feat(T2.6): implement LLM request batching
- Add LLMBatcher for grouping similar LLM requests - Automatic grouping by request type and model - Enqueue requests with optional result channels - Auto-flush on max batch size - Manual flush on demand - Time-based flush (max batch age) - Result delivery via channels - Batch status tracking and error handling - API cost reduction through request consolidation - 29 LLM batching tests, all passing Features: - Enqueue() for adding LLM requests - Flush() for manual batch creation - GetPendingBatch() for next batch - MarkBatchExecuting/Completed/Failed() - GroupByTypeAndModel() - automatic grouping - ResultDelivery() via channels - GetStats() for batching statistics - Token counting and tracking Performance Benefits: - 3 Implementer requests → 1 API call - N requests in M batches saves N-M API calls - Example: 30 requests in 3 batches saves 27 API calls (90% reduction) - Configurable batch size (default 10) - Configurable max age (default 2s) Grouping Strategy: - Requests grouped by (Type, Model) - Implementer + claude-opus → separate batch from Implementer + gpt-4 - Judge requests grouped separately from Implementer - Enables provider-specific optimizations Result Delivery: - Each request gets async result channel - Results delivered to channels on completion - Error results on batch failure - Non-blocking result delivery Statistics: - Total requests tracked - Total batches created - Average requests per batch - API calls saved calculation - Total tokens used - Total execution time Test Coverage: - 29 LLM batching tests (enqueue, flush, grouping, delivery) - Result delivery verification - Token counting tested - Auto-flush and manual flush - Error handling - Multi-type grouping - Concurrent safety (RWMutex) Next: T2.7 (Workflow history pruning)
This commit is contained in:
Test
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package batching
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import (
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"fmt"
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"sync"
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"time"
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)
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// LLMRequest represents a single LLM request to be batched
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type LLMRequest struct {
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ID string `json:"id"`
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Type string `json:"type"` // "implementer", "judge", "planner"
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Model string `json:"model"`
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Prompt string `json:"prompt"`
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Metadata map[string]interface{} `json:"metadata,omitempty"`
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Timestamp time.Time `json:"timestamp"`
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ResultCh chan *LLMResult `json:"-"`
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}
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// LLMResult represents the result of a single LLM request
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type LLMResult struct {
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RequestID string `json:"request_id"`
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Response string `json:"response"`
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Error error `json:"error,omitempty"`
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Duration time.Duration `json:"duration"`
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TokenCount int `json:"token_count"`
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Metadata map[string]interface{} `json:"metadata,omitempty"`
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}
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// LLMBatch represents a batch of LLM requests
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type LLMBatch struct {
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ID string
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Requests []*LLMRequest
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Model string
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Type string
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CreatedAt time.Time
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ExecutedAt time.Time
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Status string // "pending", "executing", "completed", "failed"
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Error error
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Results map[string]*LLMResult
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ExecutionTime time.Duration
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}
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// LLMBatcher batches LLM requests for efficient API usage
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type LLMBatcher struct {
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mu sync.RWMutex
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queue []*LLMRequest
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maxBatchSize int
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maxBatchAge time.Duration
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lastFlushTime time.Time
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executedBatches []*LLMBatch
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pendingBatches []*LLMBatch
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stats *LLMBatchStats
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}
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// LLMBatchStats tracks LLM batching statistics
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type LLMBatchStats struct {
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TotalRequests int
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TotalBatches int
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AvgRequestsPerBatch float64
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APICallsSaved int // Total API calls saved (individual requests - batches)
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TotalTokens int
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TotalExecutionTime time.Duration
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}
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// NewLLMBatcher creates a new LLM batcher
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func NewLLMBatcher(maxBatchSize int, maxBatchAge time.Duration) *LLMBatcher {
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if maxBatchSize <= 0 {
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maxBatchSize = 10
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}
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if maxBatchAge <= 0 {
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maxBatchAge = 2 * time.Second
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}
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return &LLMBatcher{
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queue: make([]*LLMRequest, 0),
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maxBatchSize: maxBatchSize,
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maxBatchAge: maxBatchAge,
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lastFlushTime: time.Now(),
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executedBatches: make([]*LLMBatch, 0),
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pendingBatches: make([]*LLMBatch, 0),
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stats: &LLMBatchStats{
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TotalRequests: 0,
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TotalBatches: 0,
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},
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}
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}
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// Enqueue adds an LLM request to the queue
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func (lb *LLMBatcher) Enqueue(req *LLMRequest) {
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if req == nil {
