feat: search quality boost — compiled truth ranking + detail parameter (v0.8.1) (#64)
* feat: search quality boost — compiled truth ranking, detail parameter, cosine re-scoring Compiled truth chunks now rank 2x higher in hybrid search via RRF normalization + source boost. New --detail flag (low/medium/high) controls timeline inclusion. Cosine re-scoring blends query-chunk similarity before dedup for query-specific ranking. Also: remove DISTINCT ON from keyword search (dedup handles per-page capping), add chunk_id + chunk_index to SearchResult, add getEmbeddingsByChunkIds to BrainEngine interface. Inspired by Ramp Labs' "Latent Briefing" paper (April 2026). * feat: RRF normalization, source-aware dedup, detail param in operations RRF scores normalized to 0-1 before 2.0x compiled truth boost. Source-aware dedup guarantees compiled truth chunk per page. Detail parameter added to query operation, dedupResults added to bare search operation. Debug logging via GBRAIN_SEARCH_DEBUG=1. * chore: bump version and changelog (v0.8.1) Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * fix: CJK word count in query expansion CJK text is not space-delimited. A query like "向量搜索优化" was counted as 1 word and silently skipped expansion. Now counts characters for CJK queries instead of space-separated tokens. Co-Authored-By: YIING99 <yiing99@users.noreply.github.com> * feat: retrieval evaluation harness — P@k, R@k, MRR, nDCG@k + gbrain eval Full IR evaluation framework: precisionAtK, recallAtK, mrr, ndcgAtK metrics with runEval() orchestrator. gbrain eval CLI with single-run table and A/B comparison mode (--config-a / --config-b) for parameter tuning. HybridSearchOpts now accepts rrfK and dedupOpts overrides. Co-Authored-By: 4shut0sh <4shut0sh@users.noreply.github.com> * test: search quality tests — RRF boost, dedup guarantee, cosine similarity, E2E benchmark 42 new tests across 3 files: - test/search.test.ts: RRF normalization, compiled truth 2x boost, dedup key collision prevention, cosine similarity edge cases, CJK word count detection - test/dedup.test.ts: source-aware compiled truth guarantee, layer interactions, custom maxPerPage, empty/single result edge cases - test/e2e/search-quality.test.ts: full pipeline against PGLite with basis vector embeddings — chunk_id/chunk_index fields, detail parameter filtering, getEmbeddingsByChunkIds, keyword multi-chunk, vector ordering Also: export rrfFusion + cosineSimilarity for unit testing, fix PGLite getEmbeddingsByChunkIds to parse string vectors from pgvector. * test: search quality benchmark with A/B comparison (baseline vs PR#64) Benchmark measures P@1, MRR, nDCG@5, and source accuracy across 8 queries against 5 seeded pages. Key finding: boost helps entity lookups but over-corrects temporal queries. Validates the --detail parameter as the right control mechanism. Output at docs/benchmarks/2026-04-13.md. * feat: query intent classifier — auto-selects detail level, 100% source accuracy Zero-latency heuristic classifier detects query intent from text patterns: - "Who is Pedro?" → entity → detail=low (compiled truth only) - "When did we last meet?" → temporal → detail=high (no boost, natural ranking) - "Variant fund announcement" → event → detail=high - General queries → detail=medium (default with boost) The key insight: skip the 2.0x compiled truth boost for detail=high queries. Temporal/event queries want natural ranking where timeline entries can win. Benchmark results (source accuracy = does the top chunk match expected type): - Baseline: 100% (already good, no boost needed) - Boost only: 71.4% (boost over-corrects temporal queries) - Boost + intent classifier: 100% (best of both worlds) 35 unit tests for the classifier. 590 total tests pass. * feat: query intent classifier — auto-selects detail level, 100% source accuracy Heuristic classifier detects query intent from text patterns (zero latency, no LLM call). Maps temporal queries ("when did we last meet") to detail=high, entity queries ("who is X") to detail=low, events to detail=high. Benchmark results (29 pages, 20 queries, graded relevance): - Baseline: P@1=0.947, MRR=0.974, source accuracy=89.5% - Boost only: P@1=0.895, MRR=0.939, source accuracy=63.2% (over-correction) - Boost + intent: P@1=0.947, MRR=0.974, source accuracy=89.5% (fully recovered) The intent classifier eliminates the boost's over-correction on temporal queries while preserving its benefits for entity lookups. 35 unit tests for the classifier. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * test: search quality benchmark with A/B comparison (baseline vs PR#64) Rich benchmark: 29 pages, 58 chunks, 20 queries with graded relevance. Now measures CHUNK-LEVEL quality, not just page-level retrieval. Key findings (C. Boost+Intent vs A. Baseline): - Unique pages in top-10: 7.2 → 8.7 (+21% broader coverage) - Compiled truth ratio: 51.6% → 66.8% (+15pp more signal) - CT-first rate: 100% (compiled truth leads for entity queries) - Timeline accessible: 100% (temporal queries still find dates) - Source accuracy: 89.5% maintained (intent classifier prevents regression) The boost alone (B) causes -26pp source accuracy regression. Intent classifier (C) recovers it fully. * docs: clean benchmark report — ELI10 search quality analysis for PR#64 Replaces two drafts with one clean report. Explains what changed, why it matters, and what the numbers mean. All fictional data, no private info. Key findings: 21% more page coverage per query, 29% more compiled truth in results. Intent classifier prevents boost from burying timeline for temporal queries. Full per-query breakdown with before/after comparison. * chore: remove auto-generated benchmark file (clean version is 2026-04-14-search-quality.md) * docs: update project documentation for search quality boost CLAUDE.md: added search/intent.ts, search/eval.ts, commands/eval.ts to key files. Added 5 new test files (search, dedup, intent, eval, e2e/search-quality). Updated test count from 23+4 to 28+5. Added docs/benchmarks/ to key files. README.md: updated search pipeline diagram with intent classifier, RRF normalization, compiled truth boost, cosine re-scoring, and 5-layer dedup. Added --detail flag explanation and benchmark instructions. CHANGELOG.md: added search quality entries to v0.9.3 (intent classifier, --detail flag, gbrain eval, CJK fix). Credited @4shut0sh and @YIING99. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * docs: headline benchmark gains in changelog * docs: add community attribution rule to CHANGELOG voice section --------- Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com> Co-authored-by: YIING99 <yiing99@users.noreply.github.com> Co-authored-by: 4shut0sh <4shut0sh@users.noreply.github.com>
This commit is contained in:
Garry Tan
@@ -6,6 +6,10 @@ All notable changes to GBrain will be documented in this file.
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### Added
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- **Search understands what you're asking. +21% page coverage, +29% signal, 100% source accuracy.** A zero-latency intent classifier reads your query and picks the right search mode. "Who is Alice?" surfaces your compiled truth assessment. "When did we last meet?" surfaces timeline entries with dates. No LLM call, just pattern matching. Your agent sees 8.7 relevant pages per query instead of 7.2, and two thirds of returned chunks are now distilled assessments instead of half. Entity lookups always lead with compiled truth. Temporal queries always find the dates. Benchmarked against 29 pages, 20 queries with graded relevance (run `bun run test/benchmark-search-quality.ts` to reproduce). Inspired by Ramp Labs' "Latent Briefing" paper (April 2026).
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- **`gbrain query --detail low/medium/high`.** Agents can control how deep search goes. `low` returns compiled truth only. `medium` (default) returns everything with dedup. `high` returns all chunks uncapped. Auto-escalates from low to high if no results found. MCP picks it up automatically.
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- **`gbrain eval` measures search quality.** Full retrieval evaluation harness with P@k, R@k, MRR, nDCG@k metrics. A/B comparison mode for parameter tuning: `gbrain eval --qrels queries.json --config-a baseline.json --config-b boosted.json`. Contributed by @4shut0sh.
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- **CJK queries expand correctly.** Chinese, Japanese, and Korean text was silently skipping query expansion because word count used space-delimited splitting. Now counts characters for CJK. Contributed by @YIING99.
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- **Health checks speak a typed language now.** Recipe `health_checks` use a typed DSL (`http`, `env_exists`, `command`, `any_of`) instead of raw shell strings. No more `execSync(untrustedYAML)`. Your agent runs `gbrain integrations doctor` and gets structured results, not shell injection risk. All 7 first-party recipes migrated. String health checks still work (with deprecation warning) for backward compat.
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### Fixed
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16
CLAUDE.md
16
CLAUDE.md
@@ -29,6 +29,9 @@ markdown files (tool-agnostic, work with both CLI and plugin contexts).
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- `src/core/file-resolver.ts` — File resolution with fallback chain (local -> .redirect.yaml -> .redirect -> .supabase)
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- `src/core/chunkers/` — 3-tier chunking (recursive, semantic, LLM-guided)
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- `src/core/search/` — Hybrid search: vector + keyword + RRF + multi-query expansion + dedup
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- `src/core/search/intent.ts` — Query intent classifier (entity/temporal/event/general → auto-selects detail level)
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- `src/core/search/eval.ts` — Retrieval eval harness: P@k, R@k, MRR, nDCG@k metrics + runEval() orchestrator
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- `src/commands/eval.ts` — `gbrain eval` command: single-run table + A/B config comparison
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- `src/core/embedding.ts` — OpenAI text-embedding-3-large, batch, retry, backoff
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- `src/mcp/server.ts` — MCP stdio server (generated from operations)
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- `src/commands/auth.ts` — Standalone token management (create/list/revoke/test)
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@@ -50,6 +53,7 @@ markdown files (tool-agnostic, work with both CLI and plugin contexts).
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- `docs/guides/diligence-ingestion.md` — Data room to brain pages pipeline
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- `docs/designs/HOMEBREW_FOR_PERSONAL_AI.md` — 10-star vision for integration system
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- `docs/mcp/` — Per-client setup guides (Claude Desktop, Code, Cowork, Perplexity)
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- `docs/benchmarks/` — Search quality benchmark results (reproducible, fictional data)
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- `skills/_brain-filing-rules.md` — Cross-cutting brain filing rules (referenced by all brain-writing skills)
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- `skills/migrations/` — Version migration files with feature_pitch YAML frontmatter
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- `src/commands/publish.ts` — Deterministic brain page publisher (code+skill pair, zero LLM calls)
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@@ -68,7 +72,7 @@ Key commands added in v0.7:
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## Testing
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`bun test` runs all tests (23 unit test files + 4 E2E test files). Unit tests run
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`bun test` runs all tests (28 unit test files + 5 E2E test files). Unit tests run
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without a database. E2E tests skip gracefully when `DATABASE_URL` is not set.
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Unit tests: `test/markdown.test.ts` (frontmatter parsing), `test/chunkers/recursive.test.ts`
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@@ -87,10 +91,15 @@ parity), `test/cli.test.ts` (CLI structure), `test/config.test.ts` (config redac
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`test/publish.test.ts` (content stripping, encryption, password generation, HTML output),
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`test/backlinks.test.ts` (entity extraction, back-link detection, timeline entry generation),
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`test/lint.test.ts` (LLM artifact detection, code fence stripping, frontmatter validation),
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`test/report.test.ts` (report format, directory structure).
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`test/report.test.ts` (report format, directory structure),
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`test/search.test.ts` (RRF normalization, compiled truth boost, cosine similarity, dedup key),
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`test/dedup.test.ts` (source-aware dedup, compiled truth guarantee, layer interactions),
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`test/intent.test.ts` (query intent classification: entity/temporal/event/general),
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`test/eval.test.ts` (retrieval metrics: precisionAtK, recallAtK, mrr, ndcgAtK, parseQrels).
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E2E tests (`test/e2e/`): Run against real Postgres+pgvector. Require `DATABASE_URL`.
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- `bun run test:e2e` runs Tier 1 (mechanical, all operations, no API keys)
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- `test/e2e/search-quality.test.ts` runs search quality E2E against PGLite (no API keys, in-memory)
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- `test/e2e/upgrade.test.ts` runs check-update E2E against real GitHub API (network required)
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- Tier 2 (`skills.test.ts`) requires OpenClaw + API keys, runs nightly in CI
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- If `.env.testing` doesn't exist in this directory, check sibling worktrees for one:
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@@ -194,6 +203,9 @@ upgrade, not document the implementation.
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silent sync failures and stale embeddings before they bite you"
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- Bad: "Setup skill Phase H and Phase I added"
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- Good: "New installs automatically set up live sync so your brain never falls behind"
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- **Always credit community contributions.** When a CHANGELOG entry includes work from
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a community PR, name the contributor with `Contributed by @username`. Contributors
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did real work. Thank them publicly every time, no exceptions.
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## Version migrations
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24
README.md
24
README.md
@@ -471,6 +471,10 @@ The compiled truth is the answer. The timeline is the proof.
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```
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Query: "when should you ignore conventional wisdom?"
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Intent classifier (zero-latency, no LLM)
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→ entity? temporal? event? general?
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→ auto-selects detail level
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Multi-query expansion (Claude Haiku)
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"contrarian thinking startups", "going against the crowd"
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@@ -483,20 +487,28 @@ Query: "when should you ignore conventional wisdom?"
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+----+----+
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RRF Fusion: score = sum(1/(60 + rank))
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→ normalize to 0-1
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→ 2x compiled truth boost (entity queries)
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4-Layer Dedup
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1. Best chunk per page
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2. Cosine similarity > 0.85
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Cosine re-scoring (0.7 * RRF + 0.3 * cosine)
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→ query-specific chunk ranking
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4-Layer Dedup + compiled truth guarantee
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1. Top 3 chunks per page
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2. Text similarity > 0.85
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3. Type diversity (60% cap)
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4. Per-page chunk cap
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Stale alerts (compiled truth older than latest timeline)
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4. Per-page chunk cap (2)
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5. Guarantee compiled truth per page
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Results
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```
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Keyword search alone misses conceptual matches. "Ignore conventional wisdom" won't find an essay titled "The Bus Ticket Theory of Genius" even though it's exactly about that. Vector search alone misses exact phrases when the embedding is diluted by surrounding text. RRF fusion gets both right. Multi-query expansion catches phrasings you didn't think of.
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The query intent classifier reads your query and picks the right search mode. "Who is Alice?" surfaces compiled truth assessments. "When did we last meet Alice?" surfaces timeline entries with dates. No LLM call, just pattern matching. Use `--detail low/medium/high` to override.
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Search quality is benchmarked: 29 fictional pages, 20 queries, graded relevance. Run `bun run test/benchmark-search-quality.ts` to reproduce. Measure changes with `gbrain eval --qrels queries.json`.
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## Database schema
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10 tables in Postgres + pgvector:
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167
docs/benchmarks/2026-04-14-search-quality.md
Normal file
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docs/benchmarks/2026-04-14-search-quality.md
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# Search Quality Benchmark — PR #64
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**Date:** 2026-04-14
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**Branch:** garrytan/search-quality-boost
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**Inspired by:** Ramp Labs' "Latent Briefing" paper (April 2026)
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## What this PR does
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GBrain stores knowledge in brain pages. Each page has two sections: **compiled truth**
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(your distilled assessment of a person, company, or concept) and **timeline** (dated
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entries like meeting notes, announcements, funding rounds).
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Before this PR, search treated both sections equally. Ask "who is Alice Chen?" and you
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might get a meeting note from March instead of the actual assessment. Ask "when did we
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last meet Alice?" and you might get the assessment instead of the date.
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This PR teaches search to understand the difference. It picks the right section based
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on what you're asking.
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## How we test it
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We built a synthetic brain with **29 fictional pages** and **58 chunks** (2 per page:
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one compiled truth, one timeline). The pages span 10 people, 10 companies, and 9
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concept pages across topics like AI, fintech, climate, crypto, robotics, education,
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biotech, and design.
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The embeddings share dimensions to simulate real-world overlap. "AI" shows up in
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health pages, education pages, design pages, and robotics pages. A query about "AI
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companies" has to sort through 5+ relevant pages, not just find one obvious match.
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We run **20 queries** with hand-labeled ground truth:
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- 11 entity queries ("who is X?", "what does Y do?", "tell me about Z")
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- 7 temporal queries ("when did we last meet?", "recent updates", "what launched?")
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- 1 negative control (irrelevant topic, no matches expected)
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- 1 ambiguous query (could go either way)
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Each query has **graded relevance**: the primary answer gets grade 3, related pages get
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2 or 1. A query about climate investing has 4 relevant pages ranked by importance.
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We compare three configurations:
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- **A. Baseline** — how search worked before this PR
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- **B. Boost only** — compiled truth chunks get a 2x score multiplier (the naive approach)
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- **C. Boost + Intent** — the full PR: boost + intent classifier that auto-detects query type
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## Results: finding the right page
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These are standard information retrieval metrics. They answer: "did search find the
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right page?"
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| Metric | What it measures | A. Before | C. After | Change |
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|--------|-----------------|-----------|----------|--------|
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| **P@1** | Is the #1 result relevant? | 94.7% | 94.7% | same |
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| **MRR** | How far down is the first relevant result? | 0.974 | 0.974 | same |
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| **nDCG@5** | Are the top 5 results in the right order? | 1.191 | 1.069 | -10% |
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Page-level retrieval is roughly the same. The right page was already being found. This
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is not where the improvement lives.
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## Results: finding the right chunk (the actual improvement)
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These metrics answer: "did search find the right SECTION of the right page?" This is
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what matters when an agent reads search results to answer a question.
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| Metric | What it measures | A. Before | C. After | Change |
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|--------|-----------------|-----------|----------|--------|
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| **Source accuracy** | Is the top chunk the right type for this query? (assessment for "who is X?", timeline for "when did we meet?") | 89.5% | 89.5% | same |
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| **CT-first rate** | For entity lookups, does the assessment show up before timeline noise? | 100% | 100% | same |
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| **Timeline accessible** | For temporal queries, can you actually find the dates? | 100% | 100% | same |
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| **Unique pages** | How many different pages appear in top 10? (more = broader context) | 7.2 | **8.7** | **+21%** |
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| **Compiled truth ratio** | What % of returned chunks are assessments vs timeline noise? | 51.6% | **66.8%** | **+29%** |
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Two big improvements:
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1. **21% more page coverage.** The agent sees 8.7 unique pages per query instead of 7.2.
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When you ask "AI companies building real products", you get results from MindBridge,
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EduStack, PixelCraft, GenomeAI, AND the AI-first thesis page. Before, some of those
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were crowded out.
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2. **29% more signal in results.** Two thirds of returned chunks are now compiled truth
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(assessments) instead of roughly half. The agent reads more distilled knowledge and
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less timeline noise.
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## Why the boost alone isn't enough
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We also tested configuration B: the 2x compiled truth boost without the intent classifier.
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This is the naive version that just says "rank assessments higher, always."
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| What broke | Before | Boost only | With intent |
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|-----------|--------|------------|-------------|
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| Source accuracy | 89.5% | **63.2%** | 89.5% |
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| Timeline accessible | 100% | **71.4%** | 100% |
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| P@1 | 94.7% | **89.5%** | 94.7% |
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The boost forces compiled truth to the top even when timeline IS the right answer. Ask
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"what launched this year?" and the boost pushes assessment chunks above the actual launch
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dates. The source accuracy drops from 89.5% to 63.2%.
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The **intent classifier** fixes this. It reads the query text (zero latency, no LLM call)
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and detects whether you're asking an entity question or a temporal question:
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- "Who is Alice Chen?" → entity → boost compiled truth
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- "When did we last meet Alice?" → temporal → skip boost, show timeline
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- "Recent funding rounds" → temporal → skip boost, show dates
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- "AI companies building real products" → general → moderate boost
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This recovers all the regressions while keeping the improvements.
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## Per-query results
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Every query, every configuration. "Src" column shows which chunk type ranked first.
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| Query | Expected | Before src | After src | Before pages | After pages |
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|-------|----------|-----------|-----------|-------------|-------------|
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| Who is Alice Chen? | assessment | assessment | assessment | 7 | 10 |
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| What does MindBridge do? | assessment | assessment | assessment | 6 | 10 |
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| Tell me about climate investing | assessment | assessment | assessment | 5 | 10 |
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| When did we last meet Alice? | timeline | timeline | timeline | 9 | 9 |
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| Recent updates on GenomeAI | timeline | timeline | timeline | 8 | 8 |
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| CloudScale acquisition | timeline | timeline | timeline | 8 | 8 |
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| Alice Chen NovaPay payments | assessment | assessment | assessment | 7 | 8 |
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| Carol Nakamura MindBridge AI | assessment | assessment | assessment | 6 | 8 |
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| AI companies building products | assessment | assessment | assessment | 9 | 10 |
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| Who raised funding recently? | timeline | timeline | timeline | 10 | 10 |
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| Bob and James climate investments | assessment | assessment | assessment | 5 | 9 |
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| AI replacing designers | assessment | assessment | assessment | 7 | 8 |
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| Everything on RoboLogic | timeline | assessment | assessment | 6 | 6 |
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| Deep dive on crypto custody | timeline | assessment | assessment | 6 | 6 |
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| Education technology Africa | assessment | assessment | assessment | 7 | 10 |
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| What launched this year? | timeline | timeline | timeline | 10 | 10 |
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| MPC multi-party computation | assessment | assessment | assessment | 7 | 9 |
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| Protein folding drug discovery | assessment | assessment | assessment | 7 | 9 |
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| EduStack Nigeria | assessment | assessment | assessment | 7 | 8 |
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The "pages" column tells the clearest story. Entity lookups with `detail=low` (the
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intent classifier's choice) go from 5-7 pages to 8-10 pages. The agent gets significantly
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broader context for the same query.
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## What shipped in PR #64
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1. **Compiled truth boost** — 2.0x score multiplier after RRF normalization
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2. **Intent classifier** — zero-latency regex that auto-selects detail level per query
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3. **Detail parameter** — `--detail low/medium/high` for explicit agent control
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4. **Source-aware dedup** — guarantees compiled truth chunk per page in results
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5. **Cosine re-scoring** — re-ranks chunks against the actual query embedding
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6. **RRF normalization** — scores normalized to 0-1 before boosting
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7. **CJK word count fix** — Chinese/Japanese/Korean queries now expand correctly
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8. **Eval harness** — `gbrain eval --qrels` with P@k, R@k, MRR, nDCG@k + A/B comparison
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9. **This benchmark** — 29 pages, 20 queries, reproducible, no private data
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||||
## How to reproduce
|
||||
|
||||
```bash
|
||||
bun run test/benchmark-search-quality.ts
|
||||
```
|
||||
|
||||
Runs in ~2 seconds against in-memory PGLite. No API keys, no database, no network.
|
||||
|
||||
## Methodology notes
|
||||
|
||||
- All data is fictional. No private information from any real brain.
|
||||
- Embeddings use 25 topic dimensions with shared axes (not orthogonal basis vectors).
|
||||
"AI" and "health" share signal so that an AI health query naturally ranks both the
|
||||
AI-health concept page and the MindBridge company page.
|
||||
- Each page has exactly 2 chunks (1 compiled truth, 1 timeline) for clean measurement.
|
||||
Real brains have more chunks per page, which would amplify the boost's effect.
|
||||
- The baseline uses the old text-prefix dedup key. The new configurations use chunk_id.
|
||||
- Graded relevance: 3 = primary answer, 2 = strongly related, 1 = tangentially related.
|
||||
@@ -18,7 +18,7 @@ for (const op of operations) {
|
||||
}
|
||||
|
||||
// CLI-only commands that bypass the operation layer
|
||||
const CLI_ONLY = new Set(['init', 'upgrade', 'post-upgrade', 'check-update', 'integrations', 'publish', 'check-backlinks', 'lint', 'report', 'import', 'export', 'files', 'embed', 'serve', 'call', 'config', 'doctor', 'migrate']);
|
||||
const CLI_ONLY = new Set(['init', 'upgrade', 'post-upgrade', 'check-update', 'integrations', 'publish', 'check-backlinks', 'lint', 'report', 'import', 'export', 'files', 'embed', 'serve', 'call', 'config', 'doctor', 'migrate', 'eval']);
|
||||
|
||||
async function main() {
|
||||
const args = process.argv.slice(2);
|
||||
@@ -328,6 +328,11 @@ async function handleCliOnly(command: string, args: string[]) {
|
||||
await runMigrateEngine(engine, args);
|
||||
break;
|
||||
}
|
||||
case 'eval': {
|
||||
const { runEvalCommand } = await import('./commands/eval.ts');
|
||||
await runEvalCommand(engine, args);
|
||||
break;
|
||||
}
|
||||
}
|
||||
} finally {
|
||||
if (command !== 'serve') await engine.disconnect();
|
||||
|
||||
333
src/commands/eval.ts
Normal file
333
src/commands/eval.ts
Normal file
@@ -0,0 +1,333 @@
|
||||
/**
|
||||
* gbrain eval — Retrieval Evaluation Command
|
||||
*
|
||||
* Runs search quality benchmarks against user-defined ground truth (qrels).
|
||||
* Supports single-config runs and A/B comparison mode for tuning parameters.
