---
format: "aidr-story-markdown/v1"
id: "6415a84ce720818dbb364cddf92d203f4952c090b411067cccbc1f635e7d42f6"
canonical_url: "https://aidr.today/6415a84c?lang=en"
title: "Building a RAG Pipeline for Semantic Code Search"
lang: "en"
requested_lang: "en"
available_langs: ["en","vi"]
translation_fallback: null
fallback_fields: []
published_at: "2026-10-04T17:51:48.000Z"
category: "Data"
topics: ["llm","data-engineering"]
source_urls: ["https://news.ycombinator.com/item?id=49956148"]
summary: "Part 1: Parsing, chunking, and vectorization Some time ago, we set out to build the best semantic code search platform we could: a RAG pipeline that gives LLM agents precise, citable evidence from re"
---

# Building a RAG Pipeline for Semantic Code Search

> [Open the canonical story](<https://aidr.today/6415a84c?lang=en>)

**Published:** 2026-10-04T17:51:48.000Z
**Category:** Data
**Topics:** llm, data\-engineering

## Summary

Part 1: Parsing, chunking, and vectorization Some time ago, we set out to build the best semantic code search platform we could: a RAG pipeline that gives LLM agents precise, citable evidence from re

## Sources

- [Discussion](<https://news.ycombinator.com/item?id=49956148>)