Step-by-Step Guide to Building RAG with LlamaIndex 0.10 and Vector 0.4 for Docs Search
This article provides a step-by-step guide to building a Retrieval-Augmented Generation (RAG) pipeline for internal documentation search using LlamaIndex 0.10 and Vector 0.4. It highlights performance improvements, cost efficiency, and local deployment capabilities of the stack. The guide includes code setup, prerequisites, and benchmarks, with a complete implementation available on GitHub.
- ▪LlamaIndex 0.10 reduces vector store write latency by 42% compared to version 0.9.x when tested on 100k-document datasets.
- ▪Vector 0.4 introduces native HNSW index persistence, removing the need for custom serialization code.
- ▪An end-to-end RAG pipeline for 50k documents costs $0.12 per hour to run on a 4 vCPU, 8GB RAM instance, making it 60% cheaper than managed alternatives.
- ▪The guide includes a CLI tool for ingesting markdown files and a FastAPI-based REST API for querying with source citations and confidence scores.
- ▪Full benchmark results and the complete codebase are available in a public GitHub repository for replication and use.
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