feat(autofix): Add first pass at interactive flow #1168
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| # call LLM separately with the same memory to generate structured output insight cards | ||
| insight_sharing = InsightSharingComponent(context) | ||
| past_insights = context.state.get().get_all_insights() | ||
| insight_card = insight_sharing.invoke( | ||
| InsightSharingRequest( | ||
| latest_thought=text, | ||
| memory=self.memory, | ||
| task_description=context.state.get().get_step_description(), | ||
| past_insights=past_insights, | ||
| ) | ||
| ) | ||
| if insight_card: | ||
| if context.state.get().steps and isinstance( | ||
| context.state.get().steps[-1], DefaultStep | ||
| ): | ||
| step = cast(DefaultStep, context.state.get().steps[-1]) | ||
| step.insights.append(insight_card) | ||
| with context.state.update() as cur: | ||
| cur.steps[-1] = step |
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Hmm I think this is worth pulling out of the general agent logic and into a specific AutofixAgent that inherits from this generic Agent as codecov will be using this too and we don't need to generate insights there.
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Since in many cases we'll want to disable it for Autofix too (e.g. evals, GH Copilot), I'm adding an interactive flag to the AgentConfig so that these kinds of features are easy to enable/disable whenever we want. It defaults to False so the unit test gen code will not generate any insights.
src/seer/automation/autofix/components/insight_sharing/component.py
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| elif message.role == "tool_use": | ||
| new_messages.append( | ||
| Message(role="assistant", content=message.content, tool_calls=[]) | ||
| ) |
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tool_use is for anthropic right, we don't need to use this hack on anthropic calls right?
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I believe I did need to use this hack for Claude, I can't recall the exact error that was occurring though
This supports insight card generation, user interjection, and a root cause flow that makes sense for the new design. (#450 )