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v1/mode: model-aware enrichment downgrade + 3 corpora + variance harness
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Pass 5 (5 reps × 4 conditions × 1 file on grok-4.1-fast) showed composing
matrix corpora is anti-additive on strong models — composed lakehouse_arch
+ symbols LOST 5/5 head-to-head vs codereview_isolation (Δ −1.8 grounded
findings, p=0.031). Default flips to isolation; matrix path now auto-
downgrades when the resolved model is strong.

Mode runner:
- matrix_corpus is Vec<String> (string OR array via deserialize_string_or_vec)
- top_k=6 from each corpus, merge by score, take top 8 globally
- chunk tag prefers doc_id over source so reviewer sees [adr:009] vs [lakehouse_arch]
- is_weak_model() gate auto-downgrades codereview_lakehouse → codereview_isolation
  for strong models (default-strong; weak = :free suffix or local last-resort)
- LH_FORCE_FULL_ENRICHMENT=1 bypasses for diagnostic runs
- EnrichmentSources.downgraded_from records when the gate fires

Three corpora indexed via /vectors/index (5849 chunks total):
- lakehouse_arch_v1 — ADRs + phases + PRD + scrum spec (93 docs, 2119 chunks)
- scrum_findings_v1 — past scrum_reviews.jsonl (168 docs, 1260 chunks; EXCLUDED
  from defaults — 24% out-of-bounds line citations from cross-file drift)
- lakehouse_symbols_v1 — regex-extracted pub items + /// docs (656 docs, 2470 chunks)

Experiment infra:
- scripts/build_*_corpus.ts — re-runnable when source content changes
- scripts/mode_pass5_variance_paid.ts — N reps × M conditions on one file
- scripts/mode_pass5_summarize.ts — mean ± σ + head-to-head, parser handles
  numbered + path-with-line + path-with-symbol finding tables
- scripts/mode_compare.ts — groups by mode|corpus when sweeps span corpora
- scripts/mode_experiment.ts — default model bumped to x-ai/grok-4.1-fast,
  --corpus flag for per-call override

Decisions + open follow-ups: docs/MODE_RUNNER_TUNING_PLAN.md

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
This commit is contained in:
root 2026-04-26 17:29:17 -05:00
parent 56bf30cfd8
commit 2dbc8dbc83
10 changed files with 1043 additions and 82 deletions
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23
config/modes.toml
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@ -12,15 +12,22 @@
[[task_class]]
name = "scrum_review"
# `codereview_lakehouse` is the codebase-specific enrichment runner —
# bundles defined/imported symbols, pathway-memory bug fingerprints,
# and relevance-filtered matrix chunks into ONE precise prompt so the
# model gets it right the first call. The generic `codereview` mode
# from LLM Team is still the network fallback if execute fails.
preferred_mode = "codereview_lakehouse"
fallback_modes = ["codereview", "consensus", "ladder"]
# 2026-04-26 pass5 variance test (5 reps × 4 conditions, grok-4.1-fast,
# pathway_memory.rs): composed corpus LOST 5/5 vs isolation (Δ −1.8
# grounded findings, p=0.031). See docs/MODE_RUNNER_TUNING_PLAN.md.
# Default is now isolation — bug fingerprints + adversarial framing +
# file content carries strong models without matrix noise. The
# `codereview_lakehouse` matrix path remains available via force_mode
# (auto-downgrades to isolation on strong models — see the
# is_strong_model gate in crates/gateway/src/v1/mode.rs).
preferred_mode = "codereview_isolation"
fallback_modes = ["codereview_lakehouse", "codereview", "consensus", "ladder"]
default_model = "qwen3-coder:480b"
matrix_corpus = "distilled_procedural_v20260423102847"
# Corpora kept defined so experimental modes (codereview_matrix_only,
# pass2/pass5 sweeps) and weak-model rescue rungs can still pull them.
# scrum_findings_v1 is built but EXCLUDED — bake-off showed 24% OOB
# line citations from cross-file drift, only safe with same-file gating.
matrix_corpus = ["lakehouse_arch_v1", "lakehouse_symbols_v1"]
[[task_class]]
name = "contract_analysis"

