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

internal/embedding/model.go

@@ -2,6 +2,7 @@
 package embedding
 
 import (
+	"context"
 	"fmt"
 	"sync"
 )
@@ -44,6 +45,14 @@ type EmbeddingModel interface {
 	// EmbedBatch generates embeddings for multiple texts.
 	EmbedBatch(texts []string) ([][]float32, error)
 
+	// EmbedWithContext generates an embedding for a single text with context-aware cancellation.
+	// The context controls mutex acquisition timeout — if ctx is cancelled while waiting
+	// for the model lock, the call returns immediately with ctx.Err().
+	EmbedWithContext(ctx context.Context, text string) ([]float32, error)
+
+	// EmbedBatchWithContext generates embeddings for multiple texts with context-aware cancellation.
+	EmbedBatchWithContext(ctx context.Context, texts []string) ([][]float32, error)
+
 	// Close releases model resources.
 	Close() error
 }

internal/embedding/service.go

@@ -3,6 +3,7 @@ package embedding
 
 import (
 	"bytes"
+	"context"
 	"crypto/sha256"
 	"encoding/hex"
 	"fmt"
@@ -225,6 +226,103 @@ func (m *bgeModel) EmbedBatch(texts []string) ([][]float32, error) {
 	return results, nil
 }
 
+// acquireMutex attempts to acquire the model mutex, respecting context cancellation.
+// On success the caller MUST call the returned unlock function.
+// If ctx is cancelled while waiting, returns ctx.Err() and no unlock is needed.
+func (m *bgeModel) acquireMutex(ctx context.Context) (unlock func(), err error) {
+	acquired := make(chan struct{})
+	go func() {
+		m.mu.Lock()
+		close(acquired)
+	}()
+
+	select {
+	case <-acquired:
+		// Got the lock normally.
+		return m.mu.Unlock, nil
+	case <-ctx.Done():
+		// Context cancelled while waiting. The goroutine above will eventually
+		// acquire the mutex — we must ensure it gets unlocked.
+		go func() {
+			<-acquired
+			m.mu.Unlock()
+		}()
+		return nil, ctx.Err()
+	}
+}
+
+// EmbedWithContext generates an embedding for a single text with context-aware mutex acquisition.
+// If ctx is cancelled while waiting for the model lock, returns immediately with ctx.Err().
+func (m *bgeModel) EmbedWithContext(ctx context.Context, text string) ([]float32, error) {
+	if text == "" {
+		return make([]float32, EmbeddingDim), nil
+	}
+
+	unlock, err := m.acquireMutex(ctx)
+	if err != nil {
+		return nil, fmt.Errorf("acquire embedding lock: %w", err)
+	}
+	defer unlock()
+
+	results, err := m.computeBatch([]string{text})
+	if err != nil {
+		return nil, err
+	}
+	if len(results) == 0 {
+		return make([]float32, EmbeddingDim), nil
+	}
+	return results[0], nil
+}
+
+// EmbedBatchWithContext generates embeddings for multiple texts with context-aware mutex acquisition.
+func (m *bgeModel) EmbedBatchWithContext(ctx context.Context, texts []string) ([][]float32, error) {
+	if len(texts) == 0 {
+		return nil, nil
+	}
+
+	// Filter out empty texts and track indices
+	nonEmpty := make([]string, 0, len(texts))
+	indices := make([]int, 0, len(texts))
+	for i, t := range texts {
+		if t != "" {
+			nonEmpty = append(nonEmpty, t)
+			indices = append(indices, i)
+		}
+	}
+
+	// If all texts are empty, return zero vectors
+	if len(nonEmpty) == 0 {
+		results := make([][]float32, len(texts))
+		for i := range results {
+			results[i] = make([]float32, EmbeddingDim)
+		}
+		return results, nil
+	}
+
+	unlock, err := m.acquireMutex(ctx)
+	if err != nil {
+		return nil, fmt.Errorf("acquire embedding lock: %w", err)
+	}
+	defer unlock()
+
+	// Compute embeddings for non-empty texts
+	embeddings, err := m.computeBatch(nonEmpty)
+	if err != nil {
+		return nil, fmt.Errorf("compute batch embeddings: %w", err)
+	}
+
+	// Build result with zero vectors for empty texts
+	results := make([][]float32, len(texts))
+	for i := range results {
+		results[i] = make([]float32, EmbeddingDim)
+	}
+	for i, idx := range indices {
+		results[idx] = embeddings[i]
+	}
+
+	return results, nil
+}
+
 // computeBatch runs inference on a batch of texts. Must be called with lock held.
 func (m *bgeModel) computeBatch(sentences []string) ([][]float32, error) {
 	if len(sentences) == 0 {
@@ -509,6 +607,17 @@ func (s *Service) EmbedBatch(texts []string) ([][]float32, error) {
 	return s.model.EmbedBatch(texts)
 }
 
+// EmbedWithContext generates an embedding with context-aware cancellation.
+// If ctx is cancelled while waiting for the model lock, returns immediately.
+func (s *Service) EmbedWithContext(ctx context.Context, text string) ([]float32, error) {
+	return s.model.EmbedWithContext(ctx, text)
+}
+
+// EmbedBatchWithContext generates embeddings with context-aware cancellation.
+func (s *Service) EmbedBatchWithContext(ctx context.Context, texts []string) ([][]float32, error) {
+	return s.model.EmbedBatchWithContext(ctx, texts)
+}
+
 // Close releases model resources.
 func (s *Service) Close() error {
 	return s.model.Close()