feat: Implement executor assignment and clustering optimizations for session management

This commit is contained in:
catlog22
2025-12-20 11:29:16 +08:00
parent e1cac5dd50
commit 4de4db3c69
3 changed files with 194 additions and 33 deletions

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@@ -258,6 +258,33 @@ TodoWrite({
### Step 3: Launch Execution
**Executor Resolution** (任务级 executor 优先于全局设置):
```javascript
// 获取任务的 executor优先使用 executorAssignmentsfallback 到全局 executionMethod
function getTaskExecutor(task) {
const assignments = executionContext?.executorAssignments || {}
if (assignments[task.id]) {
return assignments[task.id].executor // 'gemini' | 'codex' | 'agent'
}
// Fallback: 全局 executionMethod 映射
const method = executionContext?.executionMethod || 'Auto'
if (method === 'Agent') return 'agent'
if (method === 'Codex') return 'codex'
// Auto: 根据复杂度
return planObject.complexity === 'Low' ? 'agent' : 'codex'
}
// 按 executor 分组任务
function groupTasksByExecutor(tasks) {
const groups = { gemini: [], codex: [], agent: [] }
tasks.forEach(task => {
const executor = getTaskExecutor(task)
groups[executor].push(task)
})
return groups
}
```
**Execution Flow**: Parallel batches concurrently → Sequential batches in order
```javascript
const parallel = executionCalls.filter(c => c.executionType === "parallel")
@@ -283,8 +310,9 @@ for (const call of sequential) {
**Option A: Agent Execution**
When to use:
- `executionMethod = "Agent"`
- `executionMethod = "Auto" AND complexity = "Low"`
- `getTaskExecutor(task) === "agent"`
- `executionMethod = "Agent"` (全局 fallback)
-`executionMethod = "Auto" AND complexity = "Low"` (全局 fallback)
**Task Formatting Principle**: Each task is a self-contained checklist. The agent only needs to know what THIS task requires, not its position or relation to other tasks.
@@ -400,8 +428,9 @@ function extractRelatedFiles(tasks) {
**Option B: CLI Execution (Codex)**
When to use:
- `executionMethod = "Codex"`
- `executionMethod = "Auto" AND complexity = "Medium" or "High"`
- `getTaskExecutor(task) === "codex"`
- `executionMethod = "Codex"` (全局 fallback)
-`executionMethod = "Auto" AND complexity = "Medium/High"` (全局 fallback)
**Task Formatting Principle**: Same as Agent - each task is a self-contained checklist. No task numbering or position awareness.
@@ -530,6 +559,15 @@ if (bash_result.status === 'failed' || bash_result.status === 'timeout') {
**Result Collection**: After completion, analyze output and collect result following `executionResult` structure (include `cliExecutionId` for resume capability)
**Option C: CLI Execution (Gemini)**
When to use: `getTaskExecutor(task) === "gemini"` (分析类任务)
```bash
# 使用与 Option B 相同的 formatBatchPrompt切换 tool 和 mode
ccw cli -p "${formatBatchPrompt(batch)}" --tool gemini --mode analysis --id ${sessionId}-${batch.groupId}
```
### Step 4: Progress Tracking
Progress tracked at batch level (not individual task level). Icons: ⚡ (parallel, concurrent), → (sequential, one-by-one)
@@ -697,10 +735,15 @@ Passed from lite-plan via global variable:
explorationAngles: string[], // List of exploration angles
explorationManifest: {...} | null, // Exploration manifest
clarificationContext: {...} | null,
executionMethod: "Agent" | "Codex" | "Auto",
executionMethod: "Agent" | "Codex" | "Auto", // 全局默认
codeReviewTool: "Skip" | "Gemini Review" | "Agent Review" | string,
originalUserInput: string,
// 任务级 executor 分配(优先于 executionMethod
executorAssignments: {
[taskId]: { executor: "gemini" | "codex" | "agent", reason: string }
},
// Session artifacts location (saved by lite-plan)
session: {
id: string, // Session identifier: {taskSlug}-{shortTimestamp}

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@@ -353,6 +353,23 @@ if (dedupedClarifications.length > 0) {