215
crates/gateway/src/v1/mode.rs
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@ -147,8 +147,31 @@ pub struct TaskClassEntry {
#[serde(default)]
pub fallback_modes: Vec<String>,
pub default_model: String,
#[serde(default)]
pub matrix_corpus: Option<String>,
/// One or more corpora the mode runner queries (top-k per corpus,
/// merged by score before the relevance filter). Accepts a single
/// string or an array in modes.toml — `deserialize_string_or_vec`
/// handles both shapes for backward compat.
#[serde(default, deserialize_with = "deserialize_string_or_vec")]
pub matrix_corpus: Vec<String>,
}
/// Accept `key = "x"` or `key = ["x", "y"]` in TOML/JSON. Empty string or
/// missing field → empty vec.
fn deserialize_string_or_vec<'de, D>(d: D) -> Result<Vec<String>, D::Error>
where D: serde::Deserializer<'de> {
use serde::de::Error;
let v = serde_json::Value::deserialize(d).map_err(D::Error::custom)?;
match v {
serde_json::Value::Null => Ok(vec![]),
serde_json::Value::String(s) if s.is_empty() => Ok(vec![]),
serde_json::Value::String(s) => Ok(vec![s]),
serde_json::Value::Array(a) => a
.into_iter()
.map(|x| x.as_str().map(String::from)
.ok_or_else(|| D::Error::custom("matrix_corpus array must contain strings")))
.collect(),
other => Err(D::Error::custom(format!("matrix_corpus must be string or array, got {other:?}"))),
}
}
#[derive(Clone, Debug, Deserialize)]
@ -234,7 +257,7 @@ pub struct DecisionTrace {
pub task_class_matched: bool,
pub source: &'static str, // "config" | "default" | "force_mode"
pub fallbacks: Vec<String>,
pub matrix_corpus: Option<String>,
pub matrix_corpus: Vec<String>,
pub notes: Vec<String>,
}
@ -279,7 +302,7 @@ pub async fn route(
task_class_matched: cfg.lookup(&req.task_class).is_some(),
source: "force_mode",
fallbacks: vec![],
matrix_corpus: None,
matrix_corpus: vec![],
notes,
},
}));
@ -349,7 +372,7 @@ pub async fn route(
task_class_matched: false,
source: "default",
fallbacks: cfg.default.fallback_modes.clone(),
matrix_corpus: None,
matrix_corpus: vec![],
notes,
},
}))
@ -419,11 +442,13 @@ pub struct ExecuteRequest {
/// runner uses its built-in forensic-review framing.
#[serde(default)]
pub user_question: Option<String>,
/// Override the matrix corpus the runner queries. Defaults to the
/// task_class's matrix_corpus from modes.toml. Use for the corpus-
/// tightening experiment (Pass 2 of the 2026-04-26 mode sweep).
#[serde(default)]
pub force_matrix_corpus: Option<String>,
/// Override the matrix corpus (or corpora) the runner queries.
/// Accepts a single string or array — same semantics as
/// modes.toml's `matrix_corpus`. Empty/missing → use the task
/// class default. Multi-corpus path: top-k retrieved from each,
/// merged and re-sorted by score before the relevance filter.
#[serde(default, deserialize_with = "deserialize_string_or_vec")]
pub force_matrix_corpus: Vec<String>,
/// Override the relevance filter threshold (default 0.3). Setting
/// to 0 keeps every chunk; raising rejects more aggressively. Used
/// to find the threshold sweet spot per task class.
@ -441,8 +466,13 @@ pub struct EnrichmentSources {
pub bug_fingerprints_count: usize,
pub matrix_chunks_kept: usize,
pub matrix_chunks_dropped: usize,
pub matrix_corpus: Option<String>,
pub matrix_corpus: Vec<String>,
pub relevance_filter_used: bool,
/// Set when the model-aware downgrade fires — records the mode the
/// caller was originally routed to before is_weak_model() flipped
/// it. None means no downgrade happened.
#[serde(skip_serializing_if = "Option::is_none")]
pub downgraded_from: Option<String>,
pub enrichment_warnings: Vec<String>,
/// Which enrichment knobs the runner used for this mode. Lets
/// the comparison aggregator group runs by signal-set.
@ -488,6 +518,30 @@ fn framing_text(f: ReviewerFraming) -> &'static str {
}
}
/// Strong-model heuristic for the model-aware enrichment downgrade.
///
/// Pass 5 variance test (2026-04-26, see docs/MODE_RUNNER_TUNING_PLAN.md)
/// proved that on `x-ai/grok-4.1-fast`, composing matrix corpora into the
/// `codereview_lakehouse` prompt LOST 5/5 head-to-head reps against the
/// matrix-free `codereview_isolation` mode. Strong models have enough
/// native capacity that bug fingerprints + adversarial framing + file
/// content carry them; matrix chunks displace depth-of-analysis.
///
/// We default to "strong" (downgrade matrix off) because most production
/// traffic uses paid models. The explicit `weak` predicate keeps the
/// list small and easy to extend — anything matching `:free` (OpenRouter
/// free tier) or the local last-resort qwen3.5 stays on the full
/// `codereview_lakehouse` path where matrix demonstrably helped during
/// the 2026-04-26 free-tier bake-off.
fn is_weak_model(model: &str) -> bool {
if model.ends_with(":free") || model.contains(":free/") {
return true;
}
// Local last-resort rung from the scrum ladder. Other local models
// can be added here as we test them.
matches!(model, "qwen3.5:latest" | "qwen3:latest")
}
pub async fn execute(
State(_state): State<V1State>,
Json(req): Json<ExecuteRequest>,
@ -507,7 +561,40 @@ pub async fn execute(
.clone()
.or_else(|| tc.map(|t| t.default_model.clone()))
.unwrap_or_else(|| cfg.default.default_model.clone());
let matrix_corpus = tc.and_then(|t| t.matrix_corpus.clone());
let matrix_corpus: Vec<String> = tc
.map(|t| t.matrix_corpus.clone())
.unwrap_or_default();
// Model-aware enrichment downgrade (2026-04-26 pass 5 finding).
// If a caller resolves `codereview_lakehouse` against a strong
// model, downgrade to `codereview_isolation` so we don't pollute
// the prompt with matrix chunks the model would do better without.
// `LH_FORCE_FULL_ENRICHMENT=1` bypasses for diagnostic runs.
// `force_mode` from the caller is treated as opt-in to the chosen
// mode and skips the downgrade — experiments need to inspect exact
// mode behavior on whatever model they pass.
let force_full = std::env::var("LH_FORCE_FULL_ENRICHMENT")
.map(|v| v == "1" || v.eq_ignore_ascii_case("true"))
.unwrap_or(false);
let downgraded_from = if mode == "codereview_lakehouse"
&& req.force_mode.is_none()
&& !force_full
&& !is_weak_model(&model)
{
tracing::info!(
target: "v1::mode",
"downgrade codereview_lakehouse -> codereview_isolation for strong model {}",
model
);
Some(mode.clone())
} else {
None
};
let mode = if downgraded_from.is_some() {
"codereview_isolation".to_string()
} else {
mode
};
if !is_native_mode(&mode) {
// Native execute is the only path implemented; LLM-Team proxy
@ -525,12 +612,17 @@ pub async fn execute(
}
// Caller can override the matrix corpus per-call (Pass 2 corpus
// tightening). Falls back to modes.toml default.
let matrix_corpus = req.force_matrix_corpus.clone().or(matrix_corpus);
// tightening). Empty force_matrix_corpus falls back to modes.toml.
let matrix_corpus: Vec<String> = if req.force_matrix_corpus.is_empty() {
matrix_corpus
} else {
req.force_matrix_corpus.clone()
};
let flags = flags_for_mode(&mode);
let mut sources = EnrichmentSources {
matrix_corpus: matrix_corpus.clone(),
flags: Some(flags),
downgraded_from: downgraded_from.clone(),
..Default::default()
};
@ -613,38 +705,64 @@ pub async fn execute(
}
}
// Step 3: matrix corpus search (if configured for this task class).
// Step 3: matrix corpus search. Multi-corpus path: query top_k from
// each, merge, re-sort by score, take top 8 overall before the
// relevance filter — orthogonal corpora (e.g. arch + symbols) get
// composed without one swamping the other on chunk count alone.
let mut raw_chunks: Vec<serde_json::Value> = vec![];
if flags.include_matrix_chunks {
if let Some(corpus) = &matrix_corpus {
let body = serde_json::json!({
"index_name": corpus,
"query": format!("{} {}\n{}", req.task_class, req.file_path, &file_content[..file_content.len().min(500)]),
"top_k": 8,
});
match client
.post("http://localhost:3100/vectors/search")
.json(&body)
.send()
.await
{
Ok(r) if r.status().is_success() => {
if let Ok(j) = r.json::<serde_json::Value>().await {
raw_chunks = j
.get("results")
.and_then(|v| v.as_array())
.cloned()
.unwrap_or_default();
if flags.include_matrix_chunks && !matrix_corpus.is_empty() {
let query_str = format!(
"{} {}\n{}",
req.task_class,
req.file_path,
&file_content[..file_content.len().min(500)]
);
let per_corpus_k = if matrix_corpus.len() == 1 { 8 } else { 6 };
for corpus in &matrix_corpus {
let body = serde_json::json!({
"index_name": corpus,
"query": query_str,
"top_k": per_corpus_k,
});
match client
.post("http://localhost:3100/vectors/search")
.json(&body)
.send()
.await
{
Ok(r) if r.status().is_success() => {
if let Ok(j) = r.json::<serde_json::Value>().await {
if let Some(arr) = j.get("results").and_then(|v| v.as_array()) {
for mut c in arr.iter().cloned() {
// Tag the corpus origin on each chunk so
// dropped/kept telemetry can attribute
// signal back to its source corpus.
if let serde_json::Value::Object(ref mut obj) = c {
obj.insert(
"corpus".to_string(),
serde_json::Value::String(corpus.clone()),
);
}
raw_chunks.push(c);
}
}
}
}
Ok(r) => sources
.enrichment_warnings
.push(format!("matrix_search[{}] HTTP {}", corpus, r.status())),
Err(e) => sources
.enrichment_warnings
.push(format!("matrix_search[{}] err: {e}", corpus)),
}
Ok(r) => sources
.enrichment_warnings
.push(format!("matrix_search HTTP {}", r.status())),
Err(e) => sources
.enrichment_warnings
.push(format!("matrix_search err: {e}")),
}
} // close `if let Some(corpus)`
// Sort merged chunks by score desc and take the global top 8.
raw_chunks.sort_by(|a, b| {
let sa = a.get("score").and_then(|v| v.as_f64()).unwrap_or(0.0);
let sb = b.get("score").and_then(|v| v.as_f64()).unwrap_or(0.0);
sb.partial_cmp(&sa).unwrap_or(std::cmp::Ordering::Equal)
});
raw_chunks.truncate(8);
}
// Step 4: relevance filter — drop adjacency pollution.
@ -709,9 +827,16 @@ pub async fn execute(
if flags.include_matrix_chunks && !kept_chunks.is_empty() {
user_prompt.push_str("📁 RELATED CONTEXT (matrix chunks):\n");
for c in &kept_chunks {
let src = c.get("source").and_then(|v| v.as_str()).unwrap_or("?");
let txt = c.get("text").and_then(|v| v.as_str()).unwrap_or("");
user_prompt.push_str(&format!(" [{}] {}\n", src, &txt[..txt.len().min(280)]));
// Prefer doc_id for the tag — corpus builders encode origin
// in doc_id (e.g. `adr:017`, `phase:19`) so the reviewer sees
// useful provenance instead of a generic source label.
let tag = c.get("doc_id").and_then(|v| v.as_str())
.filter(|s| !s.is_empty())
.or_else(|| c.get("source").and_then(|v| v.as_str()))
.unwrap_or("?");
let txt = c.get("text").or_else(|| c.get("chunk_text"))
.and_then(|v| v.as_str()).unwrap_or("");
user_prompt.push_str(&format!(" [{}] {}\n", tag, &txt[..txt.len().min(280)]));
}
user_prompt.push_str("\n");
}

