diff --git a/EMBEDDING-VALIDATION-TASK.md b/EMBEDDING-VALIDATION-TASK.md
new file mode 100644
index 0000000..d0317ab
--- /dev/null
+++ b/EMBEDDING-VALIDATION-TASK.md
@@ -0,0 +1,42 @@
+# Embedding Model Validation & Search Robustness
+
+## Context
+pgvector supports max 2000 dimensions for HNSW/IVFFlat indexes. The app must validate embedding models and gracefully handle dimension mismatches.
+
+## Tasks
+
+### 1. Revert dimension from 2560 back to 1536
+- All files changed in commit e09ea3a need reverting: 1536 everywhere
+- This includes: schema.prisma (both), migration.sql, embedding.service.ts, validate route, scripts, tests
+
+### 2. Add embedding dimension validation in admin settings
+File: `memento-note/lib/ai/services/embedding.service.ts`
+- After generating an embedding, check its dimension
+- Add a method `validateEmbeddingModel()` that:
+  - Generates a test embedding
+  - Checks dimension count
+  - Returns { valid: boolean, dimensions: number, warning?: string }
+  - If dimensions > 2000: warning "This model produces {N} dimensions. pgvector indexes support max 2000 dimensions. Semantic search will use sequential scan (slower for large note collections)."
+  - If dimensions != current DB vector dimension: warning "Dimension mismatch: model produces {N}d but DB stores {M}d. You need to reindex all notes."
+
+File: `memento-note/app/api/admin/embeddings/validate/route.ts`  
+- Use the new validateEmbeddingModel() method
+- Return dimension info in the API response
+
+File: `memento-note/app/api/admin/settings/route.ts` (or wherever embedding model is saved)
+- After saving a new embedding model, call validateEmbeddingModel()
+- Store the warning in the response so the frontend can display it
+
+### 3. Make semantic search robust
+File: `memento-note/lib/ai/services/semantic-search.service.ts`
+- In `vectorSearch()`: after generating query embedding, check dimension matches DB (1536). If not, log warning and return [] (fallback to FTS)
+- In `_doSearch()`: the existing try/catch already calls `_ftsFallback()`. Make sure this works.
+
+### 4. Update the migration SQL
+File: `memento-note/prisma/migrations/20260512120000_pgvector_and_fts_search/migration.sql`
+- Keep vector(1536) as the target type
+- The migration should work correctly
+
+### 5. Commit
+- Git add and commit with descriptive message
+- Do NOT push
diff --git a/memento-note/lib/ai/factory.ts b/memento-note/lib/ai/factory.ts
index aaf1380..7f084af 100644
--- a/memento-note/lib/ai/factory.ts
+++ b/memento-note/lib/ai/factory.ts
@@ -117,7 +117,7 @@ function createDeepSeekProvider(config: Record<string, string>, modelName: strin
 }
 
 function createOpenRouterProvider(config: Record<string, string>, modelName: string, embeddingModelName: string): CustomOpenAIProvider {
-  const apiKey = config?.OPENROUTER_API_KEY || process.env.OPENROUTER_API_KEY || '';
+  const apiKey = config?.OPENROUTER_API_KEY || process.env.OPENROUTER_API_KEY || config?.CUSTOM_OPENAI_API_KEY || process.env.CUSTOM_OPENAI_API_KEY || '';
   if (!apiKey) throw new Error('OPENROUTER_API_KEY is required when using OpenRouter provider');
   const defaults = PROVIDER_DEFAULTS.openrouter;
   return new CustomOpenAIProvider(apiKey, defaults.baseUrl, modelName || defaults.model, embeddingModelName || defaults.embeddingModel);