forked from cardosofelipe/fast-next-template
style(memory): apply ruff formatting and linting fixes
Auto-fixed linting errors and formatting issues: - Removed unused imports (F401): pytest, Any, AnalysisType, MemoryType, OutcomeType - Removed unused variable (F841): hooks variable in test - Applied consistent formatting across memory service and test files 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
This commit is contained in:
@@ -414,12 +414,14 @@ class MemoryToolService:
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if args.query.lower() in key.lower():
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value = await working.get(key)
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if value is not None:
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results.append({
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"type": "working",
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"key": key,
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"content": str(value),
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"relevance": 1.0,
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})
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results.append(
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{
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"type": "working",
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"key": key,
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"content": str(value),
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"relevance": 1.0,
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}
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)
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elif memory_type == MemoryType.EPISODIC:
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episodic = await self._get_episodic()
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@@ -430,14 +432,18 @@ class MemoryToolService:
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agent_instance_id=context.agent_instance_id,
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)
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for episode in episodes:
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results.append({
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"type": "episodic",
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"id": str(episode.id),
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"summary": episode.task_description,
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"outcome": episode.outcome.value if episode.outcome else None,
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"occurred_at": episode.occurred_at.isoformat(),
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"relevance": episode.importance_score,
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})
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results.append(
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{
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"type": "episodic",
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"id": str(episode.id),
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"summary": episode.task_description,
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"outcome": episode.outcome.value
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if episode.outcome
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else None,
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"occurred_at": episode.occurred_at.isoformat(),
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"relevance": episode.importance_score,
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}
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)
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elif memory_type == MemoryType.SEMANTIC:
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semantic = await self._get_semantic()
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@@ -448,15 +454,17 @@ class MemoryToolService:
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min_confidence=args.min_relevance,
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)
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for fact in facts:
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results.append({
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"type": "semantic",
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"id": str(fact.id),
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"subject": fact.subject,
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"predicate": fact.predicate,
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"object": fact.object,
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"confidence": fact.confidence,
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"relevance": fact.confidence,
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})
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results.append(
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{
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"type": "semantic",
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"id": str(fact.id),
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"subject": fact.subject,
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"predicate": fact.predicate,
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"object": fact.object,
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"confidence": fact.confidence,
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"relevance": fact.confidence,
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}
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)
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elif memory_type == MemoryType.PROCEDURAL:
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procedural = await self._get_procedural()
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@@ -467,15 +475,17 @@ class MemoryToolService:
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limit=args.limit,
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)
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for proc in procedures:
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results.append({
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"type": "procedural",
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"id": str(proc.id),
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"name": proc.name,
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"trigger": proc.trigger_pattern,
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"success_rate": proc.success_rate,
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"steps_count": len(proc.steps) if proc.steps else 0,
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"relevance": proc.success_rate,
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})
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results.append(
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{
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"type": "procedural",
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"id": str(proc.id),
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"name": proc.name,
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"trigger": proc.trigger_pattern,
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"success_rate": proc.success_rate,
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"steps_count": len(proc.steps) if proc.steps else 0,
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"relevance": proc.success_rate,
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}
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)
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# Sort by relevance and limit
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results.sort(key=lambda x: x.get("relevance", 0), reverse=True)
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@@ -601,7 +611,11 @@ class MemoryToolService:
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if ep.task_type:
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task_types[ep.task_type] = task_types.get(ep.task_type, 0) + 1
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if ep.outcome:
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outcome_val = ep.outcome.value if hasattr(ep.outcome, "value") else str(ep.outcome)
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outcome_val = (
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ep.outcome.value
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if hasattr(ep.outcome, "value")
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else str(ep.outcome)
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)
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outcomes[outcome_val] = outcomes.get(outcome_val, 0) + 1
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# Sort by frequency
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@@ -613,11 +627,13 @@ class MemoryToolService:
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examples = []
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if args.include_examples:
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for ep in episodes[: min(3, args.max_items)]:
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examples.append({
