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d9834a7a15 feat(ai): add reranker touchpoint to LiteLLM proxy recipe (takeover of #2455)
LiteLLM normalizes Cohere/Voyage/Jina rerank backends to the wire shape
gateway.rerank() already speaks, so a reranker touchpoint on the litellm
recipe makes any proxied rerank model reachable via
`search.reranker.model litellm:<model>` with no adapter.

Repairs from the original PR:
- path is the LEAF '/rerank' (not '/v1/rerank'): LiteLLM serves both
  /rerank and /v1/rerank, and the recipe's setup_hint allows
  LITELLM_BASE_URL with or without the /v1 suffix — pinning '/v1/rerank'
  doubled to /v1/v1/rerank (404) on /v1-suffixed bases.
- setup_hint appends the rerank guidance to master's current line instead
  of replacing it with a stale pre-/v1-suffix version.
- cost_per_1m_tokens_usd stays undefined (pricing-unknown), matching the
  recipe's embedding/chat touchpoints and budget-tracker's deliberate
  litellm exclusion from the free-provider sets (a proxy can front a paid
  provider; the touchpoint field isn't consumed by rerank pricing anyway).

Test drives gateway.rerank()'s real URL builder via the stubbed transport
for both base-URL forms; the /v1-suffixed case fails with the original
PR's path.

Co-authored-by: ozp <ozp@users.noreply.github.com>
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
2026-07-21 14:28:27 -07:00
7 changed files with 139 additions and 49 deletions
@@ -233,14 +233,13 @@ keep it or `git checkout` to throw it away. Nothing is committed for you.
**For a skill that ships with gbrain** (anything under the gbrain repo's own
`skills/`): SkillOpt refuses to overwrite it by default and writes the winner to
`skills/<name>/skillopt/proposed.md` instead (while keeping `best.md` as the
optimizer's current-best pointer), so an optimization pass can never silently
mutate a skill other people depend on. Two ways to handle that:
`skills/<name>/skillopt/best.md` instead, so an optimization pass can never
silently mutate a skill other people depend on. Two ways to handle that:
```bash
# See the proposed improvement without touching SKILL.md (works for ANY skill):
gbrain skillopt meeting-prep --split 1:1:1 --no-mutate
# → writes skills/meeting-prep/skillopt/proposed.md, updates best.md, and prints the proposal path.
# → writes skills/meeting-prep/skillopt/best.md (the proposed rewrite), prints its path. Copy what you want.
# Actually rewrite a bundled skill (explicit opt-in + an independent held-out set):
gbrain skillopt brain-ops --split 1:1:1 --allow-mutate-bundled \
+31 -1
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@@ -56,6 +56,36 @@ export const litellmProxy: Recipe = {
cost_per_1m_output_usd: undefined,
price_last_verified: '2026-06-14',
},
// LiteLLM normalizes Cohere / Voyage / Jina / etc. rerank backends to the
// same wire shape gbrain's gateway.rerank() already speaks (the
// ZeroEntropy/llama.cpp contract):
// { model, query, documents, top_n } → { results: [{ index, relevance_score }] }
// So any rerank model the user registers in their LiteLLM config is
// reachable via `gbrain config set search.reranker.model litellm:<model>`
// with no request/response adapter — same as embeddings ride the proxy.
reranker: {
models: [], // user-provided; whatever rerank models the proxy serves
// No canonical default — the proxy defines its own model ids. The user
// sets search.reranker.model explicitly (mirrors the embedding
// touchpoint's user_provided_models contract).
default_model: '',
// The proxied backend bills (Cohere/Voyage/…); pricing-unknown is the
// honest state — same stance as this recipe's embedding/chat
// touchpoints and budget-tracker's deliberate litellm exclusion from
// the free-provider sets.
cost_per_1m_tokens_usd: undefined,
price_last_verified: '2026-06-27',
max_payload_bytes: 5_000_000,
// LEAF path only (matches llama-server-reranker's convention). LiteLLM
// serves both `/rerank` and `/v1/rerank`, and LITELLM_BASE_URL may be
// set with or without the `/v1` suffix (the setup_hint allows both), so
// the leaf form yields a valid route either way:
// http://localhost:4000 + /rerank → /rerank ✓
// http://localhost:4000/v1 + /rerank → /v1/rerank ✓
// Pinning '/v1/rerank' here would double to /v1/v1/rerank → 404 on
// /v1-suffixed bases.
