WFGY/ProblemMap/data-contracts.md

📑 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).

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🔗 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

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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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