114
docs/MODE_RUNNER_TUNING_PLAN.md Normal file
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@ -0,0 +1,114 @@
# Mode Runner Tuning Plan
**Date:** 2026-04-26
**Branch:** `scrum/auto-apply-19814` (PR #11)
**Status:** Pass 5 variance test complete; conclusions locked. Implementation in progress.
A fresh Claude session reading this + the pass5 row range in `data/_kb/mode_experiments.jsonl` should be able to continue the work without re-running anything.
---
## What we set out to do
J's directive 2026-04-26 evening: "Mode runner experiment + corpus tightening."
Symptom in memory before the session: scrum_review's matrix corpus was kept-rate 0/2 across every call — silent failure. Question: should we tighten the corpus, build new ones, or change retrieval?
## What we built
Three new corpora indexed under `/vectors/index`:
| Corpus | Builder | Docs | Chunks | Source |
|---|---|---|---|---|
| `lakehouse_arch_v1` | `scripts/build_lakehouse_corpus.ts` | 93 | 2119 | DECISIONS.md ADRs + standalone ADRs + PHASES.md + PRD.md + CONTROL_PLANE_PRD.md + SCRUM_MASTER_SPEC.md |
| `scrum_findings_v1` | `scripts/build_scrum_findings_corpus.ts` | 168 | 1260 | Past `scrum_reviews.jsonl` rows |
| `lakehouse_symbols_v1` | `scripts/build_symbols_corpus.ts` | 656 | 2470 | Regex-extracted `pub fn|struct|enum|trait` + `///` docs from `crates/**/*.rs` |
Multi-corpus support added to the mode runner:
- `crates/gateway/src/v1/mode.rs` — `matrix_corpus` is now `Vec<String>` (string OR array in modes.toml/JSON via `deserialize_string_or_vec`)
- Top-K retrieved from each corpus, merged by score, top 8 globally before relevance filter
- Each chunk tagged with `corpus` for telemetry
- Prompt assembly prefers `doc_id` over `source` so reviewer sees `[adr:009]` not `[lakehouse_arch]`
Validation infra:
- `scripts/mode_pass5_variance_paid.ts` — N reps × M conditions on one file, paid model
- `scripts/mode_pass5_summarize.ts` — mean ± stddev + head-to-head wins/losses with parser handling 3 finding-table shapes (numbered, path-with-line, path-with-symbol)
- `scripts/mode_compare.ts` — extended grouping key to `mode|corpus` (sorted+joined when multiple corpora) so multi-corpus sweeps don't last-write-wins-clobber
## What we learned
### Single-rep bake-off (free-tier `openai/gpt-oss-120b:free`, 3 files)
Confirmed `lakehouse_arch_v1` adds +1.7 grounded findings/file vs isolation, 100% groundedness, −20s latency. **But:** matrix slightly *hurts* on small files (273-line `delta.rs`: lakehouse 7 vs isolation 9) and unlocks +9 findings on the large file (1355-line `pathway_memory.rs`).
`scrum_findings_v1` produced 24% out-of-bounds line citations from cross-file line-number drift — **dangerous, excluded from defaults**. Only safe with same-file gating (TBD if needed).
### Single-rep bake-off (paid `x-ai/grok-4.1-fast`, 3 files × 4 conditions)
Picture *flips* on a strong model. Composed corpus −1.4 grounded vs isolation. Symbols-alone slightly negative. Arch-alone negative. Suggested kitchen-sinking enrichment denigrates results when the model is good enough to handle the file directly.
### Pass 5 variance test (paid grok-4.1-fast, 5 reps × 4 conditions on `pathway_memory.rs`)
| Condition | n | mean grounded ± σ | range | H2H vs isolation | Δ mean |
|---|---|---|---|---|---|
| **isolation** | 5 | 6.2 ± 1.3 | [5–8] | baseline | — |
| arch_only | 5 | 5.2 ± 0.8 | [4–6] | 0W–3L–2T | −1.0 |
| symbols_only | 5 | 6.4 ± 1.5 | [4–8] | 3W–2L–0T | +0.2 |
| **composed (A+C)** | 5 | 4.4 ± 1.1 | [3–6] | **0W–5L–0T** | **−1.8** |
**Composed loses 5/5 head-to-head against isolation on this file with this model.** Probability under random noise = 1/2⁵ = 3.1%. Statistically significant.
Data window: rows in `data/_kb/mode_experiments.jsonl` where `ts > "2026-04-26T21:50:03Z"` and `file_path == "crates/vectord/src/pathway_memory.rs"`. Re-aggregate any time with `bun run scripts/mode_pass5_summarize.ts --since 2026-04-26T21:50:03Z`.
## Decisions taken
1. **Composed-corpus default is reverted.** `scrum_review.preferred_mode` switches from `codereview_lakehouse` → `codereview_isolation`. Matrix corpora stay defined in modes.toml but only fire when a caller explicitly forces `codereview_lakehouse` or one of the matrix-only experimental modes.
2. **Model-aware enrichment downgrade (α) is wired** in `crates/gateway/src/v1/mode.rs::execute`. When a caller resolves a "strong" model AND the resolved mode is `codereview_lakehouse`, the runner downgrades to `codereview_isolation` flag-set automatically. Strong patterns: `x-ai/grok-*`, `anthropic/*`, `openai/gpt-4*`, `openai/gpt-5*`, `deepseek/deepseek-v4*`, `moonshotai/kimi-k2*`, `google/gemini-2.5*`. Override via `LH_FORCE_FULL_ENRICHMENT=1` for diagnostic runs.
3. **`scrum_findings_v1` stays excluded from defaults** until same-file gating lands. Built and indexed; do not point any task class at it without that gate.
## Open follow-ups (not landed in this batch)
- **Same-file gating for `scrum_findings_v1`** — restrict retrieval to chunks where `file_path == focus_file` so cross-file line-number drift can't happen. Then it becomes a per-file "what was found before" signal.
- **Variance test on small files** — pass 5 was 1 file (the largest, where matrix-hurt was sharpest). Confirm direction holds on 273-line / 333-line files. ~15 min × 2 files = ~30 min.
- **Verify weak-model gain holds with α** — the bake-off showed matrix helps free-tier `gpt-oss-120b:free` on the large file. After α is wired, re-run on a free-tier model to confirm full enrichment still fires for it. ~5 min.
- **Higher-signal matrix (β fork)** — if we ever want matrix back as a default, it can't be whole-ADR/whole-section chunks. Better: only retrieve chunks where the focus file's defined symbols appear. Tighter signal, fewer chunks. Postponed.
## Reference data + tools
- **Mode-runner code:** `crates/gateway/src/v1/mode.rs`
- **Mode config:** `config/modes.toml`
- **Per-call experiment log:** `data/_kb/mode_experiments.jsonl`
- **Sweep harnesses:**
- `scripts/mode_experiment.ts` — files × modes × 1 rep (default model: `x-ai/grok-4.1-fast`)
- `scripts/mode_pass2_corpus_sweep.ts` — corpus × threshold sweep
- `scripts/mode_pass3_variance.ts` — temp × reps on one mode
- `scripts/mode_pass5_variance_paid.ts` — N reps × M conditions on one file
- **Aggregators:**
- `scripts/mode_compare.ts` — full per-mode comparison with grounding check
- `scripts/mode_pass5_summarize.ts` — variance + head-to-head, robust to 3 table shapes
- **Corpus builders (re-runnable when source docs / scrum_reviews / source code change):**
- `scripts/build_lakehouse_corpus.ts`
- `scripts/build_scrum_findings_corpus.ts`
- `scripts/build_symbols_corpus.ts`
## Re-entry recipe (fresh session)
```bash
cd /home/profit/lakehouse
git log --oneline scrum/auto-apply-19814 -10 # what's recent
cat docs/MODE_RUNNER_TUNING_PLAN.md # this file
bun run scripts/mode_pass5_summarize.ts --since 2026-04-26T21:50:03Z # locked result
curl -s http://localhost:3100/v1/mode/list | jq '.task_classes.scrum_review' # current config
```
If you want to reproduce the bake-off:
```bash
# Strong model variance test (~17 min):
bun run scripts/mode_pass5_variance_paid.ts
# Weak-model regression (~10 min):
LH_MODEL=openai/gpt-oss-120b:free LH_REPS=3 bun run scripts/mode_pass5_variance_paid.ts
```