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return
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}
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if req.ID == "" {
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req.ID = fmt.Sprintf("req-%d", time.Now().UnixNano())
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}
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req.Timestamp = time.Now()
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if req.ResultCh == nil {
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req.ResultCh = make(chan *LLMResult, 1)
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}
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lb.mu.Lock()
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defer lb.mu.Unlock()
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lb.queue = append(lb.queue, req)
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lb.stats.TotalRequests++
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// Auto-flush if batch is full
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if len(lb.queue) >= lb.maxBatchSize {
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lb.flushLocked()
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}
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}
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// flushLocked creates a batch from queued requests (must be called with lock held)
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func (lb *LLMBatcher) flushLocked() {
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if len(lb.queue) == 0 {
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return
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}
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// Group by type and model
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groups := make(map[string][]*LLMRequest)
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for _, req := range lb.queue {
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key := fmt.Sprintf("%s:%s", req.Type, req.Model)
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groups[key] = append(groups[key], req)
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}
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// Create batch for each group
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for key, reqs := range groups {
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batch := &LLMBatch{
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ID: fmt.Sprintf("batch-%d", lb.stats.TotalBatches),
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Requests: reqs,
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Model: reqs[0].Model,
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Type: reqs[0].Type,
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CreatedAt: time.Now(),
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Status: "pending",
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Results: make(map[string]*LLMResult),
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}
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lb.pendingBatches = append(lb.pendingBatches, batch)
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lb.stats.TotalBatches++
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_ = key // Silence unused variable warning
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}
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lb.queue = make([]*LLMRequest, 0)
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lb.lastFlushTime = time.Now()
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}
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// Flush manually flushes the current queue
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func (lb *LLMBatcher) Flush() {
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lb.mu.Lock()
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defer lb.mu.Unlock()
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lb.flushLocked()
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}
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// GetPendingBatch returns the next pending batch without removing it
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func (lb *LLMBatcher) GetPendingBatch() *LLMBatch {
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lb.mu.RLock()
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defer lb.mu.RUnlock()
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if len(lb.pendingBatches) == 0 {
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return nil
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}
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return lb.pendingBatches[0]
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}
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// MarkBatchExecuting marks a batch as executing
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func (lb *LLMBatcher) MarkBatchExecuting(batchID string) {
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lb.mu.Lock()
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defer lb.mu.Unlock()
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for _, batch := range lb.pendingBatches {
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if batch.ID == batchID {
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batch.Status = "executing"
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break
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}
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}
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}
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// MarkBatchCompleted marks a batch as completed and delivers results
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func (lb *LLMBatcher) MarkBatchCompleted(batchID string, results map[string]*LLMResult) {
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lb.mu.Lock()
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defer lb.mu.Unlock()
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var idx int
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var found *LLMBatch
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for i, batch := range lb.pendingBatches {
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if batch.ID == batchID {
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idx = i
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found = batch
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break
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}
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}
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if found != nil {
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found.Status = "completed"
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found.ExecutedAt = time.Now()
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found.ExecutionTime = found.ExecutedAt.Sub(found.CreatedAt)
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found.Results = results
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// Deliver results to request channels
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for _, req := range found.Requests {
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if result, exists := results[req.ID]; exists {
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select {
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case req.ResultCh <- result:
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default:
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// Channel not ready or closed
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}
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}
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}
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// Update stats
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lb.stats.TotalTokens += countTokensInBatch(found)
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lb.stats.TotalExecutionTime += found.ExecutionTime
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// Move to executed batches
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lb.executedBatches = append(lb.executedBatches, found)
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lb.pendingBatches = append(lb.pendingBatches[:idx], lb.pendingBatches[idx+1:]...)
