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- Agents & Orchestration — orchestration frameworks and guardrails
- WFGY Global Fix Map — main Emergency Room, 300+ structured fixes
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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
-
Retrieval returns plausible but wrong chunks
Fix No.1: Hallucination & Chunk Drift →
Hallucination
Also review Retrieval Playbook →
Retrieval Playbook -
High vector similarity, wrong meaning
Fix No.5: Embedding ≠ Semantic →
Embedding ≠ Semantic -
Hybrid chains with BM25 or HyDE degrade compared to single retriever
Pattern: Query Parsing Split →
Query Parsing Split
Apply Rerankers for ordering control →
Rerankers -
Facts exist in the store but never show up
Pattern: Vectorstore Fragmentation →
Vectorstore Fragmentation -
Citations missing or inconsistent across LCEL steps
Fix No.8: Retrieval Traceability + Data Contracts →
Retrieval Traceability · Data Contracts -
Long Runnable chains flatten style and drift logically
Fix No.3 and No.9: Context Drift and Entropy Collapse →
Context Drift · Entropy Collapse -
Agent loops, role confusion, memory overwrite
Fix No.13: Multi-Agent Chaos →
Multi-Agent Problems · Role Drift · Memory Overwrite -
Model sounds confident but is wrong
Fix No.4: Bluffing / Overconfidence →
Bluffing -
Model merges two sources into one answer
Pattern: Symbolic Constraint Unlock →
Symbolic Constraint Unlock
# 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
-
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_revandmem_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
-
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
| 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 |
| 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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