176
scripts/build_lakehouse_corpus.ts Normal file
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@ -0,0 +1,176 @@
#!/usr/bin/env bun
/**
* Build the `lakehouse_arch_v1` corpus — Option A from 2026-04-26
* corpus-tightening pass. Sources: DECISIONS.md ADRs, standalone
* ADR-NNN-*.md docs, PHASES.md per-phase entries, PRD.md,
* CONTROL_PLANE_PRD.md, SCRUM_MASTER_SPEC.md sections.
*
* doc_id encodes origin (adr:017, phase:19, prd:executive_summary, ...)
* so the reviewer prompt's [tag] surfaces useful context.
*
* Usage:
* bun run scripts/build_lakehouse_corpus.ts # build
* bun run scripts/build_lakehouse_corpus.ts --dry-run # show docs, don't POST
* bun run scripts/build_lakehouse_corpus.ts --print # dump first chunk + count
*/
import { readFileSync, readdirSync } from "node:fs";
import { resolve } from "node:path";
const ROOT = resolve(import.meta.dir, "..");
const GATEWAY = process.env.LH_GATEWAY ?? "http://localhost:3100";
const INDEX_NAME = process.env.LH_CORPUS_NAME ?? "lakehouse_arch_v1";
const SOURCE_LABEL = "lakehouse_arch";
const CHUNK_SIZE = Number(process.env.LH_CHUNK_SIZE ?? 1500);
const OVERLAP = Number(process.env.LH_OVERLAP ?? 150);
interface Doc { id: string; text: string }
function slug(s: string): string {
return s
.toLowerCase()
.replace(/[^a-z0-9]+/g, "_")
.replace(/^_+|_+$/g, "")
.slice(0, 60);
}
// Split DECISIONS.md by `## ADR-NNN: title`. Drop date line so it doesn't
// dilute the embedding (ADRs are about intent, not when they happened).
function chunkDecisionsMd(md: string): Doc[] {
const docs: Doc[] = [];
const sections = md.split(/^## ADR-(\d+):\s*(.+)$/m);
// sections = [preamble, num, title, body, num, title, body, ...]
for (let i = 1; i < sections.length; i += 3) {
const num = sections[i].padStart(3, "0");
const title = sections[i + 1].trim();
const body = sections[i + 2]
.replace(/^\*\*Date:\*\*.*$/m, "")
.trim();
docs.push({
id: `adr:${num}`,
text: `# ADR-${num}: ${title}\n\n${body}`,
});
}
return docs;
}
// Standalone ADR-NNN-*.md files in docs/ — keep one doc per file.
function chunkStandaloneAdrs(dir: string): Doc[] {
const docs: Doc[] = [];
for (const f of readdirSync(dir)) {
const m = f.match(/^ADR-(\d+)-(.+)\.md$/);
if (!m) continue;
const num = m[1].padStart(3, "0");
const slug_ = slug(m[2]);
docs.push({
id: `adr_doc:${num}_${slug_}`,
text: readFileSync(resolve(dir, f), "utf8"),
});
}
return docs;
}
// PHASES.md uses `## Phase N: title` headings + nested checklists. Split
// by phase. Sub-bullets stay with their parent phase so context is intact.
function chunkPhasesMd(md: string): Doc[] {
const docs: Doc[] = [];
const sections = md.split(/^## (Phase[^\n]*)$/m);
for (let i = 1; i < sections.length; i += 2) {
const heading = sections[i].trim();
const body = sections[i + 1].trim();
if (!body) continue;
const phase_num_match = heading.match(/Phase\s+(\S+)/);
const id_part = phase_num_match
? `phase:${slug(phase_num_match[1])}`
: `phase:${slug(heading)}`;
docs.push({ id: id_part, text: `## ${heading}\n${body}` });
}
return docs;
}
// Generic doc: split by `## Section` (top-level inside a single doc). If
// the section list is empty, return the whole file as one doc and let the
// server-side chunker handle it.
function chunkBySectionH2(filePath: string, originPrefix: string): Doc[] {
const md = readFileSync(filePath, "utf8");
const sections = md.split(/^## (.+)$/m);
if (sections.length < 3) {
return [{ id: `${originPrefix}:_full`, text: md }];
}
const docs: Doc[] = [];
// Capture preamble (before any ## heading) if non-trivial
if (sections[0].trim().length > 200) {
docs.push({
id: `${originPrefix}:_preamble`,
text: sections[0].trim(),
});
}
for (let i = 1; i < sections.length; i += 2) {
const heading = sections[i].trim();
const body = sections[i + 1].trim();
if (!body) continue;
docs.push({
id: `${originPrefix}:${slug(heading)}`,
text: `## ${heading}\n${body}`,
});
}
return docs;
}
function buildAllDocs(): Doc[] {
const docs: Doc[] = [];
docs.push(...chunkDecisionsMd(readFileSync(resolve(ROOT, "docs/DECISIONS.md"), "utf8")));
docs.push(...chunkStandaloneAdrs(resolve(ROOT, "docs")));
docs.push(...chunkPhasesMd(readFileSync(resolve(ROOT, "docs/PHASES.md"), "utf8")));
docs.push(...chunkBySectionH2(resolve(ROOT, "docs/PRD.md"), "prd"));
docs.push(...chunkBySectionH2(resolve(ROOT, "docs/CONTROL_PLANE_PRD.md"), "ctrl_prd"));
docs.push(...chunkBySectionH2(resolve(ROOT, "docs/SCRUM_MASTER_SPEC.md"), "scrum_spec"));
return docs;
}
async function main() {
const dryRun = process.argv.includes("--dry-run") || process.argv.includes("--print");
const printOnly = process.argv.includes("--print");
const docs = buildAllDocs();
const totalBytes = docs.reduce((s, d) => s + d.text.length, 0);
const expectedChunks = Math.ceil(totalBytes / (CHUNK_SIZE - OVERLAP));
console.log(`[corpus] ${docs.length} documents · ${totalBytes} bytes · ~${expectedChunks} chunks at ${CHUNK_SIZE}/${OVERLAP}`);
console.log(`[corpus] origins: ${[...new Set(docs.map(d => d.id.split(":")[0]))].join(", ")}`);
if (printOnly) {
console.log("\n[corpus] first 3 doc IDs:");
docs.slice(0, 3).forEach(d => console.log(` ${d.id} (${d.text.length} bytes) ${d.text.slice(0, 80).replace(/\n/g, " ")}…`));
console.log("\n[corpus] last 3 doc IDs:");
docs.slice(-3).forEach(d => console.log(` ${d.id} (${d.text.length} bytes) ${d.text.slice(0, 80).replace(/\n/g, " ")}…`));
return;
}
if (dryRun) return;
const body = {
index_name: INDEX_NAME,
source: SOURCE_LABEL,
documents: docs,
chunk_size: CHUNK_SIZE,
overlap: OVERLAP,
};
console.log(`[corpus] POST ${GATEWAY}/vectors/index → ${INDEX_NAME}`);
const r = await fetch(`${GATEWAY}/vectors/index`, {
method: "POST",
headers: { "content-type": "application/json" },
body: JSON.stringify(body),
signal: AbortSignal.timeout(60_000),
});
if (!r.ok) {
console.error(`[corpus] HTTP ${r.status}: ${await r.text()}`);
process.exit(1);
}
const j: any = await r.json();
console.log(`[corpus] job ${j.job_id} · ${j.documents} docs → ${j.chunks} chunks queued`);
console.log(`[corpus] poll: curl -s ${GATEWAY}/vectors/jobs/${j.job_id} | jq`);
console.log(`[corpus] verify: curl -s '${GATEWAY}/vectors/indexes' | jq '.[]|select(.index_name=="${INDEX_NAME}")'`);
}
main().catch(e => { console.error(e); process.exit(1); });

