mirror of
https://github.com/lukaszraczylo/claude-mnemonic.git
synced 2026-10-12 04:55:18 +00:00
fix: prevent MCP server hanging by adding concurrency, timeouts, and context propagation (#45)
Root cause: synchronous MCP request processing combined with missing context propagation to the embedding layer caused indefinite hangs when ONNX inference was slow or the database was contended. Changes: - MCP server: dispatch each request in its own goroutine with semaphore (cap 10) and WaitGroup for clean shutdown drain - Embedding: add context-aware mutex acquisition (acquireMutex) so callers can bail out instead of blocking forever on a stuck ONNX model - Vector client: propagate context through getOrComputeEmbedding and replace bare RLock() calls with context-aware acquireRLockWithContext - Worker handlers: add 15s request-scoped timeouts to all search/context handlers (handleSearchByPrompt, handleContextInject, handleFileContext, handleContextCount, handleGetObservations/Summaries/Prompts) - Worker HTTP server: set WriteTimeout=60s (was 0); SSE endpoint extends deadline per-request via http.ResponseController Fixes #45
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@@ -3,6 +3,7 @@ package embedding
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import (
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"bytes"
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"context"
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"crypto/sha256"
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"encoding/hex"
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"fmt"
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@@ -225,6 +226,103 @@ func (m *bgeModel) EmbedBatch(texts []string) ([][]float32, error) {
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return results, nil
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}
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// acquireMutex attempts to acquire the model mutex, respecting context cancellation.
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// On success the caller MUST call the returned unlock function.
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// If ctx is cancelled while waiting, returns ctx.Err() and no unlock is needed.
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func (m *bgeModel) acquireMutex(ctx context.Context) (unlock func(), err error) {
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acquired := make(chan struct{})
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go func() {
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m.mu.Lock()
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close(acquired)
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}()
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select {
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case <-acquired:
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// Got the lock normally.
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return m.mu.Unlock, nil
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case <-ctx.Done():
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// Context cancelled while waiting. The goroutine above will eventually
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// acquire the mutex — we must ensure it gets unlocked.
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go func() {
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<-acquired
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m.mu.Unlock()
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}()
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return nil, ctx.Err()
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}
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}
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// EmbedWithContext generates an embedding for a single text with context-aware mutex acquisition.
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// If ctx is cancelled while waiting for the model lock, returns immediately with ctx.Err().
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func (m *bgeModel) EmbedWithContext(ctx context.Context, text string) ([]float32, error) {
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if text == "" {
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return make([]float32, EmbeddingDim), nil
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}
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unlock, err := m.acquireMutex(ctx)
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if err != nil {
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return nil, fmt.Errorf("acquire embedding lock: %w", err)
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}
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defer unlock()
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results, err := m.computeBatch([]string{text})
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if err != nil {
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return nil, err
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}
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if len(results) == 0 {
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return make([]float32, EmbeddingDim), nil
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}
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return results[0], nil
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}
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// EmbedBatchWithContext generates embeddings for multiple texts with context-aware mutex acquisition.
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func (m *bgeModel) EmbedBatchWithContext(ctx context.Context, texts []string) ([][]float32, error) {
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if len(texts) == 0 {
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return nil, nil
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}
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// Filter out empty texts and track indices
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nonEmpty := make([]string, 0, len(texts))
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indices := make([]int, 0, len(texts))
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for i, t := range texts {
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if t != "" {
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nonEmpty = append(nonEmpty, t)
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indices = append(indices, i)
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}
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}
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// If all texts are empty, return zero vectors
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if len(nonEmpty) == 0 {
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results := make([][]float32, len(texts))
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for i := range results {
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results[i] = make([]float32, EmbeddingDim)
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}
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return results, nil
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}
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unlock, err := m.acquireMutex(ctx)
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if err != nil {
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return nil, fmt.Errorf("acquire embedding lock: %w", err)
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}
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defer unlock()
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// Compute embeddings for non-empty texts
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embeddings, err := m.computeBatch(nonEmpty)
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if err != nil {
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return nil, fmt.Errorf("compute batch embeddings: %w", err)
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}
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// Build result with zero vectors for empty texts
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results := make([][]float32, len(texts))
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for i := range results {
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results[i] = make([]float32, EmbeddingDim)
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}
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for i, idx := range indices {
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results[idx] = embeddings[i]
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}
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return results, nil
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}
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// computeBatch runs inference on a batch of texts. Must be called with lock held.
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func (m *bgeModel) computeBatch(sentences []string) ([][]float32, error) {
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if len(sentences) == 0 {
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@@ -509,6 +607,17 @@ func (s *Service) EmbedBatch(texts []string) ([][]float32, error) {
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return s.model.EmbedBatch(texts)
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}
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// EmbedWithContext generates an embedding with context-aware cancellation.
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// If ctx is cancelled while waiting for the model lock, returns immediately.
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func (s *Service) EmbedWithContext(ctx context.Context, text string) ([]float32, error) {
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return s.model.EmbedWithContext(ctx, text)
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}
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// EmbedBatchWithContext generates embeddings with context-aware cancellation.
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func (s *Service) EmbedBatchWithContext(ctx context.Context, texts []string) ([][]float32, error) {
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return s.model.EmbedBatchWithContext(ctx, texts)
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}
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// Close releases model resources.
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func (s *Service) Close() error {
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return s.model.Close()
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