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AI Agents · Lesson

Building a RAG Chain End-to-End

Stitch it together: loader -> splitter -> embeddings -> vector store -> retriever -> prompt -> model.

Project Goal

Combine loaders, splitters, vector stores, and LCEL into a complete production-shaped RAG chain.

Step 1: Load and Chunk

from langchain_community.document_loaders import PyPDFLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter

loader = PyPDFLoader('handbook.pdf')
docs = loader.load()
splitter = RecursiveCharacterTextSplitter(chunk_size=800, chunk_overlap=100)
chunks = splitter.split_documents(docs)
print(f'{len(chunks)} chunks loaded')

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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