94
scripts/build_scrum_findings_corpus.ts Normal file
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@ -0,0 +1,94 @@
#!/usr/bin/env bun
/**
* Build the `scrum_findings_v1` corpus — Option B from 2026-04-26
* corpus pass. Self-feeding: each accepted scrum review's
* `suggestions_preview` becomes a document, indexed under doc_id
* `review:<file_slug>:<ts_compact>` so multi-iteration coexists.
*
* Re-run this whenever scrum_reviews.jsonl grows; the index_name stays
* stable and the gateway will re-register metadata.
*/
import { readFileSync } from "node:fs";
import { resolve } from "node:path";
const ROOT = resolve(import.meta.dir, "..");
const GATEWAY = process.env.LH_GATEWAY ?? "http://localhost:3100";
const INDEX_NAME = process.env.LH_CORPUS_NAME ?? "scrum_findings_v1";
const SOURCE_LABEL = "scrum_findings";
const CHUNK_SIZE = Number(process.env.LH_CHUNK_SIZE ?? 1500);
const OVERLAP = Number(process.env.LH_OVERLAP ?? 150);
const MIN_PREVIEW_BYTES = 200; // skip stub rows
interface Doc { id: string; text: string }
function slugFile(path: string): string {
return path.replace(/^crates\//, "").replace(/[^a-z0-9]+/gi, "_").slice(0, 40);
}
function compactTs(iso: string): string {
return iso.replace(/[-:T]/g, "").slice(0, 14); // 20260424T110656
}
function buildDocs(): Doc[] {
const lines = readFileSync(resolve(ROOT, "data/_kb/scrum_reviews.jsonl"), "utf8").split("\n").filter(Boolean);
const docs: Doc[] = [];
const idCounts = new Map<string, number>();
for (const line of lines) {
let row: any;
try { row = JSON.parse(line); } catch { continue; }
const file = row.file ?? "";
const preview = row.suggestions_preview ?? "";
if (!file || preview.length < MIN_PREVIEW_BYTES) continue;
const ts = compactTs(row.reviewed_at ?? "");
const baseId = `review:${slugFile(file)}:${ts || "no_ts"}`;
// Multiple reviews with same ts (rare but possible) get a counter.
const count = (idCounts.get(baseId) ?? 0) + 1;
idCounts.set(baseId, count);
const id = count === 1 ? baseId : `${baseId}_${count}`;
const header = `File: ${file}\nReviewed: ${row.reviewed_at ?? "?"}\nModel: ${row.accepted_model ?? "?"}\nVerdict: ${row.verdict ?? "?"}\nFindings: ${row.findings_count ?? "?"}\n\n`;
docs.push({ id, text: header + preview });
}
return docs;
}
async function main() {
const dryRun = process.argv.includes("--dry-run") || process.argv.includes("--print");
const printOnly = process.argv.includes("--print");
const docs = buildDocs();
const totalBytes = docs.reduce((s, d) => s + d.text.length, 0);
console.log(`[corpus-B] ${docs.length} reviews · ${totalBytes} bytes · target chunk_size=${CHUNK_SIZE}`);
console.log(`[corpus-B] file coverage: ${new Set(docs.map(d => d.id.split(":")[1])).size} unique files`);
if (printOnly) {
docs.slice(0, 3).forEach(d => console.log(` ${d.id} (${d.text.length}b) ${d.text.slice(0, 80).replace(/\n/g, " ")}…`));
return;
}
if (dryRun) return;
const r = await fetch(`${GATEWAY}/vectors/index`, {
method: "POST",
headers: { "content-type": "application/json" },
body: JSON.stringify({
index_name: INDEX_NAME,
source: SOURCE_LABEL,
documents: docs,
chunk_size: CHUNK_SIZE,
overlap: OVERLAP,
}),
signal: AbortSignal.timeout(60_000),
});
if (!r.ok) {
console.error(`[corpus-B] HTTP ${r.status}: ${await r.text()}`);
process.exit(1);
}
const j: any = await r.json();
console.log(`[corpus-B] job ${j.job_id} · ${j.documents} docs → ${j.chunks} chunks queued`);
}
main().catch(e => { console.error(e); process.exit(1); });

141
scripts/build_symbols_corpus.ts Normal file
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@ -0,0 +1,141 @@
#!/usr/bin/env bun
/**
* Build the `lakehouse_symbols_v1` corpus — Option C from 2026-04-26
* pass. Extracts public Rust items with their /// doc comments from
* crates/**\/*.rs. Regex-based — covers ~80% of definitions without
* pulling in a syn-based parser.
*
* doc_id: `symbol:<crate>::<kind>::<name>` e.g. symbol:vectord::struct::PathwayTrace
*
* Each chunk includes: doc comment (if any) + signature + 1-2 lines
* after the brace so reviewer sees field types / variants for structs
* and enums.
*/
import { readFileSync, readdirSync, statSync } from "node:fs";
import { resolve, relative } from "node:path";
const ROOT = resolve(import.meta.dir, "..");
const GATEWAY = process.env.LH_GATEWAY ?? "http://localhost:3100";
const INDEX_NAME = process.env.LH_CORPUS_NAME ?? "lakehouse_symbols_v1";
const SOURCE_LABEL = "lakehouse_symbols";
const CHUNK_SIZE = Number(process.env.LH_CHUNK_SIZE ?? 800);
const OVERLAP = Number(process.env.LH_OVERLAP ?? 80);
interface Doc { id: string; text: string }
function walkRs(dir: string): string[] {
const out: string[] = [];
for (const entry of readdirSync(dir)) {
if (entry === "target" || entry.startsWith(".")) continue;
const full = resolve(dir, entry);
const st = statSync(full);
if (st.isDirectory()) out.push(...walkRs(full));
else if (entry.endsWith(".rs")) out.push(full);
}
return out;
}
function crateOf(rsPath: string): string {
const rel = relative(resolve(ROOT, "crates"), rsPath);
return rel.split("/")[0];
}
// Match pub fn|struct|enum|trait declarations. Capture the (optional)
// preceding contiguous /// doc block and a few lines after for signature
// + body preview. Skips items inside `mod tests` blocks and #[cfg(test)].
const ITEM_RE = /(?:^[ \t]*\/\/\/.*\n)*[ \t]*pub(?:\([^)]+\))?[ \t]+(fn|struct|enum|trait|async[ \t]+fn)[ \t]+([A-Za-z_][A-Za-z0-9_]*)/gm;
function extractItems(src: string, crate: string, relPath: string): Doc[] {
const docs: Doc[] = [];
const seen = new Set<string>();
// Quick test-module guard: drop everything from a `mod tests {` line
// onward. Coarse but adequate — public items inside tests are rare.
const cutoff = src.search(/^(#\[cfg\(test\)\]|mod tests\b)/m);
const usable = cutoff > 0 ? src.slice(0, cutoff) : src;
for (const m of usable.matchAll(ITEM_RE)) {
const matchStart = m.index!;
const kind = m[1].replace(/^async[ \t]+/, "async_");
const name = m[2];
// Walk backward to capture the contiguous /// doc block above.
const lines = usable.slice(0, matchStart).split("\n");
const docLines: string[] = [];
for (let i = lines.length - 1; i >= 0; i--) {
const t = lines[i].trim();
if (t.startsWith("///")) docLines.unshift(t.replace(/^\/\/\/\s?/, ""));
else if (t === "" || t.startsWith("#[")) continue;
else break;
}
// Capture signature + ~6 lines of body preview.
const after = usable.slice(matchStart, matchStart + 800);
const bodyEnd = after.search(/\n\}\n|\n\n[a-z#]/);
const body = bodyEnd > 0 ? after.slice(0, Math.min(bodyEnd, 800)) : after.slice(0, 800);
const id = `symbol:${crate}::${kind}::${name}`;
if (seen.has(id)) continue;
seen.add(id);
const header = `${crate}::${name} (${kind}) — ${relPath}`;
const docText = docLines.length > 0 ? `\n${docLines.join("\n")}\n` : "\n";
docs.push({ id, text: `${header}\n${docText}\n\`\`\`rust\n${body}\n\`\`\`` });
}
return docs;
}
function buildDocs(): Doc[] {
const cratesDir = resolve(ROOT, "crates");
const docs: Doc[] = [];
for (const f of walkRs(cratesDir)) {
const src = readFileSync(f, "utf8");
const crate = crateOf(f);
const rel = relative(ROOT, f);
docs.push(...extractItems(src, crate, rel));
}
return docs;
}
async function main() {
const printOnly = process.argv.includes("--print");
const dryRun = process.argv.includes("--dry-run") || printOnly;
const docs = buildDocs();
const totalBytes = docs.reduce((s, d) => s + d.text.length, 0);
const byCrate = new Map<string, number>();
for (const d of docs) {
const c = d.id.split("::")[0].replace("symbol:", "");
byCrate.set(c, (byCrate.get(c) ?? 0) + 1);
}
console.log(`[corpus-C] ${docs.length} symbols · ${totalBytes} bytes · chunk_size=${CHUNK_SIZE}`);
console.log(`[corpus-C] by crate: ${[...byCrate.entries()].map(([k, v]) => `${k}=${v}`).join(", ")}`);
if (printOnly) {
docs.slice(0, 3).forEach(d => console.log(` ${d.id} (${d.text.length}b)\n ${d.text.slice(0, 200).replace(/\n/g, "\n ")}\n`));
return;
}
if (dryRun) return;
const r = await fetch(`${GATEWAY}/vectors/index`, {
method: "POST",
headers: { "content-type": "application/json" },
body: JSON.stringify({
index_name: INDEX_NAME,
source: SOURCE_LABEL,
documents: docs,
chunk_size: CHUNK_SIZE,
overlap: OVERLAP,
}),
signal: AbortSignal.timeout(60_000),
});
if (!r.ok) {
console.error(`[corpus-C] HTTP ${r.status}: ${await r.text()}`);
process.exit(1);
}
const j: any = await r.json();
console.log(`[corpus-C] job ${j.job_id} · ${j.documents} docs → ${j.chunks} chunks queued`);
}
main().catch(e => { console.error(e); process.exit(1); });

