@jackhua/mini-langchain/dist/vectorstores/memory.d.ts

A lightweight TypeScript implementation of LangChain with cost optimization features

/**
 * In-memory vector store implementation
 */
import { VectorStore, Embeddings } from './base';
import { Document } from '../core/types';
/**
 * Simple in-memory vector store
 */
export declare class MemoryVectorStore extends VectorStore {
    private vectors;
    private documents;
    constructor(embeddings: Embeddings);
    /**
     * Add documents to the vector store
     */
    addDocuments(documents: Document[]): Promise<string[]>;
    /**
     * Add vectors directly
     */
    addVectors(vectors: number[][], documents: Document[]): Promise<string[]>;
    /**
     * Similarity search
     */
    similaritySearch(query: string, k?: number, filter?: Record<string, any>): Promise<Document[]>;
    /**
     * Similarity search with score
     */
    similaritySearchWithScore(query: string, k?: number, filter?: Record<string, any>): Promise<[Document, number][]>;
    /**
     * Similarity search by vector
     */
    similaritySearchVectorWithScore(query: number[], k: number, filter?: Record<string, any>): Promise<[Document, number][]>;
    /**
     * Delete documents
     */
    delete(params?: {
        ids?: string[];
        filter?: Record<string, any>;
    }): Promise<void>;
    /**
     * Calculate cosine similarity between two vectors
     */
    private cosineSimilarity;
    /**
     * Check if metadata matches filter
     */
    private matchesFilter;
    /**
     * Create from texts
     */
    static fromTexts(texts: string[], metadatas: Record<string, any>[] | Record<string, any>, embeddings: Embeddings): Promise<MemoryVectorStore>;
    /**
     * Create from documents
     */
    static fromDocuments(docs: Document[], embeddings: Embeddings): Promise<MemoryVectorStore>;
    /**
     * Get all documents
     */
    getAllDocuments(): Document[];
    /**
     * Get document count
     */
    getDocumentCount(): number;
}
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