📑 Data Contracts — Stable Interfaces for RAG & Agents

Everything WFGY touches is JSON-first and versioned. These “contracts” make pipelines observable, reproducible, and easy to debug.

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Retrieval Playbook · Traceability · Eval · Ops · Patterns: SCU · Memory Desync

0) Envelope (required for all records)

{
  "schema_version": "1.0.0",
  "event": "ingest.write | retrieve.run | rerank.run | answer.decide",
  "ts": "2025-08-13T10:22:59Z",
  "trace_id": "uuid",
  "agent_id": "scout|medic|engineer|retriever|system",
  "mem_rev": "r42", 
  "mem_hash": "sha256:..."
}

mem_rev/mem_hash prevent memory overwrite and desync.
Use the same envelope for logs and datasets.

1) Chunk record

Purpose: atomic, traceable text unit for retrieval.

{
  "$schema": "https://wfgy.dev/schemas/chunk-1.0.json",
  "chunk_id": "c_00123",
  "doc_id": "d_wfgy_paper",
  "section_id": "s_intro",
  "span": {"line_start": 120, "line_end": 154},
  "lang": "en",
  "text": "Delta-S measures semantic stress ...",
  "hash": "sha256:...",
  "embedding": {
    "model": "sentence-transformers/all-MiniLM-L6-v2",
    "dim": 384,
    "vector": [0.012, -0.044, ...],
    "normalized": true,
    "metric": "cosine"
  },
  "anchors": ["ΔS", "semantic stress"]
}

Rules

Keep original text + normalized text (case/punctuation) if you apply normalization downstream.
Always store metric and normalized.

2) Query record

{
  "$schema": "https://wfgy.dev/schemas/query-1.0.json",
  "q_id": "q_2025_08_13_0001",
  "text": "How does ΔS detect retrieval failure?",
  "lang": "en",
  "hyde": "Generate a canonical query about ... (optional)",
  "tokens": {"count": 12, "analyzer": "icu"},
  "hints": {"doc_id": ["d_wfgy_paper"], "section_id": []}
}

3) Retrieval result (candidate)

{
  "$schema": "https://wfgy.dev/schemas/retrieved-1.0.json",
  "q_id": "q_2025_08_13_0001",
  "ranker": "dense|bm25|hybrid",
  "k": 50,
  "items": [
    {
      "chunk_id": "c_00123",
      "doc_id": "d_wfgy_paper",
      "score": 0.812,           // retriever-native score
      "cosine": 0.91,           // optional explicit cosine
      "ΔS_q_ctx": 0.36,         // optional, if ground anchor available
      "source": "dense",
      "features": {"bm25": 8.3, "dense": 0.91}
    }
  ]
}

4) Rerank result

{
  "$schema": "https://wfgy.dev/schemas/rerank-1.0.json",
  "q_id": "q_2025_08_13_0001",
  "model": "BAAI/bge-reranker-base",
  "k_in": 60,
  "k_out": 8,
  "items": [
    {
      "chunk_id": "c_00123",
      "pre_score": {"dense": 0.91, "bm25": 8.3},
      "post_score": {"ce": 0.82},
      "reason": "mentions ΔS definition and failure threshold",
      "selected": true
    }
  ]
}

5) Prompt frame (schema-locked)

{
  "$schema": "https://wfgy.dev/schemas/prompt-frame-1.0.json",
  "system": "You are a grounded assistant. Cite before you explain.",
  "task": "Answer the user's question using ONLY the cited snippets.",
  "constraints": ["No cross-source merging", "Cite line spans"],
  "citations": [
    {"id": "c_00123", "doc_id": "d_wfgy_paper", "section_id": "s_intro", "span": [120,154]}
  ],
  "question": "How does ΔS detect retrieval failure?"
}

6) Answer + trace

{
  "$schema": "https://wfgy.dev/schemas/answer-1.0.json",
  "q_id": "q_2025_08_13_0001",
  "answer_text": "ΔS measures the semantic gap ...",
  "cited_chunks": ["c_00123", "c_00987"],
  "λ_observe": "→",
  "metrics": {"ΔS_q_ctx": 0.38, "latency_ms": 922}
}

7) Metrics pack (for CI)

{
  "$schema": "https://wfgy.dev/schemas/metrics-1.0.json",
  "dataset": "goldset_v1",
  "recall@50": 0.91,
  "nDCG@10": 0.62,
  "ΔS_mean": 0.41,
  "ΔS_p95": 0.58,
  "λ_convergent_rate": 0.82
}

Acceptance checklist

✅ All records include envelope (schema_version, event, ts, trace_id, mem_rev/hash).
✅ Chunks persist metric and normalized flags.
✅ Prompts are schema-locked (cite → explain).
✅ Answers store cited chunk IDs and λ state.
✅ Metrics committed per PR (goldset.jsonl).

🔗 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
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⚙️ 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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