How to build a production RAG pipeline in Python (without a vector database)
The article discusses building a production RAG pipeline in Python without using a vector database. It emphasizes that for domain-specific corpora, BM25 retrieval can be more efficient and cost-effective than semantic search. The tutorial provides a step-by-step guide on using Meilisearch for document indexing and retrieval.
- ▪BM25 retrieval is competitive with semantic search for domain-specific corpora.
- ▪Meilisearch offers BM25 out of the box, along with typo tolerance and a simple REST API.
- ▪The tutorial includes steps for indexing documents and retrieving top-k results.
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try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 3944946) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Ayi NEDJIMI Posted on May 22 How to build a production RAG pipeline in Python (without a vector database) #ai #llm #python #tutorial Everyone reaching for a vector database when building RAG is solving the wrong problem first. For most domain-specific corpora — technical documentation, company knowledge bases, article archives — BM25 retrieval is competitive with semantic search, costs a fraction of the compute, and is dramatically simpler to operate.
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