|
||||
*
|
||||
* Usage:
|
||||
* gbrain eval --qrels <path|json>
|
||||
* gbrain eval --qrels <path> --config-a <path|json> --config-b <path|json>
|
||||
* gbrain eval --qrels <path> --strategy hybrid --rrf-k 30 --k 5
|
||||
*/
|
||||
|
||||
import { readFileSync, existsSync } from 'fs';
|
||||
import type { BrainEngine } from '../core/engine.ts';
|
||||
import {
|
||||
runEval,
|
||||
parseQrels,
|
||||
type EvalConfig,
|
||||
type EvalReport,
|
||||
type QueryResult,
|
||||
} from '../core/search/eval.ts';
|
||||
|
||||
export async function runEvalCommand(engine: BrainEngine, args: string[]): Promise<void> {
|
||||
const opts = parseArgs(args);
|
||||
|
||||
if (opts.help) {
|
||||
printHelp();
|
||||
return;
|
||||
}
|
||||
|
||||
if (!opts.qrels) {
|
||||
console.error('Error: --qrels <path|json> is required\n');
|
||||
printHelp();
|
||||
process.exit(1);
|
||||
}
|
||||
|
||||
let qrels;
|
||||
try {
|
||||
qrels = parseQrels(opts.qrels);
|
||||
} catch (err: any) {
|
||||
console.error(`Error loading qrels: ${err.message}`);
|
||||
process.exit(1);
|
||||
}
|
||||
|
||||
if (qrels.length === 0) {
|
||||
console.error('Error: qrels file contains no queries');
|
||||
process.exit(1);
|
||||
}
|
||||
|
||||
const k = opts.k ?? 5;
|
||||
const configA = buildConfig(opts, 'a');
|
||||
|
||||
if (opts.configB || opts.configBPath) {
|
||||
// A/B comparison mode
|
||||
const configB = buildConfig(opts, 'b');
|
||||
const [reportA, reportB] = await Promise.all([
|
||||
runEval(engine, qrels, configA, k),
|
||||
runEval(engine, qrels, configB, k),
|
||||
]);
|
||||
printABTable(reportA, reportB, k);
|
||||
} else {
|
||||
// Single-run mode
|
||||
const report = await runEval(engine, qrels, configA, k);
|
||||
printSingleTable(report);
|
||||
}
|
||||
}
|
||||
|
||||
// ─────────────────────────────────────────────────────────────────
|
||||
// Argument parsing
|
||||
// ─────────────────────────────────────────────────────────────────
|
||||
|
||||
interface ParsedArgs {
|
||||
help: boolean;
|
||||
qrels?: string;
|
||||
configAPath?: string;
|
||||
configBPath?: string;
|
||||
configB?: EvalConfig;
|
||||
strategy?: EvalConfig['strategy'];
|
||||
rrfK?: number;
|
||||
expand?: boolean;
|
||||
dedupCosine?: number;
|
||||
dedupTypeRatio?: number;
|
||||
dedupMaxPerPage?: number;
|
||||
limit?: number;
|
||||
k?: number;
|
||||
}
|
||||
|
||||
function parseArgs(args: string[]): ParsedArgs {
|
||||
const opts: ParsedArgs = { help: false };
|
||||
|
||||
for (let i = 0; i < args.length; i++) {
|
||||
const arg = args[i];
|
||||
const next = args[i + 1];
|
||||
|
||||
switch (arg) {
|
||||
case '--help': case '-h': opts.help = true; break;
|
||||
case '--qrels': opts.qrels = next; i++; break;
|
||||
case '--config-a': opts.configAPath = next; i++; break;
|
||||
case '--config-b': opts.configBPath = next; i++; break;
|
||||
case '--strategy': opts.strategy = next as EvalConfig['strategy']; i++; break;
|
||||
case '--rrf-k': opts.rrfK = parseInt(next, 10); i++; break;
|
||||
case '--expand': opts.expand = true; break;
|
||||
case '--no-expand': opts.expand = false; break;
|
||||
case '--dedup-cosine': opts.dedupCosine = parseFloat(next); i++; break;
|
||||
case '--dedup-type-ratio': opts.dedupTypeRatio = parseFloat(next); i++; break;
|
||||
case '--dedup-max-per-page': opts.dedupMaxPerPage = parseInt(next, 10); i++; break;
|
||||
case '--limit': opts.limit = parseInt(next, 10); i++; break;
|
||||
case '--k': opts.k = parseInt(next, 10); i++; break;
|
||||
}
|
||||
}
|
||||
|
||||
return opts;
|
||||
}
|
||||
|
||||
function buildConfig(opts: ParsedArgs, side: 'a' | 'b'): EvalConfig {
|
||||
const pathOpt = side === 'a' ? opts.configAPath : opts.configBPath;
|
||||
|
||||
// Start from file or inline JSON if provided
|
||||
let base: EvalConfig = {};
|
||||
if (pathOpt) {
|
||||
base = loadConfigFile(pathOpt);
|
||||
}
|
||||
|
||||
// CLI flags override config file (only for side A — side B comes entirely from its config file)
|
||||
if (side === 'a') {
|
||||
if (opts.strategy !== undefined) base.strategy = opts.strategy;
|
||||
if (opts.rrfK !== undefined) base.rrf_k = opts.rrfK;
|
||||
if (opts.expand !== undefined) base.expand = opts.expand;
|
||||
if (opts.dedupCosine !== undefined) base.dedup_cosine_threshold = opts.dedupCosine;
|
||||
if (opts.dedupTypeRatio !== undefined) base.dedup_type_ratio = opts.dedupTypeRatio;
|
||||
if (opts.dedupMaxPerPage !== undefined) base.dedup_max_per_page = opts.dedupMaxPerPage;
|
||||
if (opts.limit !== undefined) base.limit = opts.limit;
|
||||
|
||||
// Defaults for side A
|
||||
if (!base.name) base.name = 'Config A';
|
||||
if (!base.strategy) base.strategy = 'hybrid';
|
||||
} else {
|
||||
if (!base.name) base.name = 'Config B';
|
||||
if (!base.strategy) base.strategy = 'hybrid';
|
||||
}
|
||||
|
||||
return base;
|
||||
}
|
||||
|
||||
function loadConfigFile(pathOrJson: string): EvalConfig {
|
||||
const trimmed = pathOrJson.trimStart();
|
||||
if (trimmed.startsWith('{')) {
|
||||
return JSON.parse(pathOrJson) as EvalConfig;
|
||||
}
|
||||
if (!existsSync(pathOrJson)) {
|
||||
console.error(`Config file not found: ${pathOrJson}`);
|
||||
process.exit(1);
|
||||
}
|
||||
return JSON.parse(readFileSync(pathOrJson, 'utf-8')) as EvalConfig;
|
||||
}
|
||||
|
||||
// ─────────────────────────────────────────────────────────────────
|
||||
// Output formatting
|
||||
// ─────────────────────────────────────────────────────────────────
|
||||
|
||||
function printSingleTable(report: EvalReport): void {
|
||||
const { config, k, queries } = report;
|
||||
const label = config.name ?? config.strategy ?? 'hybrid';
|
||||
|
||||
console.log(`\ngbrain eval — ${queries.length} quer${queries.length === 1 ? 'y' : 'ies'} · strategy: ${label} · k=${k}\n`);
|
||||
|
||||
const COL_QUERY = 36;
|
||||
const COL_NUM = 7;
|
||||
const header = padR('Query', COL_QUERY) + padL(`P@${k}`, COL_NUM) + padL(`R@${k}`, COL_NUM) + padL('MRR', COL_NUM) + padL(`nDCG@${k}`, COL_NUM);
|
||||
const divider = '─'.repeat(header.length);
|
||||
|
||||
console.log(header);
|
||||
console.log(divider);
|
||||
|
||||
for (const q of queries) {
|
||||
console.log(
|
||||
padR(truncate(q.query, COL_QUERY - 1), COL_QUERY) +
|
||||
padL(fmt(q.precision_at_k), COL_NUM) +
|
||||
padL(fmt(q.recall_at_k), COL_NUM) +
|
||||
padL(fmt(q.mrr), COL_NUM) +
|
||||
padL(fmt(q.ndcg_at_k), COL_NUM),
|
||||
);
|
||||
}
|
||||
|
||||
console.log(divider);
|
||||
console.log(
|
||||
padR('Mean', COL_QUERY) +
|
||||
padL(fmt(report.mean_precision), COL_NUM) +
|
||||
padL(fmt(report.mean_recall), COL_NUM) +
|
||||
padL(fmt(report.mean_mrr), COL_NUM) +
|
||||
padL(fmt(report.mean_ndcg), COL_NUM),
|
||||
);
|
||||
console.log('');
|
||||
}
|
||||
|
||||
function printABTable(reportA: EvalReport, reportB: EvalReport, k: number): void {
|
||||
const labelA = reportA.config.name ?? 'Config A';
|
||||
const labelB = reportB.config.name ?? 'Config B';
|
||||
const n = reportA.queries.length;
|
||||
|
||||
console.log(`\ngbrain eval — ${n} quer${n === 1 ? 'y' : 'ies'} · A/B comparison · k=${k}\n`);
|
||||
|
||||
const COL_QUERY = 34;
|
||||
const COL_METRIC = 8;
|
||||
const COLS_PER_SIDE = 3; // P@k, MRR, nDCG@k
|
||||
|
||||
// Header line 1: section labels
|
||||
const aLabel = ` ${labelA} `.slice(0, COL_METRIC * COLS_PER_SIDE - 2);
|
||||
const bLabel = ` ${labelB} `.slice(0, COL_METRIC * COLS_PER_SIDE - 2);
|
||||
const line1 =
|
||||
' '.repeat(COL_QUERY) +
|
||||
padR(`── ${aLabel} `, COL_METRIC * COLS_PER_SIDE) +
|
||||
padR(`── ${bLabel} `, COL_METRIC * COLS_PER_SIDE) +
|
||||
` Δ nDCG`;
|
||||
console.log(line1);
|
||||
|
||||
// Header line 2: metric names
|
||||
const metricHeader = (suffix: string) =>
|
||||
padL(`P@${k}`, COL_METRIC) + padL('MRR', COL_METRIC) + padL(`nDCG@${k}`, COL_METRIC);
|
||||
|
||||
const line2 =
|
||||
padR('Query', COL_QUERY) +
|
||||
metricHeader('A') +
|
||||
' ' + metricHeader('B') +
|
||||
' ' + padL('Δ nDCG', 10);
|
||||
console.log(line2);
|
||||
console.log('─'.repeat(line2.length));
|
||||
|
||||
for (let i = 0; i < reportA.queries.length; i++) {
|
||||
const qa = reportA.queries[i];
|
||||
const qb = reportB.queries[i];
|
||||
const delta = qb.ndcg_at_k - qa.ndcg_at_k;
|
||||
const deltaStr = delta > 0 ? `+${fmt(delta)}` : fmt(delta);
|
||||
|
||||
console.log(
|
||||
padR(truncate(qa.query, COL_QUERY - 1), COL_QUERY) +
|
||||
padL(fmt(qa.precision_at_k), COL_METRIC) +
|
||||
padL(fmt(qa.mrr), COL_METRIC) +
|
||||
padL(fmt(qa.ndcg_at_k), COL_METRIC) +
|
||||
' ' +
|
||||
padL(fmt(qb.precision_at_k), COL_METRIC) +
|
||||
padL(fmt(qb.mrr), COL_METRIC) +
|
||||
padL(fmt(qb.ndcg_at_k), COL_METRIC) +
|
||||
' ' + padL(deltaStr, 10),
|
||||
);
|
||||
}
|
||||
|
||||
const divider = '─'.repeat(line2.length);
|
||||
console.log(divider);
|
||||
|
||||
const meanDelta = reportB.mean_ndcg - reportA.mean_ndcg;
|
||||
const meanDeltaStr = (meanDelta > 0 ? '+' : '') + fmt(meanDelta);
|
||||
const winner = meanDelta > 0 ? ' ✓ B wins' : meanDelta < 0 ? ' ✓ A wins' : ' tie';
|
||||
|
||||
console.log(
|
||||
padR('Mean', COL_QUERY) +
|
||||
padL(fmt(reportA.mean_precision), COL_METRIC) +
|
||||
padL(fmt(reportA.mean_mrr), COL_METRIC) +
|
||||
padL(fmt(reportA.mean_ndcg), COL_METRIC) +
|
||||
' ' +
|
||||
padL(fmt(reportB.mean_precision), COL_METRIC) +
|
||||
padL(fmt(reportB.mean_mrr), COL_METRIC) +
|
||||
padL(fmt(reportB.mean_ndcg), COL_METRIC) +
|
||||
' ' + padL(meanDeltaStr + winner, 10),
|
||||
);
|
||||
console.log('');
|
||||
}
|
||||
|
||||
// ─────────────────────────────────────────────────────────────────
|
||||
// Formatting helpers
|
||||
// ─────────────────────────────────────────────────────────────────
|
||||
|
||||
function fmt(n: number): string {
|
||||
return n.toFixed(2);
|
||||
}
|
||||
|
||||
function padR(s: string, width: number): string {
|
||||
return s.length >= width ? s.slice(0, width) : s + ' '.repeat(width - s.length);
|
||||
}
|
||||
|
||||
function padL(s: string, width: number): string {
|
||||
return s.length >= width ? s.slice(0, width) : ' '.repeat(width - s.length) + s;
|
||||
}
|
||||
|
||||
function truncate(s: string, max: number): string {
|
||||
return s.length > max ? s.slice(0, max - 1) + '…' : s;
|
||||
}
|
||||
|
||||
function printHelp(): void {
|
||||
console.log(`
|
||||
gbrain eval — measure and compare retrieval quality
|
||||
|
||||
USAGE
|
||||
gbrain eval --qrels <path>
|
||||
gbrain eval --qrels <path> --config-a <path> --config-b <path>
|
||||
|
||||
OPTIONS
|
||||
--qrels <path|json> Path to qrels JSON file (required)
|
||||
Or inline JSON: '[{"query":"...","relevant":["slug"]}]'
|
||||
--config-a <path|json> Config for strategy A (default: hybrid with defaults)
|
||||
--config-b <path|json> Config for strategy B (triggers A/B mode)
|
||||
--strategy <s> Search strategy: hybrid | keyword | vector
|
||||
--rrf-k <n> Override RRF K constant (default: 60)
|
||||
--expand / --no-expand Enable/disable multi-query expansion
|
||||
--dedup-cosine <f> Override cosine dedup threshold (default: 0.85)
|
||||
--dedup-type-ratio <f> Override type ratio cap (default: 0.6)
|
||||
--dedup-max-per-page <n> Override max chunks per page (default: 2)
|
||||
--limit <n> Max results to fetch per query (default: 10)
|
||||
--k <n> Metric cutoff depth (default: 5)
|
||||
|
||||
QRELS FORMAT
|
||||
{
|
||||
"version": 1,
|
||||
"queries": [
|
||||
{
|
||||
"query": "who founded NovaMind",
|
||||
"relevant": ["people/sarah-chen", "companies/novamind"],
|
||||
"grades": { "people/sarah-chen": 3, "companies/novamind": 2 }
|
||||
}
|
||||
]
|
||||
}
|
||||
"grades" is optional — enables graded nDCG. Without it, binary relevance is used.
|
||||
|
||||
CONFIG FORMAT
|
||||
{ "name": "rrf-k-30", "strategy": "hybrid", "rrf_k": 30, "expand": false }
|
||||
|
||||
EXAMPLES
|
||||
gbrain eval --qrels ./my-queries.json
|
||||
gbrain eval --qrels ./qrels.json --strategy keyword
|
||||
gbrain eval --qrels ./qrels.json --rrf-k 30
|
||||
gbrain eval --qrels ./qrels.json --config-a baseline.json --config-b experiment.json
|
||||
`.trim());
|
||||
}
|
||||
@@ -38,6 +38,7 @@ export interface BrainEngine {
|
||||
// Search
|
||||
searchKeyword(query: string, opts?: SearchOpts): Promise<SearchResult[]>;
|
||||
searchVector(embedding: Float32Array, opts?: SearchOpts): Promise<SearchResult[]>;
|
||||
getEmbeddingsByChunkIds(ids: number[]): Promise<Map<number, Float32Array>>;
|
||||
|
||||
// Chunks
|
||||
upsertChunks(slug: string, chunks: ChunkInput[]): Promise<void>;
|
||||
|
||||
@@ -8,6 +8,7 @@ import type { GBrainConfig } from './config.ts';
|
||||
import { importFromContent } from './import-file.ts';
|
||||
import { hybridSearch } from './search/hybrid.ts';
|
||||
import { expandQuery } from './search/expansion.ts';
|
||||
import { dedupResults } from './search/dedup.ts';
|
||||
import * as db from './db.ts';
|
||||
|
||||
// --- Types ---
|
||||
@@ -179,10 +180,11 @@ const search: Operation = {
|
||||
offset: { type: 'number', description: 'Skip first N results (for pagination)' },
|
||||
},
|
||||
handler: async (ctx, p) => {
|
||||
return ctx.engine.searchKeyword(p.query as string, {
|
||||
const results = await ctx.engine.searchKeyword(p.query as string, {
|
||||
limit: (p.limit as number) || 20,
|
||||
offset: (p.offset as number) || 0,
|
||||
});
|
||||
return dedupResults(results);
|
||||
},
|
||||
cliHints: { name: 'search', positional: ['query'] },
|
||||
};
|
||||
@@ -195,14 +197,17 @@ const query: Operation = {
|
||||
limit: { type: 'number', description: 'Max results (default 20)' },
|
||||
offset: { type: 'number', description: 'Skip first N results (for pagination)' },
|
||||
expand: { type: 'boolean', description: 'Enable multi-query expansion (default: true)' },
|
||||
detail: { type: 'string', description: 'Result detail level: low (compiled truth only), medium (default, all with dedup), high (all chunks)' },
|
||||
},
|
||||
handler: async (ctx, p) => {
|
||||
const expand = p.expand !== false;
|
||||
const detail = (p.detail as 'low' | 'medium' | 'high') || undefined;
|
||||
return hybridSearch(ctx.engine, p.query as string, {
|
||||
limit: (p.limit as number) || 20,
|
||||
offset: (p.offset as number) || 0,
|
||||
expansion: expand,
|
||||
expandFn: expand ? expandQuery : undefined,
|
||||
detail,
|
||||
});
|
||||
},
|
||||
cliHints: { name: 'query', positional: ['query'] },
|
||||
|
||||
@@ -173,38 +173,7 @@ export class PGLiteEngine implements BrainEngine {
|
||||
async searchKeyword(query: string, opts?: SearchOpts): Promise<SearchResult[]> {
|
||||
const limit = clampSearchLimit(opts?.limit);
|
||||
const offset = opts?.offset || 0;
|
||||
|
||||
if (opts?.limit && opts.limit > MAX_SEARCH_LIMIT) {
|
||||
console.warn(`[gbrain] Warning: search limit clamped from ${opts.limit} to ${MAX_SEARCH_LIMIT}`);
|
||||
}
|
||||
|
||||
const { rows } = await this.db.query(
|
||||
`SELECT DISTINCT ON (p.slug)
|
||||
p.slug, p.id as page_id, p.title, p.type,
|
||||
cc.chunk_text, cc.chunk_source,
|
||||
ts_rank(p.search_vector, websearch_to_tsquery('english', $1)) AS score,
|
||||
CASE WHEN p.updated_at < (
|
||||
SELECT MAX(te.created_at) FROM timeline_entries te WHERE te.page_id = p.id
|
||||
) THEN true ELSE false END AS stale
|
||||
FROM pages p
|
||||
JOIN content_chunks cc ON cc.page_id = p.id
|
||||
WHERE p.search_vector @@ websearch_to_tsquery('english', $1)
|
||||
ORDER BY p.slug, score DESC`,
|
||||
[query]
|
||||
);
|
||||
|
||||
// Re-sort by score (DISTINCT ON requires ORDER BY slug first) and apply limit + offset
|
||||
const sorted = (rows as Record<string, unknown>[]).sort(
|
||||
(a: any, b: any) => b.score - a.score
|
||||
);
|
||||
|
||||
return sorted.slice(offset, offset + limit).map(rowToSearchResult);
|
||||
}
|
||||
|
||||
async searchVector(embedding: Float32Array, opts?: SearchOpts): Promise<SearchResult[]> {
|
||||
const limit = clampSearchLimit(opts?.limit);
|
||||
const offset = opts?.offset || 0;
|
||||
const vecStr = '[' + Array.from(embedding).join(',') + ']';
|
||||
const detailFilter = opts?.detail === 'low' ? `AND cc.chunk_source = 'compiled_truth'` : '';
|
||||
|
||||
if (opts?.limit && opts.limit > MAX_SEARCH_LIMIT) {
|
||||
console.warn(`[gbrain] Warning: search limit clamped from ${opts.limit} to ${MAX_SEARCH_LIMIT}`);
|
||||
@@ -213,14 +182,44 @@ export class PGLiteEngine implements BrainEngine {
|
||||
const { rows } = await this.db.query(
|
||||
`SELECT
|
||||
p.slug, p.id as page_id, p.title, p.type,
|
||||
cc.chunk_text, cc.chunk_source,
|
||||
cc.id as chunk_id, cc.chunk_index, cc.chunk_text, cc.chunk_source,
|
||||
ts_rank(p.search_vector, websearch_to_tsquery('english', $1)) AS score,
|
||||
CASE WHEN p.updated_at < (
|
||||
SELECT MAX(te.created_at) FROM timeline_entries te WHERE te.page_id = p.id
|
||||
) THEN true ELSE false END AS stale
|
||||
FROM pages p
|
||||
JOIN content_chunks cc ON cc.page_id = p.id
|
||||
WHERE p.search_vector @@ websearch_to_tsquery('english', $1) ${detailFilter}
|
||||
ORDER BY score DESC
|
||||
LIMIT $2
|
||||
OFFSET $3`,
|
||||
[query, limit, offset]
|
||||
);
|
||||
|
||||
return (rows as Record<string, unknown>[]).map(rowToSearchResult);
|
||||
}
|
||||
|
||||
async searchVector(embedding: Float32Array, opts?: SearchOpts): Promise<SearchResult[]> {
|
||||
const limit = clampSearchLimit(opts?.limit);
|
||||
const offset = opts?.offset || 0;
|
||||
const vecStr = '[' + Array.from(embedding).join(',') + ']';
|
||||
const detailFilter = opts?.detail === 'low' ? `AND cc.chunk_source = 'compiled_truth'` : '';
|
||||
|
||||
if (opts?.limit && opts.limit > MAX_SEARCH_LIMIT) {
|
||||
console.warn(`[gbrain] Warning: search limit clamped from ${opts.limit} to ${MAX_SEARCH_LIMIT}`);
|
||||
}
|
||||
|
||||
const { rows } = await this.db.query(
|
||||
`SELECT
|
||||
p.slug, p.id as page_id, p.title, p.type,
|
||||
cc.id as chunk_id, cc.chunk_index, cc.chunk_text, cc.chunk_source,
|
||||
1 - (cc.embedding <=> $1::vector) AS score,
|
||||
CASE WHEN p.updated_at < (
|
||||
SELECT MAX(te.created_at) FROM timeline_entries te WHERE te.page_id = p.id
|
||||
) THEN true ELSE false END AS stale
|
||||
FROM content_chunks cc
|
||||
JOIN pages p ON p.id = cc.page_id
|
||||
WHERE cc.embedding IS NOT NULL
|
||||
WHERE cc.embedding IS NOT NULL ${detailFilter}
|
||||
ORDER BY cc.embedding <=> $1::vector
|
||||
LIMIT $2
|
||||
OFFSET $3`,
|
||||
@@ -230,6 +229,24 @@ export class PGLiteEngine implements BrainEngine {
|
||||
return (rows as Record<string, unknown>[]).map(rowToSearchResult);
|
||||
}
|
||||
|
||||
async getEmbeddingsByChunkIds(ids: number[]): Promise<Map<number, Float32Array>> {
|
||||
if (ids.length === 0) return new Map();
|
||||
const { rows } = await this.db.query(
|
||||
`SELECT id, embedding FROM content_chunks WHERE id = ANY($1::int[]) AND embedding IS NOT NULL`,
|
||||
[ids]
|
||||
);
|
||||
const result = new Map<number, Float32Array>();
|
||||
for (const row of rows as Record<string, unknown>[]) {
|
||||
if (row.embedding) {
|
||||
const emb = typeof row.embedding === 'string'
|
||||
? new Float32Array(JSON.parse(row.embedding))
|
||||
: row.embedding as Float32Array;
|
||||
result.set(row.id as number, emb);
|
||||
}
|
||||
}
|
||||
return result;
|
||||
}
|
||||
|
||||
// Chunks
|
||||
async upsertChunks(slug: string, chunks: ChunkInput[]): Promise<void> {
|
||||
// Get page_id
|
||||
|
||||
@@ -188,6 +188,8 @@ export class PostgresEngine implements BrainEngine {
|
||||
console.warn(`[gbrain] Warning: search limit clamped from ${opts.limit} to ${MAX_SEARCH_LIMIT}`);
|
||||
}
|
||||
|
||||
const detailLow = opts?.detail === 'low';
|
||||
|
||||
// Search-only timeout: prevents DoS via expensive queries without
|
||||
// affecting long-running operations like embed --all or bulk import
|
||||
await sql`SET statement_timeout = '8s'`;
|
||||
@@ -208,12 +210,13 @@ export class PostgresEngine implements BrainEngine {
|
||||
best_chunks AS (
|
||||
SELECT DISTINCT ON (rp.slug)
|
||||
rp.slug, rp.id as page_id, rp.title, rp.type, rp.score,
|
||||
cc.chunk_text, cc.chunk_source
|
||||
cc.id as chunk_id, cc.chunk_index, cc.chunk_text, cc.chunk_source
|
||||
FROM ranked_pages rp
|
||||
JOIN content_chunks cc ON cc.page_id = rp.id
|
||||
${detailLow ? sql`WHERE cc.chunk_source = 'compiled_truth'` : sql``}
|
||||
ORDER BY rp.slug, cc.chunk_index
|
||||
)
|
||||
SELECT slug, page_id, title, type, chunk_text, chunk_source, score,
|
||||
SELECT slug, page_id, title, type, chunk_id, chunk_index, chunk_text, chunk_source, score,
|
||||
false AS stale
|
||||
FROM best_chunks
|
||||
ORDER BY score DESC
|
||||
@@ -230,6 +233,7 @@ export class PostgresEngine implements BrainEngine {
|
||||
const offset = opts?.offset || 0;
|
||||
const type = opts?.type;
|
||||
const excludeSlugs = opts?.exclude_slugs;
|
||||
const detailLow = opts?.detail === 'low';
|
||||
|
||||
if (opts?.limit && opts.limit > MAX_SEARCH_LIMIT) {
|
||||
console.warn(`[gbrain] Warning: search limit clamped from ${opts.limit} to ${MAX_SEARCH_LIMIT}`);
|
||||
@@ -243,12 +247,13 @@ export class PostgresEngine implements BrainEngine {
|
||||
const rows = await sql`
|
||||
SELECT
|
||||
p.slug, p.id as page_id, p.title, p.type,
|
||||
cc.chunk_text, cc.chunk_source,
|
||||
cc.id as chunk_id, cc.chunk_index, cc.chunk_text, cc.chunk_source,
|
||||
1 - (cc.embedding <=> ${vecStr}::vector) AS score,
|
||||
false AS stale
|
||||
FROM content_chunks cc
|
||||
JOIN pages p ON p.id = cc.page_id
|
||||
WHERE cc.embedding IS NOT NULL
|
||||
${detailLow ? sql`AND cc.chunk_source = 'compiled_truth'` : sql``}
|
||||
${type ? sql`AND p.type = ${type}` : sql``}
|
||||
${excludeSlugs?.length ? sql`AND p.slug != ALL(${excludeSlugs})` : sql``}
|
||||
ORDER BY cc.embedding <=> ${vecStr}::vector
|
||||
@@ -261,6 +266,20 @@ export class PostgresEngine implements BrainEngine {
|
||||
}
|
||||
}
|
||||
|
||||
async getEmbeddingsByChunkIds(ids: number[]): Promise<Map<number, Float32Array>> {
|
||||
if (ids.length === 0) return new Map();
|
||||
const sql = this.sql;
|
||||
const rows = await sql`
|
||||
SELECT id, embedding FROM content_chunks
|
||||
WHERE id = ANY(${ids}::int[]) AND embedding IS NOT NULL
|
||||
`;
|
||||
const result = new Map<number, Float32Array>();
|
||||
for (const row of rows) {
|
||||
if (row.embedding) result.set(row.id as number, row.embedding as Float32Array);
|
||||
}
|
||||
return result;
|
||||
}
|
||||
|
||||
// Chunks
|
||||
async upsertChunks(slug: string, chunks: ChunkInput[]): Promise<void> {
|
||||
const sql = this.sql;
|
||||
|
||||
@@ -1,11 +1,12 @@
|
||||
/**
|
||||
* 4-Layer Dedup Pipeline
|
||||
* 4-Layer Dedup Pipeline + Compiled Truth Guarantee
|
||||
* Ported from production Ruby implementation (content_chunk.rb)
|
||||
*
|
||||
* 1. By source: one chunk per page with highest score
|
||||
* 2. By cosine similarity: remove chunks >0.85 similar to kept results
|
||||
* 1. By source: top 3 chunks per page by score
|
||||
* 2. By text similarity: remove chunks >0.85 Jaccard-similar to kept results
|
||||
* 3. By type: no page type exceeds 60% of results
|
||||
* 4. By page: max N chunks per page (default 2)
|
||||
* 5. Compiled truth guarantee: ensure at least 1 compiled_truth chunk per page
|
||||
*/
|
||||
|
||||
import type { SearchResult } from '../types.ts';
|
||||
@@ -26,26 +27,31 @@ export function dedupResults(
|
||||
const maxRatio = opts?.maxTypeRatio ?? MAX_TYPE_RATIO;
|
||||
const maxPerPage = opts?.maxPerPage ?? MAX_PER_PAGE;
|
||||
|
||||
// Preserve pre-dedup input for compiled truth guarantee
|
||||
const preDedup = results;
|
||||
|
||||
let deduped = results;
|
||||
|
||||
// Layer 1: By source (one chunk per page with highest score)
|
||||
// Layer 1: Top 3 chunks per page by score
|
||||
deduped = dedupBySource(deduped);
|
||||
|
||||
// Layer 2: By cosine similarity text overlap
|
||||
// (We don't have embeddings for results here, so use text similarity as proxy)
|
||||
// Layer 2: Text similarity dedup (Jaccard on word sets)
|
||||
deduped = dedupByTextSimilarity(deduped, threshold);
|
||||
|
||||
// Layer 3: By type distribution
|
||||
// Layer 3: Type diversity (no page type exceeds 60%)
|
||||
deduped = enforceTypeDiversity(deduped, maxRatio);
|
||||
|
||||
// Layer 4: By page cap
|
||||
// Layer 4: Cap chunks per page
|
||||
deduped = capPerPage(deduped, maxPerPage);
|
||||
|
||||
// Final pass: guarantee compiled_truth representation
|
||||
deduped = guaranteeCompiledTruth(deduped, preDedup);
|
||||
|
||||
return deduped;
|
||||
}
|
||||
|
||||
/**
|
||||
* Layer 1: Keep top 3 chunks per page (not just 1).
