Building KernelMind Part 2: Hybrid Retrieval, Reranking, and Actually Retrieving Useful Code
The article discusses the development of KernelMind's retrieval pipeline, focusing on hybrid retrieval methods. It highlights the integration of embeddings and BM25 for improved code retrieval accuracy. The combination of these techniques has led to a more effective system for accessing relevant code snippets within repositories.
- ▪KernelMind's retrieval pipeline evolved to operate directly on chunks retrieved from FAISS instead of raw documents from MongoDB.
- ▪The integration of BM25 with embeddings allowed for better retrieval of exact operational language in code repositories.
- ▪Reciprocal Rank Fusion was implemented to combine the strengths of both retrieval systems, enhancing overall retrieval quality.
Opening excerpt (first ~120 words) tap to expand
try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 3935689) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Ishaan Mavinkurve Posted on May 18 Building KernelMind Part 2: Hybrid Retrieval, Reranking, and Actually Retrieving Useful Code #ai #llm #python #showdev By the end of the first phase of KernelMind, the repository had stopped behaving like disconnected text. Functions now had identity, relationships attached to them. The graph architecture was finally stable enough to represent execution flow across the repository.
…
Excerpt limited to ~120 words for fair-use compliance. The full article is at DEV.to (Top).
Discussion
0 commentsMore from DEV.to (Top)
