| import os |
| import sys |
| import math |
|
|
| sys.path.append(os.path.dirname(os.path.abspath(__file__))) |
| from src.core.retrieval_engine import LocalRetrievalEngine |
|
|
| class MockInferenceStep: |
| def __init__(self, operation, text, confidence, metadata): |
| self.operation = operation |
| self.text = text |
| self.confidence = confidence |
| self.metadata = metadata |
|
|
| def process_input(text, max_depth=12): |
| retriever = LocalRetrievalEngine() |
| matches = retriever.query(text, top_k=3) |
| |
| raw_steps = [] |
| if not matches: |
| raw_steps.append(MockInferenceStep( |
| operation="INITIALIZE", |
| text="Autonomous core initialized. No contextual nodes found on disk.", |
| confidence=1.0, |
| metadata={"duration_ms": 15} |
| )) |
| class BaseNode: |
| label = "System isolated. Awaiting operational ingestion data." |
| confidence = 1.0 |
| return BaseNode(), raw_steps |
|
|
| for idx, match in enumerate(matches): |
| raw_steps.append(MockInferenceStep( |
| operation="SELECT_PREMISE" if idx == 0 else "RESOLVE_RELATION", |
| text=match.get('text', ''), |
| confidence=match.get('alignment_score', 0.85), |
| metadata={"duration_ms": 40 + (idx * 15), "source": match.get('source_path')} |
| )) |
|
|
| class FinalNode: |
| label = matches[0].get('text', '')[:120] + "..." |
| confidence = matches[0].get('alignment_score', 0.90) |
|
|
| return FinalNode(), raw_steps |
|
|
| def get_ripple_payload(text, max_depth=12): |
| final_node, raw_steps = process_input(text, max_depth=max_depth) |
| step_payload = [] |
| |
| for i, step in enumerate(raw_steps): |
| sim_score = step.confidence |
| sim_score = max(0.001, min(0.999, sim_score)) |
| calculated_loss = -math.log(sim_score) * 0.1 |
| |
| step_payload.append({ |
| "id": i, |
| "operation": step.operation, |
| "confidence": float(step.confidence), |
| "free_energy": float(calculated_loss), |
| "duration_ms": int(step.metadata.get("duration_ms", 25)), |
| }) |
|
|
| total_fe = sum(s["free_energy"] for s in step_payload) |
|
|
| return { |
| "steps": step_payload, |
| "final_conclusion": { |
| "label": final_node.label, |
| "confidence": float(final_node.confidence), |
| }, |
| "total_free_energy": total_fe, |
| } |
|
|