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"summary": ep.task_description,
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"task_type": ep.task_type,
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"outcome": ep.outcome.value if ep.outcome else None,
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})
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examples.append(
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{
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"summary": ep.task_description,
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"task_type": ep.task_type,
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"outcome": ep.outcome.value if ep.outcome else None,
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}
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)
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return {
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"analysis_type": "recent_patterns",
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@@ -661,11 +677,13 @@ class MemoryToolService:
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examples = []
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if args.include_examples:
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for ep in successful[: min(3, args.max_items)]:
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examples.append({
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"summary": ep.task_description,
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"task_type": ep.task_type,
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"lessons": ep.lessons_learned,
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})
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examples.append(
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{
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"summary": ep.task_description,
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"task_type": ep.task_type,
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"lessons": ep.lessons_learned,
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}
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)
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return {
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"analysis_type": "success_factors",
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@@ -694,9 +712,7 @@ class MemoryToolService:
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failure_by_task[task].append(ep)
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# Most common failure types
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failure_counts = {
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task: len(eps) for task, eps in failure_by_task.items()
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}
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failure_counts = {task: len(eps) for task, eps in failure_by_task.items()}
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top_failures = sorted(failure_counts.items(), key=lambda x: x[1], reverse=True)[
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: args.max_items
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]
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@@ -704,12 +720,14 @@ class MemoryToolService:
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examples = []
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if args.include_examples:
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for ep in failed[: min(3, args.max_items)]:
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examples.append({
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"summary": ep.task_description,
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"task_type": ep.task_type,
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"lessons": ep.lessons_learned,
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"error": ep.outcome_details,
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})
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examples.append(
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{
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"summary": ep.task_description,
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"task_type": ep.task_type,
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"lessons": ep.lessons_learned,
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"error": ep.outcome_details,
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}
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)
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return {
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"analysis_type": "failure_patterns",
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@@ -794,15 +812,21 @@ class MemoryToolService:
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insights = []
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if top_tasks:
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insights.append(f"Most common task type: {top_tasks[0][0]} ({top_tasks[0][1]} occurrences)")
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insights.append(
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f"Most common task type: {top_tasks[0][0]} ({top_tasks[0][1]} occurrences)"
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)
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total = sum(outcome_dist.values())
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if total > 0:
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success_rate = outcome_dist.get("success", 0) / total
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if success_rate > 0.8:
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insights.append("High success rate observed - current approach is working well")
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insights.append(
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"High success rate observed - current approach is working well"
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)
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elif success_rate < 0.5:
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insights.append("Success rate below 50% - consider reviewing procedures")
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insights.append(
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"Success rate below 50% - consider reviewing procedures"
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)
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return insights
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@@ -839,9 +863,13 @@ class MemoryToolService:
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if top_failures:
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worst_task, count = top_failures[0]
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tips.append(f"'{worst_task}' has most failures ({count}) - needs procedure review")
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tips.append(
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f"'{worst_task}' has most failures ({count}) - needs procedure review"
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)
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tips.append("Review lessons_learned from past failures before attempting similar tasks")
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tips.append(
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"Review lessons_learned from past failures before attempting similar tasks"
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)
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return tips
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@@ -912,7 +940,11 @@ class MemoryToolService:
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outcomes = {"success": 0, "failure": 0, "partial": 0, "abandoned": 0}
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for ep in recent_episodes:
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if ep.outcome:
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key = ep.outcome.value if hasattr(ep.outcome, "value") else str(ep.outcome)
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key = (
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ep.outcome.value
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if hasattr(ep.outcome, "value")
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else str(ep.outcome)
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)
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if key in outcomes:
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outcomes[key] += 1
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@@ -942,7 +974,8 @@ class MemoryToolService:
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# Filter by minimum success rate if specified
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procedures = [
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p for p in all_procedures
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p
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for p in all_procedures
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if args.min_success_rate is None or p.success_rate >= args.min_success_rate
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][: args.limit]
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