@juspay/neurolink/dist/rag/retrieval/vectorQueryTool.d.ts

Universal AI Development Platform with working MCP integration, multi-provider support, voice (TTS/STT/realtime), and professional CLI. 58+ external MCP servers discoverable, multimodal file processing, RAG pipelines. Build, test, and deploy AI applicatio

/**
 * Vector Query Tool
 *
 * Provides semantic search capabilities for RAG pipelines.
 * Integrates with vector stores and supports metadata filtering and reranking.
 */
import { z } from "zod";
import type { MetadataFilter, RequestContext, VectorQueryResponse, VectorQueryResult, VectorQueryToolConfig, VectorStore } from "../../types/index.js";
/**
 * Creates a vector query tool for semantic search
 * Follows NeuroLink's factory pattern
 *
 * @param config - Tool configuration
 * @param vectorStore - Vector store instance or resolver function
 * @returns Tool object with execute method
 */
export declare function createVectorQueryTool(config: VectorQueryToolConfig, vectorStore: VectorStore | ((context: RequestContext) => VectorStore)): {
    name: string;
    description: string;
    parameters: z.ZodObject<{
        topK: z.ZodOptional<z.ZodNumber>;
        filter?: z.ZodOptional<z.ZodRecord<z.ZodString, z.ZodUnknown>> | undefined;
        query: z.ZodString;
    }, z.core.$strip>;
    /**
     * Execute the vector query
     * @param params - Query parameters
     * @param context - Optional request context
     * @returns Query results with relevant context
     */
    execute: (params: {
        query: string;
        filter?: MetadataFilter;
        topK?: number;
    }, context?: RequestContext) => Promise<VectorQueryResponse>;
};
/**
 * In-memory vector store implementation for testing and development
 */
export declare class InMemoryVectorStore implements VectorStore {
    private vectors;
    /**
     * Add vectors to an index
     */
    upsert(indexName: string, items: Array<{
        id: string;
        vector: number[];
        metadata?: Record<string, unknown>;
    }>): Promise<void>;
    /**
     * Query vectors by similarity
     */
    query(params: {
        indexName: string;
        queryVector: number[];
        topK?: number;
        filter?: MetadataFilter;
        includeVectors?: boolean;
    }): Promise<VectorQueryResult[]>;
    /**
     * Delete vectors from an index
     */
    delete(indexName: string, ids: string[]): Promise<void>;
    /**
     * Check if metadata matches filter
     */
    private matchesFilter;
    /**
     * Calculate cosine similarity between two vectors
     */
    private cosineSimilarity;
}