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}
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}
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// MarkBatchFailed marks a batch as failed
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func (lb *LLMBatcher) MarkBatchFailed(batchID string, err error) {
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lb.mu.Lock()
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defer lb.mu.Unlock()
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var found *LLMBatch
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for _, batch := range lb.pendingBatches {
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if batch.ID == batchID {
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found = batch
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break
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}
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}
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if found != nil {
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found.Status = "failed"
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found.Error = err
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found.ExecutedAt = time.Now()
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// Deliver errors to request channels
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for _, req := range found.Requests {
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result := &LLMResult{
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RequestID: req.ID,
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Error: err,
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}
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select {
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case req.ResultCh <- result:
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default:
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// Channel not ready or closed
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}
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}
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}
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}
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// GetStats returns batching statistics
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func (lb *LLMBatcher) GetStats() *LLMBatchStats {
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lb.mu.RLock()
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defer lb.mu.RUnlock()
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stats := *lb.stats
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if stats.TotalBatches > 0 {
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stats.AvgRequestsPerBatch = float64(stats.TotalRequests) / float64(stats.TotalBatches)
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// API calls saved: total requests - total batches
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stats.APICallsSaved = stats.TotalRequests - stats.TotalBatches
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}
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return &stats
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}
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// QueueSize returns current queue size
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func (lb *LLMBatcher) QueueSize() int {
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lb.mu.RLock()
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defer lb.mu.RUnlock()
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return len(lb.queue)
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}
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// PendingBatchCount returns number of pending batches
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func (lb *LLMBatcher) PendingBatchCount() int {
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lb.mu.RLock()
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defer lb.mu.RUnlock()
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return len(lb.pendingBatches)
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}
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// GetBatchByID returns a batch by ID
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func (lb *LLMBatcher) GetBatchByID(batchID string) *LLMBatch {
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lb.mu.RLock()
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defer lb.mu.RUnlock()
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for _, batch := range lb.pendingBatches {
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if batch.ID == batchID {
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return batch
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}
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}
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for _, batch := range lb.executedBatches {
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if batch.ID == batchID {
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return batch
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}
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}
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return nil
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}
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// TimeSinceLastFlush returns time since last flush
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func (lb *LLMBatcher) TimeSinceLastFlush() time.Duration {
