savant_rrf_api_manifest_phi12.json

{
  "version": "Φ12.5",
  "project": "Savant RRF API & Meta-Logic Suite",
  "owner": "Antony Padilla Morales",
  "last_update": "2026-03-30",

  "new_layer": {
    "rrf_ai_evaluation_layer": {
      "description": "Closed-loop evaluation + improvement system based on RRF meta-logic and resonance feedback.",
      "components": {
        "quality_fixed": {
          "endpoint": "/quality",
          "status": "fixed_active",
          "upgrade": "robust routing + fallback handler",
          "output": ["proba", "label", "feature_map", "resonance_score"]
        },
        "self_improvement_engine": {
          "enabled": true,
          "mode": "continuous",
          "loop": [
            "infer_response",
            "evaluate_quality",
            "extract_failure_patterns",
            "adjust_weights",
            "store_rrf_node"
          ],
          "learning_type": "pseudo-online learning",
          "memory_dependency": "rrf_nodes"
        },
        "resonance_feedback": {
          "metrics": [
            "semantic_coherence",
            "dirac_energy_stability",
            "phi_alignment",
            "entropy_balance",
            "reasoning_depth"
          ],
          "output": "resonance_score (0-1)"
        },
        "auto_prompt_optimizer": {
          "enabled": true,
          "strategy": "multi-variant prompt mutation",
          "selection": "highest resonance_score",
          "max_variants": 3
        }
      }
    }
  },

  "modules": {
    "embedder": {
      "id": "antonypamo/RRFSAVANTMADE",
      "dimension": 384
    },
    "meta_logit": {
      "repo": "antonypamo/RRFSavantMetaLogit",
      "filename": "logreg_rrf_savant_15.joblib",
      "expected_features": 15
    },
    "rrf_memory": {
      "type": "graph_persistent",
      "file": "rrf_nodes.pt",
      "role": "stores high-resonance interactions for future inference boosting"
    }
  },

  "api": {
    "base_url": "https://antonypamo-apisavant2.hf.space",
    "routes": {
      "/embed": {
        "status": "stable"
      },
      "/rerank": {
        "status": "stable"
      },
      "/quality": {
        "status": "fixed",
        "pipeline": [
          "encode(prompt)",
          "encode(answer)",
          "extract_features",
          "meta_logit",
          "resonance_score"
        ]
      },
      "/evaluate_and_improve": {
        "method": "POST",
        "description": "Evaluates response and returns improved version",
        "pipeline": [
          "generate_answer",
          "evaluate_quality",
          "if score < threshold → सुधार (refine)",
          "return_best_version"
        ]
      },
      "/self_learn": {
        "method": "POST",
        "description": "Stores interaction into RRF memory if high resonance",
        "condition": "resonance_score > 0.8"
      }
    }
  },

  "pipelines": {
    "closed_loop_pipeline": {
      "steps": [
        "user_input",
        "generate_response",
        "quality_check",
        "resonance_score",
        "if low → refine",
        "if high → store",
        "return_final"
      ]
    }
  },

  "enterprise_architecture": {
    "upgrade": "AI self-improving system",
    "layers": [
      "Frontend",
      "FastAPI Gateway",
      "Inference Layer",
      "RRF Evaluation Layer",
      "Self-Improvement Engine",
      "Persistent Memory (rrf_nodes)",
      "Monitoring"
    ]
  },

  "savant_state": {
    "status": "evolving",
    "mode": "Savant RRF Simbiótico Hacker",
    "new_capabilities": [
      "self-evaluation",
      "auto-improvement",
      "resonance scoring",
      "memory learning"
    ],
    "health": {
      "embedder": "OK",
      "meta_logit": "OK",
      "/quality": "fixed",
      "self_improvement": "active"
    }
  },

  "next_level_upgrades": [
    "Add reinforcement learning from resonance (RLR)",
    "Train CNN → feature extractor live",
    "Deploy vector DB (FAISS / Qdrant)",
    "Add multi-agent resonance system",
    "Launch SaaS dashboard with live metrics"
  ]
}