**IMPORTANT**: Phase 3 is **planning only** - NO code execution. All execution happens in Phase 5 via lite-execute.
**Executor Assignment** (Claude 智能分配plan 生成后执行):
```javascript
// 分配规则(优先级从高到低):
// 1. 用户明确指定:"用 gemini 分析..." → gemini, "codex 实现..." → codex
// 2. 任务类型推断:
// - 分析|审查|评估|探索 → gemini
// - 实现|创建|修改|修复 → codex (复杂) 或 agent (简单)
// 3. 默认 → agent
const executorAssignments = {} // { taskId: { executor: 'gemini'|'codex'|'agent', reason: string } }
plan.tasks.forEach(task => {
// Claude 根据上述规则语义分析,为每个 task 分配 executor
executorAssignments[task.id] = { executor: '...', reason: '...' }
})
```
**Low Complexity** - Direct planning by Claude:
```javascript
// Step 1: Read schema
@@ -532,9 +549,13 @@ executionContext = {
explorationAngles: manifest.explorations.map(e => e.angle),
explorationManifest: manifest,
clarificationContext: clarificationContext || null,
executionMethod: userSelection.execution_method,
executionMethod: userSelection.execution_method, // 全局默认,可被 executorAssignments 覆盖
codeReviewTool: userSelection.code_review_tool,
originalUserInput: task_description,
// 任务级 executor 分配(优先于全局 executionMethod
executorAssignments: executorAssignments, // { taskId: { executor, reason } }
session: {
id: sessionId,
folder: sessionFolder,

View File

@@ -301,13 +301,65 @@ export class SessionClusteringService {
return intersection.size / union.size;
}
/**
* Find the most relevant existing cluster for a set of session IDs
* Returns the cluster with highest session overlap
*/
private findExistingClusterForSessions(sessionIds: string[]): SessionCluster | null {
if (sessionIds.length === 0) return null;
const clusterCounts = new Map<string, number>();
let maxCount = 0;
let bestClusterId: string | null = null;
for (const sessionId of sessionIds) {
const clusters = this.coreMemoryStore.getSessionClusters(sessionId);
for (const cluster of clusters) {
if (cluster.status !== 'active') continue;
const count = (clusterCounts.get(cluster.id) || 0) + 1;
clusterCounts.set(cluster.id, count);
if (count > maxCount) {
maxCount = count;
bestClusterId = cluster.id;
}
}
}
if (bestClusterId) {
return this.coreMemoryStore.getCluster(bestClusterId);
}
return null;
}
/**
* Determine if a new cluster should merge with an existing one
* Based on 70% session overlap threshold
*/
private shouldMergeWithExisting(newClusterSessions: SessionMetadataCache[], existingCluster: SessionCluster): boolean {
const MERGE_THRESHOLD = 0.7;
const existingMembers = this.coreMemoryStore.getClusterMembers(existingCluster.id);
const newSessionIds = new Set(newClusterSessions.map(s => s.session_id));
const existingSessionIds = new Set(existingMembers.map(m => m.session_id));
if (newSessionIds.size === 0) return false;
const intersection = new Set([...newSessionIds].filter(id => existingSessionIds.has(id)));
const overlapRatio = intersection.size / newSessionIds.size;
return overlapRatio > MERGE_THRESHOLD;
}
/**
* Run auto-clustering algorithm
* Optimized to prevent duplicate clusters by checking existing clusters first
*/
async autocluster(options?: ClusteringOptions): Promise<ClusteringResult> {
// 1. Collect sessions
const sessions = await this.collectSessions(options);
console.log(`[Clustering] Collected ${sessions.length} sessions`);
// 1. Collect only unclustered sessions to prevent re-clustering
const sessions = await this.collectSessions({ ...options, scope: 'unclustered' });
console.log(`[Clustering] Collected ${sessions.length} unclustered sessions`);
// 2. Update metadata cache
for (const session of sessions) {
@@ -327,43 +379,88 @@ export class SessionClusteringService {
}
// 4. Agglomerative clustering
const clusters = this.agglomerativeClustering(sessions, relevanceMatrix, CLUSTER_THRESHOLD);
console.log(`[Clustering] Generated ${clusters.length} clusters`);