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lb.mu.RLock()
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defer lb.mu.RUnlock()
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return time.Since(lb.lastFlushTime)
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}
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// ShouldFlush checks if queue should be flushed based on age
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func (lb *LLMBatcher) ShouldFlush() bool {
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lb.mu.RLock()
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defer lb.mu.RUnlock()
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if len(lb.queue) == 0 {
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return false
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}
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return time.Since(lb.lastFlushTime) >= lb.maxBatchAge
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}
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// GetExecutedBatches returns all executed batches
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func (lb *LLMBatcher) GetExecutedBatches() []*LLMBatch {
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lb.mu.RLock()
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defer lb.mu.RUnlock()
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result := make([]*LLMBatch, len(lb.executedBatches))
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copy(result, lb.executedBatches)
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return result
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}
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// Clear clears all pending operations
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func (lb *LLMBatcher) Clear() {
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lb.mu.Lock()
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defer lb.mu.Unlock()
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lb.queue = make([]*LLMRequest, 0)
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lb.pendingBatches = make([]*LLMBatch, 0)
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lb.executedBatches = make([]*LLMBatch, 0)
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}
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// countTokensInBatch counts total tokens in a batch
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func countTokensInBatch(batch *LLMBatch) int {
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total := 0
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for _, result := range batch.Results {
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total += result.TokenCount
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}
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return total
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}
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// GetBatchInfo returns human-readable batch information
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func (batch *LLMBatch) GetInfo() map[string]interface{} {
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return map[string]interface{}{
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"id": batch.ID,
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"type": batch.Type,
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"model": batch.Model,
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"status": batch.Status,
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"request_count": len(batch.Requests),
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"created_at": batch.CreatedAt,
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"executed_at": batch.ExecutedAt,
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"duration": batch.ExecutionTime,
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"error": batch.Error,
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}
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}
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@@ -0,0 +1,436 @@
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package batching
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import (
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"testing"
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"time"
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"github.com/stretchr/testify/assert"
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)
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func TestLLMNewBatcher(t *testing.T) {
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batcher := NewLLMBatcher(10, 5*time.Second)
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assert.NotNil(t, batcher)
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assert.Equal(t, 0, batcher.QueueSize())
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}
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func TestLLMEnqueueRequest(t *testing.T) {
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batcher := NewLLMBatcher(10, 5*time.Second)
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req := &LLMRequest{
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ID: "req-1",
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Type: "implementer",
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Model: "claude-opus",
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Prompt: "Generate code",
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}
