Initial commit: RA-H Open Source Edition

Local-first knowledge management system with BYO API keys. Features: - 3-panel UI (Nodes | Focus | Helpers) - SQLite + sqlite-vec for vector search - Agent system (Easy/Hard mode orchestrators) - Content extraction (YouTube, PDF, web) - Integrate workflow for connection discovery - Dimension system with auto-assignment Tech stack: - Next.js 15 + TypeScript + Tailwind CSS - Anthropic (Claude) + OpenAI (GPT) via Vercel AI SDK Setup: npm install && npm rebuild better-sqlite3 scripts/dev/bootstrap-local.sh npm run dev MIT License
2025-12-15 16:14:28 +11:00
commit 733d1c3407
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+import { NextResponse } from 'next/server';
+import { getSQLiteClient } from '@/services/database/sqlite-client';
+import { chunkService } from '@/services/database/chunks';
+
+export async function GET() {
+  try {
+    const sqlite = getSQLiteClient();
+    
+    // Test basic database connection
+    const connectionTest = await sqlite.testConnection();
+    if (!connectionTest) {
+      return NextResponse.json({
+        status: 'error',
+        message: 'Database connection failed',
+        details: null
+      });
+    }
+
+    // Check if vector extension is loaded
+    const vectorExtensionTest = await sqlite.checkVectorExtension();
+    
+    let vectorStats = null;
+    let chunkStats = null;
+    let vectorHealth = 'unknown';
+
+    try {
+      // Get chunk counts
+      const totalChunks = await chunkService.getChunkCount();
+      const chunksWithoutEmbeddings = await chunkService.getChunksWithoutEmbeddings();
+      const vectorizedCount = totalChunks - chunksWithoutEmbeddings.length;
+
+      chunkStats = {
+        total_chunks: totalChunks,
+        vectorized_chunks: vectorizedCount,
+        missing_embeddings: chunksWithoutEmbeddings.length,
+        coverage_percentage: totalChunks > 0 ? Math.round((vectorizedCount / totalChunks) * 100) : 0
+      };
+
+      // Test vector table health by attempting a simple query
+      if (vectorExtensionTest) {
+        try {
+          const result = sqlite.query('SELECT COUNT(*) as count FROM vec_chunks');
+          const vecCount = Number(result.rows[0].count);
+          
+          vectorStats = {
+            vec_chunks_count: vecCount,
+            matches_chunk_embeddings: vecCount === vectorizedCount
+          };
+          
+          vectorHealth = vecCount === vectorizedCount ? 'healthy' : 'inconsistent';
+        } catch (vecError: any) {
+          vectorHealth = 'corrupted';
+          vectorStats = {
+            error: vecError.message,
+            suggestion: 'Vector table may be corrupted and need recreation'
+          };
+        }
+      } else {
+        vectorHealth = 'extension_unavailable';
+      }
+
+    } catch (error: any) {
+      return NextResponse.json({
+        status: 'error',
+        message: 'Failed to collect vector statistics',
+        details: error.message
+      });
+    }
+
+    return NextResponse.json({
+      status: 'success',
+      data: {
+        database_connected: connectionTest,
+        vector_extension_loaded: vectorExtensionTest,
+        vector_health: vectorHealth,
+        chunk_stats: chunkStats,
+        vector_stats: vectorStats,
+        recommendations: generateRecommendations(vectorHealth, chunkStats, vectorStats)
+      }
+    });
+
+  } catch (error: any) {
+    console.error('Vector health check failed:', error);
+    return NextResponse.json({
+      status: 'error',
+      message: 'Health check failed',
+      details: error.message
+    });
+  }
+}
+
+function generateRecommendations(
+  vectorHealth: string, 
+  chunkStats: any, 
+  vectorStats: any
+): string[] {
+  const recommendations: string[] = [];
+
+  if (vectorHealth === 'corrupted') {
+    recommendations.push('Vector tables are corrupted - restart the application to trigger automatic healing');
+  }
+
+  if (vectorHealth === 'extension_unavailable') {
+    recommendations.push('Vector extension not loaded - check sqlite-vec installation');
+  }
+
+  if (chunkStats && chunkStats.coverage_percentage < 95) {
+    recommendations.push(`${chunkStats.missing_embeddings} chunks missing embeddings - consider running embedding generation`);
+  }
+
+  if (vectorStats && !vectorStats.matches_chunk_embeddings) {
+    recommendations.push('Vector count does not match chunk embeddings - database inconsistency detected');
+  }
+
+  if (recommendations.length === 0) {
+    recommendations.push('Vector search system is healthy');
+  }
+
+  return recommendations;
+}