Cadre

Capstone 02 — RAG over Codebase (Cross-Repo Semantic Search)

Every serious engineering org in 2026 runs an internal code search that understands meaning, not just strings. Sourcegraph Amp, Cursor's codebase answers, Augment's enterprise graph, Aider's repomap, Pinterest's internal MCP — same shape. Ingest many repos, parse with tree-sitter, embed function- and class-level chunks, hybrid-search, re-rank, answer with citations. This capstone asks you to build one that handles 2M lines of code across 10 repos and survives incremental re-indexing on every git push.

Capstone 30 hr Python (ingestion), TypeScript (API + UI)

Prerequisites: Phase 5 (NLP foundations), Phase 7 (transformers), Phase 11 (LLM engineering), Phase 13 (tools), Phase 17 (infrastructure)

#Problem

By 2026 every frontier coding agent ships with a codebase retrieval layer because context windows alone do not solve cross-repo questions. Claude's 1M-token context helps; it does not eliminate the need for ranked retrieval. Naive cosine search over raw chunks poisons results on generated code, on monorepo duplication, and on the long tail of rarely-imported symbols. The production answer is a hybrid (dense + BM25) search over AST-aware chunks with a re-ranker, backed by a graph of symbol references.

You learn this by indexing a real fleet — not one tutorial repo — and measuring MRR@10, citation faithfulness, and incremental freshness. The failure modes are infrastructural: a 100k-file monorepo, a push that retouches half the files, a query that needs to cross four repos to answer correctly.

#Problem

Keep going. The rest of this lesson is one step away.