Reranking

Reranking re-scores search results using git metadata signals combined with task-specific weight profiles. Default semantic search returns results ranked by vector similarity only — but an agent investigating a production bug needs different results than an agent onboarding a new developer, even for the same query.

How It Works

1. Vector search returns candidates ranked by embedding similarity
2. Reranking re-scores each result using a weighted combination of trajectory signals
3. Final ranking reflects both semantic relevance and code quality/history signals

Presets

For search_code (practical development)

Preset	Signals	Best for
relevance	similarity only	General code lookup (default)
recent	similarity 0.7 + recency 0.3	Sprint review, incident response
stable	similarity 0.7 + stability 0.3	Finding reliable implementations

For semantic_search / hybrid_search (analytics)

Preset	Key signals	Best for
relevance	similarity only	General lookup (default)
techDebt	age + churn + bugFix + volatility	Legacy code assessment
hotspots	chunkChurn + burstActivity + bugFix + volatility	Bug-prone areas, risk assessment
codeReview	recency + burstActivity + density + chunkChurn	Recent changes review
onboarding	documentation + stability	New developer entry points
securityAudit	age + ownership + bugFix + pathRisk + volatility	Old critical code, security review
refactoring	chunkChurn + chunkSize + volatility + bugFix + age	Refactor candidates
ownership	ownership + knowledgeSilo	Knowledge silos, bus factor
impactAnalysis	similarity + imports	Dependency chains, blast radius

Custom Weights

Any signal can be combined with arbitrary weights:

{
  "rerank": {
    "custom": {
      "similarity": 0.4,
      "burstActivity": 0.3,
      "bugFix": 0.2,
      "pathRisk": 0.1
    }
  }
}

Available Scoring Signals

19 signals are available for composition:

Key	Signal Source	Description
similarity	Vector search	Embedding similarity score
recency	git (prefers chunk-level)	Inverse of ageDays
stability	git (prefers chunk-level)	Inverse of commitCount
churn	git (prefers chunk-level)	Direct commitCount
age	git (prefers chunk-level)	Direct ageDays
ownership	git	Author concentration (dominantAuthorPct)
chunkSize	chunk metadata	Lines of code in chunk
documentation	chunk metadata	Is documentation file
imports	file metadata	Import/dependency count
bugFix	git (prefers chunk-level)	bugFixRate
volatility	git	churnVolatility (stddev of commit gaps)
density	git	changeDensity (commits/month)
chunkChurn	git chunk-level	chunkCommitCount
relativeChurnNorm	git	Churn relative to file size
burstActivity	git	recencyWeightedFreq — recent burst of changes
pathRisk	file metadata	Security-sensitive path pattern (0 or 1)
knowledgeSilo	git	Single-contributor flag (1 / 0.5 / 0)
chunkRelativeChurn	git chunk-level	Chunk's share of file churn

All presets automatically prefer chunk-level data when available (e.g., chunkCommitCount over commitCount for churn signals).

Combining Filters with Reranking

Filters (Qdrant conditions) narrow the candidate set. Reranking re-orders the filtered results. Use both for precise queries:

Goal	Filter	Rerank
Recent bugs in auth	git.ageDays <= 14 + pathPattern: **/auth/**	hotspots
Old single-owner code	git.ageDays >= 90 + git.commitCount >= 5	ownership
Recently active TypeScript	language: typescript + git.ageDays <= 30	codeReview
Large stable functions	chunkType: function + git.commitCount <= 3	onboarding

👉 Full agentic reranking workflows — how agents chain presets for bug investigation, code review, refactoring, and more.