feat(ai): implement intelligent auto-tagging system

- Added multi-provider AI infrastructure (OpenAI/Ollama)
- Implemented real-time tag suggestions with debounced analysis
- Created AI diagnostics and database maintenance tools in Settings
- Added automated garbage collection for orphan labels
- Refined UX with deterministic color hashing and interactive ghost tags

keep-notes/lib/ai/providers/ollama.ts

@@ -0,0 +1,77 @@
+import { AIProvider, TagSuggestion } from '../types';
+
+export class OllamaProvider implements AIProvider {
+  private baseUrl: string;
+  private modelName: string;
+
+  constructor(baseUrl: string = 'http://localhost:11434/api', modelName: string = 'llama3') {
+    this.baseUrl = baseUrl.endsWith('/') ? baseUrl.slice(0, -1) : baseUrl;
+    this.modelName = modelName;
+  }
+
+  async generateTags(content: string): Promise<TagSuggestion[]> {
+    try {
+      const response = await fetch(`${this.baseUrl}/generate`, {
+        method: 'POST',
+        headers: { 'Content-Type': 'application/json' },
+        body: JSON.stringify({
+          model: this.modelName,
+        prompt: `Analyse la note suivante et extrais les concepts clés sous forme de tags courts (1-3 mots max).
+        
+        Règles:
+        - Pas de mots de liaison (le, la, pour, et...).
+        - Garde les expressions composées ensemble (ex: "semaine prochaine", "New York").
+        - Normalise en minuscules sauf noms propres.
+        - Maximum 5 tags.
+
+        Réponds UNIQUEMENT sous forme de liste JSON d'objets : [{"tag": "string", "confidence": number}].
+        
+        Contenu de la note: "${content}"`,
+          stream: false,
+        }),
+      });
+
+      if (!response.ok) throw new Error(`Ollama error: ${response.statusText}`);
+
+      const data = await response.json();
+      const text = data.response;
+
+      const jsonMatch = text.match(/\[\s*\{.*\}\s*\]/s);
+      if (jsonMatch) {
+        return JSON.parse(jsonMatch[0]);
+      }
+
+      // Support pour le format { "tags": [...] }
+      const objectMatch = text.match(/\{\s*"tags"\s*:\s*(\[.*\])\s*\}/s);
+      if (objectMatch && objectMatch[1]) {
+        return JSON.parse(objectMatch[1]);
+      }
+      
+      return [];
+    } catch (e) {
+      console.error('Erreur API directe Ollama:', e);
+      return [];
+    }
+  }
+
+  async getEmbeddings(text: string): Promise<number[]> {
+    try {
+      const response = await fetch(`${this.baseUrl}/embeddings`, {
+        method: 'POST',
+        headers: { 'Content-Type': 'application/json' },
+        body: JSON.stringify({
+          model: this.modelName,
+          prompt: text,
+        }),
+      });
+
+      if (!response.ok) throw new Error(`Ollama error: ${response.statusText}`);
+
+      const data = await response.json();
+      return data.embedding;
+    } catch (e) {
+      console.error('Erreur embeddings directs Ollama:', e);
+      return [];
+    }
+  }
+}

keep-notes/lib/ai/providers/openai.ts

@@ -0,0 +1,46 @@
+import { openai } from '@ai-sdk/openai';
+import { generateObject, embed } from 'ai';
+import { z } from 'zod';
+import { AIProvider, TagSuggestion } from '../types';
+
+export class OpenAIProvider implements AIProvider {
+  private model: any;
+
+  constructor(apiKey: string, modelName: string = 'gpt-4o-mini') {
+    this.model = openai(modelName);
+  }
+
+  async generateTags(content: string): Promise<TagSuggestion[]> {
+    try {
+      const { object } = await generateObject({
+        model: this.model,
+        schema: z.object({
+          tags: z.array(z.object({
+            tag: z.string().describe('Le nom du tag, court et en minuscules'),
+            confidence: z.number().min(0).max(1).describe('Le niveau de confiance entre 0 et 1')
+          }))
+        }),
+        prompt: `Analyse la note suivante et suggère entre 1 et 5 tags pertinents. 
+        Contenu de la note: "${content}"`,
+      });
+
+      return object.tags;
+    } catch (e) {
+      console.error('Erreur génération tags OpenAI:', e);
+      return [];
+    }
+  }
+
+  async getEmbeddings(text: string): Promise<number[]> {
+    try {
+      const { embedding } = await embed({
+        model: openai.embedding('text-embedding-3-small'),
+        value: text,
+      });
+      return embedding;
+    } catch (e) {
+      console.error('Erreur embeddings OpenAI:', e);
+      return [];
+    }
+  }
+}