path: '/rerank',
},
},
setup_hint: 'Run LiteLLM (https://docs.litellm.ai) in front of any provider; set LITELLM_BASE_URL (include the /v1 suffix if your proxy serves the OpenAI route there, e.g. http://localhost:4000/v1) + pass --embedding-model litellm:<model> and --embedding-dimensions <N>.',
setup_hint: 'Run LiteLLM (https://docs.litellm.ai) in front of any provider; set LITELLM_BASE_URL (include the /v1 suffix if your proxy serves the OpenAI route there, e.g. http://localhost:4000/v1) + pass --embedding-model litellm:<model> and --embedding-dimensions <N>. For rerank: register a rerank model in LiteLLM and set search.reranker.model litellm:<model-name>.',
};
+4 -10
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@@ -93,13 +93,7 @@ import { resolveLrSchedule } from './lr-schedule.ts';
import { preflight, formatPreflightReport } from './preflight.ts';
import { isRejected, loadRejectedBuffer, makeRejectedEntry, saveRejectedBuffer } from './rejected-buffer.ts';
import { runReflect, runOneShotRewrite, describeJudges } from './reflect.ts';
import {
acceptCandidate,
proposedPath as proposedFilePath,
revertAllPending,
skillPath,
writeProposed,
} from './version-store.ts';
import { acceptCandidate, bestPath, revertAllPending, skillPath, writeProposed } from './version-store.ts';
import { runValidationGate, scoreSkillOnTasks } from './validate-gate.ts';
import { ROLLOUT_SUCCESS_THRESHOLD } from './types.ts';
import type { SkillOptOpts, EditOp, RunReceipt, BenchmarkTask } from './types.ts';
@@ -708,9 +702,9 @@ async function runOptimizationLoop(
// to the catch's assignment values only (it can't prove the async callback ran).
const finalOutcome = outcome as 'accepted' | 'no_improvement' | 'aborted' | 'errored';
if (!mutateDecision.mutate && finalOutcome === 'accepted') {
// writeProposed() emitted both the best pointer and the stable review
// artifact in the accept branch. SKILL.md remains untouched.
proposedPath = proposedFilePath(skillsDir, skillName);
// best.md was written by writeProposed() in the accept branch (no-mutate
// path); it doubles as proposed.md for human review. SKILL.md untouched.
proposedPath = bestPath(skillsDir, skillName);
} else if (mutateDecision.mutate) {
mutatedSkillFile = finalOutcome === 'accepted';
}
+9 -15
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@@ -23,7 +23,6 @@
*
* history.json
* best.md
* proposed.md
* versions/
* v0001_e1_s1.md
* v0002_e1_s2.md
@@ -53,10 +52,6 @@ export function bestPath(skillsDir: string, skillName: string): string {
return path.join(skilloptDir(skillsDir, skillName), 'best.md');
}
export function proposedPath(skillsDir: string, skillName: string): string {
return path.join(skilloptDir(skillsDir, skillName), 'proposed.md');
}
export function skillPath(skillsDir: string, skillName: string): string {
return path.join(skillsDir, skillName, 'SKILL.md');
}
@@ -176,18 +171,17 @@ export function acceptCandidate(input: AcceptInput): AcceptResult {
}
/**
* Write the candidate to both `best.md` and `proposed.md` WITHOUT touching
* SKILL.md or the history ledger. `best.md` remains the optimizer's current
* best pointer; `proposed.md` is the stable human-review artifact promised by
* `--no-mutate`. Returns the proposal path. Each write is atomic (.tmp + rename).
* Write the candidate to `best.md` (which doubles as `proposed.md`) WITHOUT
* touching SKILL.md or the history ledger. Used by the `--no-mutate` /
* bundled-without-allow paths: the optimizer found a better candidate but the
* caller opted out of in-place mutation, so we surface it for human review.
* Returns the path written. Atomic (.tmp + rename).
*/
export function writeProposed(skillsDir: string, skillName: string, candidateText: string): string {
const best = bestPath(skillsDir, skillName);
const proposed = proposedPath(skillsDir, skillName);
fs.mkdirSync(path.dirname(best), { recursive: true });
atomicWrite(best, candidateText);
atomicWrite(proposed, candidateText);
return proposed;
const p = bestPath(skillsDir, skillName);
fs.mkdirSync(path.dirname(p), { recursive: true });
atomicWrite(p, candidateText);
return p;
}
/**
+88
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@@ -0,0 +1,88 @@
/**
* litellm-proxy reranker touchpoint smoke.
*
* Sibling of recipe-llama-server-reranker.test.ts. Pins the reranker
* touchpoint on the LiteLLM proxy recipe so:
* - the touchpoint exists with the LEAF '/rerank' path (LiteLLM serves both
* /rerank and /v1/rerank, so the leaf form is valid whether or not the
* user's LITELLM_BASE_URL carries the /v1 suffix the setup_hint allows)
* - a /v1-suffixed base URL does NOT produce /v1/v1/rerank (the original
* community PR pinned '/v1/rerank' which 404s on /v1-suffixed bases)
* - models: [] (user-provided; proxy defines the model ids)
* - pricing stays undefined (proxy can front a paid provider — same honest
* pricing-unknown stance as the embedding/chat touchpoints)
*
* The gateway.rerank() URL tests drive the real URL builder via the stubbed
* transport (same seam as test/ai/rerank.test.ts).
