Building a Modular RAG Pipeline in Python
summary
A Python modular RAG pipeline is built with component interfaces and runtime wiring via importlib so you can swap chunkers, retrievers and vector stores; a Streamlit UI demos Chromadb, SQLite retriever, Langmith tracing and Azure for speed; the demo indexes data, runs retrievals and shows latencies (indexing seconds, retrieval ~1–7s), a batch run returns 3 of 4 batch answers correct, and the claim is that the framework makes it easy to compare quality, cost and speed tradeoffs.
AI-written summary · about
chapters (11)
- 0:00Introduction
- 3:09What is RAG pipeline
- 6:41RAG pipeline considerations
- 7:26Core RAG components
- 9:09Modular RAG pipeline breakdown
- 12:40Live demo
- 15:51Custom components
- 17:36SQLite retriever
- 20:29Batch evaluation
- 22:16Code walkthrough
- 25:52Summary
related talks
01h 16mForget RAG Pipelines—Build Production Ready Agents in 15 Mins
33mRAG Pipelines Letting You Down? Fitch Group's Solution for Financial Documents
42mTake Your RAG To The Max!
27mBuilding a Reproducible RAG Pipeline
41mFrom RAGs to riches: Build an AI document interrogation app in 30 mins
01h 28mBuilding a RAG System on Node.js
31mEasy RAG with LangChain4J and Docker
27mOptimizing RAG Systems in Practice: Challenges and Proven Solutions