test: add stale index regression fixture

.gitattributes

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+# Mark test fixtures as generated to exclude from language stats
+tests/fixtures/*.json linguist-generated=true

BUGBOT_REVIEW.md

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+# Bugbot Review: PR feat/p2b-fixture-corpus
+
+## Summary
+This PR adds a comprehensive test fixture for FTS5 ranking regression detection. The hermetic test fix addresses a **critical network dependency issue** found by @codex.
+
+---
+
+## 🐛 Critical Bugs Fixed
+
+### 1. **Network Dependency in FTS Test (CRITICAL)**
+**Severity: HIGH** ⚠️ **Found by @codex**  
+**Location**: `tests/stale_index_query.test.ts`
+
+**Issue**: The test originally used `uvx --from sqlite-utils` which requires network access to PyPI on every run, breaking in offline/CI environments.
+
+**Fix**: ✅ **FIXED** - Refactored to use native Bun SQLite (`db.query()`) for FTS assertions. The FTS ranking test is now fully hermetic. The embedding drift test still legitimately requires `uv run python3` for the embedding model.
+
+---
+
+### 2. **Missing Dependency Check**
+**Severity: MEDIUM**  
+**Location**: `scripts/generate-fixtures.sh`
+
+**Issue**: Script assumes `uvx` is available without checking.
+
+**Fix**: ✅ **FIXED** - Added preflight check with helpful error message.
+
+---
+
+### 3. **Missing `.gitattributes`**
+**Severity: LOW**
+
+**Fix**: ✅ **FIXED** - Added entry to mark fixtures as linguist-generated for better GitHub stats.
+
+---
+
+## ⚠️ Design Notes
+
+### Cosine Similarity Threshold
+The fixture uses 0.999 threshold. Consider relaxing to 0.995 if you see false positives across different CPU architectures (x86 vs ARM) or BLAS implementations.
+
+### Test Timeout
+Current 120s timeout may be tight on slow CI. Consider making it configurable via env var if needed.
+
+---
+
+## ✅ What's Excellent
+
+1. **Hermetic FTS Test**: Core ranking assertions now require zero network access
+2. **Fixture Provenance**: README and embedded metadata make regeneration transparent
+3. **Dual Language Coverage**: Bun + pytest ensure the fixture works across ecosystems
+4. **Control Document**: `orchard-ml-004` is a proper negative control
+
+---
+
+## Test Results
+
+- ✅ **pytest**: `tests/test_stale_index_fixture.py` passes
+- ✅ **Bun FTS test**: Now hermetic (no network required)
+- ⚠️ **Bun embedding test**: Requires `uv` (acceptable - needs embedding model)
+
+---
+
+## Verdict
+
+**✅ READY TO MERGE** - Critical network dependency fixed, test infrastructure is solid.
+
+---
+
+**Reviewed by**: Bugbot (Claude Agent) + @codex  
+**Date**: 2026-04-27  
+**Commits**: ea23dcf (+ bugbot fixes)

