f516ca4be6
- DSPyLLMAdapter now accepts dspy.LM instead of model string, uses dspy.context(lm=...) - DSPyJudgeAdapter, DSPyProposerAdapter, DSPySyntheticAdapter each accept and use own LM - OptimizationConfig gains per-model api_base/api_key_env override fields - cli/app.py creates separate dspy.LM per adapter with per-model overrides - New unit tests verify each adapter isolates its LM from global config Fixes Bug #1 (multi-model config not wired) and Bug #2 (DSPyLLMAdapter ignores model param). Co-Authored-By: Paperclip <noreply@paperclip.ing>
52 lines
1.7 KiB
Python
52 lines
1.7 KiB
Python
"""
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Adapter: Reflective Mutation Proposer.
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Implements the ProposerPort via the DSPy InstructionProposer.
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Converts trajectories into readable format for the LLM proposer.
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"""
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from __future__ import annotations
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import dspy
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from prometheus.domain.entities import Prompt, Trajectory
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from prometheus.domain.ports import ProposerPort
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from prometheus.infrastructure.dspy_modules import InstructionProposer
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class DSPyProposerAdapter(ProposerPort):
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"""Uses evaluation trajectories to build a failure report and propose a new prompt."""
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def __init__(self, lm: dspy.LM) -> None:
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self._lm = lm
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self._proposer = InstructionProposer()
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def propose(
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self,
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current_prompt: Prompt,
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trajectories: list[Trajectory],
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task_description: str,
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) -> Prompt:
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failure_examples = self._format_failures(trajectories)
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with dspy.context(lm=self._lm):
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pred = self._proposer(
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current_instruction=current_prompt.text,
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task_description=task_description,
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failure_examples=failure_examples,
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)
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return Prompt(text=pred.new_instruction)
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@staticmethod
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def _format_failures(trajectories: list[Trajectory]) -> str:
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"""Convert trajectories into a structured textual report."""
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sections: list[str] = []
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for i, t in enumerate(trajectories, 1):
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section = (
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f"# Example {i}\n"
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f"## Input\n{t.input_text}\n\n"
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f"## Generated Output\n{t.output_text}\n\n"
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f"## Score\n{t.score:.2f}\n\n"
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f"## Feedback\n{t.feedback}\n"
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)
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sections.append(section)
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return "\n---\n".join(sections)
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