wisdomforge

/** * Embedding utilities for Qdrant MCP Server * * This file contains utility functions for generating and managing embeddings. * Using Qdrant's built-in FastEmbed support for efficient embedding generation. */ import axios from 'axios'; import { VECTOR_SIZE, QDRANT_URL, QDRANT_API_KEY } from '../configs/qdrant'; import { EmbeddingResponse } from '../types/declarations/embedding'; /** * Generate an embedding vector for the provided text * * Uses Qdrant's built-in FastEmbed capability * * @param text The text to generate an embedding for * @returns A vector representation of the text */ export async function generateEmbedding(text: string): Promise<number[]> { try { // Using Qdrant's server-side FastEmbed integration const headers: Record<string, string> = { 'Content-Type': 'application/json' }; if (QDRANT_API_KEY) { headers['api-key'] = QDRANT_API_KEY; } const response = await axios.post<EmbeddingResponse>( `${QDRANT_URL}/embeddings`, { text: text, model: 'fastembed', // Use the default FastEmbed model }, { headers } ); return response.data.embedding; } catch (error) { console.error('Error generating embedding with FastEmbed:', error); // Fall back to mock implementation in case of errors console.warn('Falling back to mock embeddings due to API error.'); return Array.from({ length: VECTOR_SIZE }, () => Math.random() - 0.5); } } /** * Helper function to calculate cosine similarity between two vectors * * @param a First vector * @param b Second vector * @returns Cosine similarity score (0-1) */ export function cosineSimilarity(a: number[], b: number[]): number { if (a.length !== b.length) { throw new Error('Vectors must be of the same length'); } let dotProduct = 0; let aMagnitude = 0; let bMagnitude = 0; for (let i = 0; i < a.length; i++) { dotProduct += a[i] * b[i]; aMagnitude += a[i] * a[i]; bMagnitude += b[i] * b[i]; } aMagnitude = Math.sqrt(aMagnitude); bMagnitude = Math.sqrt(bMagnitude); if (aMagnitude === 0 || bMagnitude === 0) { return 0; } return dotProduct / (aMagnitude * bMagnitude); }