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vectorstores.ts
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vectorstores.ts
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import type { EmbeddingsInterface } from "./embeddings.js";
import type { DocumentInterface } from "./documents/document.js";
import {
BaseRetriever,
BaseRetrieverInterface,
type BaseRetrieverInput,
} from "./retrievers/index.js";
import { Serializable } from "./load/serializable.js";
import {
CallbackManagerForRetrieverRun,
Callbacks,
} from "./callbacks/manager.js";
/**
* Type for options when adding a document to the VectorStore.
*/
// eslint-disable-next-line @typescript-eslint/no-explicit-any
type AddDocumentOptions = Record<string, any>;
/**
* Type for options when performing a maximal marginal relevance search.
*/
export type MaxMarginalRelevanceSearchOptions<FilterType> = {
k: number;
fetchK?: number;
lambda?: number;
filter?: FilterType;
};
/**
* Type for options when performing a maximal marginal relevance search
* with the VectorStoreRetriever.
*/
export type VectorStoreRetrieverMMRSearchKwargs = {
fetchK?: number;
lambda?: number;
};
/**
* Type for input when creating a VectorStoreRetriever instance.
*/
export type VectorStoreRetrieverInput<V extends VectorStoreInterface> =
BaseRetrieverInput &
(
| {
vectorStore: V;
k?: number;
filter?: V["FilterType"];
searchType?: "similarity";
}
| {
vectorStore: V;
k?: number;
filter?: V["FilterType"];
searchType: "mmr";
searchKwargs?: VectorStoreRetrieverMMRSearchKwargs;
}
);
export interface VectorStoreRetrieverInterface<
V extends VectorStoreInterface = VectorStoreInterface
> extends BaseRetrieverInterface {
vectorStore: V;
addDocuments(
documents: DocumentInterface[],
options?: AddDocumentOptions
): Promise<string[] | void>;
}
/**
* Class for performing document retrieval from a VectorStore. Can perform
* similarity search or maximal marginal relevance search.
*/
export class VectorStoreRetriever<
V extends VectorStoreInterface = VectorStoreInterface
>
extends BaseRetriever
implements VectorStoreRetrieverInterface
{
static lc_name() {
return "VectorStoreRetriever";
}
get lc_namespace() {
return ["langchain_core", "vectorstores"];
}
vectorStore: V;
k = 4;
searchType = "similarity";
searchKwargs?: VectorStoreRetrieverMMRSearchKwargs;
filter?: V["FilterType"];
_vectorstoreType(): string {
return this.vectorStore._vectorstoreType();
}
constructor(fields: VectorStoreRetrieverInput<V>) {
super(fields);
this.vectorStore = fields.vectorStore;
this.k = fields.k ?? this.k;
this.searchType = fields.searchType ?? this.searchType;
this.filter = fields.filter;
if (fields.searchType === "mmr") {
this.searchKwargs = fields.searchKwargs;
}
}
async _getRelevantDocuments(
query: string,
runManager?: CallbackManagerForRetrieverRun
): Promise<DocumentInterface[]> {
if (this.searchType === "mmr") {
if (typeof this.vectorStore.maxMarginalRelevanceSearch !== "function") {
throw new Error(
`The vector store backing this retriever, ${this._vectorstoreType()} does not support max marginal relevance search.`
);
}
return this.vectorStore.maxMarginalRelevanceSearch(
query,
{
k: this.k,
filter: this.filter,
...this.searchKwargs,
},
runManager?.getChild("vectorstore")
);
}
return this.vectorStore.similaritySearch(
query,
this.k,
this.filter,
runManager?.getChild("vectorstore")
);
}
async addDocuments(
documents: DocumentInterface[],
options?: AddDocumentOptions
): Promise<string[] | void> {
return this.vectorStore.addDocuments(documents, options);
}
}
export interface VectorStoreInterface extends Serializable {
FilterType: object | string;
embeddings: EmbeddingsInterface;
_vectorstoreType(): string;
addVectors(
vectors: number[][],
documents: DocumentInterface[],
options?: AddDocumentOptions
): Promise<string[] | void>;
addDocuments(
documents: DocumentInterface[],
options?: AddDocumentOptions
): Promise<string[] | void>;
// eslint-disable-next-line @typescript-eslint/no-explicit-any
delete(_params?: Record<string, any>): Promise<void>;
similaritySearchVectorWithScore(
query: number[],
k: number,
filter?: this["FilterType"]
): Promise<[DocumentInterface, number][]>;
similaritySearch(
query: string,
k?: number,
filter?: this["FilterType"],
callbacks?: Callbacks
): Promise<DocumentInterface[]>;
similaritySearchWithScore(
query: string,
k?: number,
filter?: this["FilterType"],
callbacks?: Callbacks
): Promise<[DocumentInterface, number][]>;
/**
* Return documents selected using the maximal marginal relevance.
* Maximal marginal relevance optimizes for similarity to the query AND diversity
* among selected documents.
*
* @param {string} query - Text to look up documents similar to.
* @param {number} options.k - Number of documents to return.
* @param {number} options.fetchK - Number of documents to fetch before passing to the MMR algorithm.
* @param {number} options.lambda - Number between 0 and 1 that determines the degree of diversity among the results,
* where 0 corresponds to maximum diversity and 1 to minimum diversity.
* @param {this["FilterType"]} options.filter - Optional filter
* @param _callbacks
*
* @returns {Promise<DocumentInterface[]>} - List of documents selected by maximal marginal relevance.