const minClusterSize = options?.minClusterSize || 2;
// 5. Create session_clusters
// Early return if not enough sessions
if (sessions.length < minClusterSize) {
console.log('[Clustering] Not enough unclustered sessions to form new clusters');
return { clustersCreated: 0, sessionsProcessed: sessions.length, sessionsClustered: 0 };
}
const newPotentialClusters = this.agglomerativeClustering(sessions, relevanceMatrix, CLUSTER_THRESHOLD);
console.log(`[Clustering] Generated ${newPotentialClusters.length} potential clusters`);
// 5. Process clusters: create new or merge with existing
let clustersCreated = 0;
let clustersMerged = 0;
let sessionsClustered = 0;
for (const cluster of clusters) {
if (cluster.length < (options?.minClusterSize || 2)) {
for (const clusterSessions of newPotentialClusters) {
if (clusterSessions.length < minClusterSize) {
continue; // Skip small clusters
}
const clusterName = this.generateClusterName(cluster);
const clusterIntent = this.generateClusterIntent(cluster);
const sessionIds = clusterSessions.map(s => s.session_id);
const existingCluster = this.findExistingClusterForSessions(sessionIds);
const clusterRecord = this.coreMemoryStore.createCluster({
name: clusterName,
description: `Auto-generated cluster with ${cluster.length} sessions`,
intent: clusterIntent,
status: 'active'
});
// Check if we should merge with an existing cluster
if (existingCluster && this.shouldMergeWithExisting(clusterSessions, existingCluster)) {
const existingMembers = this.coreMemoryStore.getClusterMembers(existingCluster.id);
const existingSessionIds = new Set(existingMembers.map(m => m.session_id));
// Add members
cluster.forEach((session, index) => {
this.coreMemoryStore.addClusterMember({
cluster_id: clusterRecord.id,
session_id: session.session_id,
session_type: session.session_type as 'core_memory' | 'workflow' | 'cli_history' | 'native',
sequence_order: index + 1,
relevance_score: 1.0 // TODO: Calculate based on centrality
// Only add sessions not already in the cluster
const newSessions = clusterSessions.filter(s => !existingSessionIds.has(s.session_id));
if (newSessions.length > 0) {
newSessions.forEach((session, index) => {
this.coreMemoryStore.addClusterMember({
cluster_id: existingCluster.id,
session_id: session.session_id,
session_type: session.session_type as 'core_memory' | 'workflow' | 'cli_history' | 'native',
sequence_order: existingMembers.length + index + 1,
relevance_score: 1.0
});
});
// Update cluster description
this.coreMemoryStore.updateCluster(existingCluster.id, {
description: `Auto-generated cluster with ${existingMembers.length + newSessions.length} sessions`
});
clustersMerged++;
sessionsClustered += newSessions.length;
console.log(`[Clustering] Merged ${newSessions.length} sessions into existing cluster '${existingCluster.name}'`);
}
} else {
// Create new cluster
const clusterName = this.generateClusterName(clusterSessions);
const clusterIntent = this.generateClusterIntent(clusterSessions);
const clusterRecord = this.coreMemoryStore.createCluster({
name: clusterName,
description: `Auto-generated cluster with ${clusterSessions.length} sessions`,
intent: clusterIntent,
status: 'active'
});
});
clustersCreated++;
sessionsClustered += cluster.length;
// Add members
clusterSessions.forEach((session, index) => {
this.coreMemoryStore.addClusterMember({
cluster_id: clusterRecord.id,
session_id: session.session_id,
session_type: session.session_type as 'core_memory' | 'workflow' | 'cli_history' | 'native',
sequence_order: index + 1,
relevance_score: 1.0
});
});
clustersCreated++;
sessionsClustered += clusterSessions.length;
}
}
console.log(`[Clustering] Summary: ${clustersCreated} created, ${clustersMerged} merged`);
return {
clustersCreated,
sessionsProcessed: sessions.length,