50 lines
1.6 KiB
Python
50 lines
1.6 KiB
Python
"""Dynamic recomposition: build higher-order insights with traceability."""
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from __future__ import annotations
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from dataclasses import dataclass, field
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from typing import Any
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from fusionagi.schemas.atomic import AtomicSemanticUnit
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from fusionagi.reasoning.tot import ThoughtNode
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@dataclass
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class RecomposedResponse:
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"""Recomposed response with traceability to atomic units."""
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summary: str = ""
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key_claims: list[str] = field(default_factory=list)
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unit_refs: list[str] = field(default_factory=list)
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confidence: float = 0.0
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metadata: dict[str, Any] = field(default_factory=dict)
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def recompose(
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thought_nodes: list[ThoughtNode],
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atomic_units: list[AtomicSemanticUnit],
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) -> RecomposedResponse:
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"""Build higher-order insights from selected thought nodes."""
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unit_refs: set[str] = set()
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key_claims: list[str] = []
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summaries: list[str] = []
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for node in thought_nodes:
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if node.thought:
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summaries.append(node.thought[:200])
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key_claims.append(node.thought[:150])
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for uid in node.unit_refs:
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unit_refs.add(uid)
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summary = " ".join(summaries[:3]) if summaries else "No insights."
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if len(summaries) > 3:
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summary += " [truncated]"
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avg_score = sum(n.score for n in thought_nodes) / len(thought_nodes) if thought_nodes else 0.0
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return RecomposedResponse(
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summary=summary,
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key_claims=key_claims[:10],
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unit_refs=list(unit_refs),
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confidence=min(1.0, avg_score),
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metadata={"node_count": len(thought_nodes), "unit_count": len(unit_refs)},
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)
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