# 📑 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. > **Quick Nav** > [Retrieval Playbook](./retrieval-playbook.md) · > [Traceability](./retrieval-traceability.md) · > [Eval](./eval/README.md) · > [Ops](./ops/README.md) · > Patterns: [SCU](./patterns/pattern_symbolic_constraint_unlock.md) · > [Memory Desync](./patterns/pattern_memory_desync.md) --- ## 0) Envelope (required for all records) ```json { "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. ```json { "$schema": "https://wfgy.dev/schemas/chunk-1.0.json", "chunk_id": "c_00123", "doc_id": "d_wfgy_paper", "section_id": "s_intro", "span": {"line_start": 120, "line_end": 154}, "lang": "en", "text": "Delta-S measures semantic stress ...", "hash": "sha256:...", "embedding": { "model": "sentence-transformers/all-MiniLM-L6-v2", "dim": 384, "vector": [0.012, -0.044, ...], "normalized": true, "metric": "cosine" }, "anchors": ["ΔS", "semantic stress"] } ``` **Rules** * Keep **original text** + **normalized text** (case/punctuation) if you apply normalization downstream. * Always store `metric` and `normalized`. --- ## 2) Query record ```json { "$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) ```json { "$schema": "https://wfgy.dev/schemas/retrieved-1.0.json", "q_id": "q_2025_08_13_0001", "ranker": "dense|bm25|hybrid", "k": 50, "items": [ { "chunk_id": "c_00123", "doc_id": "d_wfgy_paper", "score": 0.812, // retriever-native score "cosine": 0.91, // optional explicit cosine "ΔS_q_ctx": 0.36, // optional, if ground anchor available "source": "dense", "features": {"bm25": 8.3, "dense": 0.91} } ] } ``` --- ## 4) Rerank result ```json { "$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) ```json { "$schema": "https://wfgy.dev/schemas/prompt-frame-1.0.json", "system": "You are a grounded assistant. Cite before you explain.", "task": "Answer the user's question using ONLY the cited snippets.", "constraints": ["No cross-source merging", "Cite line spans"], "citations": [ {"id": "c_00123", "doc_id": "d_wfgy_paper", "section_id": "s_intro", "span": [120,154]} ], "question": "How does ΔS detect retrieval failure?" } ``` --- ## 6) Answer + trace ```json { "$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) ```json { "$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](https://github.com/onestardao/WFGY/blob/main/I_am_not_lizardman/WFGY_All_Principles_Return_to_One_v1.0_PSBigBig_Public.pdf) | 1️⃣ Download · 2️⃣ Upload to your LLM · 3️⃣ Ask “Answer using WFGY + \” | | **TXT OS (plain-text OS)** | [TXTOS.txt](https://github.com/onestardao/WFGY/blob/main/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](/recognition/README.md) | External citations, integrations, and ecosystem proof | | ⚙️ Engine | [WFGY 1.0](/legacy/README.md) | Original PDF tension engine and early logic sketch (legacy reference) | | ⚙️ Engine | [WFGY 2.0](/core/README.md) | Production tension kernel for RAG and agent systems | | ⚙️ Engine | [WFGY 3.0](/TensionUniverse/EventHorizon/README.md) | TXT based Singularity tension engine (131 S class set) | | 🗺️ Map | [Problem Map 1.0](/ProblemMap/README.md) | Flagship 16 problem RAG failure taxonomy and fix map | | 🗺️ Map | [Problem Map 2.0](/ProblemMap/wfgy-rag-16-problem-map-global-debug-card.md) | Global Debug Card for RAG and agent pipeline diagnosis | | 🗺️ Map | [Problem Map 3.0](/ProblemMap/wfgy-ai-problem-map-troubleshooting-atlas.md) | Global AI troubleshooting atlas and failure pattern map | | 🧰 App | [TXT OS](/OS/README.md) | .txt semantic OS with fast bootstrap | | 🧰 App | [Blah Blah Blah](/OS/BlahBlahBlah/README.md) | Abstract and paradox Q&A built on TXT OS | | 🧰 App | [Blur Blur Blur](/OS/BlurBlurBlur/README.md) | Text to image generation with semantic control | | 🏡 Onboarding | [Starter Village](/StarterVillage/README.md) | Guided entry point for new users | If this repository helped, starring it improves discovery so more builders can find the docs and tools. 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