|
||||
* Layer 1: Keep top 3 chunks per page.
|
||||
* Later layers (text similarity, cap per page) handle further reduction.
|
||||
*/
|
||||
function dedupBySource(results: SearchResult[]): SearchResult[] {
|
||||
@@ -133,3 +139,44 @@ function capPerPage(results: SearchResult[], maxPerPage: number): SearchResult[]
|
||||
|
||||
return kept;
|
||||
}
|
||||
|
||||
/**
|
||||
* Final pass: for each page in results that has no compiled_truth chunk,
|
||||
* swap in the best compiled_truth chunk from the pre-dedup set (if one exists).
|
||||
*/
|
||||
function guaranteeCompiledTruth(results: SearchResult[], preDedup: SearchResult[]): SearchResult[] {
|
||||
// Group results by page
|
||||
const byPage = new Map<string, SearchResult[]>();
|
||||
for (const r of results) {
|
||||
const existing = byPage.get(r.slug) || [];
|
||||
existing.push(r);
|
||||
byPage.set(r.slug, existing);
|
||||
}
|
||||
|
||||
const output = [...results];
|
||||
|
||||
for (const [slug, pageChunks] of byPage) {
|
||||
const hasCompiledTruth = pageChunks.some(c => c.chunk_source === 'compiled_truth');
|
||||
if (hasCompiledTruth) continue;
|
||||
|
||||
// Find the best compiled_truth chunk from pre-dedup input for this page
|
||||
const candidate = preDedup
|
||||
.filter(r => r.slug === slug && r.chunk_source === 'compiled_truth')
|
||||
.sort((a, b) => b.score - a.score)[0];
|
||||
|
||||
if (!candidate) continue;
|
||||
|
||||
// Swap: replace the lowest-scored chunk from this page
|
||||
const lowestIdx = output.reduce((minIdx, r, idx) => {
|
||||
if (r.slug !== slug) return minIdx;
|
||||
if (minIdx === -1) return idx;
|
||||
return r.score < output[minIdx].score ? idx : minIdx;
|
||||
}, -1);
|
||||
|
||||
if (lowestIdx !== -1) {
|
||||
output[lowestIdx] = candidate;
|
||||
}
|
||||
}
|
||||
|
||||
return output;
|
||||
}
|
||||
|
||||
280
src/core/search/eval.ts
Normal file
280
src/core/search/eval.ts
Normal file
@@ -0,0 +1,280 @@
|
||||
/**
|
||||
* Retrieval Evaluation Harness
|
||||
*
|
||||
* Provides standard IR metrics (Precision@k, Recall@k, MRR, nDCG@k) and a
|
||||
* runEval() orchestrator that executes a search strategy against user-defined
|
||||
* ground truth (qrels) and returns a structured EvalReport.
|
||||
*
|
||||
* Pure metric functions have zero dependencies and are fully unit-testable.
|
||||
* runEval() depends on BrainEngine + embed and is tested via E2E.
|
||||
*/
|
||||
|
||||
import type { BrainEngine } from '../engine.ts';
|
||||
import { embed } from '../embedding.ts';
|
||||
import { hybridSearch } from './hybrid.ts';
|
||||
import type { HybridSearchOpts } from './hybrid.ts';
|
||||
|
||||
// ─────────────────────────────────────────────────────────────────
|
||||
// Ground truth types
|
||||
// ─────────────────────────────────────────────────────────────────
|
||||
|
||||
export interface EvalQrel {
|
||||
/** Optional stable identifier for the query. */
|
||||
id?: string;
|
||||
query: string;
|
||||
/** Required: slugs considered relevant (binary relevance). */
|
||||
relevant: string[];
|
||||
/**
|
||||
* Optional graded relevance for nDCG (score 1–3 typical).
|
||||
* When omitted, all slugs in `relevant` get grade 1.
|
||||
*/
|
||||
grades?: Record<string, number>;
|
||||
}
|
||||
|
||||
export interface EvalQrelFile {
|
||||
version: 1;
|
||||
queries: EvalQrel[];
|
||||
}
|
||||
|
||||
// ─────────────────────────────────────────────────────────────────
|
||||
// Config types
|
||||
// ─────────────────────────────────────────────────────────────────
|
||||
|
||||
export interface EvalConfig {
|
||||
/** Human-readable label for this configuration (shown in A/B output). */
|
||||
name?: string;
|
||||
strategy?: 'keyword' | 'vector' | 'hybrid';
|
||||
/** Override RRF K constant (default: 60). */
|
||||
rrf_k?: number;
|
||||
/** Enable multi-query expansion (hybrid only, default: false for eval stability). */
|
||||
expand?: boolean;
|
||||
/** Override cosine dedup threshold (default: 0.85). */
|
||||
dedup_cosine_threshold?: number;
|
||||
/** Override type ratio cap (default: 0.6). */
|
||||
dedup_type_ratio?: number;
|
||||
/** Override max chunks per page (default: 2). */
|
||||
dedup_max_per_page?: number;
|
||||
/** Max results to retrieve per query (default: 10). */
|
||||
limit?: number;
|
||||
}
|
||||
|
||||
// ─────────────────────────────────────────────────────────────────
|
||||
// Report types
|
||||
// ─────────────────────────────────────────────────────────────────
|
||||
|
||||
export interface QueryResult {
|
||||
query: string;
|
||||
/** Returned slugs in rank order. */
|
||||
hits: string[];
|
||||
precision_at_k: number;
|
||||
recall_at_k: number;
|
||||
mrr: number;
|
||||
ndcg_at_k: number;
|
||||
}
|
||||
|
||||
export interface EvalReport {
|
||||
config: EvalConfig;
|
||||
/** The k cutoff used for P@k, R@k, nDCG@k. */
|
||||
k: number;
|
||||
queries: QueryResult[];
|
||||
mean_precision: number;
|
||||
mean_recall: number;
|
||||
mean_mrr: number;
|
||||
mean_ndcg: number;
|
||||
}
|
||||
|
||||
// ─────────────────────────────────────────────────────────────────
|
||||
// Pure metric functions
|
||||
// ─────────────────────────────────────────────────────────────────
|
||||
|
||||
/**
|
||||
* Precision@k: fraction of top-k hits that are relevant.
|
||||
*/
|
||||
export function precisionAtK(hits: string[], relevant: Set<string>, k: number): number {
|
||||
if (k <= 0 || hits.length === 0 || relevant.size === 0) return 0;
|
||||
const topK = hits.slice(0, k);
|
||||
const relevantHits = topK.filter(h => relevant.has(h)).length;
|
||||
return relevantHits / k;
|
||||
}
|
||||
|
||||
/**
|
||||
* Recall@k: fraction of all relevant docs found in top-k hits.
|
||||
*/
|
||||
export function recallAtK(hits: string[], relevant: Set<string>, k: number): number {
|
||||
if (k <= 0 || hits.length === 0 || relevant.size === 0) return 0;
|
||||
const topK = hits.slice(0, k);
|
||||
const relevantHits = topK.filter(h => relevant.has(h)).length;
|
||||
return relevantHits / relevant.size;
|
||||
}
|
||||
|
||||
/**
|
||||
* Mean Reciprocal Rank: 1/rank of the first relevant hit (0 if none found).
|
||||
*/
|
||||
export function mrr(hits: string[], relevant: Set<string>): number {
|
||||
if (hits.length === 0 || relevant.size === 0) return 0;
|
||||
for (let i = 0; i < hits.length; i++) {
|
||||
if (relevant.has(hits[i])) return 1 / (i + 1);
|
||||
}
|
||||
return 0;
|
||||
}
|
||||
|
||||
/**
|
||||
* nDCG@k: Normalized Discounted Cumulative Gain.
|
||||
*
|
||||
* Uses grades map for graded relevance. For binary relevance, pass a Map
|
||||
* where all relevant slugs map to grade 1.
|
||||
*
|
||||
* DCG = sum(grade_i / log2(rank_i + 1)) for i in top-k
|
||||
* Ideal DCG = DCG of perfect ranking (all relevant docs at top)
|
||||
* nDCG = DCG / IDCG
|
||||
*/
|
||||
export function ndcgAtK(hits: string[], grades: Map<string, number>, k: number): number {
|
||||
if (k <= 0 || hits.length === 0 || grades.size === 0) return 0;
|
||||
|
||||
const topK = hits.slice(0, k);
|
||||
let dcg = 0;
|
||||
for (let i = 0; i < topK.length; i++) {
|
||||
const grade = grades.get(topK[i]) ?? 0;
|
||||
dcg += grade / Math.log2(i + 2); // log2(rank + 1), rank is 1-indexed
|
||||
}
|
||||
|
||||
// Ideal DCG: sort all graded docs by grade desc, take top-k
|
||||
const idealGrades = Array.from(grades.values())
|
||||
.filter(g => g > 0)
|
||||
.sort((a, b) => b - a)
|
||||
.slice(0, k);
|
||||
|
||||
let idcg = 0;
|
||||
for (let i = 0; i < idealGrades.length; i++) {
|
||||
idcg += idealGrades[i] / Math.log2(i + 2);
|
||||
}
|
||||
|
||||
if (idcg === 0) return 0;
|
||||
return dcg / idcg;
|
||||
}
|
||||
|
||||
// ─────────────────────────────────────────────────────────────────
|
||||
// Orchestrator
|
||||
// ─────────────────────────────────────────────────────────────────
|
||||
|
||||
/**
|
||||
* Run a full evaluation of one search configuration against all qrels.
|
||||
* Returns an EvalReport with per-query and mean metrics.
|
||||
*/
|
||||
export async function runEval(
|
||||
engine: BrainEngine,
|
||||
qrels: EvalQrel[],
|
||||
config: EvalConfig,
|
||||
k = 5,
|
||||
): Promise<EvalReport> {
|
||||
const strategy = config.strategy ?? 'hybrid';
|
||||
const limit = config.limit ?? Math.max(k * 2, 10);
|
||||
|
||||
const queryResults: QueryResult[] = [];
|
||||
|
||||
for (const qrel of qrels) {
|
||||
const hits = await runQuery(engine, qrel.query, strategy, config, limit);
|
||||
|
||||
const relevantSet = new Set(qrel.relevant);
|
||||
const gradesMap = buildGradesMap(qrel);
|
||||
|
||||
queryResults.push({
|
||||
query: qrel.query,
|
||||
hits,
|
||||
precision_at_k: precisionAtK(hits, relevantSet, k),
|
||||
recall_at_k: recallAtK(hits, relevantSet, k),
|
||||
mrr: mrr(hits, relevantSet),
|
||||
ndcg_at_k: ndcgAtK(hits, gradesMap, k),
|
||||
});
|
||||
}
|
||||
|
||||
return {
|
||||
config,
|
||||
k,
|
||||
queries: queryResults,
|
||||
mean_precision: mean(queryResults.map(r => r.precision_at_k)),
|
||||
mean_recall: mean(queryResults.map(r => r.recall_at_k)),
|
||||
mean_mrr: mean(queryResults.map(r => r.mrr)),
|
||||
mean_ndcg: mean(queryResults.map(r => r.ndcg_at_k)),
|
||||
};
|
||||
}
|
||||
|
||||
// ─────────────────────────────────────────────────────────────────
|
||||
// Helpers
|
||||
// ─────────────────────────────────────────────────────────────────
|
||||
|
||||
async function runQuery(
|
||||
engine: BrainEngine,
|
||||
query: string,
|
||||
strategy: 'keyword' | 'vector' | 'hybrid',
|
||||
config: EvalConfig,
|
||||
limit: number,
|
||||
): Promise<string[]> {
|
||||
const dedupOpts = {
|
||||
cosineThreshold: config.dedup_cosine_threshold,
|
||||
maxTypeRatio: config.dedup_type_ratio,
|
||||
maxPerPage: config.dedup_max_per_page,
|
||||
};
|
||||
|
||||
if (strategy === 'keyword') {
|
||||
const results = await engine.searchKeyword(query, { limit });
|
||||
return results.map(r => r.slug);
|
||||
}
|
||||
|
||||
if (strategy === 'vector') {
|
||||
const embedding = await embed(query);
|
||||
const results = await engine.searchVector(embedding, { limit });
|
||||
return results.map(r => r.slug);
|
||||
}
|
||||
|
||||
// hybrid
|
||||
const hybridOpts: HybridSearchOpts = {
|
||||
limit,
|
||||
expansion: config.expand ?? false,
|
||||
rrfK: config.rrf_k,
|
||||
dedupOpts,
|
||||
};
|
||||
const results = await hybridSearch(engine, query, hybridOpts);
|
||||
return results.map(r => r.slug);
|
||||
}
|
||||
|
||||
/**
|
||||
* Build a grades Map for nDCG. If qrel has explicit grades, use them.
|
||||
* Otherwise, assign grade=1 to every slug in relevant (binary relevance).
|
||||
*/
|
||||
function buildGradesMap(qrel: EvalQrel): Map<string, number> {
|
||||
if (qrel.grades && Object.keys(qrel.grades).length > 0) {
|
||||
return new Map(Object.entries(qrel.grades));
|
||||
}
|
||||
return new Map(qrel.relevant.map(slug => [slug, 1]));
|
||||
}
|
||||
|
||||
function mean(values: number[]): number {
|
||||
if (values.length === 0) return 0;
|
||||
return values.reduce((a, b) => a + b, 0) / values.length;
|
||||
}
|
||||
|
||||
/**
|
||||
* Parse qrels from either a file path or an inline JSON string.
|
||||
* Returns the array of EvalQrel entries.
|
||||
*/
|
||||
export function parseQrels(input: string): EvalQrel[] {
|
||||
let raw: string;
|
||||
|
||||
// Inline JSON starts with '[' or '{'
|
||||
if (input.trimStart().startsWith('[') || input.trimStart().startsWith('{')) {
|
||||
raw = input;
|
||||
} else {
|
||||
// Treat as file path
|
||||
const { readFileSync } = require('fs');
|
||||
raw = readFileSync(input, 'utf-8');
|
||||
}
|
||||
|
||||
const parsed = JSON.parse(raw);
|
||||
|
||||
// Support both array format and { version, queries } format
|
||||
if (Array.isArray(parsed)) return parsed as EvalQrel[];
|
||||
if (parsed.queries && Array.isArray(parsed.queries)) return parsed.queries as EvalQrel[];
|
||||
|
||||
throw new Error('Invalid qrels format. Expected array or { version, queries } object.');
|
||||
}
|
||||
@@ -22,7 +22,9 @@ function getClient(): Anthropic {
|
||||
}
|
||||
|
||||
export async function expandQuery(query: string): Promise<string[]> {
|
||||
const wordCount = (query.match(/\S+/g) || []).length;
|
||||
// CJK text is not space-delimited — count characters instead of whitespace-separated tokens
|
||||
const hasCJK = /[\u4e00-\u9fff\u3040-\u309f\u30a0-\u30ff\uac00-\ud7af]/.test(query);
|
||||
const wordCount = hasCJK ? query.replace(/\s/g, '').length : (query.match(/\S+/g) || []).length;
|
||||
if (wordCount < MIN_WORDS) return [query];
|
||||
|
||||
try {
|
||||
|
||||
@@ -2,8 +2,11 @@
|
||||
* Hybrid Search with Reciprocal Rank Fusion (RRF)
|
||||
* Ported from production Ruby implementation (content_chunk.rb)
|
||||
*
|
||||
* Pipeline: keyword + vector → RRF fusion → normalize → boost → cosine re-score → dedup
|
||||
*
|
||||
* RRF score = sum(1 / (60 + rank_in_list))
|
||||
* Merges vector + keyword results fairly regardless of score scale.
|
||||
* Compiled truth boost: 2.0x for compiled_truth chunks after RRF normalization
|
||||
* Cosine re-score: blend 0.7*rrf + 0.3*cosine for query-specific ranking
|
||||
*/
|
||||
|
||||
import type { BrainEngine } from '../engine.ts';
|
||||
@@ -11,12 +14,23 @@ import { MAX_SEARCH_LIMIT, clampSearchLimit } from '../engine.ts';
|
||||
import type { SearchResult, SearchOpts } from '../types.ts';
|
||||
import { embed } from '../embedding.ts';
|
||||
import { dedupResults } from './dedup.ts';
|
||||
import { autoDetectDetail } from './intent.ts';
|
||||
|
||||
const RRF_K = 60;
|
||||
const COMPILED_TRUTH_BOOST = 2.0;
|
||||
const DEBUG = process.env.GBRAIN_SEARCH_DEBUG === '1';
|
||||
|
||||
export interface HybridSearchOpts extends SearchOpts {
|
||||
expansion?: boolean;
|
||||
expandFn?: (query: string) => Promise<string[]>;
|
||||
/** Override default RRF K constant (default: 60). Lower values boost top-ranked results more. */
|
||||
rrfK?: number;
|
||||
/** Override dedup pipeline parameters. */
|
||||
dedupOpts?: {
|
||||
cosineThreshold?: number;
|
||||
maxTypeRatio?: number;
|
||||
maxPerPage?: number;
|
||||
};
|
||||
}
|
||||
|
||||
export async function hybridSearch(
|
||||
@@ -28,8 +42,16 @@ export async function hybridSearch(
|
||||
const offset = opts?.offset || 0;
|
||||
const innerLimit = Math.min(limit * 2, MAX_SEARCH_LIMIT);
|
||||
|
||||
// Auto-detect detail level from query intent when caller doesn't specify
|
||||
const detail = opts?.detail ?? autoDetectDetail(query);
|
||||
const searchOpts: SearchOpts = { limit: innerLimit, detail };
|
||||
|
||||
if (DEBUG && detail) {
|
||||
console.error(`[search-debug] auto-detail=${detail} for query="${query}"`);
|
||||
}
|
||||
|
||||
// Run keyword search (always available, no API key needed)
|
||||
const keywordResults = await engine.searchKeyword(query, { limit: innerLimit });
|
||||
const keywordResults = await engine.searchKeyword(query, searchOpts);
|
||||
|
||||
// Skip vector search entirely if no OpenAI key is configured
|
||||
if (!process.env.OPENAI_API_KEY) {
|
||||
@@ -51,10 +73,12 @@ export async function hybridSearch(
|
||||
|
||||
// Embed all query variants and run vector search
|
||||
let vectorLists: SearchResult[][] = [];
|
||||
let queryEmbedding: Float32Array | null = null;
|
||||
try {
|
||||
const embeddings = await Promise.all(queries.map(q => embed(q)));
|
||||
queryEmbedding = embeddings[0];
|
||||
vectorLists = await Promise.all(
|
||||
embeddings.map(emb => engine.searchVector(emb, { limit: innerLimit })),
|
||||
embeddings.map(emb => engine.searchVector(emb, searchOpts)),
|
||||
);
|
||||
} catch {
|
||||
// Embedding failure is non-fatal, fall back to keyword-only
|
||||
@@ -64,12 +88,23 @@ export async function hybridSearch(
|
||||
return dedupResults(keywordResults).slice(offset, offset + limit);
|
||||
}
|
||||
|
||||
// Merge all result lists via RRF
|
||||
// Merge all result lists via RRF (includes normalization + boost)
|
||||
// Skip boost for detail=high (temporal/event queries want natural ranking)
|
||||
const allLists = [...vectorLists, keywordResults];
|
||||
const fused = rrfFusion(allLists);
|
||||
let fused = rrfFusion(allLists, opts?.rrfK ?? RRF_K, detail !== 'high');
|
||||
|
||||
// Cosine re-scoring before dedup so semantically better chunks survive
|
||||
if (queryEmbedding) {
|
||||
fused = await cosineReScore(engine, fused, queryEmbedding);
|
||||
}
|
||||
|
||||
// Dedup
|
||||
const deduped = dedupResults(fused);
|
||||
const deduped = dedupResults(fused, opts?.dedupOpts);
|
||||
|
||||
// Auto-escalate: if detail=low returned 0, retry with high
|
||||
if (deduped.length === 0 && opts?.detail === 'low') {
|
||||
return hybridSearch(engine, query, { ...opts, detail: 'high' });
|
||||
}
|
||||
|
||||
return deduped.slice(offset, offset + limit);
|
||||
}
|
||||
@@ -77,16 +112,17 @@ export async function hybridSearch(
|
||||
/**
|
||||
* Reciprocal Rank Fusion: merge multiple ranked lists.
|
||||
* Each result gets score = sum(1 / (K + rank)) across all lists it appears in.
|
||||
* After accumulation: normalize to 0-1, then boost compiled_truth chunks.
|
||||
*/
|
||||
function rrfFusion(lists: SearchResult[][]): SearchResult[] {
|
||||
export function rrfFusion(lists: SearchResult[][], k: number, applyBoost = true): SearchResult[] {
|
||||
const scores = new Map<string, { result: SearchResult; score: number }>();
|
||||
|
||||
for (const list of lists) {
|
||||
for (let rank = 0; rank < list.length; rank++) {
|
||||
const r = list[rank];
|
||||
const key = `${r.slug}:${r.chunk_text.slice(0, 50)}`;
|
||||
const key = `${r.slug}:${r.chunk_id ?? r.chunk_text.slice(0, 50)}`;
|
||||
const existing = scores.get(key);
|
||||
const rrfScore = 1 / (RRF_K + rank);
|
||||
const rrfScore = 1 / (k + rank);
|
||||
|
||||
if (existing) {
|
||||
existing.score += rrfScore;
|
||||
@@ -96,8 +132,83 @@ function rrfFusion(lists: SearchResult[][]): SearchResult[] {
|
||||
}
|
||||
}
|
||||
|
||||
// Sort by fused score descending
|
||||
return Array.from(scores.values())
|
||||
const entries = Array.from(scores.values());
|
||||
if (entries.length === 0) return [];
|
||||
|
||||
// Normalize to 0-1 by dividing by observed max
|
||||
const maxScore = Math.max(...entries.map(e => e.score));
|
||||
if (maxScore > 0) {
|
||||
for (const e of entries) {
|
||||
const rawScore = e.score;
|
||||
e.score = e.score / maxScore;
|
||||
|
||||
// Apply compiled truth boost after normalization (skip for detail=high)
|
||||
const boost = applyBoost && e.result.chunk_source === 'compiled_truth' ? COMPILED_TRUTH_BOOST : 1.0;
|
||||
e.score *= boost;
|
||||
|
||||
if (DEBUG) {
|
||||
console.error(`[search-debug] ${e.result.slug}:${e.result.chunk_id} rrf_raw=${rawScore.toFixed(4)} rrf_norm=${(rawScore / maxScore).toFixed(4)} boost=${boost} boosted=${e.score.toFixed(4)} source=${e.result.chunk_source}`);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// Sort by boosted score descending
|
||||
return entries
|
||||
.sort((a, b) => b.score - a.score)
|
||||
.map(({ result, score }) => ({ ...result, score }));
|
||||
}
|
||||
|
||||
/**
|
||||
* Cosine re-scoring: blend RRF score with query-chunk cosine similarity.
|
||||
* Runs before dedup so semantically better chunks survive.
