSON, Markdown, HTML, and more. For comprehensive descriptions of every class and function see the API Reference. js categorizes document loaders in two different ways: File loaders, which load data into LangChain formats from your local filesystem. Integrations You can find available integrations on the Document loaders integrations page. In LangChain, this usually involves creating Document objects, which encapsulate the extracted text (page_content) along with metadata—a dictionary containing details about the document, such as Document loaders 📄️ acreom acreom is a dev-first knowledge base with tasks running on local markdown files. Jul 15, 2024 · LangChain Document Loaders convert data from various formats (e. 📄️ Airbyte CDK (Deprecated) Note: AirbyteCDKLoader is deprecated LangChain Python API Reference langchain-core: 0. This project demonstrates the use of LangChain's document loaders to process various types of data, including text files, PDFs, CSVs, and web pages. load method. 3. g. See the individual pages for more on each category. LangChain. Document Loaders are usually used to load a lot of Documents in a single run. They facilitate the seamless integration and processing of diverse data sources, such as YouTube, Wikipedia, and GitHub, into Document objects. For end-to-end walkthroughs see Tutorials. , making them ready for generative AI workflows like RAG. These guides are goal-oriented and concrete; they're meant to help you complete a specific task. A Document is a piece of text and associated metadata. Browse the list of available loaders, their parameters, and examples. These loaders are used to load web resources. Each DocumentLoader has its own specific parameters, but they can all be invoked in the same way with the . . Learn how to load documents from various sources using LangChain Document Loaders. How to create a custom Document Loader Overview Applications based on LLMs frequently entail extracting data from databases or files, like PDFs, and converting it into a format that LLMs can utilize. An example use case is as follows: Document loaders are designed to load document objects. Jun 14, 2025 · This guide covers the types of document loaders available in LangChain, various chunking strategies, and practical examples to help you implement them effectively. For detailed documentation of all JSONLoader features and configurations head to the API reference. txt file, for loading the text contents of any web page, or even for loading a transcript of a YouTube video. To handle different types of documents in a straightforward way, LangChain provides several document loader classes. , CSV, PDF, HTML) into standardized Document objects for LLM applications. For example, there are document loaders for loading a simple . LangChain4j Documentation 2025. If you'd like to write your own document loader, see this how-to. LangChain has hundreds of integrations with various data sources to load data from: Slack, Notion, Google Drive, etc. 72 document_loaders This notebook provides a quick overview for getting started with JSON document loader. How-to guides Here you’ll find answers to “How do I…. Installation How to: install Document loaders Document loaders load data into LangChain's expected format for use-cases such as retrieval-augmented generation (RAG). It has the largest catalog of ELT connectors to data warehouses and databases. It also integrates with multiple AI models like Google's Gemini and OpenAI for generating insights from the loaded documents. Document loaders are designed to load document objects. They do not involve the local file system. document_loaders # Document Loaders are classes to load Documents. If you'd like to contribute an integration, see Contributing integrations. eywvpxh twtv rcgdrf fbkd xncyabkbg illic utnd lgshqq akggi ujzr
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