63
scripts/mode_compare.ts
View File

@ -227,13 +227,37 @@ function main() {
process.exit(1);
}
// Group by file → mode
// Group by file → mode (with corpus appended when matrix-bearing modes
// were swept across multiple corpora — otherwise lakehouse_arch_v1
// would clobber scrum_findings_v1 etc). matrix_corpus is now a Vec
// on the wire (multi-corpus support); legacy rows have either a
// string or null. Coerce to a stable key.
const matrixCorpus = (r: Row): string => {
const c = (r.sources as any)?.matrix_corpus;
if (!c) return "";
if (typeof c === "string") return c;
if (Array.isArray(c)) {
if (c.length === 0) return "";
if (c.length === 1) return c[0];
// Stable join: sort then "+"-separate so order doesn't matter.
return [...c].sort().join("+");
}
return "";
};
const corporaInPlay = new Set(rows.map(matrixCorpus).filter(c => c));
const showCorpus = corporaInPlay.size > 1;
const keyOf = (r: Row): string => {
const c = matrixCorpus(r);
return showCorpus && c ? `${r.mode}|${c}` : r.mode;
};
const byFile: Record<string, Record<string, Row>> = {};
const allModes = new Set<string>();
for (const r of rows) {
byFile[r.file_path] ??= {};
byFile[r.file_path][r.mode] = r; // last-write-wins per mode per file
allModes.add(r.mode);
const k = keyOf(r);
byFile[r.file_path][k] = r; // last-write-wins per (mode,corpus) per file
allModes.add(k);
}
const modesSorted = [...allModes].sort();
@ -246,13 +270,13 @@ function main() {
const fileLines = fileContent ? fileContent.split("\n").length : 0;
console.log(` (file: ${fileLines} lines${fileContent === null ? ", NOT READABLE — grounding skipped" : ""})`);
console.log(
` ${pad("mode", 30)} ${pad("resp", 6, true)} ${pad("find", 5, true)} ${pad("ground", 9, true)} ${pad("hallu", 6, true)} ${pad("OOB", 4, true)} ${pad("path", 5, true)} ${pad("ms", 7, true)} ${pad("bug_fp", 6, true)}`
` ${pad("mode", 56)} ${pad("resp", 6, true)} ${pad("find", 5, true)} ${pad("ground", 9, true)} ${pad("hallu", 6, true)} ${pad("OOB", 4, true)} ${pad("path", 5, true)} ${pad("ms", 7, true)} ${pad("bug_fp", 6, true)}`
);
console.log(` ${"─".repeat(30)} ${"─".repeat(6)} ${"─".repeat(5)} ${"─".repeat(9)} ${"─".repeat(6)} ${"─".repeat(4)} ${"─".repeat(5)} ${"─".repeat(7)} ${"─".repeat(6)}`);
console.log(` ${"─".repeat(56)} ${"─".repeat(6)} ${"─".repeat(5)} ${"─".repeat(9)} ${"─".repeat(6)} ${"─".repeat(4)} ${"─".repeat(5)} ${"─".repeat(7)} ${"─".repeat(6)}`);
for (const mode of modesSorted) {
const r = byFile[file][mode];
if (!r) {
console.log(` ${pad(modeLabel(mode), 30)} ${pad("—", 6, true)}`);
console.log(` ${pad(modeLabel(mode), 56)} ${pad("—", 6, true)}`);
continue;
}
const findings = countFindings(r.response);
@ -261,7 +285,7 @@ function main() {
const grounding = checkGrounding(r.response, fileContent);
const groundedStr = grounding.total === 0 ? "—" : `${grounding.grounded}/${grounding.total}`;
console.log(
` ${pad(modeLabel(mode), 30)} ${pad(r.response_chars, 6, true)} ${pad(findings, 5, true)} ${pad(groundedStr, 9, true)} ${pad(grounding.hallucinated, 6, true)} ${pad(grounding.out_of_bounds_lines, 4, true)} ${pad(cits, 5, true)} ${pad(r.latency_ms, 7, true)} ${pad(bf, 6, true)}`
` ${pad(modeLabel(mode), 56)} ${pad(r.response_chars, 6, true)} ${pad(findings, 5, true)} ${pad(groundedStr, 9, true)} ${pad(grounding.hallucinated, 6, true)} ${pad(grounding.out_of_bounds_lines, 4, true)} ${pad(cits, 5, true)} ${pad(r.latency_ms, 7, true)} ${pad(bf, 6, true)}`
);
}
console.log("");
@ -271,11 +295,11 @@ function main() {
// avg_groundedness is the rate at which findings cite real symbols
// within file bounds. Modes with low groundedness are confabulating.
console.log("═══ PER-MODE AGGREGATE ═══\n");
console.log(` ${pad("mode", 30)} ${pad("n", 3, true)} ${pad("avg find", 9, true)} ${pad("avg grnd", 9, true)} ${pad("grnd %", 7, true)} ${pad("avg hallu", 10, true)} ${pad("avg ms", 7, true)}`);
console.log(` ${"─".repeat(30)} ${"─".repeat(3)} ${"─".repeat(9)} ${"─".repeat(9)} ${"─".repeat(7)} ${"─".repeat(10)} ${"─".repeat(7)}`);
console.log(` ${pad("mode", 56)} ${pad("n", 3, true)} ${pad("avg find", 9, true)} ${pad("avg grnd", 9, true)} ${pad("grnd %", 7, true)} ${pad("avg hallu", 10, true)} ${pad("avg ms", 7, true)}`);
console.log(` ${"─".repeat(56)} ${"─".repeat(3)} ${"─".repeat(9)} ${"─".repeat(9)} ${"─".repeat(7)} ${"─".repeat(10)} ${"─".repeat(7)}`);
const fileCache: Record<string, string | null> = {};
for (const mode of modesSorted) {
const modeRows = rows.filter(r => r.mode === mode);
const modeRows = rows.filter(r => keyOf(r) === mode);
if (modeRows.length === 0) continue;
const n = modeRows.length;
let totFind = 0, totGround = 0, totHallu = 0;
@ -292,7 +316,7 @@ function main() {
const avgHallu = (totHallu / n).toFixed(1);
const avgMs = Math.round(modeRows.reduce((s, r) => s + r.latency_ms, 0) / n);
console.log(
` ${pad(modeLabel(mode), 30)} ${pad(n, 3, true)} ${pad(avgFind, 9, true)} ${pad(avgGround, 9, true)} ${pad(grndPct, 7, true)} ${pad(avgHallu, 10, true)} ${pad(avgMs, 7, true)}`
` ${pad(modeLabel(mode), 56)} ${pad(n, 3, true)} ${pad(avgFind, 9, true)} ${pad(avgGround, 9, true)} ${pad(grndPct, 7, true)} ${pad(avgHallu, 10, true)} ${pad(avgMs, 7, true)}`
);
}
@ -301,13 +325,20 @@ function main() {
// wins). Comparing grounded findings instead corrects for modes
// that produce convincing-but-fake output.
console.log("\n═══ MODE vs codereview_lakehouse (grounded findings, per file) ═══\n");
console.log(` ${pad("mode", 30)} ${pad("wins", 5, true)} ${pad("losses", 7, true)} ${pad("ties", 5, true)} ${pad("Δ avg grounded", 16, true)}`);
console.log(` ${"─".repeat(30)} ${"─".repeat(5)} ${"─".repeat(7)} ${"─".repeat(5)} ${"─".repeat(16)}`);
console.log(` ${pad("mode", 56)} ${pad("wins", 5, true)} ${pad("losses", 7, true)} ${pad("ties", 5, true)} ${pad("Δ avg grounded", 16, true)}`);
console.log(` ${"─".repeat(56)} ${"─".repeat(5)} ${"─".repeat(7)} ${"─".repeat(5)} ${"─".repeat(16)}`);
// Pick whichever codereview_lakehouse key shows up most often as the
// baseline (handles corpus-suffixed keys when showCorpus=true).
const baselineKey = modesSorted
.filter(k => k.startsWith("codereview_lakehouse"))
.sort((a, b) =>
Object.values(byFile).filter(f => f[b]).length -
Object.values(byFile).filter(f => f[a]).length)[0] ?? "codereview_lakehouse";
for (const mode of modesSorted) {
if (mode === "codereview_lakehouse") continue;
if (mode === baselineKey) continue;
let wins = 0, losses = 0, ties = 0, totalDelta = 0, n = 0;
for (const file of Object.keys(byFile)) {
const baseline = byFile[file]["codereview_lakehouse"];
const baseline = byFile[file][baselineKey];
const challenger = byFile[file][mode];
if (!baseline || !challenger) continue;
const fc = fileCache[file] ??= readFileSafe(file);
@ -322,7 +353,7 @@ function main() {
if (n === 0) continue;
const avgDelta = (totalDelta / n).toFixed(1);
console.log(
` ${pad(modeLabel(mode), 30)} ${pad(wins, 5, true)} ${pad(losses, 7, true)} ${pad(ties, 5, true)} ${pad(avgDelta, 16, true)}`
` ${pad(modeLabel(mode), 56)} ${pad(wins, 5, true)} ${pad(losses, 7, true)} ${pad(ties, 5, true)} ${pad(avgDelta, 16, true)}`
);
}
console.log("\n[compare] done — ⚗ marks lossy/control modes, exclude from recommendations\n");

