@forge-ml/rag/dist/stores/vectorStore/redis/index.d.ts

A RAG (Retrieval-Augmented Generation) package for Forge ML

import { RedisClientType } from "redis";
import { Embedding, VectorStore } from "../../../types";
import { VECTOR_MODEL_DIM } from "../types";
declare class RedisVectorStore implements VectorStore {
    client: RedisClientType;
    constructor(url: string);
    createIndex(opts?: {
        dim: VECTOR_MODEL_DIM;
    }): Promise<void>;
    addEmbedding(embedding: Embedding): Promise<"OK" | null>;
    storeEmbeddings(embeddings: Embedding[]): Promise<void>;
    queryEmbeddings(params: {
        query: number[];
        k: number;
        documentIds?: string[];
    }): Promise<{
        chunkId: string;
        documentId: string;
        score: number;
    }[]>;
    knnSearchEmbeddings({ inputVector, k, documentIds, }: {
        inputVector: number[];
        k: number;
        documentIds?: string[];
    }): Promise<{
        total: number;
        documents: {
            id: string;
            value: {
                [x: string]: string | number | any | any[] | null;
            };
        }[];
    }>;
}
export default RedisVectorStore;