Create api/router_consensus.py

api/router_consensus.py

@@ -1,18 +1,18 @@
+# api/router_consensus.py
 from fastapi import APIRouter, Depends
 import numpy as np
 from typing import List
 from consensus.topo import TopologicalConsciousness
 from consensus.metrics import weighted_average_text
-from .schemas import AskRequest, AskResponse
+from api.schemas import AskRequest, AskResponse
 from adapters.base import BaseModelAdapter
 
 router = APIRouter(prefix="/consensus", tags=["consensus"])
 
 def get_models() -> List[BaseModelAdapter]:
-    # TODO: wire real adapters here
     from adapters.openai_adapter import OpenAIAdapter
-    from adapters.anthropic_adapter import AnthropicAdapter
-    return [OpenAIAdapter(), AnthropicAdapter()]
+    # add others later (Anthropic, etc.)
+    return [OpenAIAdapter(), OpenAIAdapter()]
 
 @router.post("/ask", response_model=AskResponse)
 def ask(req: AskRequest, models: List[BaseModelAdapter] = Depends(get_models)):
@@ -23,12 +23,7 @@ def ask(req: AskRequest, models: List[BaseModelAdapter] = Depends(get_models)):
     if pc < req.min_confidence:
         return AskResponse(answer=None, confidence=pc,
                            warning="Models disagree - low confidence")
-    # weights from principal eigenvector implicit in pc isn’t returned;
-    # build a simple softmax over average pairwise sims as weights
-    E = np.stack(embs)
-    sims = E @ E.T
-    w = sims.mean(axis=1)
-    w = np.maximum(w, 0)
-    w = w / (w.sum() + 1e-12)
-    answer = weighted_average_text(responses, w)
-    return AskResponse(answer=answer, confidence=pc, weights=w.tolist())
+    E = np.stack(embs); sims = E @ E.T
+    w = sims.mean(axis=1); w = np.clip(w, 0, None); w = w/(w.sum()+1e-12)
+    return AskResponse(answer=weighted_average_text(responses, w),
+                       confidence=pc, weights=w.tolist())