9.2 KiB
LlamaIndex Guardrails and Patterns
🧭 Quick Return to Map
You are in a sub-page of Automation Platforms.
To reorient, go back here:
- Automation Platforms — stabilize no-code workflows and integrations
- WFGY Global Fix Map — main Emergency Room, 300+ structured fixes
- WFGY Problem Map 1.0 — 16 reproducible failure modes
Think of this page as a desk within a ward.
If you need the full triage and all prescriptions, return to the Emergency Room lobby.
Use this page when your RAG or agent pipeline runs in LlamaIndex. It maps common orchestration and indexing failures to exact structural fixes in the Problem Map and gives a minimal recipe you can embed in an index or query engine.
Core acceptance
- ΔS(question, retrieved) ≤ 0.45
- coverage ≥ 0.70 for the target section
- λ remains convergent across 3 paraphrases
Typical breakpoints and the right fix
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Index built but retriever fires before it is ready Fix No.14: Bootstrap Ordering → Open
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First queries after deploy fail due to env mismatch / missing secret Fix No.16: Pre-Deploy Collapse → Open
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Background ingestion + retriever race → deadlocks or empty results Fix No.15: Deployment Deadlock → Open
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Embedding similarity looks good, but meaning diverges Fix No.5: Embedding ≠ Semantic → Open
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Answers cite wrong snippet or skip citations entirely Fix No.8: Retrieval Traceability → Open Enforce payload contracts: Data Contracts → Open
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Hybrid retrievers (BM25 + dense) underperform single retriever Pattern: Query Parsing Split → Open Review: Rerankers → Open
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Some docs indexed but never surface Pattern: Vectorstore Fragmentation → Open
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Two unrelated docs blended in one answer Pattern: Symbolic Constraint Unlock (SCU) → Open
Minimal setup checklist for any LlamaIndex pipeline
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Warm-up fence before query engine Ensure index hash and vectorstore state are valid. If not, retry with capped backoff. Spec: Bootstrap Ordering
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Idempotency key Compute
dedupe_key = sha256(doc_id + rev + index_hash). Drop duplicates at ingestion. -
Retriever output contract Require fields:
snippet_id,section_id,source_url,offsets,tokens. Enforce cite-then-explain. Specs: Data Contracts · Retrieval Traceability -
Observability probes Log ΔS(question, retrieved) and λ transitions at each step. Alert if ΔS ≥ 0.60 or λ flips divergent. Overview: RAG Architecture & Recovery
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Concurrency guard One writer per index. Use locks or queue mode. Fix: Deployment Deadlock
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Eval before publish Coverage ≥ 0.70 and ΔS ≤ 0.45 required. Eval: RAG Precision/Recall
Copy-paste prompt for LlamaIndex Query Engine
I uploaded TXT OS and WFGY Problem Map files.
This retriever produced {k} docs with fields {snippet_id, section_id, source_url, offsets}.
Steps:
1. Enforce cite-then-explain. If citations missing, fail fast and suggest fix.
2. If ΔS(question, retrieved) ≥ 0.60, propose minimal structural fix referencing:
retrieval-playbook, retrieval-traceability, data-contracts, rerankers.
3. Return JSON plan:
{ "citations": [...], "answer": "...", "λ_state": "...", "ΔS": 0.xx, "next_fix": "..." }
Common LlamaIndex gotchas
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Too many retrievers chained without λ check Add λ variance clamp. Reject divergent paths.
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Index rebuild silently drops sections Enforce contracts and log ΔS across ingestion runs.
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Async queries race against ingestion Add warm-up fence and bootstrap ordering.
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Chunk drift from mismatched parsers Normalize with section detection. See: Section Detection
When to escalate
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ΔS stays ≥ 0.60 even after chunking and retriever fixes → Rebuild vectorstore with explicit metric and normalization. Spec: Retrieval Playbook
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Identical queries yield inconsistent answers → Check memory drift and version skew. Spec: Context Drift
🔗 Quick-Start Downloads
| Tool | Link | 3-Step Setup |
|---|---|---|
| WFGY 1.0 PDF | Engine Paper | 1) Download · 2) Upload to LLM · 3) Ask “Use WFGY to fix my automation bug” |
| TXT OS | TXTOS.txt | 1) Download · 2) Paste into LLM · 3) Type “hello world” |
🧭 Explore More
| Module | Description | Link |
|---|---|---|
| WFGY Core | WFGY 2.0 engine is live: full symbolic reasoning architecture and math stack | View → |
| Problem Map 1.0 | Initial 16-mode diagnostic and symbolic fix framework | View → |
| Problem Map 2.0 | RAG-focused failure tree, modular fixes, and pipelines | View → |
| Semantic Clinic Index | Expanded failure catalog: prompt injection, memory bugs, logic drift | View → |
| Semantic Blueprint | Layer-based symbolic reasoning & semantic modulations | View → |
| Benchmark vs GPT-5 | Stress test GPT-5 with full WFGY reasoning suite | View → |
| 🧙♂️ Starter Village 🏡 | New here? Lost in symbols? Click here and let the wizard guide you through | Start → |
👑 Early Stargazers: See the Hall of Fame —
⭐ WFGY Engine 2.0 is already unlocked. ⭐ Star the repo to help others discover it and unlock more on the Unlock Board.