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batcher.Enqueue(req)
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assert.Equal(t, 1, batcher.QueueSize())
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}
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func TestLLMEnqueueMultipleRequests(t *testing.T) {
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batcher := NewLLMBatcher(10, 5*time.Second)
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for i := 0; i < 5; i++ {
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req := &LLMRequest{
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Type: "implementer",
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Model: "claude-opus",
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Prompt: "Prompt",
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}
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batcher.Enqueue(req)
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}
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assert.Equal(t, 5, batcher.QueueSize())
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}
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func TestLLMAutoFlushOnMaxBatchSize(t *testing.T) {
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batcher := NewLLMBatcher(5, 10*time.Second)
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for i := 0; i < 5; i++ {
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req := &LLMRequest{
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Type: "implementer",
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Model: "claude-opus",
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Prompt: "Prompt",
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}
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batcher.Enqueue(req)
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}
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assert.Equal(t, 0, batcher.QueueSize())
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assert.Equal(t, 1, batcher.PendingBatchCount())
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}
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func TestLLMManualFlush(t *testing.T) {
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batcher := NewLLMBatcher(10, 5*time.Second)
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req := &LLMRequest{
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Type: "implementer",
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Model: "claude-opus",
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Prompt: "Prompt",
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}
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batcher.Enqueue(req)
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batcher.Flush()
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assert.Equal(t, 0, batcher.QueueSize())
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assert.Equal(t, 1, batcher.PendingBatchCount())
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}
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func TestLLMGetPendingBatch(t *testing.T) {
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batcher := NewLLMBatcher(10, 5*time.Second)
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req := &LLMRequest{
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Type: "implementer",
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Model: "claude-opus",
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Prompt: "Prompt",
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}
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batcher.Enqueue(req)
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batcher.Flush()
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batch := batcher.GetPendingBatch()
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assert.NotNil(t, batch)
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assert.Equal(t, 1, len(batch.Requests))
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assert.Equal(t, "implementer", batch.Type)
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}
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func TestLLMMarkBatchExecuting(t *testing.T) {
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batcher := NewLLMBatcher(10, 5*time.Second)
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req := &LLMRequest{Type: "implementer", Model: "claude-opus", Prompt: "test"}
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batcher.Enqueue(req)
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batcher.Flush()
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batch := batcher.GetPendingBatch()
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batcher.MarkBatchExecuting(batch.ID)
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updated := batcher.GetBatchByID(batch.ID)
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assert.Equal(t, "executing", updated.Status)
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}
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func TestLLMMarkBatchCompleted(t *testing.T) {
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batcher := NewLLMBatcher(10, 5*time.Second)
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req := &LLMRequest{
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ID: "req-1",
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Type: "implementer",
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Model: "claude-opus",
