better-qdrant-mcp-server/build/types.d.ts

MCP server for enhanced Qdrant vector database functionality

import { QdrantClient } from '@qdrant/js-client-rest';
export type EmbeddingService = 'openai' | 'openrouter' | 'fastembed' | 'ollama';
export interface EmbeddingServiceConfig {
    type: EmbeddingService;
    apiKey?: string;
    endpoint?: string;
    model?: string;
}
export interface QdrantConfig {
    url: string;
    apiKey?: string;
}
export interface ServerConfig {
    qdrant: QdrantConfig;
    embedding: EmbeddingServiceConfig;
}
export interface Collection {
    name: string;
    vectorSize: number;
    distance: 'Cosine' | 'Euclid' | 'Dot';
}
export interface SearchResult {
    id: string;
    score: number;
    payload: Record<string, any>;
    vector?: number[];
}
export interface EmbeddingGenerator {
    generateEmbeddings(texts: string[]): Promise<number[][]>;
    vectorSize: number;
}
export interface QdrantService {
    client: QdrantClient;
    listCollections(): Promise<string[]>;
    createCollection(name: string, vectorSize: number): Promise<void>;
    deleteCollection(name: string): Promise<void>;
    addDocuments(collection: string, documents: {
        id: string;
        vector: number[];
        payload: Record<string, any>;
    }[]): Promise<void>;
    search(collection: string, vector: number[], limit?: number): Promise<SearchResult[]>;
}