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AI Agents · Lesson

Loaders, Splitters and Vector Stores

Use DocumentLoaders for PDFs/HTML, TextSplitters for chunking, and VectorStores for retrieval.

Three Pillars of RAG in LangChain

  1. Document Loaders — read raw data into Documents
  2. Text Splitters — chunk Documents
  3. Vector Stores — embed and index chunks

Document Loaders

LangChain has 100+ loaders. Examples:

from langchain_community.document_loaders import PyPDFLoader, WebBaseLoader, TextLoader

pdf_docs = PyPDFLoader('handbook.pdf').load()
web_docs = WebBaseLoader('https://example.com').load()
md_docs = TextLoader('README.md').load()

All lessons in this course

  1. LangChain Architecture: Models, Prompts, Chains
  2. Loaders, Splitters and Vector Stores
  3. LCEL (LangChain Expression Language)
  4. Building a RAG Chain End-to-End
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