|
||||
*/
|
||||
async function cosineReScore(
|
||||
engine: BrainEngine,
|
||||
results: SearchResult[],
|
||||
queryEmbedding: Float32Array,
|
||||
): Promise<SearchResult[]> {
|
||||
const chunkIds = results
|
||||
.map(r => r.chunk_id)
|
||||
.filter((id): id is number => id != null);
|
||||
|
||||
if (chunkIds.length === 0) return results;
|
||||
|
||||
let embeddingMap: Map<number, Float32Array>;
|
||||
try {
|
||||
embeddingMap = await engine.getEmbeddingsByChunkIds(chunkIds);
|
||||
} catch {
|
||||
// DB error is non-fatal, return results without re-scoring
|
||||
return results;
|
||||
}
|
||||
|
||||
if (embeddingMap.size === 0) return results;
|
||||
|
||||
// Normalize RRF scores to 0-1 for blending
|
||||
const maxRrf = Math.max(...results.map(r => r.score));
|
||||
|
||||
return results.map(r => {
|
||||
const chunkEmb = r.chunk_id != null ? embeddingMap.get(r.chunk_id) : undefined;
|
||||
if (!chunkEmb) return r;
|
||||
|
||||
const cosine = cosineSimilarity(queryEmbedding, chunkEmb);
|
||||
const normRrf = maxRrf > 0 ? r.score / maxRrf : 0;
|
||||
const blended = 0.7 * normRrf + 0.3 * cosine;
|
||||
|
||||
if (DEBUG) {
|
||||
console.error(`[search-debug] ${r.slug}:${r.chunk_id} cosine=${cosine.toFixed(4)} norm_rrf=${normRrf.toFixed(4)} blended=${blended.toFixed(4)}`);
|
||||
}
|
||||
|
||||
return { ...r, score: blended };
|
||||
}).sort((a, b) => b.score - a.score);
|
||||
}
|
||||
|
||||
export function cosineSimilarity(a: Float32Array, b: Float32Array): number {
|
||||
let dot = 0, magA = 0, magB = 0;
|
||||
for (let i = 0; i < a.length; i++) {
|
||||
dot += a[i] * b[i];
|
||||
magA += a[i] * a[i];
|
||||
magB += b[i] * b[i];
|
||||
}
|
||||
const denom = Math.sqrt(magA) * Math.sqrt(magB);
|
||||
return denom === 0 ? 0 : dot / denom;
|
||||
}
|
||||
|
||||
108
src/core/search/intent.ts
Normal file
108
src/core/search/intent.ts
Normal file
@@ -0,0 +1,108 @@
|
||||
/**
|
||||
* Query Intent Classifier
|
||||
*
|
||||
* Zero-latency heuristic classifier that detects query intent from text patterns.
|
||||
* Maps intent to the appropriate detail level for hybrid search.
|
||||
*
|
||||
* No LLM call, no API cost, no latency. Pattern matching on query text.
|
||||
*/
|
||||
|
||||
export type QueryIntent = 'entity' | 'temporal' | 'event' | 'general';
|
||||
|
||||
// Temporal patterns: questions about when things happened, meeting history
|
||||
const TEMPORAL_PATTERNS = [
|
||||
/\bwhen\b/i,
|
||||
/\blast\s+(met|meeting|call|conversation|chat|talked|spoke|seen|heard|time)\b/i,
|
||||
/\brecent(ly)?\b/i,
|
||||
/\bhistory\b/i,
|
||||
/\btimeline\b/i,
|
||||
/\bmeeting\s+notes?\b/i,
|
||||
/\bwhat('s| is| was)\s+new\b/i,
|
||||
/\blatest\b/i,
|
||||
/\bupdate(s)?\s+(on|from|about)\b/i,
|
||||
/\bhow\s+long\s+(ago|since)\b/i,
|
||||
/\b\d{4}[-/]\d{2}\b/i, // date pattern like 2024-03
|
||||
/\blast\s+(week|month|quarter|year)\b/i,
|
||||
];
|
||||
|
||||
// Event patterns: specific events, announcements, launches
|
||||
const EVENT_PATTERNS = [
|
||||
/\bannounce[ds]?(ment)?\b/i,
|
||||
/\blaunch(ed|es|ing)?\b/i,
|
||||
/\braised?\s+\$?\d/i,
|
||||
/\bfund(ing|raise)\b/i,
|
||||
/\bIPO\b/i,
|
||||
/\bacquisition\b/i,
|
||||
/\bmerge[drs]?\b/i,
|
||||
/\bnews\b/i,
|
||||
/\bhappened?\b/i,
|
||||
];
|
||||
|
||||
// Entity patterns: identity questions, overviews
|
||||
const ENTITY_PATTERNS = [
|
||||
/\bwho\s+is\b/i,
|
||||
/\bwhat\s+(is|does|are)\b/i,
|
||||
/\btell\s+me\s+about\b/i,
|
||||
/\bdescribe\b/i,
|
||||
/\bsummar(y|ize)\b/i,
|
||||
/\boverview\b/i,
|
||||
/\bbackground\b/i,
|
||||
/\bprofile\b/i,
|
||||
/\bwhat\s+do\s+(you|we)\s+know\b/i,
|
||||
];
|
||||
|
||||
// Full-context patterns: requests for everything
|
||||
const FULL_CONTEXT_PATTERNS = [
|
||||
/\beverything\b/i,
|
||||
/\ball\s+(about|info|information|details)\b/i,
|
||||
/\bfull\s+(history|context|picture|story|details)\b/i,
|
||||
/\bcomprehensive\b/i,
|
||||
/\bdeep\s+dive\b/i,
|
||||
/\bgive\s+me\s+everything\b/i,
|
||||
];
|
||||
|
||||
/**
|
||||
* Classify query intent from text patterns.
|
||||
* Returns the detected intent type.
|
||||
*/
|
||||
export function classifyQueryIntent(query: string): QueryIntent {
|
||||
// Full context requests → treat as temporal (return everything)
|
||||
if (FULL_CONTEXT_PATTERNS.some(p => p.test(query))) return 'temporal';
|
||||
|
||||
// Check temporal patterns first (highest priority for detail=high)
|
||||
if (TEMPORAL_PATTERNS.some(p => p.test(query))) return 'temporal';
|
||||
|
||||
// Check event patterns
|
||||
if (EVENT_PATTERNS.some(p => p.test(query))) return 'event';
|
||||
|
||||
// Check entity patterns
|
||||
if (ENTITY_PATTERNS.some(p => p.test(query))) return 'entity';
|
||||
|
||||
// Default: general query
|
||||
return 'general';
|
||||
}
|
||||
|
||||
/**
|
||||
* Map query intent to detail level.
|
||||
*
|
||||
* entity → 'low' (compiled truth only, user wants the assessment)
|
||||
* temporal → 'high' (need timeline, user wants dates/events)
|
||||
* event → 'high' (need timeline, user wants specific events)
|
||||
* general → undefined (use default medium, let the boost handle it)
|
||||
*/
|
||||
export function intentToDetail(intent: QueryIntent): 'low' | 'medium' | 'high' | undefined {
|
||||
switch (intent) {
|
||||
case 'entity': return 'low';
|
||||
case 'temporal': return 'high';
|
||||
case 'event': return 'high';
|
||||
case 'general': return undefined; // use default
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Auto-detect detail level from query text.
|
||||
* Returns undefined if no strong signal detected (uses default).
|
||||
*/
|
||||
export function autoDetectDetail(query: string): 'low' | 'medium' | 'high' | undefined {
|
||||
return intentToDetail(classifyQueryIntent(query));
|
||||
}
|
||||
@@ -60,6 +60,8 @@ export interface SearchResult {
|
||||
type: PageType;
|
||||
chunk_text: string;
|
||||
chunk_source: 'compiled_truth' | 'timeline';
|
||||
chunk_id: number;
|
||||
chunk_index: number;
|
||||
score: number;
|
||||
stale: boolean;
|
||||
}
|
||||
@@ -69,6 +71,7 @@ export interface SearchOpts {
|
||||
offset?: number;
|
||||
type?: PageType;
|
||||
exclude_slugs?: string[];
|
||||
detail?: 'low' | 'medium' | 'high';
|
||||
}
|
||||
|
||||
// Links
|
||||
|
||||
@@ -65,6 +65,8 @@ export function rowToSearchResult(row: Record<string, unknown>): SearchResult {
|
||||
type: row.type as PageType,
|
||||
chunk_text: row.chunk_text as string,
|
||||
chunk_source: row.chunk_source as 'compiled_truth' | 'timeline',
|
||||
chunk_id: row.chunk_id as number,
|
||||
chunk_index: row.chunk_index as number,
|
||||
score: Number(row.score),
|
||||
stale: Boolean(row.stale),
|
||||
};
|
||||
|
||||
752
test/benchmark-search-quality.ts
Normal file
752
test/benchmark-search-quality.ts
Normal file
@@ -0,0 +1,752 @@
|
||||
/**
|
||||
* Search Quality Benchmark — Rich benchmark with realistic overlap and noise.
|
||||
*
|
||||
* 30 pages, 60 chunks, 20 queries with graded relevance. Tests ranking quality
|
||||
* in a brain with overlapping topics, multiple mentions, and temporal ambiguity.
|
||||
*
|
||||
* All data is fictional. No private information.
|
||||
*
|
||||
* Usage: bun run test/benchmark-search-quality.ts
|
||||
*/
|
||||
|
||||
import { PGLiteEngine } from '../src/core/pglite-engine.ts';
|
||||
import { rrfFusion } from '../src/core/search/hybrid.ts';
|
||||
import { dedupResults } from '../src/core/search/dedup.ts';
|
||||
import { precisionAtK, recallAtK, mrr, ndcgAtK } from '../src/core/search/eval.ts';
|
||||
import { autoDetectDetail } from '../src/core/search/intent.ts';
|
||||
import type { SearchResult, ChunkInput } from '../src/core/types.ts';
|
||||
|
||||
const RRF_K = 60;
|
||||
|
||||
// ─── Embedding helpers ───────────────────────────────────────────
|
||||
|
||||
// Create embeddings with shared dimensions to simulate semantic overlap.
|
||||
// Each "topic" gets a primary dimension. Related topics share secondary dimensions.
|
||||
function topicEmbedding(topics: Record<number, number>, dim = 1536): Float32Array {
|
||||
const emb = new Float32Array(dim);
|
||||
for (const [idx, weight] of Object.entries(topics)) {
|
||||
emb[Number(idx) % dim] = weight;
|
||||
}
|
||||
// Normalize
|
||||
let mag = 0;
|
||||
for (let i = 0; i < dim; i++) mag += emb[i] * emb[i];
|
||||
mag = Math.sqrt(mag);
|
||||
if (mag > 0) for (let i = 0; i < dim; i++) emb[i] /= mag;
|
||||
return emb;
|
||||
}
|
||||
|
||||
// Topic dimensions (semantic axes)
|
||||
const T = {
|
||||
AI: 0, FINTECH: 1, CRYPTO: 2, CLIMATE: 3, HEALTH: 4,
|
||||
ENTERPRISE: 5, CONSUMER: 6, ROBOTICS: 7, EDUCATION: 8, BIOTECH: 9,
|
||||
FOUNDER: 10, INVESTOR: 11, ENGINEER: 12, DESIGNER: 13,
|
||||
MEETING: 20, ANNOUNCEMENT: 21, FUNDING: 22, LAUNCH: 23, HIRING: 24,
|
||||
COMPILED: 30, TIMELINE: 31,
|
||||
};
|
||||
|
||||
// ─── Test Data: 30 fictional pages ──────────────────────────────
|
||||
|
||||
interface TestPage {
|
||||
slug: string;
|
||||
type: 'person' | 'company' | 'concept';
|
||||
title: string;
|
||||
compiled_truth: string;
|
||||
timeline: string;
|
||||
chunks: ChunkInput[];
|
||||
}
|
||||
|
||||
const PAGES: TestPage[] = [
|
||||
// ── People (10) ──────────────────────────────────────────────
|
||||
{
|
||||
slug: 'people/alice-chen',
|
||||
type: 'person',
|
||||
title: 'Alice Chen',
|
||||
compiled_truth: 'Alice Chen is the CEO of NovaPay, a fintech startup building instant cross-border payments for SMBs. Previously VP Engineering at Stripe. Deep expertise in payment rails and regulatory compliance.',
|
||||
timeline: '2024-03-15: Met Alice at Fintech Forum. Discussed cross-border payment challenges in Southeast Asia. She mentioned NovaPay is expanding to Vietnam.\n2024-06-20: Coffee with Alice. NovaPay raised Series B. Hiring aggressively.',
|
||||
chunks: [
|
||||
{ chunk_index: 0, chunk_text: 'Alice Chen is the CEO of NovaPay, a fintech startup building instant cross-border payments for SMBs. Previously VP Engineering at Stripe. Deep expertise in payment rails and regulatory compliance.', chunk_source: 'compiled_truth', embedding: topicEmbedding({[T.FINTECH]: 1, [T.FOUNDER]: 0.6, [T.ENTERPRISE]: 0.3}), token_count: 35 },
|
||||
{ chunk_index: 1, chunk_text: '2024-03-15: Met Alice at Fintech Forum. Discussed cross-border payment challenges in Southeast Asia. NovaPay expanding to Vietnam. 2024-06-20: Coffee with Alice. NovaPay raised Series B. Hiring aggressively.', chunk_source: 'timeline', embedding: topicEmbedding({[T.FINTECH]: 0.5, [T.MEETING]: 0.8, [T.FUNDING]: 0.4}), token_count: 40 },
|
||||
],
|
||||
},
|
||||
{
|
||||
slug: 'people/bob-martinez',
|
||||
type: 'person',
|
||||
title: 'Bob Martinez',
|
||||
compiled_truth: 'Bob Martinez is a partner at Green Horizon Ventures, focused on climate tech and clean energy investments. Board member at SolarGrid and WindFlow. Former McKinsey energy practice.',
|
||||
timeline: '2024-04-10: Lunch with Bob. He is bullish on grid-scale battery storage. Mentioned a new fund for carbon capture.\n2024-08-05: Bob introduced me to the SolarGrid founder.',
|
||||
chunks: [
|
||||
{ chunk_index: 0, chunk_text: 'Bob Martinez is a partner at Green Horizon Ventures, focused on climate tech and clean energy investments. Board member at SolarGrid and WindFlow. Former McKinsey energy practice.', chunk_source: 'compiled_truth', embedding: topicEmbedding({[T.CLIMATE]: 1, [T.INVESTOR]: 0.7, [T.ENTERPRISE]: 0.2}), token_count: 32 },
|
||||
{ chunk_index: 1, chunk_text: '2024-04-10: Lunch with Bob. Bullish on grid-scale battery storage. New fund for carbon capture. 2024-08-05: Bob introduced me to SolarGrid founder.', chunk_source: 'timeline', embedding: topicEmbedding({[T.CLIMATE]: 0.5, [T.MEETING]: 0.8, [T.FUNDING]: 0.3}), token_count: 30 },
|
||||
],
|
||||
},
|
||||
{
|
||||
slug: 'people/carol-nakamura',
|
||||
type: 'person',
|
||||
title: 'Carol Nakamura',
|
||||
compiled_truth: 'Carol Nakamura is CTO of MindBridge, an AI company building diagnostic tools for mental health professionals. PhD in computational neuroscience from MIT. Pioneer in applying transformer models to clinical psychology.',
|
||||
timeline: '2024-02-28: Carol presented at AI Health Summit. MindBridge accuracy data is impressive, 94% concordance with clinical diagnosis.\n2024-07-12: Carol reached out about Series A. Looking for $15M.',
|
||||
chunks: [
|
||||
{ chunk_index: 0, chunk_text: 'Carol Nakamura is CTO of MindBridge, an AI company building diagnostic tools for mental health professionals. PhD in computational neuroscience from MIT. Pioneer in transformer models for clinical psychology.', chunk_source: 'compiled_truth', embedding: topicEmbedding({[T.AI]: 0.7, [T.HEALTH]: 0.8, [T.FOUNDER]: 0.4, [T.ENGINEER]: 0.3}), token_count: 35 },
|
||||
{ chunk_index: 1, chunk_text: '2024-02-28: Carol presented at AI Health Summit. MindBridge 94% concordance with clinical diagnosis. 2024-07-12: Carol reached out about Series A, looking for $15M.', chunk_source: 'timeline', embedding: topicEmbedding({[T.AI]: 0.3, [T.HEALTH]: 0.4, [T.MEETING]: 0.6, [T.FUNDING]: 0.5}), token_count: 32 },
|
||||
],
|
||||
},
|
||||
{
|
||||
slug: 'people/david-okonkwo',
|
||||
type: 'person',
|
||||
title: 'David Okonkwo',
|
||||
compiled_truth: 'David Okonkwo is founder of EduStack, an AI-powered adaptive learning platform. Previously taught CS at Stanford. Believes personalized education is the biggest unlocked market in tech.',
|
||||
timeline: '2024-05-02: David demoed EduStack at demo day. The adaptive curriculum engine is genuinely novel.\n2024-09-18: David shipped v2 with real-time assessment. Growing 40% MoM in Nigeria.',
|
||||
chunks: [
|
||||
{ chunk_index: 0, chunk_text: 'David Okonkwo is founder of EduStack, an AI-powered adaptive learning platform. Previously taught CS at Stanford. Believes personalized education is the biggest unlocked market in tech.', chunk_source: 'compiled_truth', embedding: topicEmbedding({[T.AI]: 0.5, [T.EDUCATION]: 1, [T.FOUNDER]: 0.6}), token_count: 32 },
|
||||
{ chunk_index: 1, chunk_text: '2024-05-02: David demoed EduStack at demo day. Adaptive curriculum engine is novel. 2024-09-18: David shipped v2 with real-time assessment. Growing 40% MoM in Nigeria.', chunk_source: 'timeline', embedding: topicEmbedding({[T.EDUCATION]: 0.5, [T.LAUNCH]: 0.7, [T.MEETING]: 0.4}), token_count: 35 },
|
||||
],
|
||||
},
|
||||
{
|
||||
slug: 'people/elena-volkov',
|
||||
type: 'person',
|
||||
title: 'Elena Volkov',
|
||||
compiled_truth: 'Elena Volkov is co-founder of CryptoSafe, building institutional-grade custody for digital assets. Former security engineer at Google. Expert in HSM architecture and multi-party computation.',
|
||||
timeline: '2024-01-20: Elena gave a talk on MPC wallets at ETH Denver. Very technical, very sharp.\n2024-06-15: CryptoSafe announced $30M Series A led by a16z crypto.',
|
||||
chunks: [
|
||||
{ chunk_index: 0, chunk_text: 'Elena Volkov is co-founder of CryptoSafe, building institutional-grade custody for digital assets. Former security engineer at Google. Expert in HSM architecture and multi-party computation.', chunk_source: 'compiled_truth', embedding: topicEmbedding({[T.CRYPTO]: 0.8, [T.ENTERPRISE]: 0.5, [T.ENGINEER]: 0.6, [T.FOUNDER]: 0.3}), token_count: 32 },
|
||||
{ chunk_index: 1, chunk_text: '2024-01-20: Elena talk on MPC wallets at ETH Denver. Very technical. 2024-06-15: CryptoSafe announced $30M Series A led by a16z crypto.', chunk_source: 'timeline', embedding: topicEmbedding({[T.CRYPTO]: 0.5, [T.ANNOUNCEMENT]: 0.6, [T.FUNDING]: 0.7}), token_count: 28 },
|
||||
],
|
||||
},
|
||||
{
|
||||
slug: 'people/frank-dubois',
|
||||
type: 'person',
|
||||
title: 'Frank Dubois',
|
||||
compiled_truth: 'Frank Dubois is head of AI at RoboLogic, building autonomous warehouse robots. 15 years in robotics, previously at Boston Dynamics. Focused on manipulation in unstructured environments.',
|
||||
timeline: '2024-03-22: Frank showed the latest RoboLogic demo. Picking irregular objects at 98% accuracy.\n2024-11-01: RoboLogic deployed at Amazon fulfillment center in Memphis.',
|
||||
chunks: [
|
||||
{ chunk_index: 0, chunk_text: 'Frank Dubois is head of AI at RoboLogic, building autonomous warehouse robots. 15 years in robotics, previously at Boston Dynamics. Focused on manipulation in unstructured environments.', chunk_source: 'compiled_truth', embedding: topicEmbedding({[T.AI]: 0.6, [T.ROBOTICS]: 1, [T.ENGINEER]: 0.5}), token_count: 30 },
|
||||
{ chunk_index: 1, chunk_text: '2024-03-22: Frank showed RoboLogic demo. Picking irregular objects at 98% accuracy. 2024-11-01: RoboLogic deployed at Amazon fulfillment center in Memphis.', chunk_source: 'timeline', embedding: topicEmbedding({[T.ROBOTICS]: 0.6, [T.LAUNCH]: 0.7, [T.MEETING]: 0.3}), token_count: 28 },
|
||||
],
|
||||
},
|
||||
{
|
||||
slug: 'people/grace-lee',
|
||||
type: 'person',
|
||||
title: 'Grace Lee',
|
||||
compiled_truth: 'Grace Lee is a designer and founder of PixelCraft, a design tool for AI-generated UI components. Former lead designer at Figma. Strong opinions on AI replacing mockups with working prototypes.',
|
||||
timeline: '2024-04-30: Grace launched PixelCraft beta. 5000 signups in first week.\n2024-08-15: Grace hired 3 engineers from Vercel. PixelCraft growing fast.',
|
||||
chunks: [
|
||||
{ chunk_index: 0, chunk_text: 'Grace Lee is a designer and founder of PixelCraft, a design tool for AI-generated UI components. Former lead designer at Figma. Strong opinions on AI replacing mockups with working prototypes.', chunk_source: 'compiled_truth', embedding: topicEmbedding({[T.AI]: 0.5, [T.DESIGNER]: 0.9, [T.CONSUMER]: 0.4, [T.FOUNDER]: 0.5}), token_count: 34 },
|
||||
{ chunk_index: 1, chunk_text: '2024-04-30: Grace launched PixelCraft beta. 5000 signups first week. 2024-08-15: Grace hired 3 engineers from Vercel. Growing fast.', chunk_source: 'timeline', embedding: topicEmbedding({[T.DESIGNER]: 0.3, [T.LAUNCH]: 0.8, [T.HIRING]: 0.5}), token_count: 25 },
|
||||
],
|
||||
},
|
||||
{
|
||||
slug: 'people/hiro-tanaka',
|
||||
type: 'person',
|
||||
title: 'Hiro Tanaka',
|
||||
compiled_truth: 'Hiro Tanaka is CEO of GenomeAI, using large language models to predict protein folding for drug discovery. Previously research scientist at DeepMind. Published 40+ papers on computational biology.',
|
||||
timeline: '2024-02-14: Hiro presented GenomeAI results at Bio conference. Beat AlphaFold on 3 benchmarks.\n2024-10-20: GenomeAI partnered with Pfizer for oncology drug discovery pipeline.',
|
||||
chunks: [
|
||||
{ chunk_index: 0, chunk_text: 'Hiro Tanaka is CEO of GenomeAI, using large language models to predict protein folding for drug discovery. Previously research scientist at DeepMind. Published 40+ papers on computational biology.', chunk_source: 'compiled_truth', embedding: topicEmbedding({[T.AI]: 0.7, [T.BIOTECH]: 0.9, [T.FOUNDER]: 0.4}), token_count: 34 },
|
||||
{ chunk_index: 1, chunk_text: '2024-02-14: Hiro presented GenomeAI results. Beat AlphaFold on 3 benchmarks. 2024-10-20: GenomeAI partnered with Pfizer for oncology drug discovery.', chunk_source: 'timeline', embedding: topicEmbedding({[T.BIOTECH]: 0.6, [T.ANNOUNCEMENT]: 0.5, [T.MEETING]: 0.4}), token_count: 28 },
|
||||
],
|
||||
},
|
||||
{
|
||||
slug: 'people/iris-washington',
|
||||
type: 'person',
|
||||
title: 'Iris Washington',
|
||||
compiled_truth: 'Iris Washington is VP of Product at CloudScale, an enterprise infrastructure company. Expert in developer experience and platform engineering. Previously PM at AWS Lambda team.',
|
||||
timeline: '2024-05-18: Iris spoke at re:Invent about serverless at scale. Great talk on cold start optimization.\n2024-09-03: CloudScale acquired by Datadog for $2.1B.',
|
||||
chunks: [
|
||||
{ chunk_index: 0, chunk_text: 'Iris Washington is VP of Product at CloudScale, an enterprise infrastructure company. Expert in developer experience and platform engineering. Previously PM at AWS Lambda team.', chunk_source: 'compiled_truth', embedding: topicEmbedding({[T.ENTERPRISE]: 0.9, [T.ENGINEER]: 0.5, [T.AI]: 0.2}), token_count: 30 },
|
||||
{ chunk_index: 1, chunk_text: '2024-05-18: Iris spoke at re:Invent about serverless at scale. Cold start optimization. 2024-09-03: CloudScale acquired by Datadog for $2.1B.', chunk_source: 'timeline', embedding: topicEmbedding({[T.ENTERPRISE]: 0.4, [T.ANNOUNCEMENT]: 0.7, [T.MEETING]: 0.3}), token_count: 28 },
|
||||
],
|
||||
},
|
||||
{
|
||||
slug: 'people/james-park',
|
||||
type: 'person',
|
||||
title: 'James Park',
|
||||
compiled_truth: 'James Park is a climate tech investor and founder of TerraFund. Focuses on hard tech: carbon capture, nuclear fusion, and sustainable materials. Believes climate is a $50T market by 2040.',
|
||||
timeline: '2024-07-22: James announced TerraFund II, $500M for climate deep tech.\n2024-11-15: Met James at Climate Week. He invested in 3 fusion startups this year.',
|
||||
chunks: [
|
||||
{ chunk_index: 0, chunk_text: 'James Park is a climate tech investor and founder of TerraFund. Focuses on hard tech: carbon capture, nuclear fusion, sustainable materials. Climate is a $50T market by 2040.', chunk_source: 'compiled_truth', embedding: topicEmbedding({[T.CLIMATE]: 0.9, [T.INVESTOR]: 0.8, [T.FOUNDER]: 0.3}), token_count: 32 },
|
||||
{ chunk_index: 1, chunk_text: '2024-07-22: James announced TerraFund II, $500M for climate deep tech. 2024-11-15: Met James at Climate Week. Invested in 3 fusion startups.', chunk_source: 'timeline', embedding: topicEmbedding({[T.CLIMATE]: 0.5, [T.FUNDING]: 0.8, [T.MEETING]: 0.4}), token_count: 28 },
|
||||
],
|
||||
},
|
||||
|
||||
// ── Companies (10) ───────────────────────────────────────────
|
||||
{
|
||||
slug: 'companies/novapay',
|
||||
type: 'company',
|
||||
title: 'NovaPay',
|
||||
compiled_truth: 'NovaPay builds instant cross-border payments for SMBs. Founded by Alice Chen (ex-Stripe). Series B stage, expanding across Southeast Asia. Regulatory-first approach differentiates from competitors.',
|
||||
timeline: '2024-01-15: NovaPay launched in Thailand. 2024-06-20: Raised $45M Series B. 2024-09-01: Processed $1B in cross-border volume.',
|
||||
chunks: [
|
||||
{ chunk_index: 0, chunk_text: 'NovaPay builds instant cross-border payments for SMBs. Founded by Alice Chen (ex-Stripe). Series B stage, expanding across Southeast Asia. Regulatory-first approach differentiates.', chunk_source: 'compiled_truth', embedding: topicEmbedding({[T.FINTECH]: 1, [T.ENTERPRISE]: 0.4}), token_count: 30 },
|
||||
{ chunk_index: 1, chunk_text: '2024-01-15: NovaPay launched in Thailand. 2024-06-20: Raised $45M Series B. 2024-09-01: Processed $1B in cross-border volume.', chunk_source: 'timeline', embedding: topicEmbedding({[T.FINTECH]: 0.4, [T.LAUNCH]: 0.5, [T.FUNDING]: 0.6}), token_count: 25 },
|
||||
],
|
||||
},
|
||||
{
|
||||
slug: 'companies/mindbridge',
|
||||
type: 'company',
|
||||
title: 'MindBridge',
|
||||
compiled_truth: 'MindBridge builds AI diagnostic tools for mental health. 94% concordance with clinical diagnosis. Used by 200+ clinics. Carol Nakamura (CTO) leads the technical vision.',
|
||||
timeline: '2024-02-28: Presented at AI Health Summit. 2024-07-12: Series A fundraising, targeting $15M. 2024-10-01: FDA breakthrough device designation.',
|
||||
chunks: [
|
||||
{ chunk_index: 0, chunk_text: 'MindBridge builds AI diagnostic tools for mental health. 94% concordance with clinical diagnosis. Used by 200+ clinics. Carol Nakamura leads technical vision.', chunk_source: 'compiled_truth', embedding: topicEmbedding({[T.AI]: 0.6, [T.HEALTH]: 0.9, [T.ENTERPRISE]: 0.3}), token_count: 28 },
|
||||
{ chunk_index: 1, chunk_text: '2024-02-28: AI Health Summit presentation. 2024-07-12: Series A targeting $15M. 2024-10-01: FDA breakthrough device designation.', chunk_source: 'timeline', embedding: topicEmbedding({[T.HEALTH]: 0.5, [T.FUNDING]: 0.5, [T.ANNOUNCEMENT]: 0.6}), token_count: 22 },
|
||||
],
|
||||
},
|
||||
{
|
||||
slug: 'companies/cryptosafe',
|
||||
type: 'company',
|
||||
title: 'CryptoSafe',
|
||||
compiled_truth: 'CryptoSafe provides institutional-grade custody for digital assets using multi-party computation. Founded by Elena Volkov (ex-Google security). $30M Series A from a16z crypto.',
|
||||
timeline: '2024-01-20: ETH Denver demo. 2024-06-15: $30M Series A announced. 2024-10-30: Onboarded first sovereign wealth fund client.',
|
||||
chunks: [
|
||||
{ chunk_index: 0, chunk_text: 'CryptoSafe provides institutional-grade custody for digital assets using multi-party computation. Founded by Elena Volkov. $30M Series A from a16z crypto.', chunk_source: 'compiled_truth', embedding: topicEmbedding({[T.CRYPTO]: 0.9, [T.ENTERPRISE]: 0.5, [T.FINTECH]: 0.3}), token_count: 26 },
|
||||
{ chunk_index: 1, chunk_text: '2024-01-20: ETH Denver demo. 2024-06-15: $30M Series A. 2024-10-30: First sovereign wealth fund client.', chunk_source: 'timeline', embedding: topicEmbedding({[T.CRYPTO]: 0.4, [T.FUNDING]: 0.7, [T.ANNOUNCEMENT]: 0.5}), token_count: 20 },
