Document Loading, Splitting, and Embedding
PyPDFLoader, RecursiveCharacterTextSplitter, OpenAIEmbeddings, embedding strategies.
The RAG Ingestion Pipeline
Before an LLM can answer from your documents, you must load, split, and embed them. This lesson covers that ingestion pipeline, the foundation of every Retrieval-Augmented Generation system.
pip install langchain-community pypdf langchain-openaiLoading a PDF
PyPDFLoader reads a PDF and returns a list of Document objects, one per page. Each Document has page_content (the text) and metadata (source, page number).
from langchain_community.document_loaders import PyPDFLoader
loader = PyPDFLoader("handbook.pdf")
docs = loader.load()
print(len(docs), "pages")All lessons in this course
- LangChain Architecture and LCEL
- Document Loading, Splitting, and Embedding
- Vector Stores: Chroma and FAISS
- Building a RAG Q&A System End-to-End