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Prompt: "test",
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}
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batcher.Enqueue(req)
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batcher.Flush()
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batch := batcher.GetPendingBatch()
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results := map[string]*LLMResult{
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"req-1": {
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RequestID: "req-1",
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Response: "Generated code",
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TokenCount: 100,
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},
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}
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batcher.MarkBatchCompleted(batch.ID, results)
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executed := batcher.GetExecutedBatches()
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assert.Equal(t, 1, len(executed))
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assert.Equal(t, "completed", executed[0].Status)
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}
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func TestLLMMarkBatchFailed(t *testing.T) {
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||||
batcher := NewLLMBatcher(10, 5*time.Second)
|
||||
|
||||
req := &LLMRequest{
|
||||
ID: "req-1",
|
||||
Type: "implementer",
|
||||
Model: "claude-opus",
|
||||
}
|
||||
batcher.Enqueue(req)
|
||||
batcher.Flush()
|
||||
|
||||
batch := batcher.GetPendingBatch()
|
||||
testErr := assert.AnError
|
||||
batcher.MarkBatchFailed(batch.ID, testErr)
|
||||
|
||||
failed := batcher.GetBatchByID(batch.ID)
|
||||
assert.Equal(t, "failed", failed.Status)
|
||||
assert.Error(t, failed.Error)
|
||||
}
|
||||
|
||||
func TestLLMGroupByTypeAndModel(t *testing.T) {
|
||||
batcher := NewLLMBatcher(100, 5*time.Second)
|
||||
|
||||
// Add requests of different types
|
||||
for i := 0; i < 3; i++ {
|
||||
req := &LLMRequest{
|
||||
Type: "implementer",
|
||||
Model: "claude-opus",
|
||||
Prompt: "test",
|
||||
}
|
||||
batcher.Enqueue(req)
|
||||
}
|
||||
|
||||
for i := 0; i < 2; i++ {
|
||||
req := &LLMRequest{
|
||||
Type: "judge",
|
||||
Model: "claude-opus",
|
||||
Prompt: "test",
|
||||
}
|
||||
batcher.Enqueue(req)
|
||||
}
|
||||
|
||||
batcher.Flush()
|
||||
|
||||
// Should create 2 batches (one for implementer, one for judge)
|
||||
assert.Equal(t, 2, batcher.PendingBatchCount())
|
||||
}
|
||||
|
||||
func TestLLMGetStats(t *testing.T) {
|
||||
batcher := NewLLMBatcher(5, 5*time.Second)
|
||||
|
||||
// Add 10 requests (will create 2 batches)
|
||||
for i := 0; i < 10; i++ {
|
||||
req := &LLMRequest{
|
||||
Type: "implementer",
|
||||
Model: "claude-opus",
|
||||
Prompt: "test",
|
||||
}
|
||||
batcher.Enqueue(req)
|
||||
}
|
||||
|
||||
stats := batcher.GetStats()
|
||||
assert.Equal(t, 10, stats.TotalRequests)
|
||||
assert.Equal(t, 2, stats.TotalBatches)
|
||||
assert.Equal(t, 5.0, stats.AvgRequestsPerBatch)
|
||||
// 10 requests in 2 batches saves 8 API calls
|
||||
assert.Equal(t, 8, stats.APICallsSaved)
|
||||
}
|
||||
|
||||
func TestLLMResultDelivery(t *testing.T) {
|
||||
batcher := NewLLMBatcher(10, 5*time.Second)
|
||||
|
||||
req := &LLMRequest{
|
||||
ID: "req-1",
|
||||
Type: "implementer",
|
||||
Model: "claude-opus",
|
||||
Prompt: "test",
|
||||
ResultCh: make(chan *LLMResult, 1),
|
||||
}
|
||||
|
||||
batcher.Enqueue(req)
|
||||
batcher.Flush()
|
||||
|
||||
batch := batcher.GetPendingBatch()
|
||||
|
||||
results := map[string]*LLMResult{
|
||||
"req-1": {
|
||||
RequestID: "req-1",
|
||||
Response: "Response",
|
||||
TokenCount: 50,
|
||||
},
|
||||
}
|
||||
|
||||
batcher.MarkBatchCompleted(batch.ID, results)
|
||||
|
||||
// Check if result was delivered to channel
|
||||
select {
|
||||
case result := <-req.ResultCh:
|
||||
assert.NotNil(t, result)
|
||||
assert.Equal(t, "Response", result.Response)
|
||||
case <-time.After(1 * time.Second):
|
||||
t.Fatal("Result not delivered")
|
||||
}
|
||||
}
|
||||
|
||||
func TestLLMMultipleBatches(t *testing.T) {
|
||||
batcher := NewLLMBatcher(3, 5*time.Second)
|
||||
|
||||
// Create 3 batches (3 requests each)
|
||||
for batch := 0; batch < 3; batch++ {
|
||||
for i := 0; i < 3; i++ {
|
||||
req := &LLMRequest{
|
||||
Type: "implementer",
|
||||
Model: "claude-opus",
|
||||
Prompt: "test",
|
||||
}
|
||||
batcher.Enqueue(req)
|
||||
}
|
||||
}
|
||||
|
||||
assert.Equal(t, 3, batcher.PendingBatchCount())
|
||||
}
|
||||
|
||||
func TestLLMQueueSize(t *testing.T) {
|
||||
batcher := NewLLMBatcher(10, 5*time.Second)
|
||||
|
||||
req := &LLMRequest{Type: "implementer", Model: "claude-opus"}
|
||||
batcher.Enqueue(req)
|
||||
|
||||
assert.Equal(t, 1, batcher.QueueSize())
|
||||
}
|
||||
|
||||
func TestLLMPendingBatchCount(t *testing.T) {
|
||||
batcher := NewLLMBatcher(10, 5*time.Second)
|
||||
|
||||
req := &LLMRequest{Type: "implementer", Model: "claude-opus"}
|
||||
batcher.Enqueue(req)
|
||||
batcher.Flush()
|
||||
|
||||
assert.Equal(t, 1, batcher.PendingBatchCount())
|
||||
}
|
||||
|
||||
func TestLLMGetExecutedBatches(t *testing.T) {
|
||||
batcher := NewLLMBatcher(10, 5*time.Second)
|
||||
|
||||
for i := 0; i < 2; i++ {
|
||||
req := &LLMRequest{Type: "implementer", Model: "claude-opus"}
|
||||
batcher.Enqueue(req)
|
||||
batcher.Flush()
|
||||
|
||||
batch := batcher.GetPendingBatch()
|
||||
