feat: Enhance embedding management and model configuration

- Updated embedding_manager.py to include backend parameter in model configuration.
- Modified model_manager.py to utilize cache_name for ONNX models.
- Refactored hybrid_search.py to improve embedder initialization based on backend type.
- Added backend column to vector_store.py for better model configuration management.
- Implemented migration for existing database to include backend information.
- Enhanced API settings implementation with comprehensive provider and endpoint management.
- Introduced LiteLLM integration guide detailing configuration and usage.
- Added examples for LiteLLM usage in TypeScript.

ccw/examples/litellm-usage.ts

@@ -0,0 +1,77 @@
+/**
+ * LiteLLM Usage Examples
+ * Demonstrates how to use the LiteLLM TypeScript client
+ */
+
+import { getLiteLLMClient, getLiteLLMStatus } from '../src/tools/litellm-client';
+
+async function main() {
+  console.log('=== LiteLLM TypeScript Bridge Examples ===\n');
+
+  // Example 1: Check availability
+  console.log('1. Checking LiteLLM availability...');
+  const status = await getLiteLLMStatus();
+  console.log('   Status:', status);
+  console.log('');
+
+  if (!status.available) {
+    console.log('❌ LiteLLM is not available. Please install ccw-litellm:');
+    console.log('   pip install ccw-litellm');
+    return;
+  }
+
+  const client = getLiteLLMClient();
+
+  // Example 2: Get configuration
+  console.log('2. Getting configuration...');
+  try {
+    const config = await client.getConfig();
+    console.log('   Config:', config);
+  } catch (error) {
+    console.log('   Error:', error.message);
+  }
+  console.log('');
+
+  // Example 3: Generate embeddings
+  console.log('3. Generating embeddings...');
+  try {
+    const texts = ['Hello world', 'Machine learning is amazing'];
+    const embedResult = await client.embed(texts, 'default');
+    console.log('   Dimensions:', embedResult.dimensions);
+    console.log('   Vectors count:', embedResult.vectors.length);
+    console.log('   First vector (first 5 dims):', embedResult.vectors[0]?.slice(0, 5));
+  } catch (error) {
+    console.log('   Error:', error.message);
+  }
+  console.log('');
+
+  // Example 4: Single message chat
+  console.log('4. Single message chat...');
+  try {
+    const response = await client.chat('What is 2+2?', 'default');
+    console.log('   Response:', response);
+  } catch (error) {
+    console.log('   Error:', error.message);
+  }
+  console.log('');
+
+  // Example 5: Multi-turn chat
+  console.log('5. Multi-turn chat...');
+  try {
+    const chatResponse = await client.chatMessages([
+      { role: 'system', content: 'You are a helpful math tutor.' },
+      { role: 'user', content: 'What is the Pythagorean theorem?' }
+    ], 'default');
+    console.log('   Content:', chatResponse.content);
+    console.log('   Model:', chatResponse.model);
+    console.log('   Usage:', chatResponse.usage);
+  } catch (error) {
+    console.log('   Error:', error.message);
+  }
+  console.log('');
+
+  console.log('=== Examples completed ===');
+}
+
+// Run examples
+main().catch(console.error);