zzet--gortex/bench/baselines

Retrieval baselines bench

Reproducible per-baseline NDCG@10 + latency table across six candidate retrieval systems. The harness ships six adapters behind a single uniform Go-side surface; per-adapter Available() checks let --smoke verify wiring without paying for the heavy ones.

Supported adapters:

Adapter	Type	Install
ripgrep	Go-native	brew install ripgrep (or distro equivalent)
probe	Go-native	cargo install probe-ai
colgrep	Python	pip install colgrep (requires CUDA-capable GPU)
grepai	Python	pip install grepai-cli
coderankembed	Python+model	pip install sentence-transformers transformers torch (model ~440MB on first run)
semble	Python	pip install semble

Running

# Smoke check: report which adapters are available right now
go run ./bench/baselines -smoke

# Full table against the gortex repo itself, all adapters
go run ./bench/baselines

# Just the locally-installable ones (ripgrep + probe)
go run ./bench/baselines -against ripgrep,probe

# JSON output for downstream tooling
go run ./bench/baselines -format json -json bench/results/baselines.json

Flags:

• -repo PATH — corpus to query (default .)
• -queries PATH — JSON query set (default queries.json here)
• -groundtruth PATH — JSON per-query expected file paths (default groundtruth.json here)
• -against LIST — comma-separated adapter names; unknown names error out so a typo doesn't silently drop a baseline
• -top-k N — top-K per query (default 10, matches NDCG@10)
• -out PATH — primary output (default stdout)
• -json PATH — companion JSON metrics output
• -format markdown|json — primary format
• -smoke — skip runs; only probe Available() for each adapter
• -timeout DURATION — per-adapter wall-clock cap (default 5m)

How NDCG@10 is computed

For each query the harness computes:

DCG@10  = Σ (gain_i / log2(i + 2))   for i in [0, min(K, |hits|))
IDCG@10 = Σ (1     / log2(i + 2))    for i in [0, min(K, |expected|))
NDCG@10 = DCG@10 / IDCG@10

with gain_i = 1 when hits[i] is in the ground-truth set, 0 otherwise. The reported number is the mean across queries with a non-empty ground-truth entry. Queries the adapter failed on (Error set) are skipped from the mean — they're shown in the per-query JSON for debugging but don't penalize the headline.

Adding an adapter

1. Add a struct in adapters.go that satisfies the adapter interface (Name, Available, Run).
2. Register it in allAdapters() — that single list drives the CLI, the smoke output, and adapterByName.
3. For Python baselines: embed pythonBaselineAdapter and call pythonModuleAvailable / runPythonModule (or runPythonScript if you need a custom wrapper). Document the install in the table above.