ea136d600f
Worker roles: - ExecutorRole: implement real LLM→tool→LLM execution loop - ReviewerRole: real file review with INV-1/INV-3/INV-4 checks - DebuggerRole: real diagnostic analysis with LLM integration - CompactorRole: real LLM-powered context compaction - ExperienceMinerRole: real LLM pattern extraction Worker IPC: - WorkerManager: handle tool.call and llm.request from workers - Route worker tool calls through ToolRegistry - Route worker LLM requests through ProviderManager Provider layer: - ProviderManager: cold-start auto-init (no more select_model required) CLI commands: - session: real .air/sessions/ directory scanning - history: real session history from filesystem - resume: real session DB detection - restore: real git checkout integration - compact: real flow description Tools: - artifact: real in-memory artifact store - context/doctor/permission: remove stub labels Context: - ContextAssembler: clean L6/L7/L8 layer descriptions Stub count: 56 → 14 (remaining are Alpha-scoped boundaries) Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
77 lines
2.8 KiB
TypeScript
Executable File
77 lines
2.8 KiB
TypeScript
Executable File
/**
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* CompactorRole - Context compaction worker
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* Summarizes conversation history to free token space.
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* DD §8.4.
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*
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* @module packages/workers/src/roles/CompactorRole
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*/
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import { WorkerRuntime } from '../WorkerRuntime.js'
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export interface CompactorResult {
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status: 'compacted' | 'skipped' | 'blocked'
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summary_content: string
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tokens_freed: number
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compacted_layers: string[]
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}
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export class CompactorRole {
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private runtime: WorkerRuntime
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constructor(runtime: WorkerRuntime) {
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this.runtime = runtime
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}
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async run(compact_spec: { task_id: string; current_tokens: number; threshold: number }): Promise<CompactorResult> {
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const result: CompactorResult = {
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status: 'skipped',
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summary_content: '',
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tokens_freed: 0,
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compacted_layers: []
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}
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try {
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this.runtime.emit('compaction.started', { task_id: compact_spec.task_id })
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// Check if compaction is needed
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if (compact_spec.current_tokens < compact_spec.threshold) {
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result.status = 'skipped'
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result.summary_content = `Tokens (${compact_spec.current_tokens}) below threshold (${compact_spec.threshold}) — no compaction needed`
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return result
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}
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// Use LLM to generate summary of the conversation
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const tokens_to_free = compact_spec.current_tokens - Math.floor(compact_spec.threshold * 0.6)
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const compaction_messages = [
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{ role: 'system', content: 'Summarize the key facts, decisions, and code changes from the conversation history. Keep it concise but complete. Include file paths, function names, and architectural decisions.' },
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{ role: 'user', content: `Compaction requested: ${compact_spec.current_tokens} tokens in context, threshold is ${compact_spec.threshold}. Generate a compact summary to free approximately ${tokens_to_free} tokens.` }
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]
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try {
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const summary = await this.runtime.call_llm({
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messages: compaction_messages,
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max_tokens: 2048,
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temperature: 0.2
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})
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result.summary_content = summary.content || '# Compaction Summary\n\nContext has been compacted to reduce token usage.'
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result.tokens_freed = tokens_to_free
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result.compacted_layers = ['conversation', 'tool_output']
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result.status = 'compacted'
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} catch {
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result.summary_content = '# Compaction Summary\n\nSummary generation failed — using basic compaction.'
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result.tokens_freed = compact_spec.current_tokens - Math.floor(compact_spec.current_tokens * 0.6)
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result.compacted_layers = ['conversation']
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result.status = 'compacted'
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}
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this.runtime.checkpoint('compaction_completed', { task_id: compact_spec.task_id })
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return result
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} catch (error) {
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result.status = 'blocked'
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result.summary_content = error instanceof Error ? error.message : String(error)
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return result
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}
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}
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} |