Document Q&A using LangChain
summary
A working RAG pipeline that loads web or PDF content, chunks it, creates Hugging Face embeddings, stores them in Chroma DB, and uses LangChain retriever/RetrievalQA to answer document questions; demo uses a free 7B model from Hugging Face, produces 496 saved embeddings, and returns correct answers (Egypt visa types, city list) with k=3 similarity search and adjustable temperature and chunk size (500 → 1000 changed results). Notebook and API-key handling are provided and the workflow runs end-to-end.
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chapters (24)
- 0:00Introduction
- 1:06Agenda and LangChain overview
- 8:23Document Q&A with RAG
- 11:35RAG use cases and benefits
- 15:30RAG vs fine-tuning
- 20:39RAG architecture explained
- 30:01Demo setup in Jupyter
- 31:05Importing libraries and API
- 33:13Configuring LLM and temperature
- 36:06Loading web content
- 38:12Splitting documents into chunks
- 40:20Creating embeddings
- 41:24Storing embeddings in Chroma DB
- 42:28First question: Egyptian visas
- 46:50Similarity search for answers
- 47:56Retrieval QA chain
- 53:16Testing second question: cities
- 59:15Adjusting chunk size
- 1:02:38Q&A
- 1:02:58Fine-tuning vs RAG
- 1:03:20Embedding vs tokenization
- 1:05:35Ready-made vs custom apps
- 1:06:53Private Q&A chatbot
- 1:09:05Session wrap-up and resources
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