445865e429
Some checks failed
- Integrate GPU scoring inline into reasoning/multi_path.py (auto-uses GPU when available) - Integrate GPU deduplication into multi_agent/consensus_engine.py - Add semantic_search() method to memory/semantic_graph.py with GPU acceleration - Integrate GPU training into self_improvement/training.py AutoTrainer - Fix all 758 ruff lint issues (whitespace, import sorting, unused imports, ambiguous vars, undefined names) - Fix all 40 mypy type errors across the codebase (no-any-return, union-attr, arg-type, etc.) - Fix deprecated ruff config keys (select/ignore -> [tool.ruff.lint]) - Add .dockerignore to exclude .venv/, tests/, docs/ from Docker builds - Add type hints and docstrings to verification/outcome.py - Fix E402 import ordering in witness_agent.py - Fix F821 undefined names in vector_pgvector.py and native.py - Fix E741 ambiguous variable names in reflective.py and recommender.py All 276 tests pass. 0 ruff errors. 0 mypy errors. Co-Authored-By: Nakamoto, S <defi@defi-oracle.io>
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.reasoning.tot import ThoughtNode
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from fusionagi.schemas.atomic import AtomicSemanticUnit
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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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