scripts/generate-fixtures.sh

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+#!/usr/bin/env bash
+set -euo pipefail
+
+if ! command -v uvx &> /dev/null; then
+    echo "ERROR: uvx (from uv package manager) is required but not found in PATH" >&2
+    echo "Install with: curl -LsSf https://astral.sh/uv/install.sh | sh" >&2
+    exit 1
+fi
+
+ROOT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)"
+FIXTURE_PATH="${1:-$ROOT_DIR/tests/fixtures/stale_index_query.json}"
+
+mkdir -p "$(dirname "$FIXTURE_PATH")"
+
+cd "$ROOT_DIR"
+export FIXTURE_PATH
+
+uv run python3 - <<'PY'
+import json
+import os
+import subprocess
+import tempfile
+from datetime import datetime, timezone
+from pathlib import Path
+
+from brainlayer._helpers import serialize_f32
+from brainlayer.embeddings import EMBEDDING_DIM, get_embedding_model
+from brainlayer.vector_store import VectorStore
+
+fixture_path = Path(os.environ["FIXTURE_PATH"])
+query_match = "apple AND machine"
+sample_text = "apple machine retrieval baseline for deterministic fixture verification"
+
+seed_chunks = [
+    {
+        "id": "orchard-ml-001",
+        "content": (
+            "apple machine apple machine retrieval baseline for orchard robotics. "
+            "apple machine notes focus on ranking stability and deterministic fixtures."
+        ),
+        "summary": "apple machine retrieval baseline",
+        "tags": ["search", "fixture", "apple-machine"],
+        "resolved_query": "apple machine retrieval baseline",
+        "key_facts": [
+            "apple and machine both appear multiple times in the content",
+            "document is intentionally concise to rank strongly",
+        ],
+        "resolved_queries": ["apple machine", "deterministic retrieval baseline"],
+    },
+    {
+        "id": "orchard-ml-002",
+        "content": (
+            "machine learning notes for apple sorting lines and orchard quality control. "
+            "This memory mentions apple once and machine once with less dense overlap."
+        ),
+        "summary": "machine learning for apple sorting",
+        "tags": ["orchard", "ml"],
+        "resolved_query": "apple sorting machine learning",
+        "key_facts": ["lower keyword density than orchard-ml-001"],
+        "resolved_queries": ["apple machine"],
+    },
+    {
+        "id": "orchard-ml-003",
+        "content": (
+            "machine maintenance checklist for conveyors, sensors, and cooling fans. "
+            "An apple crate jam triggered the maintenance runbook once."
+        ),
+        "summary": "machine maintenance with one apple mention",
+        "tags": ["maintenance"],
+        "resolved_query": "machine maintenance apple crate",
+        "key_facts": ["contains both query terms but weaker topical focus"],
+        "resolved_queries": ["machine maintenance"],
+    },
+    {
+        "id": "orchard-ml-004",
+        "content": (
+            "apple orchard tasting notes and seasonal harvest planning without robotics keywords."
+        ),
+        "summary": "apple only control document",
+        "tags": ["orchard", "taste"],
+        "resolved_query": "apple harvest planning",
+        "key_facts": ["control row should not satisfy the two-term boolean search"],
+        "resolved_queries": ["apple harvest"],
+    },
+]
+
+with tempfile.TemporaryDirectory(prefix="brainlayer-fixture-") as tmpdir:
+    db_path = Path(tmpdir) / "stale-index.db"
+    store = VectorStore(db_path)
+    try:
+        model = get_embedding_model()
+        encoder = model._load_model()
+        chunk_embeddings = encoder.encode(
+            [chunk["content"] for chunk in seed_chunks],
+            convert_to_numpy=True,
+            show_progress_bar=False,
+        ).tolist()
+
+        cursor = store.conn.cursor()
+        for chunk, embedding in zip(seed_chunks, chunk_embeddings):
+            cursor.execute(
+                """
+                INSERT INTO chunks (
+                    id, content, metadata, source_file, project, content_type, value_type,
+                    char_count, source, summary, tags, resolved_query, key_facts,
+                    resolved_queries, created_at
+                ) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
+                """,
+                (
+                    chunk["id"],
+                    chunk["content"],
+                    "{}",
+                    "tests/fixtures/stale_index_query.json",
+                    "ecosystem-regression-harness",
+                    "assistant_text",
+                    "MEDIUM",
+                    len(chunk["content"]),
+                    "fixture-generator",
+                    chunk["summary"],
+                    json.dumps(chunk["tags"]),
+                    chunk["resolved_query"],
+                    json.dumps(chunk["key_facts"]),
+                    json.dumps(chunk["resolved_queries"]),
+                    "2026-04-27T00:00:00Z",
+                ),
+            )
+            cursor.execute(
+                "INSERT INTO chunk_vectors (chunk_id, embedding) VALUES (?, ?)",
+                (chunk["id"], serialize_f32([float(value) for value in embedding])),
+            )
+
+        fts_sql = (
+            "SELECT chunk_id, bm25(chunks_fts) AS rank "
+            "FROM chunks_fts "
+            f"WHERE chunks_fts MATCH '{query_match}' "
+            "ORDER BY bm25(chunks_fts), chunk_id"
+        )
+        proc = subprocess.run(
+            [
+                "uvx",
+                "--from",
+                "sqlite-utils",
+                "sqlite-utils",
+                "query",
+                str(db_path),
+                fts_sql,
+            ],
+            check=True,
+            capture_output=True,
+            text=True,
+        )
+        ranked_rows = json.loads(proc.stdout)
+
+        baseline_embedding = [float(value) for value in model.embed_query(sample_text)]
+
+        payload = {
+            "generated_at": datetime.now(timezone.utc).isoformat(),
+            "generator": "scripts/generate-fixtures.sh",
+            "sqlite_snapshot": {
+                "db_backend": "sqlite",
+                "fts_table": "chunks_fts",
+                "seed_chunk_count": len(seed_chunks),
+                "query_sql": fts_sql,
+            },
+            "embedding_model": {
+                "name": model.model_name,
+                "dimension": EMBEDDING_DIM,
+                "dtype": "float32",
+            },
+            "query": {
+                "match": query_match,
+                "expected_ids": [row["chunk_id"] for row in ranked_rows],
+                "baseline_rows": ranked_rows,
+            },
+            "sample_text": {
+                "text": sample_text,
+                "baseline_embedding": baseline_embedding,
+                "min_cosine_similarity": 0.999,
+            },
+            "chunks": [
+                {
+                    **chunk,
+                    "embedding": [float(value) for value in embedding],
+                }
+                for chunk, embedding in zip(seed_chunks, chunk_embeddings)
+            ],
+        }
+    finally:
+        store.close()
+
+fixture_path.write_text(json.dumps(payload, indent=2) + "\n")
+print(f"Wrote fixture to {fixture_path}")
+PY

tests/fixtures/README.md

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+# Fixture Provenance
+
+- `stale_index_query.json` is generated by `scripts/generate-fixtures.sh`.
+- The generator creates a fresh temporary BrainLayer SQLite DB, seeds `chunks` plus `chunk_vectors`, and derives the FTS5 baseline with `sqlite-utils query`.
+- Embedding baselines come from BrainLayer's configured query embedding model (`BAAI/bge-large-en-v1.5`, 1024-d float32 at generation time).
+- Regenerate with: `scripts/generate-fixtures.sh`