fix: switch embedding dimension from 1536 to 2560 for qwen-embedding-4b

memento-note/lib/ai/providers/custom-openai.ts

@@ -1,13 +1,13 @@
 import { createOpenAI } from '@ai-sdk/openai';
-import { generateObject, generateText as aiGenerateText, embed, stepCountIs } from 'ai';
+import { generateObject, generateText as aiGenerateText, stepCountIs } from 'ai';
 import { z } from 'zod';
 import { AIProvider, TagSuggestion, TitleSuggestion, ToolUseOptions, ToolCallResult } from '../types';
 
 export class CustomOpenAIProvider implements AIProvider {
   private model: any;
-  private embeddingModel: any;
   private apiKey: string;
   private baseUrl: string;
+  private embeddingModelName: string;
 
   constructor(
     apiKey: string,
@@ -17,6 +17,7 @@ export class CustomOpenAIProvider implements AIProvider {
   ) {
     this.apiKey = apiKey;
     this.baseUrl = baseUrl.endsWith('/') ? baseUrl.slice(0, -1) : baseUrl;
+    this.embeddingModelName = embeddingModelName;
     // Create OpenAI-compatible client with custom base URL
     // Use .chat() to force /chat/completions endpoint (avoids Responses API)
     const customClient = createOpenAI({
@@ -44,7 +45,16 @@ export class CustomOpenAIProvider implements AIProvider {
     });
 
     this.model = customClient.chat(modelName);
-    this.embeddingModel = customClient.embedding(embeddingModelName);
+  }
+
+  private async fetchWithTimeout(url: string, options: RequestInit, timeoutMs: number = 60_000): Promise<Response> {
+    const controller = new AbortController()
+    const timer = setTimeout(() => controller.abort(), timeoutMs)
+    try {
+      return await fetch(url, { ...options, signal: controller.signal })
+    } finally {
+      clearTimeout(timer)
+    }
   }
 
   async generateTags(content: string): Promise<TagSuggestion[]> {
@@ -70,13 +80,40 @@ export class CustomOpenAIProvider implements AIProvider {
 
   async getEmbeddings(text: string): Promise<number[]> {
     try {
-      const { embedding } = await embed({
-        model: this.embeddingModel,
-        value: text,
+      const response = await this.fetchWithTimeout(`${this.baseUrl}/embeddings`, {
+        method: 'POST',
+        headers: {
+          'Content-Type': 'application/json',
+          'Authorization': `Bearer ${this.apiKey}`,
+          'HTTP-Referer': 'https://localhost:3000',
+          'X-Title': 'Memento AI',
+        },
+        body: JSON.stringify({
+          model: this.embeddingModelName,
+          input: text,
+        }),
       });
-      return embedding;
+
+      if (!response.ok) {
+        const errText = await response.text();
+        throw new Error(`${this.baseUrl}/embeddings error ${response.status}: ${errText}`);
+      }
+
+      const data = await response.json();
+
+      // Standard OpenAI-compatible response: { data: [{ embedding: number[] }] }
+      if (data.data && Array.isArray(data.data) && data.data[0]?.embedding) {
+        return data.data[0].embedding;
+      }
+
+      // Fallback: some providers return { embedding: number[] }
+      if (data.embedding && Array.isArray(data.embedding)) {
+        return data.embedding;
+      }
+
+      throw new Error(`Unexpected embeddings response shape: ${JSON.stringify(data)}`);
     } catch (e) {
-      console.error('Error generating embeddings (Custom OpenAI):', e);
+      console.error('Error generating embeddings (CustomOpenAI):', e);
       throw e;
     }
   }

memento-note/lib/ai/providers/openrouter.ts

@@ -1,21 +1,35 @@
 import { createOpenAI } from '@ai-sdk/openai';
-import { generateObject, generateText as aiGenerateText, embed, stepCountIs } from 'ai';
+import { generateObject, generateText as aiGenerateText, stepCountIs } from 'ai';
 import { z } from 'zod';
 import { AIProvider, TagSuggestion, TitleSuggestion, ToolUseOptions, ToolCallResult } from '../types';
 
 export class OpenRouterProvider implements AIProvider {
   private model: any;
-  private embeddingModel: any;
+  private apiKey: string;
+  private baseUrl: string;
+  private embeddingModelName: string;
 
   constructor(apiKey: string, modelName: string = 'anthropic/claude-3-haiku', embeddingModelName: string = 'openai/text-embedding-3-small') {
+    this.apiKey = apiKey;
+    this.baseUrl = 'https://openrouter.ai/api/v1';
+    this.embeddingModelName = embeddingModelName;
     // Create OpenAI-compatible client for OpenRouter
     const openrouter = createOpenAI({
-      baseURL: 'https://openrouter.ai/api/v1',
+      baseURL: this.baseUrl,
       apiKey: apiKey,
     });
 
     this.model = openrouter.chat(modelName);
-    this.embeddingModel = openrouter.embedding(embeddingModelName);
+  }
+
+  private async fetchWithTimeout(url: string, options: RequestInit, timeoutMs: number = 60_000): Promise<Response> {
+    const controller = new AbortController()
+    const timer = setTimeout(() => controller.abort(), timeoutMs)
+    try {
+      return await fetch(url, { ...options, signal: controller.signal })
+    } finally {
+      clearTimeout(timer)
+    }
   }
 
   async generateTags(content: string): Promise<TagSuggestion[]> {
@@ -41,11 +55,38 @@ export class OpenRouterProvider implements AIProvider {
 
   async getEmbeddings(text: string): Promise<number[]> {
     try {
-      const { embedding } = await embed({
-        model: this.embeddingModel,
-        value: text,
+      const response = await this.fetchWithTimeout(`${this.baseUrl}/embeddings`, {
+        method: 'POST',
+        headers: {
+          'Content-Type': 'application/json',
+          'Authorization': `Bearer ${this.apiKey}`,
+          'HTTP-Referer': 'https://localhost:3000',
+          'X-Title': 'Memento AI',
+        },
+        body: JSON.stringify({
+          model: this.embeddingModelName,
+          input: text,
+        }),
       });
-      return embedding;
+
+      if (!response.ok) {
+        const errText = await response.text();
+        throw new Error(`OpenRouter embeddings error ${response.status}: ${errText}`);
+      }
+
+      const data = await response.json();
+
+      // OpenRouter returns { data: [{ embedding: number[] }] }
+      if (data.data && Array.isArray(data.data) && data.data[0]?.embedding) {
+        return data.data[0].embedding;
+      }
+
+      // Fallback: some OpenAI-compatible providers return { embedding: number[] }
+      if (data.embedding && Array.isArray(data.embedding)) {
+        return data.embedding;
+      }
+
+      throw new Error(`Unexpected OpenRouter embeddings response shape: ${JSON.stringify(data)}`);
     } catch (e) {
       console.error('Error generating embeddings (OpenRouter):', e);
       throw e;

memento-note/lib/ai/services/embedding.service.ts

@@ -1,7 +1,7 @@
 /**
  * Embedding Service
  * Generates vector embeddings for semantic search and similarity analysis.
- * Stores embeddings as native pgvector(1536) in PostgreSQL.
+ * Stores embeddings as native pgvector(2560) in PostgreSQL.
  */
 
 import { getAIProvider } from '../factory'
@@ -14,7 +14,7 @@ export interface EmbeddingResult {
 }
 
 export class EmbeddingService {
-  private readonly EMBEDDING_DIMENSION = 1536
+  private readonly EMBEDDING_DIMENSION = 2560
 
   async generateEmbedding(text: string): Promise<EmbeddingResult> {
     if (!text || text.trim().length === 0) {