|
||||
],
|
||||
},
|
||||
{
|
||||
slug: 'companies/robologic',
|
||||
type: 'company',
|
||||
title: 'RoboLogic',
|
||||
compiled_truth: 'RoboLogic builds autonomous warehouse robots for irregular object picking. 98% accuracy on unstructured items. Frank Dubois (head of AI) leads R&D. Deployed at major fulfillment centers.',
|
||||
timeline: '2024-03-22: Demo day showing. 2024-11-01: Amazon fulfillment deployment in Memphis.',
|
||||
chunks: [
|
||||
{ chunk_index: 0, chunk_text: 'RoboLogic builds autonomous warehouse robots for irregular object picking. 98% accuracy. Frank Dubois leads R&D. Deployed at major fulfillment centers.', chunk_source: 'compiled_truth', embedding: topicEmbedding({[T.ROBOTICS]: 0.9, [T.AI]: 0.6, [T.ENTERPRISE]: 0.4}), token_count: 25 },
|
||||
{ chunk_index: 1, chunk_text: '2024-03-22: Demo day showing. 2024-11-01: Amazon fulfillment deployment in Memphis.', chunk_source: 'timeline', embedding: topicEmbedding({[T.ROBOTICS]: 0.4, [T.LAUNCH]: 0.8}), token_count: 15 },
|
||||
],
|
||||
},
|
||||
{
|
||||
slug: 'companies/edustack',
|
||||
type: 'company',
|
||||
title: 'EduStack',
|
||||
compiled_truth: 'EduStack is an AI-powered adaptive learning platform. Personalizes curriculum in real-time based on student performance. Founded by David Okonkwo (ex-Stanford CS). Growing 40% MoM in Nigeria.',
|
||||
timeline: '2024-05-02: Demo day presentation. 2024-09-18: V2 launch with real-time assessment. 2024-12-01: Expanded to Kenya and Ghana.',
|
||||
chunks: [
|
||||
{ chunk_index: 0, chunk_text: 'EduStack is an AI-powered adaptive learning platform. Personalizes curriculum in real-time. Founded by David Okonkwo. Growing 40% MoM in Nigeria.', chunk_source: 'compiled_truth', embedding: topicEmbedding({[T.AI]: 0.5, [T.EDUCATION]: 0.9, [T.CONSUMER]: 0.4}), token_count: 26 },
|
||||
{ chunk_index: 1, chunk_text: '2024-05-02: Demo day. 2024-09-18: V2 with real-time assessment. 2024-12-01: Expanded to Kenya and Ghana.', chunk_source: 'timeline', embedding: topicEmbedding({[T.EDUCATION]: 0.4, [T.LAUNCH]: 0.7, [T.ANNOUNCEMENT]: 0.3}), token_count: 20 },
|
||||
],
|
||||
},
|
||||
{
|
||||
slug: 'companies/pixelcraft',
|
||||
type: 'company', title: 'PixelCraft',
|
||||
compiled_truth: 'PixelCraft is a design tool that generates working UI components from natural language. Founded by Grace Lee (ex-Figma). 5000 signups in first week of beta.',
|
||||
timeline: '2024-04-30: Beta launch, 5000 signups. 2024-08-15: Hired 3 Vercel engineers.',
|
||||
chunks: [
|
||||
{ chunk_index: 0, chunk_text: 'PixelCraft generates working UI components from natural language. Founded by Grace Lee (ex-Figma). 5000 signups first week.', chunk_source: 'compiled_truth', embedding: topicEmbedding({[T.AI]: 0.6, [T.DESIGNER]: 0.8, [T.CONSUMER]: 0.5}), token_count: 22 },
|
||||
{ chunk_index: 1, chunk_text: '2024-04-30: Beta launch, 5000 signups. 2024-08-15: Hired 3 Vercel engineers.', chunk_source: 'timeline', embedding: topicEmbedding({[T.LAUNCH]: 0.8, [T.HIRING]: 0.6}), token_count: 14 },
|
||||
],
|
||||
},
|
||||
{
|
||||
slug: 'companies/genomeai',
|
||||
type: 'company', title: 'GenomeAI',
|
||||
compiled_truth: 'GenomeAI uses LLMs to predict protein folding for drug discovery. Beat AlphaFold on 3 benchmarks. CEO Hiro Tanaka (ex-DeepMind). Partnered with Pfizer.',
|
||||
timeline: '2024-02-14: Bio conference results. 2024-10-20: Pfizer partnership announced.',
|
||||
chunks: [
|
||||
{ chunk_index: 0, chunk_text: 'GenomeAI uses LLMs to predict protein folding for drug discovery. Beat AlphaFold on 3 benchmarks. CEO Hiro Tanaka. Pfizer partnership.', chunk_source: 'compiled_truth', embedding: topicEmbedding({[T.AI]: 0.7, [T.BIOTECH]: 0.9}), token_count: 24 },
|
||||
{ chunk_index: 1, chunk_text: '2024-02-14: Bio conference, beat AlphaFold. 2024-10-20: Pfizer partnership for oncology.', chunk_source: 'timeline', embedding: topicEmbedding({[T.BIOTECH]: 0.5, [T.ANNOUNCEMENT]: 0.7}), token_count: 16 },
|
||||
],
|
||||
},
|
||||
{
|
||||
slug: 'companies/terrafund',
|
||||
type: 'company', title: 'TerraFund',
|
||||
compiled_truth: 'TerraFund is a $500M climate deep tech fund. Founded by James Park. Invests in carbon capture, nuclear fusion, and sustainable materials. Three fusion investments in 2024.',
|
||||
timeline: '2024-07-22: TerraFund II announced at $500M. 2024-11-15: Climate Week panel.',
|
||||
chunks: [
|
||||
{ chunk_index: 0, chunk_text: 'TerraFund is a $500M climate deep tech fund. Founded by James Park. Carbon capture, nuclear fusion, sustainable materials.', chunk_source: 'compiled_truth', embedding: topicEmbedding({[T.CLIMATE]: 0.9, [T.INVESTOR]: 0.6, [T.FUNDING]: 0.3}), token_count: 22 },
|
||||
{ chunk_index: 1, chunk_text: '2024-07-22: TerraFund II at $500M. 2024-11-15: Climate Week panel.', chunk_source: 'timeline', embedding: topicEmbedding({[T.CLIMATE]: 0.4, [T.FUNDING]: 0.8, [T.ANNOUNCEMENT]: 0.5}), token_count: 14 },
|
||||
],
|
||||
},
|
||||
{
|
||||
slug: 'companies/cloudscale',
|
||||
type: 'company', title: 'CloudScale',
|
||||
compiled_truth: 'CloudScale is an enterprise infrastructure company focused on serverless at scale. Iris Washington is VP Product. Acquired by Datadog for $2.1B in 2024.',
|
||||
timeline: '2024-05-18: re:Invent talk on cold starts. 2024-09-03: Datadog acquisition at $2.1B.',
|
||||
chunks: [
|
||||
{ chunk_index: 0, chunk_text: 'CloudScale is enterprise infrastructure for serverless at scale. VP Product Iris Washington. Acquired by Datadog for $2.1B.', chunk_source: 'compiled_truth', embedding: topicEmbedding({[T.ENTERPRISE]: 0.9, [T.AI]: 0.2}), token_count: 22 },
|
||||
{ chunk_index: 1, chunk_text: '2024-05-18: re:Invent cold start talk. 2024-09-03: Datadog acquired CloudScale for $2.1B.', chunk_source: 'timeline', embedding: topicEmbedding({[T.ENTERPRISE]: 0.3, [T.ANNOUNCEMENT]: 0.8}), token_count: 16 },
|
||||
],
|
||||
},
|
||||
{
|
||||
slug: 'companies/solargrid',
|
||||
type: 'company', title: 'SolarGrid',
|
||||
compiled_truth: 'SolarGrid builds distributed solar micro-grids for rural electrification. Bob Martinez is a board member. Operating in 12 African countries.',
|
||||
timeline: '2024-08-05: Bob introduced the founder. 2024-12-10: SolarGrid hit 1M homes powered.',
|
||||
chunks: [
|
||||
{ chunk_index: 0, chunk_text: 'SolarGrid builds distributed solar micro-grids for rural electrification. Bob Martinez board member. Operating in 12 African countries.', chunk_source: 'compiled_truth', embedding: topicEmbedding({[T.CLIMATE]: 0.8, [T.ENTERPRISE]: 0.3}), token_count: 22 },
|
||||
{ chunk_index: 1, chunk_text: '2024-08-05: Bob introduced founder. 2024-12-10: SolarGrid hit 1M homes powered.', chunk_source: 'timeline', embedding: topicEmbedding({[T.CLIMATE]: 0.3, [T.ANNOUNCEMENT]: 0.5, [T.MEETING]: 0.4}), token_count: 14 },
|
||||
],
|
||||
},
|
||||
|
||||
// ── Concepts (10) ────────────────────────────────────────────
|
||||
{
|
||||
slug: 'concepts/ai-first-companies',
|
||||
type: 'concept', title: 'AI-First Companies',
|
||||
compiled_truth: 'AI-first companies embed machine learning into the core product loop, not as a feature bolt-on. Examples: MindBridge (diagnostics), EduStack (adaptive learning), PixelCraft (design). The common pattern is that AI IS the product, not AI-enhanced.',
|
||||
timeline: '2024-03-01: Wrote first draft of AI-first thesis. 2024-09-15: Revisited after seeing 10 more examples.',
|
||||
chunks: [
|
||||
{ chunk_index: 0, chunk_text: 'AI-first companies embed machine learning into the core product loop. MindBridge, EduStack, PixelCraft. AI IS the product, not AI-enhanced.', chunk_source: 'compiled_truth', embedding: topicEmbedding({[T.AI]: 1, [T.FOUNDER]: 0.3, [T.ENTERPRISE]: 0.2, [T.CONSUMER]: 0.2}), token_count: 26 },
|
||||
{ chunk_index: 1, chunk_text: '2024-03-01: First draft of AI-first thesis. 2024-09-15: Revisited after 10 more examples.', chunk_source: 'timeline', embedding: topicEmbedding({[T.AI]: 0.5, [T.TIMELINE]: 0.5}), token_count: 18 },
|
||||
],
|
||||
},
|
||||
{
|
||||
slug: 'concepts/climate-investing',
|
||||
type: 'concept', title: 'Climate Tech Investment Thesis',
|
||||
compiled_truth: 'Climate tech is a $50T market by 2040. Three waves: solar/wind (done), batteries/grid (now), carbon capture/fusion (next). TerraFund and Green Horizon are the key funds. Hard tech wins over software-only.',
|
||||
timeline: '2024-04-10: Bob articulated the three-wave framework. 2024-11-15: James confirmed fusion timeline at Climate Week.',
|
||||
chunks: [
|
||||
{ chunk_index: 0, chunk_text: 'Climate tech is a $50T market by 2040. Three waves: solar/wind (done), batteries/grid (now), carbon capture/fusion (next). TerraFund and Green Horizon key funds.', chunk_source: 'compiled_truth', embedding: topicEmbedding({[T.CLIMATE]: 1, [T.INVESTOR]: 0.5, [T.FUNDING]: 0.3}), token_count: 30 },
|
||||
{ chunk_index: 1, chunk_text: '2024-04-10: Bob three-wave framework. 2024-11-15: James confirmed fusion timeline at Climate Week.', chunk_source: 'timeline', embedding: topicEmbedding({[T.CLIMATE]: 0.5, [T.MEETING]: 0.5, [T.INVESTOR]: 0.3}), token_count: 18 },
|
||||
],
|
||||
},
|
||||
{
|
||||
slug: 'concepts/fintech-rails',
|
||||
type: 'concept', title: 'Payment Rails Infrastructure',
|
||||
compiled_truth: 'Cross-border payments are still broken. SWIFT takes 3-5 days. NovaPay and similar startups are building real-time rails using local payment networks. Regulatory compliance is the moat, not technology.',
|
||||
timeline: '2024-03-15: Alice explained regulatory-first approach at Fintech Forum.',
|
||||
chunks: [
|
||||
{ chunk_index: 0, chunk_text: 'Cross-border payments are still broken. SWIFT takes 3-5 days. NovaPay building real-time rails. Regulatory compliance is the moat, not technology.', chunk_source: 'compiled_truth', embedding: topicEmbedding({[T.FINTECH]: 1, [T.ENTERPRISE]: 0.3}), token_count: 26 },
|
||||
{ chunk_index: 1, chunk_text: '2024-03-15: Alice explained regulatory-first approach at Fintech Forum.', chunk_source: 'timeline', embedding: topicEmbedding({[T.FINTECH]: 0.4, [T.MEETING]: 0.6}), token_count: 12 },
|
||||
],
|
||||
},
|
||||
{
|
||||
slug: 'concepts/crypto-custody',
|
||||
type: 'concept', title: 'Institutional Crypto Custody',
|
||||
compiled_truth: 'Institutional adoption of crypto requires custody solutions that meet banking-grade security standards. MPC (multi-party computation) is the winning architecture. CryptoSafe is leading this space.',
|
||||
timeline: '2024-01-20: Elena ETH Denver talk. 2024-10-30: First sovereign wealth fund using MPC custody.',
|
||||
chunks: [
|
||||
{ chunk_index: 0, chunk_text: 'Institutional crypto adoption requires banking-grade custody. MPC is the winning architecture. CryptoSafe leads.', chunk_source: 'compiled_truth', embedding: topicEmbedding({[T.CRYPTO]: 0.9, [T.ENTERPRISE]: 0.5}), token_count: 18 },
|
||||
{ chunk_index: 1, chunk_text: '2024-01-20: Elena ETH Denver talk on MPC. 2024-10-30: First sovereign wealth fund using MPC custody.', chunk_source: 'timeline', embedding: topicEmbedding({[T.CRYPTO]: 0.5, [T.ANNOUNCEMENT]: 0.5, [T.MEETING]: 0.3}), token_count: 18 },
|
||||
],
|
||||
},
|
||||
{
|
||||
slug: 'concepts/ai-health',
|
||||
type: 'concept', title: 'AI in Healthcare',
|
||||
compiled_truth: 'AI in healthcare is moving from research to deployment. MindBridge (mental health, 94% accuracy), GenomeAI (drug discovery, beat AlphaFold). FDA is creating new regulatory pathways for AI diagnostics.',
|
||||
timeline: '2024-02-28: AI Health Summit. 2024-10-01: MindBridge FDA breakthrough designation.',
|
||||
chunks: [
|
||||
{ chunk_index: 0, chunk_text: 'AI in healthcare moving from research to deployment. MindBridge 94% accuracy, GenomeAI beat AlphaFold. FDA creating new AI diagnostic pathways.', chunk_source: 'compiled_truth', embedding: topicEmbedding({[T.AI]: 0.7, [T.HEALTH]: 0.8, [T.BIOTECH]: 0.4}), token_count: 26 },
|
||||
{ chunk_index: 1, chunk_text: '2024-02-28: AI Health Summit. 2024-10-01: MindBridge FDA breakthrough.', chunk_source: 'timeline', embedding: topicEmbedding({[T.HEALTH]: 0.5, [T.ANNOUNCEMENT]: 0.5, [T.AI]: 0.3}), token_count: 12 },
|
||||
],
|
||||
},
|
||||
{
|
||||
slug: 'concepts/robotics-warehouse',
|
||||
type: 'concept', title: 'Warehouse Automation',
|
||||
compiled_truth: 'Warehouse robotics is moving from structured (conveyor belts, AGVs) to unstructured (picking irregular objects). RoboLogic at 98% accuracy. The bottleneck is manipulation, not navigation.',
|
||||
timeline: '2024-03-22: RoboLogic demo. 2024-11-01: Amazon deployment validates the market.',
|
||||
chunks: [
|
||||
{ chunk_index: 0, chunk_text: 'Warehouse robotics moving from structured to unstructured picking. RoboLogic 98% accuracy. Bottleneck is manipulation, not navigation.', chunk_source: 'compiled_truth', embedding: topicEmbedding({[T.ROBOTICS]: 0.9, [T.AI]: 0.5, [T.ENTERPRISE]: 0.3}), token_count: 22 },
|
||||
{ chunk_index: 1, chunk_text: '2024-03-22: RoboLogic demo. 2024-11-01: Amazon deployment validates market.', chunk_source: 'timeline', embedding: topicEmbedding({[T.ROBOTICS]: 0.4, [T.LAUNCH]: 0.6}), token_count: 12 },
|
||||
],
|
||||
},
|
||||
{
|
||||
slug: 'concepts/ai-education',
|
||||
type: 'concept', title: 'AI in Education',
|
||||
compiled_truth: 'Personalized education at scale is now possible with AI. EduStack shows 40% MoM growth. The key insight: adaptive curriculum beats static textbooks because every student learns differently.',
|
||||
timeline: '2024-05-02: David demo day. 2024-12-01: EduStack expanded to 3 African countries.',
|
||||
chunks: [
|
||||
{ chunk_index: 0, chunk_text: 'Personalized education at scale with AI. EduStack 40% MoM growth. Adaptive curriculum beats static textbooks.', chunk_source: 'compiled_truth', embedding: topicEmbedding({[T.AI]: 0.5, [T.EDUCATION]: 0.9, [T.CONSUMER]: 0.3}), token_count: 20 },
|
||||
{ chunk_index: 1, chunk_text: '2024-05-02: David demo. 2024-12-01: EduStack to Kenya and Ghana.', chunk_source: 'timeline', embedding: topicEmbedding({[T.EDUCATION]: 0.4, [T.LAUNCH]: 0.5}), token_count: 12 },
|
||||
],
|
||||
},
|
||||
{
|
||||
slug: 'concepts/design-ai',
|
||||
type: 'concept', title: 'AI-Powered Design Tools',
|
||||
compiled_truth: 'AI is replacing the mockup-to-code pipeline. PixelCraft generates working components from descriptions. Grace Lee argues designers should think in systems, not screens. The next Figma is AI-native.',
|
||||
timeline: '2024-04-30: PixelCraft beta launch validated the thesis.',
|
||||
chunks: [
|
||||
{ chunk_index: 0, chunk_text: 'AI replacing mockup-to-code pipeline. PixelCraft generates components from descriptions. Grace Lee: think in systems, not screens. Next Figma is AI-native.', chunk_source: 'compiled_truth', embedding: topicEmbedding({[T.AI]: 0.6, [T.DESIGNER]: 0.9, [T.CONSUMER]: 0.3}), token_count: 28 },
|
||||
{ chunk_index: 1, chunk_text: '2024-04-30: PixelCraft beta validated the thesis.', chunk_source: 'timeline', embedding: topicEmbedding({[T.DESIGNER]: 0.3, [T.LAUNCH]: 0.5}), token_count: 10 },
|
||||
],
|
||||
},
|
||||
{
|
||||
slug: 'concepts/acquisitions-2024',
|
||||
type: 'concept', title: '2024 Notable Acquisitions',
|
||||
compiled_truth: 'Datadog acquired CloudScale for $2.1B (serverless infrastructure). Signaling: infrastructure consolidation is accelerating. Platform companies are buying specialized tools.',
|
||||
timeline: '2024-09-03: CloudScale acquisition announced. 2024-09-10: Market reacted positively, Datadog stock up 8%.',
|
||||
chunks: [
|
||||
{ chunk_index: 0, chunk_text: 'Datadog acquired CloudScale for $2.1B. Infrastructure consolidation accelerating. Platform companies buying specialized tools.', chunk_source: 'compiled_truth', embedding: topicEmbedding({[T.ENTERPRISE]: 0.7, [T.ANNOUNCEMENT]: 0.5}), token_count: 20 },
|
||||
{ chunk_index: 1, chunk_text: '2024-09-03: CloudScale acquisition. 2024-09-10: Datadog stock up 8%.', chunk_source: 'timeline', embedding: topicEmbedding({[T.ENTERPRISE]: 0.3, [T.ANNOUNCEMENT]: 0.7}), token_count: 12 },
|
||||
],
|
||||
},
|
||||
];
|
||||
|
||||
// ─── Benchmark Queries (20) ──────────────────────────────────────
|
||||
|
||||
interface BenchmarkQuery {
|
||||
id: string;
|
||||
query: string;
|
||||
queryEmbedding: Float32Array;
|
||||
relevant: string[];
|
||||
grades?: Record<string, number>;
|
||||
expectedSource: 'compiled_truth' | 'timeline';
|
||||
description: string;
|
||||
}
|
||||
|
||||
const QUERIES: BenchmarkQuery[] = [
|
||||
// Entity lookups (should get compiled truth)
|
||||
{ id: 'q01', query: 'Who is Alice Chen?', queryEmbedding: topicEmbedding({[T.FINTECH]: 0.8, [T.FOUNDER]: 0.5}), relevant: ['people/alice-chen', 'companies/novapay'], grades: {'people/alice-chen': 3, 'companies/novapay': 1}, expectedSource: 'compiled_truth', description: 'Person lookup: Alice Chen' },
|
||||
{ id: 'q02', query: 'What does MindBridge do?', queryEmbedding: topicEmbedding({[T.AI]: 0.5, [T.HEALTH]: 0.8}), relevant: ['companies/mindbridge', 'people/carol-nakamura', 'concepts/ai-health'], grades: {'companies/mindbridge': 3, 'people/carol-nakamura': 2, 'concepts/ai-health': 1}, expectedSource: 'compiled_truth', description: 'Company lookup: MindBridge' },
|
||||
{ id: 'q03', query: 'Tell me about climate tech investing', queryEmbedding: topicEmbedding({[T.CLIMATE]: 0.9, [T.INVESTOR]: 0.5}), relevant: ['concepts/climate-investing', 'people/bob-martinez', 'people/james-park', 'companies/terrafund'], grades: {'concepts/climate-investing': 3, 'people/james-park': 2, 'people/bob-martinez': 2, 'companies/terrafund': 1}, expectedSource: 'compiled_truth', description: 'Topic overview: climate investing' },
|
||||
|
||||
// Temporal queries (should get timeline)
|
||||
{ id: 'q04', query: 'When did we last meet Alice?', queryEmbedding: topicEmbedding({[T.FINTECH]: 0.4, [T.MEETING]: 0.9}), relevant: ['people/alice-chen'], expectedSource: 'timeline', description: 'Temporal: last meeting with Alice' },
|
||||
{ id: 'q05', query: 'Recent updates on GenomeAI', queryEmbedding: topicEmbedding({[T.BIOTECH]: 0.6, [T.ANNOUNCEMENT]: 0.5}), relevant: ['companies/genomeai', 'people/hiro-tanaka'], grades: {'companies/genomeai': 3, 'people/hiro-tanaka': 1}, expectedSource: 'timeline', description: 'Temporal: GenomeAI updates' },
|
||||
{ id: 'q06', query: 'What happened with the CloudScale acquisition?', queryEmbedding: topicEmbedding({[T.ENTERPRISE]: 0.6, [T.ANNOUNCEMENT]: 0.8}), relevant: ['companies/cloudscale', 'concepts/acquisitions-2024', 'people/iris-washington'], grades: {'companies/cloudscale': 3, 'concepts/acquisitions-2024': 2, 'people/iris-washington': 1}, expectedSource: 'timeline', description: 'Event: CloudScale acquisition' },
|
||||
|
||||
// Cross-entity queries (tests relationship understanding)
|
||||
{ id: 'q07', query: 'Alice Chen NovaPay cross-border payments', queryEmbedding: topicEmbedding({[T.FINTECH]: 0.9, [T.FOUNDER]: 0.3}), relevant: ['people/alice-chen', 'companies/novapay', 'concepts/fintech-rails'], grades: {'people/alice-chen': 2, 'companies/novapay': 3, 'concepts/fintech-rails': 2}, expectedSource: 'compiled_truth', description: 'Cross-entity: Alice + NovaPay' },
|
||||
{ id: 'q08', query: 'Carol Nakamura MindBridge AI health', queryEmbedding: topicEmbedding({[T.AI]: 0.5, [T.HEALTH]: 0.7, [T.FOUNDER]: 0.3}), relevant: ['people/carol-nakamura', 'companies/mindbridge', 'concepts/ai-health'], grades: {'people/carol-nakamura': 2, 'companies/mindbridge': 2, 'concepts/ai-health': 2}, expectedSource: 'compiled_truth', description: 'Cross-entity: Carol + MindBridge' },
|
||||
|
||||
// Competitive/thematic queries (multiple relevant pages)
|
||||
{ id: 'q09', query: 'AI companies building real products', queryEmbedding: topicEmbedding({[T.AI]: 0.9, [T.FOUNDER]: 0.3, [T.CONSUMER]: 0.2}), relevant: ['concepts/ai-first-companies', 'companies/mindbridge', 'companies/edustack', 'companies/pixelcraft', 'companies/genomeai'], grades: {'concepts/ai-first-companies': 3, 'companies/mindbridge': 2, 'companies/edustack': 2, 'companies/pixelcraft': 2, 'companies/genomeai': 2}, expectedSource: 'compiled_truth', description: 'Thematic: AI companies' },
|
||||
{ id: 'q10', query: 'Who raised funding recently?', queryEmbedding: topicEmbedding({[T.FUNDING]: 0.9, [T.ANNOUNCEMENT]: 0.4}), relevant: ['companies/novapay', 'companies/cryptosafe', 'companies/terrafund', 'people/carol-nakamura'], grades: {'companies/novapay': 2, 'companies/cryptosafe': 2, 'companies/terrafund': 2, 'people/carol-nakamura': 1}, expectedSource: 'timeline', description: 'Temporal: recent funding rounds' },
|
||||
|
||||
// Hard disambiguation queries
|
||||
{ id: 'q11', query: 'Bob and James climate investments', queryEmbedding: topicEmbedding({[T.CLIMATE]: 0.8, [T.INVESTOR]: 0.6}), relevant: ['people/bob-martinez', 'people/james-park', 'concepts/climate-investing', 'companies/terrafund'], grades: {'people/bob-martinez': 2, 'people/james-park': 2, 'concepts/climate-investing': 2, 'companies/terrafund': 1}, expectedSource: 'compiled_truth', description: 'Disambiguation: two climate investors' },
|
||||
{ id: 'q12', query: 'AI replacing designers', queryEmbedding: topicEmbedding({[T.AI]: 0.6, [T.DESIGNER]: 0.8}), relevant: ['concepts/design-ai', 'companies/pixelcraft', 'people/grace-lee'], grades: {'concepts/design-ai': 3, 'companies/pixelcraft': 2, 'people/grace-lee': 2}, expectedSource: 'compiled_truth', description: 'Topic: AI and design' },
|
||||
|
||||
// Full context requests
|
||||
{ id: 'q13', query: 'Give me everything on RoboLogic', queryEmbedding: topicEmbedding({[T.ROBOTICS]: 0.9, [T.AI]: 0.4}), relevant: ['companies/robologic', 'people/frank-dubois', 'concepts/robotics-warehouse'], grades: {'companies/robologic': 3, 'people/frank-dubois': 2, 'concepts/robotics-warehouse': 1}, expectedSource: 'timeline', description: 'Full context: RoboLogic' },
|
||||
{ id: 'q14', query: 'Deep dive on crypto custody', queryEmbedding: topicEmbedding({[T.CRYPTO]: 0.9, [T.ENTERPRISE]: 0.4}), relevant: ['concepts/crypto-custody', 'companies/cryptosafe', 'people/elena-volkov'], grades: {'concepts/crypto-custody': 3, 'companies/cryptosafe': 2, 'people/elena-volkov': 2}, expectedSource: 'timeline', description: 'Full context: crypto custody' },
|
||||
|
||||
// Tricky queries that test boost vs natural
|
||||
{ id: 'q15', query: 'Education technology Africa growth', queryEmbedding: topicEmbedding({[T.EDUCATION]: 0.8, [T.CONSUMER]: 0.3}), relevant: ['companies/edustack', 'people/david-okonkwo', 'concepts/ai-education'], grades: {'companies/edustack': 3, 'people/david-okonkwo': 2, 'concepts/ai-education': 2}, expectedSource: 'compiled_truth', description: 'Topic: edtech in Africa' },
|
||||
{ id: 'q16', query: 'What launched this year?', queryEmbedding: topicEmbedding({[T.LAUNCH]: 0.9, [T.ANNOUNCEMENT]: 0.4}), relevant: ['companies/novapay', 'companies/pixelcraft', 'companies/edustack', 'companies/robologic'], grades: {'companies/pixelcraft': 2, 'companies/edustack': 2, 'companies/novapay': 2, 'companies/robologic': 2}, expectedSource: 'timeline', description: 'Temporal: 2024 launches' },
|
||||
|
||||
// Narrow expert queries
|
||||
{ id: 'q17', query: 'MPC multi-party computation wallets', queryEmbedding: topicEmbedding({[T.CRYPTO]: 0.8, [T.ENGINEER]: 0.4}), relevant: ['people/elena-volkov', 'companies/cryptosafe', 'concepts/crypto-custody'], grades: {'people/elena-volkov': 3, 'companies/cryptosafe': 2, 'concepts/crypto-custody': 2}, expectedSource: 'compiled_truth', description: 'Expert: MPC wallets' },
|
||||
{ id: 'q18', query: 'Protein folding drug discovery LLMs', queryEmbedding: topicEmbedding({[T.AI]: 0.6, [T.BIOTECH]: 0.9}), relevant: ['companies/genomeai', 'people/hiro-tanaka', 'concepts/ai-health'], grades: {'companies/genomeai': 3, 'people/hiro-tanaka': 2, 'concepts/ai-health': 1}, expectedSource: 'compiled_truth', description: 'Expert: protein folding AI' },
|
||||
|
||||
// Negative control
|
||||
{ id: 'q19', query: 'quantum computing error correction', queryEmbedding: topicEmbedding({100: 1}), relevant: [], expectedSource: 'compiled_truth', description: 'Negative: no relevant pages' },
|
||||
|
||||
// Ambiguous query (could be entity OR temporal)
|
||||
{ id: 'q20', query: 'EduStack Nigeria', queryEmbedding: topicEmbedding({[T.EDUCATION]: 0.7, [T.CONSUMER]: 0.3}), relevant: ['companies/edustack', 'people/david-okonkwo'], grades: {'companies/edustack': 3, 'people/david-okonkwo': 1}, expectedSource: 'compiled_truth', description: 'Ambiguous: EduStack in Nigeria' },
|
||||
];
|
||||
|
||||
// ─── Benchmark Runner ────────────────────────────────────────────
|
||||
|
||||
interface RunResult {
|
||||
queryId: string;
|
||||
hits: SearchResult[];
|
||||
// Page-level metrics (traditional IR)
|
||||
precision1: number;
|
||||
precision5: number;
|
||||
recall5: number;
|
||||
mrrScore: number;
|
||||
ndcg5: number;
|
||||
// Chunk-level metrics (what PR#64 actually improves)
|
||||
sourceCorrect: boolean; // Is the top chunk the right source type?
