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from fastapi import APIRouter, Depends |
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import numpy as np |
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from typing import List |
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from consensus.topo import TopologicalConsciousness |
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from consensus.metrics import weighted_average_text |
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from api.schemas import AskRequest, AskResponse |
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from adapters.base import BaseModelAdapter |
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router = APIRouter(prefix="/consensus", tags=["consensus"]) |
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def get_models() -> List[BaseModelAdapter]: |
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from adapters.openai_adapter import OpenAIAdapter |
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from adapters.anthropic_adapter import AnthropicAdapter |
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return [OpenAIAdapter(), AnthropicAdapter()] |
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@router.post("/ask", response_model=AskResponse) |
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def ask(req: AskRequest, models: List[BaseModelAdapter] = Depends(get_models)): |
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tc = TopologicalConsciousness(n_anyons=len(models), central_charge=627) |
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responses = [m.generate(req.prompt) for m in models] |
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embs = [np.array(m.embed_text(r), dtype=float) for r in responses] |
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pc = tc.calculate_coherence(embs) |
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if pc < req.min_confidence: |
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return AskResponse(answer=None, confidence=pc, |
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warning="Models disagree - low confidence") |
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E = np.stack(embs); sims = E @ E.T |
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w = sims.mean(axis=1); w = np.clip(w, 0, None); w = w/(w.sum()+1e-12) |
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return AskResponse(answer=weighted_average_text(responses, w), |
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confidence=pc, weights=w.tolist()) |
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