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WFGY/ProblemMap/GlobalFixMap/Agents_Orchestration/llamaindex.md

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LlamaIndex — Guardrails and Fix Patterns

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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 when your stack uses LlamaIndex (indices, query engines, retrievers, routers, agents) and you see wrong snippets, unstable reasoning, mixed sources, or silent failures that look fine in logs.

Acceptance targets

  • ΔS(question, retrieved) ≤ 0.45
  • coverage ≥ 0.70 to the intended section or record
  • λ stays convergent across 3 paraphrases

Typical breakpoints → exact fixes

Minimal LlamaIndex pattern with WFGY checks

# Pseudocode. Control points you must keep.
from llama_index.core import VectorStoreIndex
from llama_index.core.query_engine import RetrieverQueryEngine

# build index with explicit metric and normalization
index = VectorStoreIndex.from_documents(
    docs,
    embed_model=embedder,   # keep the same fn for write and read
)

retriever = index.as_retriever(similarity_top_k=10)
qe = RetrieverQueryEngine.from_args(retriever)

def assemble_prompt(context, q):
    # schema-locked: system -> task -> constraints -> citations -> answer
    return prompt.render(context=context, question=q)

def reason(msg):
    # require cite then explain in the template
    return llm(msg)

def wfgy_checks(q, context, answer):
    # compute ΔS(question, context) and record snippet↔citation mapping
    # fail fast when ΔS ≥ 0.60 or λ divergent
    return metrics_and_trace(q, context, answer)

def run(q):
    nodes = retriever.retrieve(q)
    context = join_nodes(nodes)
    msg = assemble_prompt(context, q)
    answer = reason(msg)
    return wfgy_checks(q, context, answer)

What this enforces

  • Retrieval is observable and parameterized.
  • Prompt is schema locked with cite first.
  • WFGY check runs after generation and can stop the run when ΔS is high or λ flips.
  • Traces record snippet to citation mapping for audits.

Reference specs RAG Architecture & Recovery · Retrieval Playbook · Retrieval Traceability · Data Contracts

LlamaIndex-specific gotchas

  • Mixed embedding functions or metrics between ingestion and query. Rebuild with explicit metric and unit normalization. See Embedding ≠ Semantic

  • ResponseSynthesizer rewrites citations. Enforce cite first and lock section ids. See Retrieval Traceability

  • RouterQueryEngine sends different tokenizations to dense and sparse backends. Unify analyzers first. See Query Parsing Split

  • Persistence reloads with a different embedder when swapping models. Pin versions and validate store headers. See Pre-Deploy Collapse

  • Agent tool loops with vague stopping rules. Add BBCR bridge steps and clamp variance with BBAM in the prompt. See Logic Collapse

When to escalate

  • ΔS remains ≥ 0.60 after chunking and retrieval fixes Work through the playbook and rebuild index parameters. Retrieval Playbook

  • Answers flip between runs or sessions Verify version skew and session state. Pre-Deploy Collapse

🔗 Quick-Start Downloads (60 sec)

Tool Link 3-Step Setup
WFGY 1.0 PDF Engine Paper 1️⃣ Download · 2️⃣ Upload to your LLM · 3️⃣ Ask “Answer using WFGY + <your question>”
TXT OS (plain-text OS) TXTOS.txt 1️⃣ Download · 2️⃣ Paste into any LLM chat · 3️⃣ Type “hello world” — OS boots instantly

Explore More

Layer Page What it’s for
⭐ Proof WFGY Recognition Map External citations, integrations, and ecosystem proof
⚙️ Engine WFGY 1.0 Original PDF tension engine and early logic sketch (legacy reference)
⚙️ Engine WFGY 2.0 Production tension kernel for RAG and agent systems
⚙️ Engine WFGY 3.0 TXT based Singularity tension engine (131 S class set)
🗺️ Map Problem Map 1.0 Flagship 16 problem RAG failure taxonomy and fix map
🗺️ Map Problem Map 2.0 Global Debug Card for RAG and agent pipeline diagnosis
🗺️ Map Problem Map 3.0 Global AI troubleshooting atlas and failure pattern map
🧰 App TXT OS .txt semantic OS with fast bootstrap
🧰 App Blah Blah Blah Abstract and paradox Q&A built on TXT OS
🧰 App Blur Blur Blur Text to image generation with semantic control
🏡 Onboarding Starter Village Guided entry point for new users

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