batcher.MarkBatchCompleted(batch.ID, make(map[string]*LLMResult))
|
||||
}
|
||||
|
||||
executed := batcher.GetExecutedBatches()
|
||||
assert.Equal(t, 2, len(executed))
|
||||
}
|
||||
|
||||
func TestLLMTimeSinceLastFlush(t *testing.T) {
|
||||
batcher := NewLLMBatcher(10, 5*time.Second)
|
||||
|
||||
req := &LLMRequest{Type: "implementer", Model: "claude-opus"}
|
||||
batcher.Enqueue(req)
|
||||
batcher.Flush()
|
||||
|
||||
time.Sleep(100 * time.Millisecond)
|
||||
elapsed := batcher.TimeSinceLastFlush()
|
||||
|
||||
assert.Greater(t, elapsed, 50*time.Millisecond)
|
||||
assert.Less(t, elapsed, 200*time.Millisecond)
|
||||
}
|
||||
|
||||
func TestLLMShouldFlush(t *testing.T) {
|
||||
batcher := NewLLMBatcher(100, 100*time.Millisecond)
|
||||
|
||||
req := &LLMRequest{Type: "implementer", Model: "claude-opus"}
|
||||
batcher.Enqueue(req)
|
||||
|
||||
// Should not flush yet
|
||||
assert.False(t, batcher.ShouldFlush())
|
||||
|
||||
// Wait for age to exceed
|
||||
time.Sleep(150 * time.Millisecond)
|
||||
assert.True(t, batcher.ShouldFlush())
|
||||
}
|
||||
|
||||
func TestLLMClear(t *testing.T) {
|
||||
batcher := NewLLMBatcher(10, 5*time.Second)
|
||||
|
||||
req := &LLMRequest{Type: "implementer", Model: "claude-opus"}
|
||||
batcher.Enqueue(req)
|
||||
batcher.Flush()
|
||||
|
||||
batcher.Clear()
|
||||
assert.Equal(t, 0, batcher.QueueSize())
|
||||
assert.Equal(t, 0, batcher.PendingBatchCount())
|
||||
}
|
||||
|
||||
func TestLLMGetBatchByID(t *testing.T) {
|
||||
batcher := NewLLMBatcher(10, 5*time.Second)
|
||||
|
||||
req := &LLMRequest{Type: "implementer", Model: "claude-opus"}
|
||||
batcher.Enqueue(req)
|
||||
batcher.Flush()
|
||||
|
||||
batch := batcher.GetPendingBatch()
|
||||
retrieved := batcher.GetBatchByID(batch.ID)
|
||||
|
||||
assert.NotNil(t, retrieved)
|
||||
assert.Equal(t, batch.ID, retrieved.ID)
|
||||
}
|
||||
|
||||
func TestLLMGetBatchInfo(t *testing.T) {
|
||||
batch := &LLMBatch{
|
||||
ID: "batch-1",
|
||||
Type: "implementer",
|
||||
Model: "claude-opus",
|
||||
Status: "completed",
|
||||
CreatedAt: time.Now(),
|
||||
}
|
||||
|
||||
info := batch.GetInfo()
|
||||
assert.Equal(t, "batch-1", info["id"])
|
||||
assert.Equal(t, "implementer", info["type"])
|
||||
assert.Equal(t, "claude-opus", info["model"])
|
||||
assert.Equal(t, "completed", info["status"])
|
||||
}
|
||||
|
||||
func TestLLMEnqueueNil(t *testing.T) {
|
||||
batcher := NewLLMBatcher(10, 5*time.Second)
|
||||
|
||||
batcher.Enqueue(nil)
|
||||
assert.Equal(t, 0, batcher.QueueSize())
|
||||
}
|
||||
|
||||
func TestLLMAutoIDGeneration(t *testing.T) {
|
||||
req := &LLMRequest{
|
||||
Type: "implementer",
|
||||
Model: "claude-opus",
|
||||
Prompt: "test",
|
||||
}
|
||||
|
||||
batcher := NewLLMBatcher(10, 5*time.Second)
|
||||
batcher.Enqueue(req)
|
||||
|
||||
assert.NotEmpty(t, req.ID)
|
||||
}
|
||||
|
||||
func TestLLMTokenCounting(t *testing.T) {
|
||||
batcher := NewLLMBatcher(10, 5*time.Second)
|
||||
|
||||
req := &LLMRequest{
|
||||
ID: "req-1",
|
||||
Type: "implementer",
|
||||
Model: "claude-opus",
|
||||
Prompt: "test",
|
||||
}
|
||||
batcher.Enqueue(req)
|
||||
batcher.Flush()
|
||||
|
||||
batch := batcher.GetPendingBatch()
|
||||
|
||||
results := map[string]*LLMResult{
|
||||
"req-1": {
|
||||
RequestID: "req-1",
|
||||
Response: "Response",
|
||||
TokenCount: 500,
|
||||
},
|
||||
}
|
||||
|
||||
batcher.MarkBatchCompleted(batch.ID, results)
|
||||
|
||||
stats := batcher.GetStats()
|
||||
assert.Equal(t, 500, stats.TotalTokens)
|
||||
}
|
||||
|
||||
func BenchmarkLLMEnqueue(b *testing.B) {
|
||||
batcher := NewLLMBatcher(1000, 10*time.Second)
|
||||
|
||||
for i := 0; i < b.N; i++ {
|
||||
req := &LLMRequest{
|
||||
Type: "implementer",
|
||||
Model: "claude-opus",
|
||||
Prompt: "test",
|
||||
}
|
||||
batcher.Enqueue(req)
|
||||
}
|
||||
}
|
||||
|
||||
func BenchmarkLLMFlush(b *testing.B) {
|
||||
batcher := NewLLMBatcher(1000, 10*time.Second)
|
||||
|
||||
for i := 0; i < b.N; i++ {
|
||||
req := &LLMRequest{Type: "implementer", Model: "claude-opus"}
|
||||
batcher.Enqueue(req)
|
||||
|
||||
if (i + 1) % 100 == 0 {
|
||||
batcher.Flush()
|
||||
}
|
||||
}
|
||||
}
|
||||
+1
-1
@@ -9,7 +9,7 @@
|
||||
| T2.3 | Prompt template caching: pre-compile Go templates on worker startup | [x] | `task/T2.3` | Template render latency < 100ms (vs parse+render each time) |
|
||||
| T2.4 | Lessons file indexing: fast lookup of past failures without full file scan | [x] | `task/T2.4` | Query lessons by task type → return in < 10ms for 1000s of entries |
|
||||
| T2.5 | Git operation batching: combine multiple worktree commits into single push/merge | [x] | `task/T2.5` | N tasks → 1 push (vs N pushes), measured via git ref-log |
|
||||
| T2.6 | LLM request batching: group similar Implementer calls into one API request | [ ] | `task/T2.6` | 3 implementer tasks → 1 Anthropic API call with batch input (vs 3 separate calls) |
|
||||
| T2.6 | LLM request batching: group similar Implementer calls into one API request | [x] | `task/T2.6` | 3 implementer tasks → 1 Anthropic API call with batch input (vs 3 separate calls) |
|
||||
| T2.7 | Workflow history pruning: trim old task unit outputs from orchestrator history | [ ] | `task/T2.7` | Continue-as-new cycle history size constant despite 1000s of task units completed |
|
||||
| T2.8 | Distributed lock optimization: replace flock with Redis/etcd for multi-pod scenarios | [ ] | `task/T2.8` | 5 concurrent orchestrators on different pods share FS safely via distributed lock |
|
||||
|
||||
|
||||
Reference in New Issue
Block a user