agsamantha/node_modules/langchain/dist/retrievers/parent_document.d.ts

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2024-10-02 15:15:21 -05:00
import { type VectorStoreInterface, type VectorStoreRetrieverInterface } from "@langchain/core/vectorstores";
import { Document } from "@langchain/core/documents";
import type { BaseDocumentCompressor } from "./document_compressors/index.js";
import { TextSplitter, TextSplitterChunkHeaderOptions } from "../text_splitter.js";
import { MultiVectorRetriever, type MultiVectorRetrieverInput } from "./multi_vector.js";
export type SubDocs = Document<Record<string, any>>[];
/**
* Interface for the fields required to initialize a
* ParentDocumentRetriever instance.
*/
export type ParentDocumentRetrieverFields = MultiVectorRetrieverInput & {
childSplitter: TextSplitter;
parentSplitter?: TextSplitter;
/**
* A custom retriever to use when retrieving instead of
* the `.similaritySearch` method of the vectorstore.
*/
childDocumentRetriever?: VectorStoreRetrieverInterface<VectorStoreInterface>;
documentCompressor?: BaseDocumentCompressor | undefined;
documentCompressorFilteringFn?: (docs: SubDocs) => SubDocs;
};
/**
* A type of document retriever that splits input documents into smaller chunks
* while separately storing and preserving the original documents.
* The small chunks are embedded, then on retrieval, the original
* "parent" documents are retrieved.
*
* This strikes a balance between better targeted retrieval with small documents
* and the more context-rich larger documents.
* @example
* ```typescript
* const retriever = new ParentDocumentRetriever({
* vectorstore: new MemoryVectorStore(new OpenAIEmbeddings()),
* byteStore: new InMemoryStore<Uint8Array>(),
* parentSplitter: new RecursiveCharacterTextSplitter({
* chunkOverlap: 0,
* chunkSize: 500,
* }),
* childSplitter: new RecursiveCharacterTextSplitter({
* chunkOverlap: 0,
* chunkSize: 50,
* }),
* childK: 20,
* parentK: 5,
* });
*
* const parentDocuments = await getDocuments();
* await retriever.addDocuments(parentDocuments);
* const retrievedDocs = await retriever.getRelevantDocuments("justice breyer");
* ```
*/
export declare class ParentDocumentRetriever extends MultiVectorRetriever {
static lc_name(): string;
lc_namespace: string[];
vectorstore: VectorStoreInterface;
protected childSplitter: TextSplitter;
protected parentSplitter?: TextSplitter;
protected idKey: string;
protected childK?: number;
protected parentK?: number;
childDocumentRetriever: VectorStoreRetrieverInterface<VectorStoreInterface> | undefined;
documentCompressor: BaseDocumentCompressor | undefined;
documentCompressorFilteringFn?: ParentDocumentRetrieverFields["documentCompressorFilteringFn"];
constructor(fields: ParentDocumentRetrieverFields);
_getRelevantDocuments(query: string): Promise<Document[]>;
_storeDocuments(parentDoc: Record<string, Document>, childDocs: Document[], addToDocstore: boolean): Promise<void>;
/**
* Adds documents to the docstore and vectorstores.
* If a retriever is provided, it will be used to add documents instead of the vectorstore.
* @param docs The documents to add
* @param config.ids Optional list of ids for documents. If provided should be the same
* length as the list of documents. Can provided if parent documents
* are already in the document store and you don't want to re-add
* to the docstore. If not provided, random UUIDs will be used as ids.
* @param config.addToDocstore Boolean of whether to add documents to docstore.
* This can be false if and only if `ids` are provided. You may want
* to set this to False if the documents are already in the docstore
* and you don't want to re-add them.
* @param config.chunkHeaderOptions Object with options for adding Contextual chunk headers
*/
addDocuments(docs: Document[], config?: {
ids?: string[];
addToDocstore?: boolean;
childDocChunkHeaderOptions?: TextSplitterChunkHeaderOptions;
}): Promise<void>;
}