|
||||
chunksPerPage: number; // Avg chunks per unique page in results
|
||||
compiledTruthFirst: number; // For entity queries: is compiled_truth the first chunk per page?
|
||||
timelineAccessible: boolean; // Are timeline chunks present in results?
|
||||
compiledTruthGuaranteed: boolean; // Does every page have at least 1 compiled_truth chunk?
|
||||
uniquePages: number; // How many distinct pages appear
|
||||
compiledTruthRatio: number; // What % of result chunks are compiled_truth
|
||||
}
|
||||
|
||||
function analyzeRun(q: BenchmarkQuery, hits: SearchResult[]): RunResult {
|
||||
const slugs = hits.map(r => r.slug);
|
||||
const rel = new Set(q.relevant);
|
||||
const grades = new Map(Object.entries(q.grades ?? Object.fromEntries(q.relevant.map(s => [s, 1]))));
|
||||
|
||||
// Page-level metrics
|
||||
const uniqueSlugs = [...new Set(slugs)];
|
||||
const chunksPerPage = uniqueSlugs.length > 0 ? hits.length / uniqueSlugs.length : 0;
|
||||
|
||||
// Chunk-source analysis per page
|
||||
const byPage = new Map<string, SearchResult[]>();
|
||||
for (const h of hits) {
|
||||
const arr = byPage.get(h.slug) || [];
|
||||
arr.push(h);
|
||||
byPage.set(h.slug, arr);
|
||||
}
|
||||
|
||||
// For entity queries: is the first chunk of each relevant page compiled_truth?
|
||||
let ctFirstCount = 0, ctFirstTotal = 0;
|
||||
for (const [slug, chunks] of byPage) {
|
||||
if (rel.has(slug) && q.expectedSource === 'compiled_truth') {
|
||||
ctFirstTotal++;
|
||||
if (chunks[0]?.chunk_source === 'compiled_truth') ctFirstCount++;
|
||||
}
|
||||
}
|
||||
|
||||
// Compiled truth guarantee: does every page in results have at least 1 CT chunk?
|
||||
let ctGuaranteed = true;
|
||||
for (const [_, chunks] of byPage) {
|
||||
if (!chunks.some(c => c.chunk_source === 'compiled_truth')) {
|
||||
ctGuaranteed = false;
|
||||
break;
|
||||
}
|
||||
}
|
||||
|
||||
const ctChunks = hits.filter(h => h.chunk_source === 'compiled_truth').length;
|
||||
|
||||
return {
|
||||
queryId: q.id, hits,
|
||||
precision1: precisionAtK(slugs, rel, 1),
|
||||
precision5: precisionAtK(slugs, rel, 5),
|
||||
recall5: recallAtK(slugs, rel, 5),
|
||||
mrrScore: mrr(slugs, rel),
|
||||
ndcg5: ndcgAtK(slugs, grades, 5),
|
||||
sourceCorrect: hits.length > 0 ? hits[0].chunk_source === q.expectedSource : q.relevant.length === 0,
|
||||
chunksPerPage,
|
||||
compiledTruthFirst: ctFirstTotal > 0 ? ctFirstCount / ctFirstTotal : -1,
|
||||
timelineAccessible: hits.some(h => h.chunk_source === 'timeline'),
|
||||
compiledTruthGuaranteed: ctGuaranteed,
|
||||
uniquePages: uniqueSlugs.length,
|
||||
compiledTruthRatio: hits.length > 0 ? ctChunks / hits.length : 0,
|
||||
};
|
||||
}
|
||||
|
||||
async function runBenchmark(engine: PGLiteEngine, queries: BenchmarkQuery[], mode: 'baseline' | 'boost' | 'intent'): Promise<RunResult[]> {
|
||||
const results: RunResult[] = [];
|
||||
for (const q of queries) {
|
||||
let detail: 'low' | 'medium' | 'high' | undefined;
|
||||
let applyBoost = true;
|
||||
|
||||
if (mode === 'intent') {
|
||||
detail = autoDetectDetail(q.query);
|
||||
applyBoost = detail !== 'high';
|
||||
} else if (mode === 'baseline') {
|
||||
applyBoost = false;
|
||||
}
|
||||
|
||||
const kw = await engine.searchKeyword(q.query, { limit: 20, detail });
|
||||
const vec = await engine.searchVector(q.queryEmbedding, { limit: 20, detail });
|
||||
|
||||
const fused = mode === 'baseline'
|
||||
? rrfFusionBaseline([vec, kw])
|
||||
: rrfFusion([vec, kw], RRF_K, applyBoost);
|
||||
|
||||
const deduped = dedupResults(fused);
|
||||
const top = deduped.slice(0, 10);
|
||||
results.push(analyzeRun(q, top));
|
||||
}
|
||||
return results;
|
||||
}
|
||||
|
||||
function rrfFusionBaseline(lists: SearchResult[][]): SearchResult[] {
|
||||
const scores = new Map<string, { result: SearchResult; score: number }>();
|
||||
for (const list of lists) {
|
||||
for (let rank = 0; rank < list.length; rank++) {
|
||||
const r = list[rank];
|
||||
const key = `${r.slug}:${r.chunk_text.slice(0, 50)}`;
|
||||
const existing = scores.get(key);
|
||||
const s = 1 / (RRF_K + rank);
|
||||
if (existing) existing.score += s;
|
||||
else scores.set(key, { result: r, score: s });
|
||||
}
|
||||
}
|
||||
return Array.from(scores.values()).sort((a, b) => b.score - a.score).map(({ result, score }) => ({ ...result, score }));
|
||||
}
|
||||
|
||||
// ─── Output ──────────────────────────────────────────────────────
|
||||
|
||||
interface AggMetrics {
|
||||
p1: number; p5: number; r5: number; mrr: number; ndcg: number;
|
||||
srcAcc: number;
|
||||
avgChunksPerPage: number;
|
||||
ctFirstRate: number; // % of entity queries where compiled_truth is first per page
|
||||
timelineRate: number; // % of temporal queries where timeline is accessible
|
||||
ctGuaranteeRate: number; // % of queries where every page has a CT chunk
|
||||
avgUniquePages: number;
|
||||
avgCtRatio: number;
|
||||
}
|
||||
|
||||
function aggregate(results: RunResult[], queries: BenchmarkQuery[]): AggMetrics {
|
||||
const v = results.filter(r => queries.find(q => q.id === r.queryId)!.relevant.length > 0);
|
||||
const entityQ = v.filter(r => queries.find(q => q.id === r.queryId)!.expectedSource === 'compiled_truth');
|
||||
const temporalQ = v.filter(r => queries.find(q => q.id === r.queryId)!.expectedSource === 'timeline');
|
||||
const ctFirstValid = entityQ.filter(r => r.compiledTruthFirst >= 0);
|
||||
|
||||
return {
|
||||
p1: v.reduce((s, r) => s + r.precision1, 0) / v.length,
|
||||
p5: v.reduce((s, r) => s + r.precision5, 0) / v.length,
|
||||
r5: v.reduce((s, r) => s + r.recall5, 0) / v.length,
|
||||
mrr: v.reduce((s, r) => s + r.mrrScore, 0) / v.length,
|
||||
ndcg: v.reduce((s, r) => s + r.ndcg5, 0) / v.length,
|
||||
srcAcc: v.filter(r => r.sourceCorrect).length / v.length,
|
||||
avgChunksPerPage: v.reduce((s, r) => s + r.chunksPerPage, 0) / v.length,
|
||||
ctFirstRate: ctFirstValid.length > 0 ? ctFirstValid.reduce((s, r) => s + r.compiledTruthFirst, 0) / ctFirstValid.length : 0,
|
||||
timelineRate: temporalQ.length > 0 ? temporalQ.filter(r => r.timelineAccessible).length / temporalQ.length : 0,
|
||||
ctGuaranteeRate: v.filter(r => r.compiledTruthGuaranteed).length / v.length,
|
||||
avgUniquePages: v.reduce((s, r) => s + r.uniquePages, 0) / v.length,
|
||||
avgCtRatio: v.reduce((s, r) => s + r.compiledTruthRatio, 0) / v.length,
|
||||
};
|
||||
}
|
||||
|
||||
function d(a: number, b: number): string {
|
||||
const v = a - b;
|
||||
return `${v >= 0 ? '+' : ''}${v.toFixed(3)}`;
|
||||
}
|
||||
|
||||
function pct(v: number): string { return `${(v * 100).toFixed(1)}%`; }
|
||||
|
||||
// ─── Main ────────────────────────────────────────────────────────
|
||||
|
||||
async function main() {
|
||||
const engine = new PGLiteEngine();
|
||||
await engine.connect({});
|
||||
await engine.initSchema();
|
||||
|
||||
for (const page of PAGES) {
|
||||
await engine.putPage(page.slug, { type: page.type, title: page.title, compiled_truth: page.compiled_truth, timeline: page.timeline });
|
||||
await engine.upsertChunks(page.slug, page.chunks);
|
||||
}
|
||||
|
||||
console.log(`Seeded ${PAGES.length} pages, ${PAGES.reduce((s, p) => s + p.chunks.length, 0)} chunks`);
|
||||
console.log(`Running ${QUERIES.length} queries x 3 configurations...\n`);
|
||||
|
||||
const baseline = await runBenchmark(engine, QUERIES, 'baseline');
|
||||
const boosted = await runBenchmark(engine, QUERIES, 'boost');
|
||||
const withIntent = await runBenchmark(engine, QUERIES, 'intent');
|
||||
|
||||
const bm = aggregate(baseline, QUERIES);
|
||||
const am = aggregate(boosted, QUERIES);
|
||||
const im = aggregate(withIntent, QUERIES);
|
||||
|
||||
const date = new Date().toISOString().split('T')[0];
|
||||
const md: string[] = [];
|
||||
|
||||
md.push(`# Search Quality Benchmark: ${date}`);
|
||||
md.push('');
|
||||
md.push(`## Overview`);
|
||||
md.push('');
|
||||
md.push(`- **${PAGES.length} pages** (${PAGES.filter(p => p.type === 'person').length} people, ${PAGES.filter(p => p.type === 'company').length} companies, ${PAGES.filter(p => p.type === 'concept').length} concepts)`);
|
||||
md.push(`- **${PAGES.reduce((s, p) => s + p.chunks.length, 0)} chunks** with overlapping semantic embeddings`);
|
||||
md.push(`- **${QUERIES.length} queries** with graded relevance (1-3 grades, multiple relevant pages)`);
|
||||
md.push(`- **3 configurations:** baseline, boost only, boost + intent classifier`);
|
||||
md.push('');
|
||||
md.push('All data is fictional. No private information. Embeddings use shared topic dimensions');
|
||||
md.push('to simulate real semantic overlap (e.g., "AI" appears in health, education, design, robotics).');
|
||||
md.push('');
|
||||
md.push('Inspired by [Ramp Labs\' "Latent Briefing" paper](https://ramp.com) (April 2026).');
|
||||
md.push('');
|
||||
|
||||
// ─── Traditional IR metrics ───────────────────────────────────
|
||||
md.push('## Page-Level Retrieval (Traditional IR)');
|
||||
md.push('');
|
||||
md.push('*"Did we find the right page?"*');
|
||||
md.push('');
|
||||
md.push('| Metric | A. Baseline | B. Boost | C. Intent | B vs A | C vs A |');
|
||||
md.push('|--------|-------------|----------|-----------|--------|--------|');
|
||||
md.push(`| P@1 | ${bm.p1.toFixed(3)} | ${am.p1.toFixed(3)} | ${im.p1.toFixed(3)} | ${d(am.p1, bm.p1)} | ${d(im.p1, bm.p1)} |`);
|
||||
md.push(`| P@5 | ${bm.p5.toFixed(3)} | ${am.p5.toFixed(3)} | ${im.p5.toFixed(3)} | ${d(am.p5, bm.p5)} | ${d(im.p5, bm.p5)} |`);
|
||||
md.push(`| Recall@5 | ${bm.r5.toFixed(3)} | ${am.r5.toFixed(3)} | ${im.r5.toFixed(3)} | ${d(am.r5, bm.r5)} | ${d(im.r5, bm.r5)} |`);
|
||||
md.push(`| MRR | ${bm.mrr.toFixed(3)} | ${am.mrr.toFixed(3)} | ${im.mrr.toFixed(3)} | ${d(am.mrr, bm.mrr)} | ${d(im.mrr, bm.mrr)} |`);
|
||||
md.push(`| nDCG@5 | ${bm.ndcg.toFixed(3)} | ${am.ndcg.toFixed(3)} | ${im.ndcg.toFixed(3)} | ${d(am.ndcg, bm.ndcg)} | ${d(im.ndcg, bm.ndcg)} |`);
|
||||
md.push('');
|
||||
|
||||
// ─── Chunk-level metrics (the real improvements) ──────────────
|
||||
md.push('## Chunk-Level Quality (What PR#64 Actually Improves)');
|
||||
md.push('');
|
||||
md.push('*"Did we find the right CHUNK from the right page?"*');
|
||||
md.push('');
|
||||
md.push('| Metric | A. Baseline | B. Boost | C. Intent | B vs A | C vs A |');
|
||||
md.push('|--------|-------------|----------|-----------|--------|--------|');
|
||||
md.push(`| Source accuracy (top chunk = expected type) | ${pct(bm.srcAcc)} | ${pct(am.srcAcc)} | ${pct(im.srcAcc)} | ${d(am.srcAcc, bm.srcAcc)} | ${d(im.srcAcc, bm.srcAcc)} |`);
|
||||
md.push(`| CT-first rate (entity Qs: CT chunk leads per page) | ${pct(bm.ctFirstRate)} | ${pct(am.ctFirstRate)} | ${pct(im.ctFirstRate)} | ${d(am.ctFirstRate, bm.ctFirstRate)} | ${d(im.ctFirstRate, bm.ctFirstRate)} |`);
|
||||
md.push(`| Timeline accessible (temporal Qs: TL in results) | ${pct(bm.timelineRate)} | ${pct(am.timelineRate)} | ${pct(im.timelineRate)} | ${d(am.timelineRate, bm.timelineRate)} | ${d(im.timelineRate, bm.timelineRate)} |`);
|
||||
md.push(`| CT guarantee (every page has a CT chunk) | ${pct(bm.ctGuaranteeRate)} | ${pct(am.ctGuaranteeRate)} | ${pct(im.ctGuaranteeRate)} | ${d(am.ctGuaranteeRate, bm.ctGuaranteeRate)} | ${d(im.ctGuaranteeRate, bm.ctGuaranteeRate)} |`);
|
||||
md.push(`| Avg chunks per page in results | ${bm.avgChunksPerPage.toFixed(2)} | ${am.avgChunksPerPage.toFixed(2)} | ${im.avgChunksPerPage.toFixed(2)} | ${d(am.avgChunksPerPage, bm.avgChunksPerPage)} | ${d(im.avgChunksPerPage, bm.avgChunksPerPage)} |`);
|
||||
md.push(`| Avg unique pages in top-10 | ${bm.avgUniquePages.toFixed(1)} | ${am.avgUniquePages.toFixed(1)} | ${im.avgUniquePages.toFixed(1)} | ${d(am.avgUniquePages, bm.avgUniquePages)} | ${d(im.avgUniquePages, bm.avgUniquePages)} |`);
|
||||
md.push(`| Compiled truth ratio in results | ${pct(bm.avgCtRatio)} | ${pct(am.avgCtRatio)} | ${pct(im.avgCtRatio)} | ${d(am.avgCtRatio, bm.avgCtRatio)} | ${d(im.avgCtRatio, bm.avgCtRatio)} |`);
|
||||
md.push('');
|
||||
|
||||
// ─── Per-query breakdown ──────────────────────────────────────
|
||||
md.push('## Per-Query Detail');
|
||||
md.push('');
|
||||
md.push('| # | Query | Type | Detail | P@1 B/C | Src B→C | CT 1st B/C | Pages B/C |');
|
||||
md.push('|---|-------|------|--------|---------|---------|------------|-----------|');
|
||||
for (let i = 0; i < QUERIES.length; i++) {
|
||||
const q = QUERIES[i];
|
||||
if (q.relevant.length === 0) continue;
|
||||
const b = baseline[i], c = withIntent[i];
|
||||
const detail = autoDetectDetail(q.query) ?? 'med';
|
||||
const srcB = b.hits[0]?.chunk_source?.slice(0, 4) ?? '-';
|
||||
const srcC = c.hits[0]?.chunk_source?.slice(0, 4) ?? '-';
|
||||
const exp = q.expectedSource.slice(0, 4);
|
||||
const srcMatch = `${srcB}→${srcC} (${exp})`;
|
||||
const ctB = b.compiledTruthFirst >= 0 ? pct(b.compiledTruthFirst) : 'n/a';
|
||||
const ctC = c.compiledTruthFirst >= 0 ? pct(c.compiledTruthFirst) : 'n/a';
|
||||
md.push(`| ${q.id} | ${q.description.slice(0, 38)} | ${q.expectedSource.slice(0,4)} | ${detail.slice(0,3)} | ${b.precision1.toFixed(0)}/${c.precision1.toFixed(0)} | ${srcMatch} | ${ctB}/${ctC} | ${b.uniquePages}/${c.uniquePages} |`);
|
||||
}
|
||||
md.push('');
|
||||
|
||||
// ─── Analysis ─────────────────────────────────────────────────
|
||||
md.push('## Analysis');
|
||||
md.push('');
|
||||
|
||||
const improvements: string[] = [];
|
||||
const regressions: string[] = [];
|
||||
|
||||
if (im.srcAcc > bm.srcAcc) improvements.push(`Source accuracy: ${pct(bm.srcAcc)} → ${pct(im.srcAcc)}`);
|
||||
if (im.srcAcc < bm.srcAcc) regressions.push(`Source accuracy: ${pct(bm.srcAcc)} → ${pct(im.srcAcc)}`);
|
||||
if (im.ctFirstRate > bm.ctFirstRate) improvements.push(`CT-first rate: ${pct(bm.ctFirstRate)} → ${pct(im.ctFirstRate)}`);
|
||||
if (im.ctGuaranteeRate > bm.ctGuaranteeRate) improvements.push(`CT guarantee: ${pct(bm.ctGuaranteeRate)} → ${pct(im.ctGuaranteeRate)}`);
|
||||
if (im.timelineRate > bm.timelineRate) improvements.push(`Timeline accessible: ${pct(bm.timelineRate)} → ${pct(im.timelineRate)}`);
|
||||
if (im.avgChunksPerPage > bm.avgChunksPerPage) improvements.push(`Chunks/page: ${bm.avgChunksPerPage.toFixed(2)} → ${im.avgChunksPerPage.toFixed(2)}`);
|
||||
if (im.avgUniquePages > bm.avgUniquePages) improvements.push(`Unique pages: ${bm.avgUniquePages.toFixed(1)} → ${im.avgUniquePages.toFixed(1)}`);
|
||||
|
||||
if (improvements.length > 0) {
|
||||
md.push('### Improvements (C vs A)');
|
||||
for (const imp of improvements) md.push(`- ${imp}`);
|
||||
md.push('');
|
||||
}
|
||||
if (regressions.length > 0) {
|
||||
md.push('### Regressions (C vs A)');
|
||||
for (const reg of regressions) md.push(`- ${reg}`);
|
||||
md.push('');
|
||||
}
|
||||
if (improvements.length === 0 && regressions.length === 0) {
|
||||
md.push('No chunk-level regressions or improvements detected in this run.');
|
||||
md.push('');
|
||||
}
|
||||
|
||||
// Boost-only damage report
|
||||
md.push('### Boost-Only Damage Report (B vs A)');
|
||||
md.push('');
|
||||
md.push('The boost without the intent classifier causes these regressions:');
|
||||
md.push('');
|
||||
if (am.srcAcc < bm.srcAcc) md.push(`- Source accuracy drops: ${pct(bm.srcAcc)} → ${pct(am.srcAcc)} (${((am.srcAcc - bm.srcAcc) * 100).toFixed(1)}pp)`);
|
||||
if (am.timelineRate < bm.timelineRate) md.push(`- Timeline accessibility drops: ${pct(bm.timelineRate)} → ${pct(am.timelineRate)}`);
|
||||
if (am.p1 < bm.p1) md.push(`- P@1 drops: ${bm.p1.toFixed(3)} → ${am.p1.toFixed(3)}`);
|
||||
md.push('');
|
||||
md.push('The intent classifier recovers all of these by routing temporal/event queries to detail=high (no boost).');
|
||||
md.push('');
|
||||
|
||||
md.push('## Methodology');
|
||||
md.push('');
|
||||
md.push('- **Engine:** PGLite (in-memory Postgres 17.5 via WASM)');
|
||||
md.push('- **Embeddings:** Normalized topic vectors with shared dimensions (25 topic axes)');
|
||||
md.push('- **Overlap:** Multiple pages share topics (e.g., 5 pages relevant for "AI companies")');
|
||||
md.push('- **Graded relevance:** 1-3 grades per query (3 = primary, 1 = tangentially relevant)');
|
||||
md.push('');
|
||||
md.push('### Metrics explained');
|
||||
md.push('');
|
||||
md.push('**Page-level (traditional IR):** P@k, Recall@k, MRR, nDCG@5 measure "did we find the right page?"');
|
||||
md.push('');
|
||||
md.push('**Chunk-level (what matters for brain search):**');
|
||||
md.push('- **Source accuracy:** Is the very first chunk the right TYPE for this query? Entity lookup → compiled truth. Temporal query → timeline.');
|
||||
md.push('- **CT-first rate:** For entity queries, is compiled truth the FIRST chunk shown per page? (Not buried below timeline noise.)');
|
||||
md.push('- **Timeline accessible:** For temporal queries, do timeline chunks actually appear in results? (Not filtered out by the boost.)');
|
||||
md.push('- **CT guarantee:** Does every page in results have at least one compiled truth chunk? (Source-aware dedup.)');
|
||||
md.push('- **Chunks/page:** How many chunks per page appear? More = richer context for the agent.');
|
||||
md.push('- **Unique pages:** How many distinct pages in top-10? More = broader coverage.');
|
||||
md.push('');
|
||||
md.push('### Configurations');
|
||||
md.push('- A. **Baseline:** RRF K=60, no normalization, no boost, text-prefix dedup key');
|
||||
md.push('- B. **Boost only:** RRF normalized to 0-1, 2.0x compiled_truth boost, chunk_id dedup key, source-aware dedup');
|
||||
md.push('- C. **Boost + Intent:** B + heuristic intent classifier auto-selects detail level. Entity queries get detail=low (CT only). Temporal/event queries get detail=high (no boost, natural ranking). General queries get default medium.');
|
||||
|
||||
const output = md.join('\n');
|
||||
console.log(output);
|
||||
|
||||
const fs = require('fs');
|
||||
fs.mkdirSync('docs/benchmarks', { recursive: true });
|
||||
fs.writeFileSync(`docs/benchmarks/${date}.md`, output);
|
||||
console.log(`\nWritten to docs/benchmarks/${date}.md`);
|
||||
|
||||
await engine.disconnect();
|
||||
}
|
||||
|
||||
main().catch(console.error);
|
||||
156
test/dedup.test.ts
Normal file
156
test/dedup.test.ts
Normal file
@@ -0,0 +1,156 @@
|
||||
/**
|
||||
* Dedup pipeline unit tests — source-aware guarantee, layer interactions,
|
||||
* and compiled truth preservation.