*/
import { describe, expect, test, afterEach } from 'bun:test';
import { getRecipe } from '../../src/core/ai/recipes/index.ts';
import {
configureGateway,
resetGateway,
rerank,
__setRerankTransportForTests,
} from '../../src/core/ai/gateway.ts';
afterEach(() => {
__setRerankTransportForTests(null);
resetGateway();
});
describe('recipe: litellm reranker touchpoint', () => {
test('declares reranker touchpoint with leaf /rerank path', () => {
const r = getRecipe('litellm')!;
const tp = r.touchpoints.reranker;
expect(tp).toBeDefined();
expect(tp!.path).toBe('/rerank');
expect(tp!.max_payload_bytes).toBe(5_000_000);
});
test('reranker touchpoint uses empty models[] for user-provided model ids', () => {
const r = getRecipe('litellm')!;
expect(r.touchpoints.reranker!.models).toEqual([]);
});
test('pricing stays undefined — proxy can front a paid provider', () => {
const r = getRecipe('litellm')!;
expect(r.touchpoints.reranker!.cost_per_1m_tokens_usd).toBeUndefined();
});
test('setup_hint keeps the /v1-suffix guidance AND mentions rerank', () => {
const r = getRecipe('litellm')!;
expect(r.setup_hint).toMatch(/\/v1 suffix/);
expect(r.setup_hint).toMatch(/search\.reranker\.model litellm:/);
});
});
describe('gateway.rerank() URL via litellm recipe', () => {
async function capturedRerankUrl(baseUrl?: string): Promise<string> {
configureGateway({
reranker_model: 'litellm:my-reranker',
env: {},
...(baseUrl ? { base_urls: { litellm: baseUrl } } : {}),
});
let capturedUrl = '';
__setRerankTransportForTests(async (url) => {
capturedUrl = url;
return new Response(
JSON.stringify({ results: [{ index: 0, relevance_score: 0.9 }] }),
{ status: 200, headers: { 'content-type': 'application/json' } },
);
});
await rerank({ query: 'q', documents: ['d'] });
return capturedUrl;
}
test('default base (no /v1 suffix) → /rerank', async () => {
const url = await capturedRerankUrl();
expect(url).toBe('http://localhost:4000/rerank');
});
test('/v1-suffixed base → /v1/rerank, NOT /v1/v1/rerank', async () => {
const url = await capturedRerankUrl('http://localhost:4000/v1');
expect(url).toBe('http://localhost:4000/v1/rerank');
expect(url).not.toContain('/v1/v1/');
});
});
+4 -4
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@@ -39,7 +39,6 @@ import { runSkillOpt } from '../../src/core/skillopt/orchestrator.ts';
import {
bestPath,
loadHistory,
proposedPath,
skillPath,
} from '../../src/core/skillopt/version-store.ts';
import { loadRejectedBuffer } from '../../src/core/skillopt/rejected-buffer.ts';
@@ -742,7 +741,7 @@ describe('skillopt T3 — F11 held-out gate, ablation opts, no-DB-pollution', ()
} finally { fixture.cleanup(); }
});
test('--no-mutate writes proposed.md and best.md, leaves SKILL.md untouched', async () => {
test('--no-mutate writes proposed.md (best.md), leaves SKILL.md untouched', async () => {
const fixture = setupFixture(SKILL_PEOPLE_ONLY, CITATIONS_BENCHMARK);
try {
installStub({
@@ -754,9 +753,10 @@ describe('skillopt T3 — F11 held-out gate, ablation opts, no-DB-pollution', ()
const result = await runOnce(fixture, { noMutate: true });
expect(result.outcome).toBe('accepted');
expect(result.mutatedSkillFile).toBe(false);
expect(result.proposedPath).toBe(proposedPath(fixture.skillsDir, SKILL));
expect(result.proposedPath).toBeDefined();
// proposed.md (best.md) exists and carries the improvement.
expect(fs.existsSync(result.proposedPath!)).toBe(true);
expect(fs.readFileSync(result.proposedPath!, 'utf8')).toContain('## Citations');
expect(fs.readFileSync(bestPath(fixture.skillsDir, SKILL), 'utf8')).toContain('## Citations');
// SKILL.md on disk is UNCHANGED (still People-only).
const skill = fs.readFileSync(skillPath(fixture.skillsDir, SKILL), 'utf8');
expect(skill).not.toContain('## Citations');
-15
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@@ -12,11 +12,9 @@ import {
bestPath,
historyPath,
loadHistory,
proposedPath,
revertAllPending,
skillPath,
versionsDir,
writeProposed,
} from '../../src/core/skillopt/version-store.ts';
let tmpDir: string;
@@ -81,19 +79,6 @@ describe('acceptCandidate (D8 two-phase commit)', () => {
});
});
describe('writeProposed', () => {
test('writes distinct best and proposed artifacts without mutating SKILL.md (#2635)', () => {
const candidate = '---\nname: test\n---\nproposed body\n';
const written = writeProposed(tmpDir, SKILL, candidate);
expect(written).toBe(proposedPath(tmpDir, SKILL));
expect(fs.readFileSync(bestPath(tmpDir, SKILL), 'utf8')).toBe(candidate);
expect(fs.readFileSync(proposedPath(tmpDir, SKILL), 'utf8')).toBe(candidate);
expect(fs.readFileSync(skillPath(tmpDir, SKILL), 'utf8')).toContain('baseline body');
});
});
describe('revertAllPending (D8 crash recovery)', () => {
test('no-op when no pending rows', () => {
const reverted = revertAllPending(tmpDir, SKILL);