Agents reason from real symbols with file path, line range, and relevance score — not from text excerpts a model "remembers." Hybrid FTS + vector search at ~1.2s p50; up to 1,479 RPS at 2,000 concurrent in keyword mode.
FAFO Memory's search_code over an AST-chunked code index. Real query captured against an index with 5,447 documents.
Source is parsed at index time and chunked by AST node. Each chunk is embedded + indexed for FTS. At query time, hybrid_search runs FTS and vector in parallel, fuses the rankings via weighted RRF, and returns chunks with full citation metadata. The substrate is recursive: it indexes its own implementation.
$ search_code(
query = "hybrid search by intent, not string match",
index = "your-codebase"
)
→ 8 ranked hits across rust + typescript
→ top result: hybrid_search() at code_search/search.rs
"FTS + vector search with file-level grouping"
→ score: 0.034 (highest), 0.017 (lowest)
→ latency: ~1.2s p50 semantic mode
→ throughput: 1,271–1,479 RPS @ 2,000 concurrent (keyword mode)
Every hit: file path · line range · language · score · code excerpt.
Agents reason from this, not from what the model "remembers."