Skip to main content

Performance Diagnostics

How to answer "is the index healthy, is search fast enough, where is time being spent". TeaRAGs exposes three diagnostic surfaces: index status, index metrics, and debug logs.

Index Status​

get_index_status reports the operational state of an indexed codebase in one call. Use it as the first diagnostic before anything else.

Returned fields (selected):

FieldWhat it tells you
indexedWhether the codebase has a collection at all
collectionNamePhysical collection name (alias-resolved)
chunkCount / fileCountSize of the index
embeddingModelThe model the collection was built with
schemaVersionQdrant payload schema version
hybridEnabledWhether sparse vectors are present
enrichment.progress{ done, total, pending, failed } per enrichment provider
enrichment.lastEnrichedAtFreshness of git.* signals
indexingInProgresstrue if another session is actively indexing

Interpreting combinations:

  • indexed: false → run /tea-rags:index or index_codebase
  • indexed: true, enrichment.progress.pending > 0 → enrichment still running; git.* signals incomplete. Wait or proceed with base-similarity search.
  • indexingInProgress: true → parallel session running. Never clear_index to "fix" this — let the other session finish.
  • embeddingModel doesn't match current config → collection was built with a different model. You'll need to reindex to switch models (different vector dimensions).

Index Metrics​

get_index_metrics is the deep diagnostic. It returns per-language, per-scope (source/test) percentile thresholds for every numeric signal. Source: ExploreFacade#getIndexMetrics.

{
  "signals": {
    "typescript": {
      "git.file.commitCount": {
        "source": {
          "count": 4823,
          "p25": 1, "p50": 3, "p75": 8, "p95": 20,
          "labelMap": { "low": 1, "typical": 3, "high": 8, "extreme": 20 }
        },
        "test": {
          "count": 1104,
          "p50": 2, "p95": 6,
          "labelMap": { "low": 1, "typical": 2, "high": 4, "extreme": 6 }
        }
      }
    }
  }
}

What to look at​

QuestionField to inspect
How big is this codebase?count per language — tells you chunk counts to budget search against
What "high churn" means herelabelMap.high for git.file.commitCount
Is the codebase polyglot?Multiple languages each > 10% of total chunks
Are test files skewing things?Compare source vs test scopes — very different thresholds confirm the split is meaningful
Health of the enrichment pipelinependingEnrichment / stalePct per provider

Polyglot detection rule​

If two or more languages each have > 10% of total chunks, treat the codebase as polyglot. Search strategy changes: always filter by language to avoid the dominant language occupying every result slot. See the search cascade rules for the full procedure.

DEBUG Mode​

DEBUG=1 (or DEBUG=true) enables full logging:

DEBUG=1 claude
# or inside MCP config:
# "env": { "DEBUG": "true" }

This turns on:

  • Full stack traces in error responses (only the top line ships in production)
  • Per-stage timing for indexing (chunking, embedding, upsert, enrichment)
  • Provider-level logs (Ollama model-info probes, OpenAI retry counts, Qdrant batch sizes)
  • Enrichment coordinator — which file failed, which commit was skipped

Logs land in ~/.tea-rags/logs/. Rotate or clean periodically — debug mode is verbose.

Where Time Goes​

Rough breakdown for a first-time index of ~100k chunks (Apple M3 Pro, Ollama local):

Stage% of totalDominant cost
File scan~1%IO on .gitignore tree walk
Chunking~10%Tree-sitter AST parse + language hook dispatch
Embedding~70%GPU inference (or CPU if WebGPU unavailable)
Upsert~10%Qdrant HTTP + sparse vector build
Enrichment (background, not on critical path)—isomorphic-git log read + jsdiff

Optimization knobs (see Performance Tuning for details):

  • EMBEDDING_TUNE_BATCH_SIZE — the biggest lever. Larger batches = fewer GPU round-trips.
  • INGEST_TUNE_CHUNKER_POOL_SIZE — parallelism for AST parsing; bump when chunking dominates (rare).
  • INGEST_TUNE_FILE_CONCURRENCY — how many files in flight through the pipeline at once.
  • TRAJECTORY_GIT_CHUNK_CONCURRENCY — parallel commits processed during chunk-churn overlay.

Provider-Specific Indicators​

Ollama slow?

  • Check if the model fits in GPU VRAM. If it spills to CPU, throughput drops 10–50×.
  • ollama ps shows active models and whether they're on GPU.
  • Consider jina-embeddings-v2-small-en (smaller dimensions) or ONNX with WebGPU.

OpenAI rate-limited?

  • INFRA_EMBEDDING_OPENAI_RATE_LIMIT retries are hidden — if latency is high but no errors surface, you're probably throttled.
  • Tier 1 (500 RPM, 1M TPM) indexes ~800k LoC in 45 min; above that, upgrade tier or switch to local.

Embedded Qdrant sluggish?

  • Check ~/.tea-rags/qdrant/daemon.port and daemon.pid are current.
  • rm ~/.tea-rags/qdrant/daemon.* forces fresh spawn if daemon is unresponsive.
  • Storage over 50 GB on SSD is fine; on spinning disks, moving QDRANT_EMBEDDED_STORAGE_PATH to SSD helps.

Health Checks for Agents​

Before relying on trajectory signals, agents should run this check:

get_index_status
  → enrichment.progress.pending == 0     (enrichment finished)
  → enrichment.lastEnrichedAt < 24h ago  (fresh)

If enrichment is lagging, fall back to similarity-only rerank (relevance preset) or wait.