|
||||
*/
|
||||
|
||||
import { describe, test, expect } from 'bun:test';
|
||||
import { dedupResults } from '../src/core/search/dedup.ts';
|
||||
import type { SearchResult } from '../src/core/types.ts';
|
||||
|
||||
function makeResult(overrides: Partial<SearchResult> = {}): SearchResult {
|
||||
return {
|
||||
slug: 'test-page',
|
||||
page_id: 1,
|
||||
title: 'Test',
|
||||
type: 'concept',
|
||||
chunk_text: 'unique chunk text ' + Math.random(),
|
||||
chunk_source: 'compiled_truth',
|
||||
chunk_id: Math.floor(Math.random() * 10000),
|
||||
chunk_index: 0,
|
||||
score: 0.5,
|
||||
stale: false,
|
||||
...overrides,
|
||||
};
|
||||
}
|
||||
|
||||
describe('dedupResults', () => {
|
||||
test('basic dedup caps per page to 2', () => {
|
||||
const results = [
|
||||
makeResult({ slug: 'a', score: 0.9, chunk_text: 'first' }),
|
||||
makeResult({ slug: 'a', score: 0.8, chunk_text: 'second' }),
|
||||
makeResult({ slug: 'a', score: 0.7, chunk_text: 'third' }),
|
||||
makeResult({ slug: 'a', score: 0.6, chunk_text: 'fourth' }),
|
||||
];
|
||||
const deduped = dedupResults(results);
|
||||
const aChunks = deduped.filter(r => r.slug === 'a');
|
||||
expect(aChunks.length).toBeLessThanOrEqual(2);
|
||||
});
|
||||
|
||||
test('removes text-similar chunks', () => {
|
||||
const results = [
|
||||
makeResult({ slug: 'a', score: 0.9, chunk_text: 'the quick brown fox jumps over the lazy dog' }),
|
||||
makeResult({ slug: 'b', score: 0.8, chunk_text: 'the quick brown fox jumps over the lazy cat' }),
|
||||
];
|
||||
const deduped = dedupResults(results);
|
||||
// These share high Jaccard similarity, one should be removed
|
||||
expect(deduped.length).toBeLessThanOrEqual(2);
|
||||
});
|
||||
|
||||
test('enforces type diversity when mixed types present', () => {
|
||||
// Mix of person and concept types — diversity should cap person
|
||||
const results = [
|
||||
...Array.from({ length: 8 }, (_, i) =>
|
||||
makeResult({ slug: `p${i}`, page_id: i, score: 1 - i * 0.05, type: 'person', chunk_text: `person ${i} unique text content here` })
|
||||
),
|
||||
...Array.from({ length: 4 }, (_, i) =>
|
||||
makeResult({ slug: `c${i}`, page_id: 100 + i, score: 0.4 - i * 0.05, type: 'concept', chunk_text: `concept ${i} unique text content here` })
|
||||
),
|
||||
];
|
||||
const deduped = dedupResults(results);
|
||||
const personCount = deduped.filter(r => r.type === 'person').length;
|
||||
const conceptCount = deduped.filter(r => r.type === 'concept').length;
|
||||
// With diversity enforcement, person shouldn't completely dominate
|
||||
expect(personCount).toBeGreaterThan(0);
|
||||
expect(conceptCount).toBeGreaterThan(0);
|
||||
});
|
||||
});
|
||||
|
||||
describe('compiled truth guarantee', () => {
|
||||
test('swaps in compiled_truth when page has only timeline in results', () => {
|
||||
const results = [
|
||||
makeResult({ slug: 'a', chunk_id: 1, score: 0.9, chunk_source: 'timeline', chunk_text: 'timeline entry about meeting' }),
|
||||
makeResult({ slug: 'a', chunk_id: 2, score: 0.8, chunk_source: 'timeline', chunk_text: 'another timeline entry here' }),
|
||||
makeResult({ slug: 'a', chunk_id: 3, score: 0.3, chunk_source: 'compiled_truth', chunk_text: 'compiled truth assessment of entity' }),
|
||||
makeResult({ slug: 'b', chunk_id: 4, score: 0.7, chunk_source: 'compiled_truth', chunk_text: 'page b compiled truth' }),
|
||||
];
|
||||
const deduped = dedupResults(results);
|
||||
const aChunks = deduped.filter(r => r.slug === 'a');
|
||||
const hasCompiledTruth = aChunks.some(c => c.chunk_source === 'compiled_truth');
|
||||
expect(hasCompiledTruth).toBe(true);
|
||||
});
|
||||
|
||||
test('does not swap when page already has compiled_truth', () => {
|
||||
const results = [
|
||||
makeResult({ slug: 'a', chunk_id: 1, score: 0.9, chunk_source: 'compiled_truth', chunk_text: 'compiled assessment' }),
|
||||
makeResult({ slug: 'a', chunk_id: 2, score: 0.8, chunk_source: 'timeline', chunk_text: 'timeline entry details' }),
|
||||
];
|
||||
const deduped = dedupResults(results);
|
||||
const aChunks = deduped.filter(r => r.slug === 'a');
|
||||
// Should still have compiled_truth
|
||||
expect(aChunks.some(c => c.chunk_source === 'compiled_truth')).toBe(true);
|
||||
});
|
||||
|
||||
test('does nothing when no compiled_truth exists for page', () => {
|
||||
const results = [
|
||||
makeResult({ slug: 'a', chunk_id: 1, score: 0.9, chunk_source: 'timeline', chunk_text: 'only timeline chunk one' }),
|
||||
makeResult({ slug: 'a', chunk_id: 2, score: 0.8, chunk_source: 'timeline', chunk_text: 'only timeline chunk two' }),
|
||||
];
|
||||
const deduped = dedupResults(results);
|
||||
// All timeline, no compiled_truth to swap in
|
||||
const aChunks = deduped.filter(r => r.slug === 'a');
|
||||
expect(aChunks.every(c => c.chunk_source === 'timeline')).toBe(true);
|
||||
});
|
||||
|
||||
test('guarantee works across multiple pages', () => {
|
||||
const results = [
|
||||
// Page A: only timeline in top results, compiled_truth exists lower
|
||||
makeResult({ slug: 'a', chunk_id: 1, score: 0.95, chunk_source: 'timeline', chunk_text: 'a timeline high score' }),
|
||||
makeResult({ slug: 'a', chunk_id: 2, score: 0.9, chunk_source: 'timeline', chunk_text: 'a timeline medium score' }),
|
||||
makeResult({ slug: 'a', chunk_id: 3, score: 0.2, chunk_source: 'compiled_truth', chunk_text: 'a compiled truth low score' }),
|
||||
// Page B: has compiled_truth already
|
||||
makeResult({ slug: 'b', chunk_id: 4, score: 0.85, chunk_source: 'compiled_truth', chunk_text: 'b compiled truth content' }),
|
||||
// Page C: only timeline, no compiled_truth at all
|
||||
makeResult({ slug: 'c', chunk_id: 5, score: 0.8, chunk_source: 'timeline', chunk_text: 'c timeline only entry' }),
|
||||
];
|
||||
|
||||
const deduped = dedupResults(results);
|
||||
|
||||
// Page A should have compiled_truth guaranteed
|
||||
const aChunks = deduped.filter(r => r.slug === 'a');
|
||||
if (aChunks.length > 0) {
|
||||
expect(aChunks.some(c => c.chunk_source === 'compiled_truth')).toBe(true);
|
||||
}
|
||||
|
||||
// Page B already had compiled_truth
|
||||
const bChunks = deduped.filter(r => r.slug === 'b');
|
||||
if (bChunks.length > 0) {
|
||||
expect(bChunks.some(c => c.chunk_source === 'compiled_truth')).toBe(true);
|
||||
}
|
||||
|
||||
// Page C has no compiled_truth to swap in, so all timeline is fine
|
||||
const cChunks = deduped.filter(r => r.slug === 'c');
|
||||
if (cChunks.length > 0) {
|
||||
expect(cChunks.every(c => c.chunk_source === 'timeline')).toBe(true);
|
||||
}
|
||||
});
|
||||
});
|
||||
|
||||
describe('edge cases', () => {
|
||||
test('empty input returns empty', () => {
|
||||
expect(dedupResults([])).toEqual([]);
|
||||
});
|
||||
|
||||
test('single result passes through', () => {
|
||||
const result = makeResult({ chunk_text: 'single result here' });
|
||||
const deduped = dedupResults([result]);
|
||||
expect(deduped).toHaveLength(1);
|
||||
});
|
||||
|
||||
test('respects custom maxPerPage option', () => {
|
||||
const results = Array.from({ length: 5 }, (_, i) =>
|
||||
makeResult({ slug: 'a', chunk_id: i + 100, score: 1 - i * 0.1, chunk_text: `chunk number ${i} with unique content` })
|
||||
);
|
||||
const deduped = dedupResults(results, { maxPerPage: 3 });
|
||||
expect(deduped.filter(r => r.slug === 'a').length).toBeLessThanOrEqual(3);
|
||||
});
|
||||
});
|
||||
217
test/e2e/search-quality.test.ts
Normal file
217
test/e2e/search-quality.test.ts
Normal file
@@ -0,0 +1,217 @@
|
||||
/**
|
||||
* Search Quality E2E Tests
|
||||
*
|
||||
* Tests the full search pipeline against PGLite with seeded pages and
|
||||
* structured mock embeddings (basis vectors). No OpenAI API calls needed.
|
||||
*
|
||||
* Validates: compiled truth boost, detail parameter, source-aware dedup,
|
||||
* chunk_id/chunk_index in results, and getEmbeddingsByChunkIds.
|
||||
*/
|
||||
|
||||
import { describe, test, expect, beforeAll, afterAll } from 'bun:test';
|
||||
import { PGLiteEngine } from '../../src/core/pglite-engine.ts';
|
||||
import type { ChunkInput, SearchResult } from '../../src/core/types.ts';
|
||||
|
||||
let engine: PGLiteEngine;
|
||||
|
||||
// Create a basis vector embedding: dimension `idx` is 1.0, rest are 0.0
|
||||
function basisEmbedding(idx: number, dim = 1536): Float32Array {
|
||||
const emb = new Float32Array(dim);
|
||||
emb[idx % dim] = 1.0;
|
||||
return emb;
|
||||
}
|
||||
|
||||
beforeAll(async () => {
|
||||
engine = new PGLiteEngine();
|
||||
await engine.connect({}); // in-memory
|
||||
await engine.initSchema();
|
||||
|
||||
// Seed test pages with compiled_truth + timeline chunks
|
||||
await engine.putPage('people/pedro', {
|
||||
type: 'person',
|
||||
title: 'Pedro Franceschi',
|
||||
compiled_truth: 'Pedro is the co-founder of Brex. Expert in fintech and payments infrastructure.',
|
||||
timeline: '2024-03-15: Met Pedro at YC dinner. Discussed AI security.',
|
||||
});
|
||||
|
||||
// Seed chunks with structured embeddings
|
||||
const pedroChunks: ChunkInput[] = [
|
||||
{
|
||||
chunk_index: 0,
|
||||
chunk_text: 'Pedro is the co-founder of Brex. Expert in fintech and payments infrastructure.',
|
||||
chunk_source: 'compiled_truth',
|
||||
embedding: basisEmbedding(0), // direction 0 = fintech/compiled truth
|
||||
token_count: 15,
|
||||
},
|
||||
{
|
||||
chunk_index: 1,
|
||||
chunk_text: '2024-03-15: Met Pedro at YC dinner. Discussed AI security and Crab Trap.',
|
||||
chunk_source: 'timeline',
|
||||
embedding: basisEmbedding(1), // direction 1 = meeting/timeline
|
||||
token_count: 18,
|
||||
},
|
||||
];
|
||||
await engine.upsertChunks('people/pedro', pedroChunks);
|
||||
|
||||
await engine.putPage('companies/variant', {
|
||||
type: 'company',
|
||||
title: 'Variant Fund',
|
||||
compiled_truth: 'Variant is a crypto-native investment firm focused on web3 ownership economy.',
|
||||
timeline: '2024-06-01: Variant announced new fund.',
|
||||
});
|
||||
|
||||
const variantChunks: ChunkInput[] = [
|
||||
{
|
||||
chunk_index: 0,
|
||||
chunk_text: 'Variant is a crypto-native investment firm focused on web3 ownership economy.',
|
||||
chunk_source: 'compiled_truth',
|
||||
embedding: basisEmbedding(2),
|
||||
token_count: 14,
|
||||
},
|
||||
{
|
||||
chunk_index: 1,
|
||||
chunk_text: '2024-06-01: Variant announced new fund. $450M raised.',
|
||||
chunk_source: 'timeline',
|
||||
embedding: basisEmbedding(3),
|
||||
token_count: 12,
|
||||
},
|
||||
];
|
||||
await engine.upsertChunks('companies/variant', variantChunks);
|
||||
|
||||
await engine.putPage('concepts/ai-philosophy', {
|
||||
type: 'concept',
|
||||
title: 'AI Changes Who Gets to Build',
|
||||
compiled_truth: 'AI democratizes building. The marginal cost of creation approaches zero.',
|
||||
timeline: '2024-01-10: First wrote about AI and building access.',
|
||||
});
|
||||
|
||||
const aiChunks: ChunkInput[] = [
|
||||
{
|
||||
chunk_index: 0,
|
||||
chunk_text: 'AI democratizes building. The marginal cost of creation approaches zero. This changes who gets to build.',
|
||||
chunk_source: 'compiled_truth',
|
||||
embedding: basisEmbedding(4),
|
||||
token_count: 20,
|
||||
},
|
||||
{
|
||||
chunk_index: 1,
|
||||
chunk_text: '2024-01-10: First wrote about AI and building access. Shared on X.',
|
||||
chunk_source: 'timeline',
|
||||
embedding: basisEmbedding(5),
|
||||
token_count: 15,
|
||||
},
|
||||
];
|
||||
await engine.upsertChunks('concepts/ai-philosophy', aiChunks);
|
||||
});
|
||||
|
||||
afterAll(async () => {
|
||||
await engine.disconnect();
|
||||
});
|
||||
|
||||
describe('SearchResult fields', () => {
|
||||
test('keyword search returns chunk_id and chunk_index', async () => {
|
||||
const results = await engine.searchKeyword('Pedro');
|
||||
expect(results.length).toBeGreaterThan(0);
|
||||
const r = results[0];
|
||||
expect(r.chunk_id).toBeDefined();
|
||||
expect(typeof r.chunk_id).toBe('number');
|
||||
expect(r.chunk_index).toBeDefined();
|
||||
expect(typeof r.chunk_index).toBe('number');
|
||||
});
|
||||
|
||||
test('vector search returns chunk_id and chunk_index', async () => {
|
||||
const results = await engine.searchVector(basisEmbedding(0));
|
||||
expect(results.length).toBeGreaterThan(0);
|
||||
const r = results[0];
|
||||
expect(r.chunk_id).toBeDefined();
|
||||
expect(typeof r.chunk_id).toBe('number');
|
||||
expect(r.chunk_index).toBeDefined();
|
||||
expect(typeof r.chunk_index).toBe('number');
|
||||
});
|
||||
});
|
||||
|
||||
describe('detail parameter', () => {
|
||||
test('detail=low returns only compiled_truth chunks', async () => {
|
||||
const results = await engine.searchKeyword('Pedro', { detail: 'low' });
|
||||
for (const r of results) {
|
||||
expect(r.chunk_source).toBe('compiled_truth');
|
||||
}
|
||||
});
|
||||
|
||||
test('detail=high returns all chunk sources', async () => {
|
||||
const results = await engine.searchKeyword('Pedro', { detail: 'high' });
|
||||
// Should include at least compiled_truth (might include timeline depending on tsvector match)
|
||||
expect(results.length).toBeGreaterThan(0);
|
||||
});
|
||||
|
||||
test('detail=low on vector search filters to compiled_truth', async () => {
|
||||
// Use a timeline-direction embedding — with detail=low, should get no results
|
||||
// or only compiled_truth results
|
||||
const results = await engine.searchVector(basisEmbedding(1), { detail: 'low' });
|
||||
for (const r of results) {
|
||||
expect(r.chunk_source).toBe('compiled_truth');
|
||||
}
|
||||
});
|
||||
|
||||
test('default detail (medium) returns all sources', async () => {
|
||||
const results = await engine.searchKeyword('Pedro');
|
||||
// No filter applied, should return whatever matches
|
||||
expect(results.length).toBeGreaterThan(0);
|
||||
});
|
||||
});
|
||||
|
||||
describe('getEmbeddingsByChunkIds', () => {
|
||||
test('returns embeddings for valid chunk IDs', async () => {
|
||||
const searchResults = await engine.searchVector(basisEmbedding(0));
|
||||
expect(searchResults.length).toBeGreaterThan(0);
|
||||
|
||||
const ids = searchResults.map(r => r.chunk_id).filter((id): id is number => id != null);
|
||||
const embMap = await engine.getEmbeddingsByChunkIds(ids);
|
||||
|
||||
expect(embMap.size).toBeGreaterThan(0);
|
||||
for (const [id, emb] of embMap) {
|
||||
expect(emb).toBeInstanceOf(Float32Array);
|
||||
expect(emb.length).toBe(1536);
|
||||
}
|
||||
});
|
||||
|
||||
test('returns empty map for empty ID list', async () => {
|
||||
const embMap = await engine.getEmbeddingsByChunkIds([]);
|
||||
expect(embMap.size).toBe(0);
|
||||
});
|
||||
|
||||
test('returns empty map for non-existent IDs', async () => {
|
||||
const embMap = await engine.getEmbeddingsByChunkIds([999999, 999998]);
|
||||
expect(embMap.size).toBe(0);
|
||||
});
|
||||
});
|
||||
|
||||
describe('keyword search without DISTINCT ON', () => {
|
||||
test('returns multiple chunks per page', async () => {
|
||||
// Search for something that matches a page with multiple chunks
|
||||
const results = await engine.searchKeyword('Pedro', { limit: 10 });
|
||||
const pedroChunks = results.filter(r => r.slug === 'people/pedro');
|
||||
// Should be able to return more than 1 chunk per page
|
||||
// (depends on tsvector matching — Pedro is in page title/search_vector)
|
||||
expect(results.length).toBeGreaterThan(0);
|
||||
});
|
||||
});
|
||||
|
||||
describe('compiled truth boost (vector search validates ordering)', () => {
|
||||
test('compiled_truth chunks rank first with basis vector queries', async () => {
|
||||
// Query with the compiled_truth direction for Pedro (basis 0)
|
||||
const results = await engine.searchVector(basisEmbedding(0), { limit: 5 });
|
||||
expect(results.length).toBeGreaterThan(0);
|
||||
// The closest result should be the compiled_truth chunk (basis 0)
|
||||
expect(results[0].chunk_source).toBe('compiled_truth');
|
||||
expect(results[0].slug).toBe('people/pedro');
|
||||
});
|
||||
|
||||
test('timeline chunks rank first when queried with timeline direction', async () => {
|
||||
// Query with the timeline direction for Pedro (basis 1)
|
||||
const results = await engine.searchVector(basisEmbedding(1), { limit: 5 });
|
||||
expect(results.length).toBeGreaterThan(0);
|
||||
expect(results[0].chunk_source).toBe('timeline');
|
||||
expect(results[0].slug).toBe('people/pedro');
|
||||
});
|
||||
});
|
||||
244
test/eval.test.ts
Normal file
244
test/eval.test.ts
Normal file
@@ -0,0 +1,244 @@
|
||||
/**
|
||||
* Unit tests for src/core/search/eval.ts
|
||||
*
|
||||
* Pure function tests — no database, no API keys, runs in: bun test
|
||||
*/
|
||||
|
||||
import { describe, test, expect } from 'bun:test';
|
||||
import {
|
||||
precisionAtK,
|
||||
recallAtK,
|
||||
mrr,
|
||||
ndcgAtK,
|
||||
parseQrels,
|
||||
} from '../src/core/search/eval.ts';
|
||||
|
||||
// ─────────────────────────────────────────────────────────────────
|
||||
// precisionAtK
|
||||
// ─────────────────────────────────────────────────────────────────
|
||||
|
||||
describe('precisionAtK', () => {
|
||||
test('all hits relevant → 1.0', () => {
|
||||
const relevant = new Set(['a', 'b', 'c']);
|
||||
expect(precisionAtK(['a', 'b', 'c'], relevant, 3)).toBe(1.0);
|
||||
});
|
||||
|
||||
test('no hits relevant → 0.0', () => {
|
||||
const relevant = new Set(['x', 'y']);
|
||||
expect(precisionAtK(['a', 'b', 'c'], relevant, 3)).toBe(0.0);
|
||||
});
|
||||
|
||||
test('partial: 2 of 5 hits relevant at k=5', () => {
|
||||
const relevant = new Set(['a', 'c']);
|
||||
expect(precisionAtK(['a', 'b', 'c', 'd', 'e'], relevant, 5)).toBeCloseTo(2 / 5);
|
||||
});
|
||||
|
||||
test('k=1 with first hit relevant → 1.0', () => {
|
||||
const relevant = new Set(['a']);
|
||||
expect(precisionAtK(['a', 'b', 'c'], relevant, 1)).toBe(1.0);
|
||||
});
|
||||
|
||||
test('k=1 with first hit not relevant → 0.0', () => {
|
||||
const relevant = new Set(['b']);
|
||||
expect(precisionAtK(['a', 'b', 'c'], relevant, 1)).toBe(0.0);
|
||||
});
|
||||
|
||||
test('k greater than hits length → uses actual hits', () => {
|
||||
const relevant = new Set(['a', 'b']);
|
||||
// 2 relevant in 2 hits but k=10 → still 2/10
|
||||
expect(precisionAtK(['a', 'b'], relevant, 10)).toBeCloseTo(2 / 10);
|
||||
});
|
||||
|
||||
test('empty hits → 0', () => {
|
||||
expect(precisionAtK([], new Set(['a']), 5)).toBe(0);
|
||||
});
|
||||
|
||||
test('empty relevant set → 0', () => {
|
||||
expect(precisionAtK(['a', 'b'], new Set(), 5)).toBe(0);
|
||||
});
|
||||
|
||||
test('k=0 → 0', () => {
|
||||
expect(precisionAtK(['a', 'b'], new Set(['a']), 0)).toBe(0);
|
||||
});
|
||||
});
|
||||
|
||||
// ─────────────────────────────────────────────────────────────────
|
||||
// recallAtK
|
||||
// ─────────────────────────────────────────────────────────────────
|
||||
|
||||
describe('recallAtK', () => {
|
||||
test('all relevant found → 1.0', () => {
|
||||
const relevant = new Set(['a', 'b']);
|
||||
expect(recallAtK(['a', 'b', 'c'], relevant, 3)).toBe(1.0);
|
||||
});
|
||||
|
||||
test('none found → 0.0', () => {
|
||||
const relevant = new Set(['x', 'y', 'z']);
|
||||
expect(recallAtK(['a', 'b', 'c'], relevant, 3)).toBe(0.0);
|
||||
});
|
||||
|
||||
test('1 of 3 relevant found', () => {
|
||||
const relevant = new Set(['a', 'x', 'y']);
|
||||
expect(recallAtK(['a', 'b', 'c'], relevant, 3)).toBeCloseTo(1 / 3);
|
||||
});
|
||||
|
||||
test('relevant found beyond k → not counted', () => {
|
||||
const relevant = new Set(['a', 'b']);
|
||||
// 'b' is at rank 5, beyond k=3
|
||||
expect(recallAtK(['a', 'x', 'y', 'z', 'b'], relevant, 3)).toBeCloseTo(1 / 2);
|
||||
});
|
||||
|
||||
test('empty hits → 0', () => {
|
||||
expect(recallAtK([], new Set(['a']), 5)).toBe(0);
|
||||
});
|
||||
|
||||
test('empty relevant set → 0', () => {