*/
maxMarginalRelevanceSearch?(
query: string,
options: MaxMarginalRelevanceSearchOptions<this["FilterType"]>,
callbacks: Callbacks | undefined
): Promise<DocumentInterface[]>;
asRetriever(
kOrFields?: number | Partial<VectorStoreRetrieverInput<this>>,
filter?: this["FilterType"],
callbacks?: Callbacks,
tags?: string[],
metadata?: Record<string, unknown>,
verbose?: boolean
): VectorStoreRetriever<this>;
}
/**
* Abstract class representing a store of vectors. Provides methods for
* adding vectors and documents, deleting from the store, and searching
* the store.
*/
export abstract class VectorStore
extends Serializable
implements VectorStoreInterface
{
declare FilterType: object | string;
// Only ever instantiated in main LangChain
lc_namespace = ["langchain", "vectorstores", this._vectorstoreType()];
embeddings: EmbeddingsInterface;
// eslint-disable-next-line @typescript-eslint/no-explicit-any
constructor(embeddings: EmbeddingsInterface, dbConfig: Record<string, any>) {
super(dbConfig);
this.embeddings = embeddings;
}
abstract _vectorstoreType(): string;
abstract addVectors(
vectors: number[][],
documents: DocumentInterface[],
options?: AddDocumentOptions
): Promise<string[] | void>;
abstract addDocuments(
documents: DocumentInterface[],
options?: AddDocumentOptions
): Promise<string[] | void>;
// eslint-disable-next-line @typescript-eslint/no-explicit-any
async delete(_params?: Record<string, any>): Promise<void> {
throw new Error("Not implemented.");
}
abstract similaritySearchVectorWithScore(
query: number[],
k: number,
filter?: this["FilterType"]
): Promise<[DocumentInterface, number][]>;
async similaritySearch(
query: string,
k = 4,
filter: this["FilterType"] | undefined = undefined,
_callbacks: Callbacks | undefined = undefined // implement passing to embedQuery later
): Promise<DocumentInterface[]> {
const results = await this.similaritySearchVectorWithScore(
await this.embeddings.embedQuery(query),
k,
filter
);
return results.map((result) => result[0]);
}
async similaritySearchWithScore(
query: string,
k = 4,
filter: this["FilterType"] | undefined = undefined,
_callbacks: Callbacks | undefined = undefined // implement passing to embedQuery later
): Promise<[DocumentInterface, number][]> {
return this.similaritySearchVectorWithScore(
await this.embeddings.embedQuery(query),
k,
filter
);
}
/**
* Return documents selected using the maximal marginal relevance.
* Maximal marginal relevance optimizes for similarity to the query AND diversity
* among selected documents.
*
* @param {string} query - Text to look up documents similar to.
* @param {number} options.k - Number of documents to return.
* @param {number} options.fetchK - Number of documents to fetch before passing to the MMR algorithm.
* @param {number} options.lambda - Number between 0 and 1 that determines the degree of diversity among the results,
* where 0 corresponds to maximum diversity and 1 to minimum diversity.
* @param {this["FilterType"]} options.filter - Optional filter
* @param _callbacks
*
* @returns {Promise<DocumentInterface[]>} - List of documents selected by maximal marginal relevance.
*/
async maxMarginalRelevanceSearch?(
query: string,
options: MaxMarginalRelevanceSearchOptions<this["FilterType"]>,
_callbacks: Callbacks | undefined // implement passing to embedQuery later
): Promise<DocumentInterface[]>;
static fromTexts(
_texts: string[],
_metadatas: object[] | object,
_embeddings: EmbeddingsInterface,
// eslint-disable-next-line @typescript-eslint/no-explicit-any
_dbConfig: Record<string, any>
): Promise<VectorStore> {
throw new Error(
"the Langchain vectorstore implementation you are using forgot to override this, please report a bug"
);
}
static fromDocuments(
_docs: DocumentInterface[],
_embeddings: EmbeddingsInterface,
// eslint-disable-next-line @typescript-eslint/no-explicit-any
_dbConfig: Record<string, any>
): Promise<VectorStore> {
throw new Error(
"the Langchain vectorstore implementation you are using forgot to override this, please report a bug"
);
}
asRetriever(
kOrFields?: number | Partial<VectorStoreRetrieverInput<this>>,
filter?: this["FilterType"],
callbacks?: Callbacks,
tags?: string[],
metadata?: Record<string, unknown>,
verbose?: boolean
): VectorStoreRetriever<this> {
if (typeof kOrFields === "number") {
return new VectorStoreRetriever({
vectorStore: this,
k: kOrFields,
filter,
tags: [...(tags ?? []), this._vectorstoreType()],
metadata,
verbose,
callbacks,
});
} else {
const params = {
vectorStore: this,
k: kOrFields?.k,
filter: kOrFields?.filter,
tags: [...(kOrFields?.tags ?? []), this._vectorstoreType()],
metadata: kOrFields?.metadata,
verbose: kOrFields?.verbose,
callbacks: kOrFields?.callbacks,
searchType: kOrFields?.searchType,
};
if (kOrFields?.searchType === "mmr") {
return new VectorStoreRetriever({
...params,
searchKwargs: kOrFields.searchKwargs,
});
}
return new VectorStoreRetriever({ ...params });
}
}
}
/**
* Abstract class extending VectorStore with functionality for saving and
* loading the vector store.
*/
export abstract class SaveableVectorStore extends VectorStore {
abstract save(directory: string): Promise<void>;
static load(
_directory: string,
_embeddings: EmbeddingsInterface
): Promise<SaveableVectorStore> {
throw new Error("Not implemented");
}
}