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WFGY/ProblemMap/GlobalFixMap/Agents_Orchestration/langchain.md

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LangChain — Guardrails and Fix Patterns

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Use this when your pipeline is built with LangChain (LCEL, Runnable*, Agents, Tools) 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 LCEL pattern with WFGY checks

# Pseudocode. Show the control points you must keep.
from langchain_core.runnables import RunnablePassthrough, RunnableMap

def retrieve(q):
    # k sweep and unified analyzer across dense and sparse
    return retriever.invoke(q, k=10)

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

def reason(msg):
    # model call runs after cite-then-explain requirement in the prompt
    return llm.invoke(msg)

def wfgy_checks(q, context, answer):
    # compute ΔS(question, context) and trace why this snippet
    # enforce thresholds and fail fast when ΔS ≥ 0.60 or λ divergent
    return metrics_and_trace(q, context, answer)

chain = (
    {"q": RunnablePassthrough()}
    | RunnableMap({"context": lambda x: retrieve(x["q"]), "q": lambda x: x["q"]})
    | RunnableMap({"msg": lambda x: assemble(x["context"], x["q"]), "q": lambda x: x["q"], "context": lambda x: x["context"]})
    | RunnableMap({"answer": lambda x: reason(x["msg"]), "q": lambda x: x["q"], "context": lambda x: x["context"]})
    | (lambda x: wfgy_checks(x["q"], x["context"], x["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.

Specs and recipes RAG Architecture & Recovery · Retrieval Playbook · Retrieval Traceability · Data Contracts

LangChain-specific gotchas

  • Mixed embedding functions across write and read paths. Rebuild with explicit metric and normalization. See Embedding ≠ Semantic

  • RunnableParallel merges outputs without source fences. Add per-source headers and forbid cross-section reuse. See Symbolic Constraint Unlock

  • Memory modules re-assert old facts after a refresh. Stamp mem_rev and mem_hash. See Memory Desync

  • Agents tool-call retry loops. Add BBCR bridge steps and clamp variance with BBAM in the prompt recipe. See Logic Collapse

When to escalate

  • ΔS remains ≥ 0.60 after chunk 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 based tension engine
Engine WFGY 2.0 Production tension kernel and math engine for RAG and agents
Engine WFGY 3.0 TXT based Singularity tension engine, 131 S class set
Map Problem Map 1.0 Flagship 16 problem RAG failure checklist and fix map
Map Problem Map 2.0 RAG focused recovery pipeline
Map Problem Map 3.0 Global Debug Card, image as a debug protocol layer
Map Semantic Clinic Symptom to family to exact fix
Map Grandma’s Clinic Plain language stories mapped to Problem Map 1.0
Onboarding Starter Village Guided tour for newcomers
App TXT OS TXT semantic OS, fast boot
App Blah Blah Blah Abstract and paradox Q and A built on TXT OS
App Blur Blur Blur Text to image with semantic control
App Blow Blow Blow Reasoning game engine and memory demo

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