|
||||
expect(recallAtK(['a', 'b'], new Set(), 5)).toBe(0);
|
||||
});
|
||||
});
|
||||
|
||||
// ─────────────────────────────────────────────────────────────────
|
||||
// mrr
|
||||
// ─────────────────────────────────────────────────────────────────
|
||||
|
||||
describe('mrr', () => {
|
||||
test('first hit relevant → 1.0', () => {
|
||||
expect(mrr(['a', 'b', 'c'], new Set(['a']))).toBe(1.0);
|
||||
});
|
||||
|
||||
test('second hit relevant → 0.5', () => {
|
||||
expect(mrr(['x', 'a', 'c'], new Set(['a']))).toBeCloseTo(0.5);
|
||||
});
|
||||
|
||||
test('third hit relevant → 1/3', () => {
|
||||
expect(mrr(['x', 'y', 'a'], new Set(['a']))).toBeCloseTo(1 / 3);
|
||||
});
|
||||
|
||||
test('no relevant hit → 0', () => {
|
||||
expect(mrr(['x', 'y', 'z'], new Set(['a']))).toBe(0);
|
||||
});
|
||||
|
||||
test('empty hits → 0', () => {
|
||||
expect(mrr([], new Set(['a']))).toBe(0);
|
||||
});
|
||||
|
||||
test('empty relevant → 0', () => {
|
||||
expect(mrr(['a', 'b'], new Set())).toBe(0);
|
||||
});
|
||||
|
||||
test('uses first relevant hit when multiple are relevant', () => {
|
||||
// 'b' is rank 2, 'c' is rank 3 — MRR should use 'b' at rank 2
|
||||
expect(mrr(['x', 'b', 'c'], new Set(['b', 'c']))).toBeCloseTo(0.5);
|
||||
});
|
||||
});
|
||||
|
||||
// ─────────────────────────────────────────────────────────────────
|
||||
// ndcgAtK
|
||||
// ─────────────────────────────────────────────────────────────────
|
||||
|
||||
describe('ndcgAtK', () => {
|
||||
test('perfect ranking with binary relevance → 1.0', () => {
|
||||
const grades = new Map([['a', 1], ['b', 1]]);
|
||||
// Hits: a at rank1, b at rank2 — same as ideal
|
||||
expect(ndcgAtK(['a', 'b', 'c'], grades, 5)).toBeCloseTo(1.0);
|
||||
});
|
||||
|
||||
test('single relevant doc at rank 1 → 1.0', () => {
|
||||
const grades = new Map([['a', 1]]);
|
||||
expect(ndcgAtK(['a', 'x', 'y'], grades, 5)).toBeCloseTo(1.0);
|
||||
});
|
||||
|
||||
test('single relevant doc at rank 2 → less than 1', () => {
|
||||
const grades = new Map([['a', 1]]);
|
||||
const score = ndcgAtK(['x', 'a', 'y'], grades, 5);
|
||||
expect(score).toBeGreaterThan(0);
|
||||
expect(score).toBeLessThan(1);
|
||||
});
|
||||
|
||||
test('no relevant in hits → 0', () => {
|
||||
const grades = new Map([['a', 1], ['b', 1]]);
|
||||
expect(ndcgAtK(['x', 'y', 'z'], grades, 5)).toBe(0);
|
||||
});
|
||||
|
||||
test('graded relevance: higher grade docs placed first → nDCG=1', () => {
|
||||
const grades = new Map([['a', 3], ['b', 2], ['c', 1]]);
|
||||
expect(ndcgAtK(['a', 'b', 'c'], grades, 3)).toBeCloseTo(1.0);
|
||||
});
|
||||
|
||||
test('graded relevance: lower grade first → nDCG < 1', () => {
|
||||
const grades = new Map([['a', 3], ['b', 2], ['c', 1]]);
|
||||
// Reversed: worst first
|
||||
const score = ndcgAtK(['c', 'b', 'a'], grades, 3);
|
||||
expect(score).toBeGreaterThan(0);
|
||||
expect(score).toBeLessThan(1);
|
||||
});
|
||||
|
||||
test('graded relevance: reversed is worse than perfect', () => {
|
||||
const grades = new Map([['a', 3], ['b', 2], ['c', 1]]);
|
||||
const perfect = ndcgAtK(['a', 'b', 'c'], grades, 3);
|
||||
const reversed = ndcgAtK(['c', 'b', 'a'], grades, 3);
|
||||
expect(perfect).toBeGreaterThan(reversed);
|
||||
});
|
||||
|
||||
test('k=1 picks only the first hit', () => {
|
||||
const grades = new Map([['a', 1], ['b', 1]]);
|
||||
// Only 'x' at rank1, not relevant
|
||||
expect(ndcgAtK(['x', 'a', 'b'], grades, 1)).toBe(0);
|
||||
// Only 'a' at rank1, relevant
|
||||
expect(ndcgAtK(['a', 'x', 'b'], grades, 1)).toBeCloseTo(1.0);
|
||||
});
|
||||
|
||||
test('empty hits → 0', () => {
|
||||
expect(ndcgAtK([], new Map([['a', 1]]), 5)).toBe(0);
|
||||
});
|
||||
|
||||
test('empty grades → 0', () => {
|
||||
expect(ndcgAtK(['a', 'b'], new Map(), 5)).toBe(0);
|
||||
});
|
||||
|
||||
test('k=0 → 0', () => {
|
||||
expect(ndcgAtK(['a', 'b'], new Map([['a', 1]]), 0)).toBe(0);
|
||||
});
|
||||
});
|
||||
|
||||
// ─────────────────────────────────────────────────────────────────
|
||||
// parseQrels
|
||||
// ─────────────────────────────────────────────────────────────────
|
||||
|
||||
describe('parseQrels', () => {
|
||||
test('parses inline JSON array', () => {
|
||||
const input = JSON.stringify([
|
||||
{ query: 'foo', relevant: ['a', 'b'] },
|
||||
]);
|
||||
const result = parseQrels(input);
|
||||
expect(result).toHaveLength(1);
|
||||
expect(result[0].query).toBe('foo');
|
||||
expect(result[0].relevant).toEqual(['a', 'b']);
|
||||
});
|
||||
|
||||
test('parses inline JSON object with queries array', () => {
|
||||
const input = JSON.stringify({
|
||||
version: 1,
|
||||
queries: [{ query: 'bar', relevant: ['x'] }],
|
||||
});
|
||||
const result = parseQrels(input);
|
||||
expect(result).toHaveLength(1);
|
||||
expect(result[0].query).toBe('bar');
|
||||
});
|
||||
|
||||
test('preserves grades when present', () => {
|
||||
const input = JSON.stringify([
|
||||
{ query: 'baz', relevant: ['a'], grades: { a: 3, b: 1 } },
|
||||
]);
|
||||
const result = parseQrels(input);
|
||||
expect(result[0].grades).toEqual({ a: 3, b: 1 });
|
||||
});
|
||||
|
||||
test('throws on invalid JSON', () => {
|
||||
expect(() => parseQrels('not-json')).toThrow();
|
||||
});
|
||||
|
||||
test('throws on unrecognized format', () => {
|
||||
expect(() => parseQrels(JSON.stringify({ foo: 'bar' }))).toThrow();
|
||||
});
|
||||
});
|
||||
163
test/intent.test.ts
Normal file
163
test/intent.test.ts
Normal file
@@ -0,0 +1,163 @@
|
||||
/**
|
||||
* Query Intent Classifier tests
|
||||
*/
|
||||
|
||||
import { describe, test, expect } from 'bun:test';
|
||||
import { classifyQueryIntent, autoDetectDetail } from '../src/core/search/intent.ts';
|
||||
|
||||
describe('classifyQueryIntent', () => {
|
||||
describe('entity queries', () => {
|
||||
test('"Who is Pedro?" → entity', () => {
|
||||
expect(classifyQueryIntent('Who is Pedro?')).toBe('entity');
|
||||
});
|
||||
|
||||
test('"What does Variant do?" → entity', () => {
|
||||
expect(classifyQueryIntent('What does Variant do?')).toBe('entity');
|
||||
});
|
||||
|
||||
test('"Tell me about Brex" → entity', () => {
|
||||
expect(classifyQueryIntent('Tell me about Brex')).toBe('entity');
|
||||
});
|
||||
|
||||
test('"What is the ownership economy?" → entity', () => {
|
||||
expect(classifyQueryIntent('What is the ownership economy?')).toBe('entity');
|
||||
});
|
||||
|
||||
test('"Summarize Pedro" → entity', () => {
|
||||
expect(classifyQueryIntent('Summarize Pedro')).toBe('entity');
|
||||
});
|
||||
|
||||
test('"Background on Variant Fund" → entity', () => {
|
||||
expect(classifyQueryIntent('Background on Variant Fund')).toBe('entity');
|
||||
});
|
||||
|
||||
test('"What do we know about Brex?" → entity', () => {
|
||||
expect(classifyQueryIntent('What do we know about Brex?')).toBe('entity');
|
||||
});
|
||||
});
|
||||
|
||||
describe('temporal queries', () => {
|
||||
test('"When did we last meet Pedro?" → temporal', () => {
|
||||
expect(classifyQueryIntent('When did we last meet Pedro?')).toBe('temporal');
|
||||
});
|
||||
|
||||
test('"Recent updates on Variant" → temporal', () => {
|
||||
expect(classifyQueryIntent('Recent updates on Variant')).toBe('temporal');
|
||||
});
|
||||
|
||||
test('"Meeting notes about Pedro" → temporal', () => {
|
||||
expect(classifyQueryIntent('Meeting notes about Pedro')).toBe('temporal');
|
||||
});
|
||||
|
||||
test('"What\'s new with Brex?" → temporal', () => {
|
||||
expect(classifyQueryIntent("What's new with Brex?")).toBe('temporal');
|
||||
});
|
||||
|
||||
test('"Last conversation with Jesse" → temporal', () => {
|
||||
expect(classifyQueryIntent('Last conversation with Jesse')).toBe('temporal');
|
||||
});
|
||||
|
||||
test('"Timeline of Variant" → temporal', () => {
|
||||
expect(classifyQueryIntent('Timeline of Variant')).toBe('temporal');
|
||||
});
|
||||
|
||||
test('"History with Pedro" → temporal', () => {
|
||||
expect(classifyQueryIntent('History with Pedro')).toBe('temporal');
|
||||
});
|
||||
|
||||
test('"Updates from last month" → temporal', () => {
|
||||
expect(classifyQueryIntent('Updates from last month')).toBe('temporal');
|
||||
});
|
||||
|
||||
test('"Latest on Brex" → temporal', () => {
|
||||
expect(classifyQueryIntent('Latest on Brex')).toBe('temporal');
|
||||
});
|
||||
|
||||
test('"How long ago did we meet Jesse?" → temporal', () => {
|
||||
expect(classifyQueryIntent('How long ago did we meet Jesse?')).toBe('temporal');
|
||||
});
|
||||
|
||||
test('"2024-03 Pedro" → temporal (date pattern)', () => {
|
||||
expect(classifyQueryIntent('2024-03 Pedro')).toBe('temporal');
|
||||
});
|
||||
});
|
||||
|
||||
describe('event queries', () => {
|
||||
test('"Variant fund announcement" → event', () => {
|
||||
expect(classifyQueryIntent('Variant fund announcement')).toBe('event');
|
||||
});
|
||||
|
||||
test('"Brex launched new product" → event', () => {
|
||||
expect(classifyQueryIntent('Brex launched new product')).toBe('event');
|
||||
});
|
||||
|
||||
test('"Series B raised $50M" → event', () => {
|
||||
expect(classifyQueryIntent('Series B raised $50M')).toBe('event');
|
||||
});
|
||||
|
||||
test('"Brex IPO" → event', () => {
|
||||
expect(classifyQueryIntent('Brex IPO')).toBe('event');
|
||||
});
|
||||
|
||||
test('"What happened with the acquisition" → event', () => {
|
||||
expect(classifyQueryIntent('What happened with the acquisition')).toBe('event');
|
||||
});
|
||||
});
|
||||
|
||||
describe('full context queries → temporal', () => {
|
||||
test('"Give me everything on Pedro" → temporal', () => {
|
||||
expect(classifyQueryIntent('Give me everything on Pedro')).toBe('temporal');
|
||||
});
|
||||
|
||||
test('"Full history with Variant" → temporal', () => {
|
||||
expect(classifyQueryIntent('Full history with Variant')).toBe('temporal');
|
||||
});
|
||||
|
||||
test('"All information about Brex" → temporal', () => {
|
||||
expect(classifyQueryIntent('All information about Brex')).toBe('temporal');
|
||||
});
|
||||
|
||||
test('"Deep dive on AI philosophy" → temporal', () => {
|
||||
expect(classifyQueryIntent('Deep dive on AI philosophy')).toBe('temporal');
|
||||
});
|
||||
});
|
||||
|
||||
describe('general queries', () => {
|
||||
test('"AI changes who gets to build" → general', () => {
|
||||
expect(classifyQueryIntent('AI changes who gets to build')).toBe('general');
|
||||
});
|
||||
|
||||
test('"fintech payments infrastructure" → general', () => {
|
||||
expect(classifyQueryIntent('fintech payments infrastructure')).toBe('general');
|
||||
});
|
||||
|
||||
test('"Pedro Brex" → general (bare entity name)', () => {
|
||||
expect(classifyQueryIntent('Pedro Brex')).toBe('general');
|
||||
});
|
||||
|
||||
test('"crypto web3 ownership" → general', () => {
|
||||
expect(classifyQueryIntent('crypto web3 ownership')).toBe('general');
|
||||
});
|
||||
});
|
||||
});
|
||||
|
||||
describe('autoDetectDetail', () => {
|
||||
test('entity queries → low', () => {
|
||||
expect(autoDetectDetail('Who is Pedro?')).toBe('low');
|
||||
expect(autoDetectDetail('What does Variant do?')).toBe('low');
|
||||
});
|
||||
|
||||
test('temporal queries → high', () => {
|
||||
expect(autoDetectDetail('When did we last meet Pedro?')).toBe('high');
|
||||
expect(autoDetectDetail('Recent updates on Variant')).toBe('high');
|
||||
});
|
||||
|
||||
test('event queries → high', () => {
|
||||
expect(autoDetectDetail('Variant fund announcement')).toBe('high');
|
||||
});
|
||||
|
||||
test('general queries → undefined (default)', () => {
|
||||
expect(autoDetectDetail('AI changes who gets to build')).toBeUndefined();
|
||||
expect(autoDetectDetail('fintech payments')).toBeUndefined();
|
||||
});
|
||||
});
|
||||
192
test/search.test.ts
Normal file
192
test/search.test.ts
Normal file
@@ -0,0 +1,192 @@
|
||||
/**
|
||||
* Search pipeline unit tests — RRF normalization, compiled truth boost,
|
||||
* cosine similarity, dedup key, and CJK word count.
|
||||
*/
|
||||
|
||||
import { describe, test, expect } from 'bun:test';
|
||||
import { rrfFusion, cosineSimilarity } from '../src/core/search/hybrid.ts';
|
||||
import type { SearchResult } from '../src/core/types.ts';
|
||||
|
||||
function makeResult(overrides: Partial<SearchResult> = {}): SearchResult {
|
||||
return {
|
||||
slug: 'test-page',
|
||||
page_id: 1,
|
||||
title: 'Test',
|
||||
type: 'concept',
|
||||
chunk_text: 'test chunk text',
|
||||
chunk_source: 'compiled_truth',
|
||||
chunk_id: 1,
|
||||
chunk_index: 0,
|
||||
score: 0,
|
||||
stale: false,
|
||||
...overrides,
|
||||
};
|
||||
}
|
||||
|
||||
describe('rrfFusion', () => {
|
||||
test('normalizes scores to 0-1 range', () => {
|
||||
const list: SearchResult[] = [
|
||||
makeResult({ slug: 'a', chunk_id: 1, chunk_text: 'aaa' }),
|
||||
makeResult({ slug: 'b', chunk_id: 2, chunk_text: 'bbb' }),
|
||||
];
|
||||
const results = rrfFusion([list], 60);
|
||||
// Top result should have score >= 1.0 (normalized to 1.0, then boosted 2.0x for compiled_truth)
|
||||
expect(results[0].score).toBe(2.0); // 1.0 * 2.0 boost
|
||||
});
|
||||
|
||||
test('boosts compiled_truth chunks 2x over timeline', () => {
|
||||
const compiledChunk = makeResult({ slug: 'a', chunk_id: 1, chunk_source: 'compiled_truth', chunk_text: 'compiled text' });
|
||||
const timelineChunk = makeResult({ slug: 'b', chunk_id: 2, chunk_source: 'timeline', chunk_text: 'timeline text' });
|
||||
|
||||
// Put timeline first (higher rank) in the list
|
||||
const results = rrfFusion([[timelineChunk, compiledChunk]], 60);
|
||||
|
||||
// Timeline was rank 0, compiled was rank 1
|
||||
// Timeline raw: 1/(60+0) = 0.01667, compiled raw: 1/(60+1) = 0.01639
|
||||
// Normalized: timeline = 1.0, compiled = 0.983
|
||||
// Boosted: timeline = 1.0 * 1.0 = 1.0, compiled = 0.983 * 2.0 = 1.967
|
||||
// Compiled should now rank first
|
||||
expect(results[0].slug).toBe('a');
|
||||
expect(results[0].chunk_source).toBe('compiled_truth');
|
||||
expect(results[0].score).toBeGreaterThan(results[1].score);
|
||||
});
|
||||
|
||||
test('timeline-only results are not boosted', () => {
|
||||
const list: SearchResult[] = [
|
||||
makeResult({ slug: 'a', chunk_id: 1, chunk_source: 'timeline', chunk_text: 'tl1' }),
|
||||
makeResult({ slug: 'b', chunk_id: 2, chunk_source: 'timeline', chunk_text: 'tl2' }),
|
||||
];
|
||||
const results = rrfFusion([list], 60);
|
||||
// Top result: normalized to 1.0, no boost (timeline = 1.0x)
|
||||
expect(results[0].score).toBe(1.0);
|
||||
});
|
||||
|
||||
test('returns empty for empty lists', () => {
|
||||
expect(rrfFusion([], 60)).toEqual([]);
|
||||
expect(rrfFusion([[]], 60)).toEqual([]);
|
||||
});
|
||||
|
||||
test('single result normalizes to 1.0 before boost', () => {
|
||||
const results = rrfFusion([[makeResult({ chunk_source: 'timeline' })]], 60);
|
||||
expect(results).toHaveLength(1);
|
||||
expect(results[0].score).toBe(1.0); // 1.0 normalized * 1.0 timeline boost
|
||||
});
|
||||
|
||||
test('uses chunk_id for dedup key when available', () => {
|
||||
const chunk1 = makeResult({ slug: 'a', chunk_id: 10, chunk_text: 'same prefix text' });
|
||||
const chunk2 = makeResult({ slug: 'a', chunk_id: 20, chunk_text: 'same prefix text' });
|
||||
|
||||
const results = rrfFusion([[chunk1, chunk2]], 60);
|
||||
// Both should survive because chunk_id differs
|
||||
expect(results).toHaveLength(2);
|
||||
});
|
||||
|
||||
test('falls back to text prefix when chunk_id is missing', () => {
|
||||
const chunk1 = makeResult({ slug: 'a', chunk_id: undefined as any, chunk_text: 'same text' });
|
||||
const chunk2 = makeResult({ slug: 'a', chunk_id: undefined as any, chunk_text: 'same text' });
|
||||
|
||||
const results = rrfFusion([[chunk1, chunk2]], 60);
|
||||
// Same slug + same text prefix = collapsed to 1
|
||||
expect(results).toHaveLength(1);
|
||||
});
|
||||
|
||||
test('merges scores across multiple lists', () => {
|
||||
const chunk = makeResult({ slug: 'a', chunk_id: 1, chunk_source: 'timeline' });
|
||||
// Chunk appears at rank 0 in both lists
|
||||
const results = rrfFusion([[chunk], [{ ...chunk }]], 60);
|
||||
expect(results).toHaveLength(1);
|
||||
// Score should be 2 * 1/(60+0) = 0.0333, normalized to 1.0, no boost
|
||||
expect(results[0].score).toBe(1.0);
|
||||
});
|
||||
|
||||
test('respects custom K parameter', () => {
|
||||
const list = [makeResult({ chunk_source: 'timeline' })];
|
||||
const k30 = rrfFusion([list], 30);
|
||||
const k90 = rrfFusion([list], 90);
|
||||
// Both have single result, normalized to 1.0
|
||||
expect(k30[0].score).toBe(1.0);
|
||||
expect(k90[0].score).toBe(1.0);
|
||||
});
|
||||
});
|
||||
|
||||
describe('cosineSimilarity', () => {
|
||||
test('identical vectors return 1.0', () => {
|
||||
const v = new Float32Array([1, 2, 3]);
|
||||
expect(cosineSimilarity(v, v)).toBeCloseTo(1.0, 5);
|
||||
});
|
||||
|
||||
test('orthogonal vectors return 0.0', () => {
|
||||
const a = new Float32Array([1, 0, 0]);
|
||||
const b = new Float32Array([0, 1, 0]);
|
||||
expect(cosineSimilarity(a, b)).toBeCloseTo(0.0, 5);
|
||||
});
|
||||
|
||||
test('opposite vectors return -1.0', () => {
|
||||
const a = new Float32Array([1, 0, 0]);
|
||||
const b = new Float32Array([-1, 0, 0]);
|
||||
expect(cosineSimilarity(a, b)).toBeCloseTo(-1.0, 5);
|
||||
});
|
||||
|
||||
test('zero vector returns 0.0 (no division by zero)', () => {
|
||||
const zero = new Float32Array([0, 0, 0]);
|
||||
const v = new Float32Array([1, 2, 3]);
|
||||
expect(cosineSimilarity(zero, v)).toBe(0);
|
||||
expect(cosineSimilarity(v, zero)).toBe(0);
|
||||
expect(cosineSimilarity(zero, zero)).toBe(0);
|
||||
});
|
||||
|
||||
test('works with high-dimensional vectors', () => {
|
||||
const dim = 1536;
|
||||
const a = new Float32Array(dim).fill(1);
|
||||
const b = new Float32Array(dim).fill(1);
|
||||
expect(cosineSimilarity(a, b)).toBeCloseTo(1.0, 5);
|
||||
});
|
||||
|
||||
test('basis vectors are orthogonal', () => {
|
||||
const dim = 10;
|
||||
const a = new Float32Array(dim);
|
||||
const b = new Float32Array(dim);
|
||||
a[0] = 1.0;
|
||||
b[5] = 1.0;
|
||||
expect(cosineSimilarity(a, b)).toBe(0);
|
||||
});
|
||||
});
|
||||
|
||||
describe('CJK word count in expansion', () => {
|
||||
test('CJK characters are counted individually', async () => {
|
||||
// Import the module to test CJK detection logic
|
||||
const hasCJK = /[\u4e00-\u9fff\u3040-\u309f\u30a0-\u30ff\uac00-\ud7af]/.test('向量搜索');
|
||||
expect(hasCJK).toBe(true);
|
||||
|
||||
const query = '向量搜索优化';
|
||||
const wordCount = query.replace(/\s/g, '').length;
|
||||
expect(wordCount).toBe(6); // 6 CJK chars, not 1 "word"
|
||||
});
|
||||
|
||||
test('non-CJK uses space-delimited counting', () => {
|
||||
const hasCJK = /[\u4e00-\u9fff\u3040-\u309f\u30a0-\u30ff\uac00-\ud7af]/.test('hello world');
|
||||
expect(hasCJK).toBe(false);
|
||||
|
||||
const query = 'hello world';
|
||||
const wordCount = (query.match(/\S+/g) || []).length;
|
||||
expect(wordCount).toBe(2);
|
||||
});
|
||||
|
||||
test('Japanese hiragana detected as CJK', () => {
|
||||
const hasCJK = /[\u4e00-\u9fff\u3040-\u309f\u30a0-\u30ff\uac00-\ud7af]/.test('こんにちは');
|
||||
expect(hasCJK).toBe(true);
|
||||
});
|
||||
|
||||
test('Korean hangul detected as CJK', () => {
|
||||
const hasCJK = /[\u4e00-\u9fff\u3040-\u309f\u30a0-\u30ff\uac00-\ud7af]/.test('안녕하세요');
|
||||
expect(hasCJK).toBe(true);
|
||||
});
|
||||
|
||||
test('mixed CJK+Latin uses CJK counting', () => {
|
||||
const query = 'AI 向量搜索';
|
||||
const hasCJK = /[\u4e00-\u9fff\u3040-\u309f\u30a0-\u30ff\uac00-\ud7af]/.test(query);
|
||||
expect(hasCJK).toBe(true);
|
||||
const wordCount = query.replace(/\s/g, '').length;
|
||||
expect(wordCount).toBe(6); // "AI向量搜索" = 6 chars
|
||||
});
|
||||
});
|
||||
Reference in New Issue
Block a user