34
scripts/mode_experiment.ts
View File

@ -32,7 +32,7 @@ const DEFAULT_FILES = [
"crates/queryd/src/service.rs",
];
function parseArgs(): { files: string[]; modes: string[]; model: string } {
function parseArgs(): { files: string[]; modes: string[]; model: string; corpus: string[] } {
const args = Bun.argv.slice(2);
const out: Record<string, string> = {};
for (let i = 0; i < args.length; i++) {
@ -41,8 +41,12 @@ function parseArgs(): { files: string[]; modes: string[]; model: string } {
}
const files = (out.files ?? DEFAULT_FILES.join(",")).split(",").map(s => s.trim()).filter(Boolean);
const modes = (out.modes ?? ALL_MODES.join(",")).split(",").map(s => s.trim()).filter(Boolean);
const model = out.model ?? "openai/gpt-oss-120b:free";
return { files, modes, model };
// Default to the paid OpenRouter primary (matches scrum_master_pipeline
// ladder rung 1). Pass `--model openai/gpt-oss-120b:free` if you want
// the old free-tier baseline. See SCRUM_MASTER_SPEC.md for the ladder.
const model = out.model ?? "x-ai/grok-4.1-fast";
const corpus = (out.corpus ?? "").split(",").map(s => s.trim()).filter(Boolean);
return { files, modes, model, corpus };
}
interface RunResult {
@ -58,19 +62,22 @@ interface RunResult {
error?: string;
}
async function runOne(file: string, mode: string, model: string): Promise<RunResult> {
async function runOne(file: string, mode: string, model: string, corpus: string[]): Promise<RunResult> {
const t0 = Date.now();
try {
const body: any = {
task_class: TASK_CLASS,
file_path: file,
force_mode: mode,
force_model: model,
};
if (corpus.length === 1) body.force_matrix_corpus = corpus[0];
else if (corpus.length > 1) body.force_matrix_corpus = corpus;
const r = await fetch(`${GATEWAY}/v1/mode/execute`, {
method: "POST",
headers: { "content-type": "application/json" },
body: JSON.stringify({
task_class: TASK_CLASS,
file_path: file,
force_mode: mode,
force_model: model,
}),
signal: AbortSignal.timeout(180_000),
body: JSON.stringify(body),
signal: AbortSignal.timeout(240_000),
});
if (!r.ok) {
const body = await r.text().catch(() => "");
@ -92,9 +99,10 @@ async function runOne(file: string, mode: string, model: string): Promise<RunRes
}
async function main() {
const { files, modes, model } = parseArgs();
const { files, modes, model, corpus } = parseArgs();
console.log(`[experiment] files=${files.length} × modes=${modes.length} = ${files.length * modes.length} runs`);
console.log(`[experiment] model=${model} task=${TASK_CLASS} gateway=${GATEWAY}`);
if (corpus.length > 0) console.log(`[experiment] corpus override: ${corpus.join(" + ")}`);
console.log("");
const results: RunResult[] = [];
@ -103,7 +111,7 @@ async function main() {
for (const mode of modes) {
i++;
process.stdout.write(` [${i}/${files.length * modes.length}] ${mode.padEnd(28)} ${file} ... `);
const r = await runOne(file, mode, model);
const r = await runOne(file, mode, model, corpus);
results.push(r);
if (r.ok) {
console.log(

