session-mcp/src/embeddings.ts

// Embeddings via LiteLLM API

const LLM_API_URL = process.env.LLM_API_URL || 'https://llm.agiliton.cloud';
const LLM_API_KEY = process.env.LLM_API_KEY || '';

interface EmbeddingResponse {
  data: Array<{
    embedding: number[];
    index: number;
  }>;
  model: string;
  usage: {
    prompt_tokens: number;
    total_tokens: number;
  };
}

/**
 * Generate embedding for text using LiteLLM API
 */
export async function getEmbedding(text: string): Promise<number[] | null> {
  if (!LLM_API_KEY) {
    console.error('LLM_API_KEY not set, skipping embedding');
    return null;
  }

  try {
    const response = await fetch(`${LLM_API_URL}/v1/embeddings`, {
      method: 'POST',
      headers: {
        'Authorization': `Bearer ${LLM_API_KEY}`,
        'Content-Type': 'application/json',
      },
      body: JSON.stringify({
        model: 'text-embedding-ada-002',
        input: text,
      }),
    });

    if (!response.ok) {
      console.error('Embedding API error:', response.status, await response.text());
      return null;
    }

    const data = await response.json() as EmbeddingResponse;
    return data.data?.[0]?.embedding || null;
  } catch (error) {
    console.error('Embedding generation failed:', error);
    return null;
  }
}

/**
 * Format embedding array for PostgreSQL vector type
 */
export function formatEmbedding(embedding: number[]): string {
  return `[${embedding.join(',')}]`;
}