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| license: other | |
| title: Savant RRF Φ12.0 – Dirac-Resonant Conceptual Quality API | |
| sdk: docker | |
| emoji: 🐢 | |
| colorFrom: red | |
| colorTo: green | |
| pinned: true | |
| short_description: API de evaluación conceptual resonante para LLM | |
| 🧠 Savant RRF Φ12.0 — Meta-Logic & Rerank API | |
| Savant RRF Φ12.0 is a production-ready FastAPI service that exposes: | |
| A meta-logic quality evaluator based on the Resonance of Reality Framework (RRF) | |
| A batched semantic reranker using a custom icosahedral-resonant embedding model | |
| A deterministic Φ-node ontology mapping layer | |
| The system combines SentenceTransformer embeddings, spectral / resonance features, and a 15-dimensional meta-logit classifier to evaluate reasoning quality and rank documents efficiently. | |
| 🔗 Live API | |
| Base URL: https://antonypamo-apisavant2.hf.space | |
| 📦 Models Used | |
| Component Model | |
| Embedder antonypamo/RRFSAVANTMADE | |
| Meta-Logic antonypamo/RRFSavantMetaLogicV2/logreg_rrf_savant.joblib | |
| Feature Dim 15 features | |
| Runtime CPU (GPU optional if available) | |
| 🧩 Φ-Node Ontology | |
| The system maps inputs to one of 8 deterministic Φ-nodes: | |
| Index Φ Node | |
| 0 Φ0_seed | |
| 1 Φ1_geometric | |
| 2 Φ2_gauge_dirac | |
| 3 Φ3_log_gravity | |
| 4 Φ4_resonance | |
| 5 Φ5_memory_symbiosis | |
| 6 Φ6_alignment | |
| 7 Φ7_meta_agi | |
| This mapping is rule-based and reproducible, derived from spectral coherence, energy, and phase features. | |
| 🚀 Endpoints Overview | |
| GET / | |
| Root discovery endpoint. | |
| { | |
| "status": "ok", | |
| "model": "RRFSavantMetaLogicV2", | |
| "version": "Φ12.0", | |
| "docs": "/docs", | |
| "endpoints": ["/manifest", "/health", "/evaluate", "/quality", "/v1/rerank"] | |
| } | |
| GET /health | |
| Lightweight health check. | |
| { "status": "ok" } | |
| GET /manifest | |
| Static manifest describing the model. | |
| { | |
| "model": "RRFSavantMetaLogicV2", | |
| "version": "Φ12.0", | |
| "encoder": "antonypamo/RRFSAVANTMADE", | |
| "features": 15, | |
| "phi_nodes": [...] | |
| } | |
| 🧪 Quality Evaluation API | |
| POST /evaluate | |
| Evaluates the conceptual quality of a (prompt, answer) pair. | |
| Request | |
| { | |
| "prompt": "Explain what a smoke test is", | |
| "answer": "A smoke test is a minimal validation..." | |
| } | |
| Response | |
| { | |
| "p_good": 0.87, | |
| "scores": { | |
| "SRRF": 0.87, | |
| "CRRF": 0.79, | |
| "E_phi": 0.82 | |
| }, | |
| "features": { | |
| "phi": 0.61, | |
| "omega": 0.22, | |
| "coherence": 0.83, | |
| "S_RRF": 0.81, | |
| "C_RRF": 0.77, | |
| "hamiltonian_energy": 48.3, | |
| "dominant_frequency": 12 | |
| }, | |
| "phi_node": "Φ4_resonance" | |
| } | |
| POST /quality | |
| Alias of /evaluate. Same input, same output. | |
| 🔍 Semantic Reranking API | |
| POST /v1/rerank | |
| Ranks documents by semantic similarity to a query using batched embedding inference. | |
| Request | |
| { | |
| "query": "What is a smoke test?", | |
| "documents": [ | |
| "A smoke test is a minimal system check", | |
| "Load tests measure concurrency", | |
| "Benchmarks compare systems" | |
| ], | |
| "alpha": 0.2 | |
| } | |
| alpha is reserved for future hybrid scoring (currently unused). | |
| Response | |
| { | |
| "model_id": "antonypamo/RRFSAVANTMADE", | |
| "results": [ | |
| { "id": 0, "score": 0.92, "rank": 1 }, | |
| { "id": 2, "score": 0.51, "rank": 2 }, | |
| { "id": 1, "score": 0.21, "rank": 3 } | |
| ] | |
| } | |
| ⚙️ Runtime Constraints | |
| Parameter Limit | |
| Max prompt length 8,000 chars | |
| Max answer length 12,000 chars | |
| Max documents 50 | |
| Max document size 6,000 chars | |
| Payload violations return HTTP 413. | |
| 🧠 Feature Vector (15D) | |
| The meta-logic classifier consumes: | |
| Spectral / resonance features: | |
| phi, omega, coherence, S_RRF, C_RRF | |
| hamiltonian_energy, dominant_frequency | |
| Φ-node one-hot encoding (8 dimensions) | |
| Total = 15 features | |
| 🛠 Running Locally | |
| pip install fastapi uvicorn sentence-transformers huggingface_hub joblib numpy | |
| export HF_TOKEN=your_token_here | |
| uvicorn app:app --host 0.0.0.0 --port 8000 | |
| Open: | |
| http://127.0.0.1:8000/docs | |
| 📈 Performance Notes | |
| Optimized for batched inference on /v1/rerank | |
| Stable under load (0% error rate in benchmarks) | |
| CPU-based by default; GPU reduces latency significantly | |
| Tail latency (p95/p99) depends on concurrency and hardware | |
| 🧩 Design Philosophy | |
| Savant RRF is not a generic classifier. | |
| It encodes: | |
| Discrete resonance physics | |
| Icosahedral symbolic structure | |
| Deterministic ontology mapping | |
| Meta-logic scoring beyond surface semantics | |
| This makes it suitable for: | |
| AI evaluation & judging | |
| RAG reranking | |
| Cognitive profiling | |
| Research-grade reasoning analysis | |
| 📄 License & Attribution | |
| © 2025 Antony Padilla Morales | |
| Resonance of Reality Framework (RRF) |