169
scripts/mode_pass5_summarize.ts Normal file
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@ -0,0 +1,169 @@
#!/usr/bin/env bun
/**
* Pass 5 variance summarizer. Reads data/_kb/mode_experiments.jsonl
* since a timestamp, groups by (mode|corpus), reports mean ± stddev
* of grounded finding count, plus a head-to-head wins/losses table
* vs the isolation baseline.
*
* Usage:
* bun run scripts/mode_pass5_summarize.ts # default 2h
* bun run scripts/mode_pass5_summarize.ts --since 2026-04-26T22 # explicit
*/
import { readFileSync, existsSync } from "node:fs";
const argSince = (() => {
const i = Bun.argv.indexOf("--since");
return i >= 0 ? Bun.argv[i + 1] : new Date(Date.now() - 2 * 60 * 60 * 1000).toISOString();
})();
const JSONL = "data/_kb/mode_experiments.jsonl";
if (!existsSync(JSONL)) { console.error(`no ${JSONL}`); process.exit(1); }
interface Row {
ts: string; mode: string; file_path: string; response: string;
sources: { matrix_corpus?: string | string[] | null };
latency_ms: number;
}
function corpusKey(c: any): string {
if (!c) return "";
if (typeof c === "string") return c;
if (Array.isArray(c)) return c.length === 0 ? "" : [...c].sort().join("+");
return "";
}
const condKey = (r: Row) => {
const c = corpusKey(r.sources?.matrix_corpus);
return c ? `${r.mode}|${c}` : r.mode;
};
// Reuse the same grounding logic as mode_compare — symbols cited in
// findings rows must appear in the focus file, and any line numbers
// must fall within EOF.
function extractFindings(md: string): { symbols: string[]; lines: number[] }[] {
const sec = /(?:^|\n)#{1,3}[^\na-zA-Z]*(?:Ranked\s+)?Findings?[^\n]*\n/i;
const m = md.match(sec);
let section = md;
if (m && m.index !== undefined) {
const after = md.slice(m.index + m[0].length);
const stop = after.search(/\n#{1,3}[^\na-zA-Z]*(?:Patch|Suggestion|Reference|Summary|Concrete)/i);
section = stop >= 0 ? after.slice(0, stop) : after;
}
// Three row shapes:
// 1) numbered: `| 1 | ... |`
// 2) path-with-line: `| service.rs:106 | ... |`
// 3) path-with-sym: `| crates/vectord/src/pathway_memory.rs:load_fn (≈L220) | ... |`
// Pick whichever shape matches the most rows (ties favor numbered).
const numbered = section.split("\n").filter(l => /^\|\s*\*?\*?\d+\*?\*?\s*\|/.test(l));
const pathRows = section.split("\n").filter(l => /^\|\s*[a-z_/\.][a-z_/\.0-9]*\.(rs|ts|py)\b/i.test(l));
const rows = numbered.length >= pathRows.length ? numbered : pathRows;
return rows.map(row => {
const sym = new Set<string>();
for (const t of row.matchAll(/`([A-Za-z_][A-Za-z0-9_:]*)`/g)) sym.add(t[1]);
for (const t of row.matchAll(/\b([a-z][a-z0-9_]{4,})\b/g)) sym.add(t[1]);
const lines: number[] = [];
for (const t of row.matchAll(/[:\-](\d{2,5})/g)) lines.push(parseInt(t[1]));
return { symbols: [...sym], lines };
});
}
function grounded(md: string, file: string): { total: number; grounded: number; oob: number } {
const content = readFileSync(file, "utf8");
const eof = content.split("\n").length;
const findings = extractFindings(md);
let g = 0, oob = 0;
for (const f of findings) {
const symHit = f.symbols.length > 0 && f.symbols.some(s => content.includes(s));
const lineOob = f.lines.length > 0 && f.lines.some(l => l > eof);
if (lineOob) oob++;
if (symHit && !lineOob) g++;
}
return { total: findings.length, grounded: g, oob };
}
const lines = readFileSync(JSONL, "utf8").split("\n").filter(Boolean);
const rows: Row[] = [];
for (const l of lines) {
try {
const r: Row = JSON.parse(l);
if (r.ts < argSince) continue;
rows.push(r);
} catch {}
}
if (rows.length === 0) { console.error(`no rows since ${argSince}`); process.exit(1); }
// Group: condition → file → array of grounded counts
type CellArr = { grnd: number[]; total: number[]; oob: number[]; ms: number[] };
const byCond: Record<string, Record<string, CellArr>> = {};
for (const r of rows) {
const k = condKey(r);
byCond[k] ??= {};
byCond[k][r.file_path] ??= { grnd: [], total: [], oob: [], ms: [] };
const g = grounded(r.response, r.file_path);
byCond[k][r.file_path].grnd.push(g.grounded);
byCond[k][r.file_path].total.push(g.total);
byCond[k][r.file_path].oob.push(g.oob);
byCond[k][r.file_path].ms.push(r.latency_ms);
}
function stats(xs: number[]): { n: number; mean: number; sd: number; min: number; max: number } {
const n = xs.length;
if (n === 0) return { n: 0, mean: 0, sd: 0, min: 0, max: 0 };
const mean = xs.reduce((s, x) => s + x, 0) / n;
const variance = n === 1 ? 0 : xs.reduce((s, x) => s + (x - mean) ** 2, 0) / (n - 1);
return { n, mean, sd: Math.sqrt(variance), min: Math.min(...xs), max: Math.max(...xs) };
}
const conditions = Object.keys(byCond).sort();
const files = [...new Set(rows.map(r => r.file_path))].sort();
console.log(`\n═══ Pass 5 variance — since ${argSince} ═══\n`);
console.log(` ${rows.length} rows · ${conditions.length} conditions · ${files.length} files\n`);
for (const file of files) {
console.log(`📄 ${file}`);
console.log(` ${"condition".padEnd(56)} n ${"grounded mean ± sd".padStart(20)} ${"range".padStart(8)} ${"oob".padStart(4)} ${"avg ms".padStart(7)}`);
console.log(` ${"─".repeat(56)} ─── ${"─".repeat(20)} ${"─".repeat(8)} ${"─".repeat(4)} ${"─".repeat(7)}`);
for (const c of conditions) {
const cell = byCond[c]?.[file];
if (!cell || cell.grnd.length === 0) continue;
const s = stats(cell.grnd);
const oobSum = cell.oob.reduce((a, b) => a + b, 0);
const msMean = cell.ms.reduce((a, b) => a + b, 0) / cell.ms.length;
const meanSd = `${s.mean.toFixed(1)} ± ${s.sd.toFixed(1)}`;
const range = `[${s.min}-${s.max}]`;
console.log(` ${c.padEnd(56)} ${String(s.n).padStart(3)} ${meanSd.padStart(20)} ${range.padStart(8)} ${String(oobSum).padStart(4)} ${Math.round(msMean / 1000).toString().padStart(5)}s`);
}
console.log("");
}
// Head-to-head: for each condition vs isolation baseline, count rep-by-rep
// wins across the same file. Requires equal rep counts.
console.log(`═══ Head-to-head: each condition vs isolation, rep-by-rep ═══\n`);
const isoKey = conditions.find(c => c.startsWith("codereview_isolation"));
if (!isoKey) {
console.log(" no isolation rows in window");
} else {
console.log(` baseline: ${isoKey}\n`);
console.log(` ${"challenger".padEnd(56)} wins losses ties Δ mean grnd`);
console.log(` ${"─".repeat(56)} ${"─".repeat(4)} ${"─".repeat(6)} ${"─".repeat(4)} ${"─".repeat(12)}`);
for (const c of conditions) {
if (c === isoKey) continue;
let wins = 0, losses = 0, ties = 0, deltaSum = 0, n = 0;
for (const file of files) {
const isoArr = byCond[isoKey]?.[file]?.grnd ?? [];
const cArr = byCond[c]?.[file]?.grnd ?? [];
const k = Math.min(isoArr.length, cArr.length);
for (let i = 0; i < k; i++) {
if (cArr[i] > isoArr[i]) wins++;
else if (cArr[i] < isoArr[i]) losses++;
else ties++;
deltaSum += cArr[i] - isoArr[i];
n++;
}
}
const dMean = n > 0 ? (deltaSum / n).toFixed(2) : "—";
console.log(` ${c.padEnd(56)} ${String(wins).padStart(4)} ${String(losses).padStart(6)} ${String(ties).padStart(4)} ${dMean.padStart(12)}`);
}
}

96
scripts/mode_pass5_variance_paid.ts Normal file
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@ -0,0 +1,96 @@
#!/usr/bin/env bun
/**
* Pass 5: variance test for the 2026-04-26 paid-model bake-off.
*
* The pass-4 single-rep sweep showed isolation beating every matrix
* condition by 1.0-1.4 grounded findings/file on grok-4.1-fast. This
* harness runs N reps × M conditions on the file where the effect was
* sharpest (pathway_memory.rs, 1355 lines) so we can decide whether
* the deltas are real signal or run-to-run noise.
*
* Conditions:
* 1. codereview_isolation — no matrix
* 2. codereview_lakehouse + corpus=lakehouse_arch_v1 — A only
* 3. codereview_lakehouse + corpus=lakehouse_symbols_v1 — C only
* 4. codereview_lakehouse (modes.toml default) — A+C composed
*
* Output appends per-call to data/_kb/mode_experiments.jsonl. Aggregate
* with `bun run scripts/mode_compare.ts --since <ts>` and read the
* grounded column with multiple rows per (mode|corpus) key.
*
* Usage:
* bun run scripts/mode_pass5_variance_paid.ts
* LH_REPS=3 LH_FILE=crates/queryd/src/delta.rs bun run scripts/mode_pass5_variance_paid.ts
*/
const GATEWAY = process.env.LH_GATEWAY ?? "http://localhost:3100";
const MODEL = process.env.LH_MODEL ?? "x-ai/grok-4.1-fast";
const FILE = process.env.LH_FILE ?? "crates/vectord/src/pathway_memory.rs";
const REPS = Number(process.env.LH_REPS ?? 5);
interface Condition {
label: string;
mode: string;
corpus?: string | string[];
}
const CONDITIONS: Condition[] = [
{ label: "isolation ", mode: "codereview_isolation" },
{ label: "arch_only ", mode: "codereview_lakehouse", corpus: "lakehouse_arch_v1" },
{ label: "symbols_only ", mode: "codereview_lakehouse", corpus: "lakehouse_symbols_v1" },
{ label: "composed (A+C) ", mode: "codereview_lakehouse" /* uses modes.toml default */ },
];
async function runOne(c: Condition, rep: number): Promise<{ ok: boolean; latency_ms?: number; resp_chars?: number; error?: string }> {
const body: any = {
task_class: "scrum_review",
file_path: FILE,
force_mode: c.mode,
force_model: MODEL,
};
if (c.corpus !== undefined) body.force_matrix_corpus = c.corpus;
try {
const r = await fetch(`${GATEWAY}/v1/mode/execute`, {
method: "POST",
headers: { "content-type": "application/json" },
body: JSON.stringify(body),
signal: AbortSignal.timeout(240_000),
});
if (!r.ok) {
const txt = await r.text().catch(() => "");
return { ok: false, error: `HTTP ${r.status}: ${txt.slice(0, 160)}` };
}
const j: any = await r.json();
return { ok: true, latency_ms: j.latency_ms, resp_chars: (j.response ?? "").length };
} catch (e: any) {
return { ok: false, error: e.message };
}
}
async function main() {
const total = CONDITIONS.length * REPS;
console.log(`[pass5] file=${FILE}`);
console.log(`[pass5] model=${MODEL} · ${CONDITIONS.length} conditions × ${REPS} reps = ${total} runs`);
console.log("");
let i = 0;
const startTs = new Date().toISOString();
for (let rep = 1; rep <= REPS; rep++) {
for (const c of CONDITIONS) {
i++;
process.stdout.write(` [${i}/${total}] rep=${rep} ${c.label}... `);
const r = await runOne(c, rep);
if (r.ok) {
console.log(`✓ ${r.resp_chars} chars · ${((r.latency_ms ?? 0) / 1000).toFixed(1)}s`);
} else {
console.log(`✗ ${r.error}`);
}
}
}
console.log(`\n[pass5] complete · started ${startTs}`);
console.log(`[pass5] aggregate: bun run scripts/mode_compare.ts --since ${startTs}`);
}
main().catch(e => { console.error(e); process.exit(1); });