langchain/dist/memory/vector_store.d.ts

Typescript bindings for langchain

import type { VectorStoreRetrieverInterface } from "@langchain/core/vectorstores";
import { BaseMemory, InputValues, MemoryVariables, OutputValues } from "@langchain/core/memory";
type Metadata = Record<string, unknown>;
type MetadataFunction = (inputValues?: InputValues, outputValues?: OutputValues) => Metadata;
/**
 * Interface for the parameters required to initialize a
 * VectorStoreRetrieverMemory instance.
 */
export interface VectorStoreRetrieverMemoryParams {
    vectorStoreRetriever: VectorStoreRetrieverInterface;
    inputKey?: string;
    outputKey?: string;
    memoryKey?: string;
    returnDocs?: boolean;
    /**
     * Metadata to be added to the document when saving context.
     */
    metadata?: Metadata | MetadataFunction;
}
/**
 * Class for managing long-term memory in Large Language Model (LLM)
 * applications. It provides a way to persist and retrieve relevant
 * documents from a vector store database, which can be useful for
 * maintaining conversation history or other types of memory in an LLM
 * application.
 * @example
 * ```typescript
 * const vectorStore = new MemoryVectorStore(new OpenAIEmbeddings());
 * const memory = new VectorStoreRetrieverMemory({
 *   vectorStoreRetriever: vectorStore.asRetriever(1),
 *   memoryKey: "history",
 * });
 *
 * // Saving context to memory
 * await memory.saveContext(
 *   { input: "My favorite food is pizza" },
 *   { output: "thats good to know" },
 * );
 * await memory.saveContext(
 *   { input: "My favorite sport is soccer" },
 *   { output: "..." },
 * );
 * await memory.saveContext({ input: "I don't the Celtics" }, { output: "ok" });
 *
 * // Loading memory variables
 * console.log(
 *   await memory.loadMemoryVariables({ prompt: "what sport should i watch?" }),
 * );
 * ```
 */
export declare class VectorStoreRetrieverMemory extends BaseMemory implements VectorStoreRetrieverMemoryParams {
    vectorStoreRetriever: VectorStoreRetrieverInterface;
    inputKey?: string;
    memoryKey: string;
    returnDocs: boolean;
    metadata?: Metadata | MetadataFunction;
    constructor(fields: VectorStoreRetrieverMemoryParams);
    get memoryKeys(): string[];
    /**
     * Method to load memory variables. It uses the vectorStoreRetriever to
     * get relevant documents based on the query obtained from the input
     * values.
     * @param values An InputValues object.
     * @returns A Promise that resolves to a MemoryVariables object.
     */
    loadMemoryVariables(values: InputValues): Promise<MemoryVariables>;
    /**
     * Method to save context. It constructs a document from the input and
     * output values (excluding the memory key) and adds it to the vector
     * store database using the vectorStoreRetriever.
     * @param inputValues An InputValues object.
     * @param outputValues An OutputValues object.
     * @returns A Promise that resolves to void.
     */
    saveContext(inputValues: InputValues, outputValues: OutputValues): Promise<void>;
}
export {};