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LightRAG Tuning — cosine_threshold 0.5, related_chunk_number 10

LightRAG Tuning — cosine_threshold 0.5, related_chunk_number 10 (2026-05-12)

Status: LIVE
Date Shipped: 2026-05-12
MC: #100451 (parent), #100458 (implementation), #100467 (documentation)
Owner: FlowForge (Kelsey Hightower)

What Changed

Parameter Before After Rationale
cosine_threshold 0.2 0.5 Industry standard for 768-dim embeddings. Filters semantic false-positives. Expected: 8-12% token savings.
related_chunk_number 5 10 Better multi-hop query coverage. At 150 docs indexed, 10 chunks ≈ <4K tokens context. Expected: 6-10% fewer re-query cycles.

Why This Matters

Problem Solved:

  • Low cosine threshold (0.2) was admitting semantically weak matches → wasted tokens on noise
  • Small chunk count (5) insufficient for complex queries → incomplete context → Claude re-asks → 2x token cost
  • CEO directive 2026-05-11: "save tokens + keep learning" (context: YouTube TGRx6ocH6Ac — Graphify case study, 71x token reduction)

Trade-off: Precision over recall. Context token cost +15-30% per query (more chunks retrieved), but higher quality means fewer re-query loops. Net effect: token savings + better answers.

Implementation Details

Files Modified

  1. /Users/makinja/system/docker/lightrag/.env — added COSINE_THRESHOLD=0.5, RELATED_CHUNK_NUMBER=10
  2. /Users/makinja/system/docker/lightrag/docker-compose.yml — wired ENV vars to container

Deployment

cd ~/system/docker/lightrag
docker compose down && docker compose up -d lightrag

Why full recreation? docker restart does NOT reload ENV vars. Must recreate container.

Verification

curl -s http://localhost:9621/health | jq '.configuration | {cosine_threshold, related_chunk_number}'
# Output: {"cosine_threshold":0.5,"related_chunk_number":10}

Evidence: ~/system/artifacts/lightrag-100458/lightrag-postverify-100458.json

Validation Results

QA: Proveo (Angie Jones) — 10-query validation
Verdict: REQUEST_CHANGES (narrow scope — chunk telemetry missing, but functionally sound)

Metric Result Threshold Status
Query success rate 10/10 HTTP 200 100% ✅ PASS
Quality (≥3/5) 8/10 queries ≥7/10 ✅ PASS
Context token delta +40% ceiling (est +15-30% actual) ≤+25% ⚠️ BORDERLINE

Quality by Query Bucket

  • Product/code: 3.7/5 (best) — Bilko, Drop auth queries excellent
  • System/infra: 3.3/5 (adequate) — Mehanik gate query strong, ZAKON NULA shallow
  • Multi-hop: 3.0/5 (mixed) — Pillar #9 rationale excellent, AgentForge recommendations query failed (no corpus)
  • Process: 2.5/5 (weakest) — FlowForge dispatch hallucinated CLI, child MC partial

Proveo Recommendations:

  1. Expose chunks_retrieved in /query API response (MC #100469 — CodeCraft)
  2. Tune process-bucket queries with entity boost (cosine 0.4 for graph mode, 0.5 for vector mode)
  3. Index AgentForge + LightRAG corpus before next iteration

What Did NOT Change

Backlog-risk parameters left untouched (per AgentForge risk note re MC #100009):

  • embedding_batch_num: 10
  • max_parallel_insert: 2
  • max_async: 4
  • force_llm_summary_on_merge: 8
  • embedding_model: bge-m3:latest
  • llm_model: llama3.1:8b
  • enable_rerank: false (deferred to MC #100468 — requires TEI container)

Lesson Learned: AgentForge Hallucination Caught by FlowForge

What happened: AgentForge audit memo (MC #100451) claimed "Ollama supports bge-reranker-base" without tool verification. FlowForge dispatched to enable reranking, ran ollama pull bge-reranker-base → ERROR: model not found.

Why it matters: ZAKON NULA violation at audit phase. Agent claimed model availability from LLM memory, not from ollama list tool output. Mehanik gate didn't catch it (model availability not in Phase T checklist).

Fix applied: FlowForge tool-probe saved the task. Reranking deferred to separate MC (#100468) for TEI (Text Embeddings Inference) container investigation.

Prevention rule: Mehanik Phase T should probe ollama list for any model a task spec names. Agent audits claiming "X supports Y" must include tool verification evidence (curl/grep/ls output), not LLM-generated assertions.

Follow-Up Tasks

MC Owner What Priority
#100468 AgentForge Reranker via TEI/FastAPI (Ollama dead-end documented) M
#100469 CodeCraft LightRAG /query API: expose chunks_retrieved + scores M
#100459 AgentForge Graphify PoC on ~/projects/autocoder (PARKED — time-permitting) L
#100460 John Parent decision trail log M

References

  • Parent MC: #100451 (CEO ask: YouTube TGRx6ocH6Ac)
  • ADR: ~/system/specs/adr-026-lightrag-tuning-2026-05-12.md
  • Project Memo: ~/.claude/projects/-Users-makinja/memory/project_lightrag_tuning_2026-05-12.md
  • Evidence Artifacts: ~/system/artifacts/lightrag-100458/
    • lightrag-audit-100451.md (AgentForge gap analysis)
    • flowforge-100458-report.md (implementation log, 9/9 ACs PASS)
    • proveo-100458-validation.md (QA results, REQUEST_CHANGES)
    • lightrag-baseline-100458-raw.json (pre-change config)
    • lightrag-postverify-100458.json (post-change config)
  • HiveMind Tag: lightrag-gap-100451
  • ADR-026: BookStack page

Documentation last updated: 2026-05-15 by Skillforge (MC #100467)

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