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WFGY/ProblemMap/GlobalFixMap/Agents_Orchestration/llamaindex.md

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LlamaIndex — Guardrails and Fix Patterns

🧭 Quick Return to Map

You are in a sub-page of Agents & Orchestration.
To reorient, go back here:

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

Module Description Link
WFGY Core Canonical framework entry point View
Problem Map Diagnostic map and navigation hub View
Tension Universe Experiments MVP experiment field View
Recognition Where WFGY is referenced or adopted View
AI Guide Anti-hallucination reading protocol for tools View

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