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upstash.ts
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upstash.ts
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import * as uuid from "uuid";
import { EmbeddingsInterface } from "@langchain/core/embeddings";
import { VectorStore } from "@langchain/core/vectorstores";
import { Index as UpstashIndex, type QueryResult } from "@upstash/vector";
import { Document, DocumentInterface } from "@langchain/core/documents";
import { chunkArray } from "@langchain/core/utils/chunk_array";
import { FakeEmbeddings } from "@langchain/core/utils/testing";
import {
AsyncCaller,
AsyncCallerParams,
} from "@langchain/core/utils/async_caller";
/**
* This interface defines the arguments for the UpstashVectorStore class.
*/
export interface UpstashVectorLibArgs extends AsyncCallerParams {
index: UpstashIndex;
filter?: string;
namespace?: string;
}
// eslint-disable-next-line @typescript-eslint/no-explicit-any
export type UpstashMetadata = Record<string, any>;
export type UpstashQueryMetadata = UpstashMetadata & {
// eslint-disable-next-line @typescript-eslint/no-explicit-any
_pageContentLC: any;
};
/**
* Type that defines the parameters for the delete method.
* It can either contain the target id(s) or the deleteAll config to reset all the vectors.
*/
export type UpstashDeleteParams =
| {
ids: string | string[];
deleteAll?: never;
}
| { deleteAll: boolean; ids?: never };
const CONCURRENT_UPSERT_LIMIT = 1000;
/**
* The main class that extends the 'VectorStore' class. It provides
* methods for interacting with Upstash index, such as adding documents,
* deleting documents, performing similarity search and more.
*/
export class UpstashVectorStore extends VectorStore {
declare FilterType: string;
index: UpstashIndex;
caller: AsyncCaller;
useUpstashEmbeddings?: boolean;
filter?: this["FilterType"];
namespace?: string;
_vectorstoreType(): string {
return "upstash";
}
constructor(embeddings: EmbeddingsInterface, args: UpstashVectorLibArgs) {
super(embeddings, args);
// Special case where the embeddings instance is a FakeEmbeddings instance. In this case, we need to disable "instanceof" rule.
// eslint-disable-next-line no-instanceof/no-instanceof
if (embeddings instanceof FakeEmbeddings) {
this.useUpstashEmbeddings = true;
}
const { index, namespace, ...asyncCallerArgs } = args;
this.index = index;
this.caller = new AsyncCaller(asyncCallerArgs);
this.filter = args.filter;
this.namespace = namespace;
}
/**
* This method adds documents to Upstash database. Documents are first converted to vectors
* using the provided embeddings instance, and then upserted to the database.
* @param documents Array of Document objects to be added to the database.
* @param options Optional object containing array of ids for the documents.
* @returns Promise that resolves with the ids of the provided documents when the upsert operation is done.
*/
async addDocuments(
documents: DocumentInterface[],
options?: { ids?: string[]; useUpstashEmbeddings?: boolean }
) {
const texts = documents.map(({ pageContent }) => pageContent);
if (this.useUpstashEmbeddings || options?.useUpstashEmbeddings) {
return this._addData(documents, options);
}
const embeddings = await this.embeddings.embedDocuments(texts);
return this.addVectors(embeddings, documents, options);
}
/**
* This method adds the provided vectors to Upstash database.
* @param vectors Array of vectors to be added to the Upstash database.
* @param documents Array of Document objects, each associated with a vector.
* @param options Optional object containing the array of ids foor the vectors.
* @returns Promise that resolves with the ids of the provided documents when the upsert operation is done.
*/
async addVectors(
vectors: number[][],
documents: DocumentInterface[],
options?: { ids?: string[] }
) {
const documentIds =
options?.ids ?? Array.from({ length: vectors.length }, () => uuid.v4());
const upstashVectors = vectors.map((vector, index) => {
const metadata = {
_pageContentLC: documents[index].pageContent,
...documents[index].metadata,
};
const id = documentIds[index];
return {
id,
vector,
metadata,
};
});
const namespace = this.index.namespace(this.namespace ?? "");
const vectorChunks = chunkArray(upstashVectors, CONCURRENT_UPSERT_LIMIT);
const batchRequests = vectorChunks.map((chunk) =>
this.caller.call(async () => namespace.upsert(chunk))
);
await Promise.all(batchRequests);
return documentIds;
}
/**
* This method adds the provided documents to Upstash database. The pageContent of the documents will be embedded by Upstash Embeddings.
* @param documents Array of Document objects to be added to the Upstash database.
* @param options Optional object containing the array of ids for the documents.
* @returns Promise that resolves with the ids of the provided documents when the upsert operation is done.
*/
protected async _addData(
documents: DocumentInterface[],
options?: { ids?: string[] }
) {
const documentIds =
options?.ids ?? Array.from({ length: documents.length }, () => uuid.v4());
const upstashVectorsWithData = documents.map((document, index) => {
const metadata = {
_pageContentLC: documents[index].pageContent,
...documents[index].metadata,
};
const id = documentIds[index];
return {
id,
data: document.pageContent,
metadata,
};
});
const namespace = this.index.namespace(this.namespace ?? "");
const vectorChunks = chunkArray(
upstashVectorsWithData,
CONCURRENT_UPSERT_LIMIT
);
const batchRequests = vectorChunks.map((chunk) =>
this.caller.call(async () => namespace.upsert(chunk))
);
await Promise.all(batchRequests);
return documentIds;
}
/**
* This method deletes documents from the Upstash database. You can either
* provide the target ids, or delete all vectors in the database.
* @param params Object containing either array of ids of the documents or boolean deleteAll.
* @returns Promise that resolves when the specified documents have been deleted from the database.
*/
async delete(params: UpstashDeleteParams): Promise<void> {
const namespace = this.index.namespace(this.namespace ?? "");
if (params.deleteAll) {
await namespace.reset();
} else if (params.ids) {
await namespace.delete(params.ids);
}
}
protected async _runUpstashQuery(
query: number[] | string,
k: number,
filter?: this["FilterType"],
options?: { includeVectors: boolean }
) {
let queryResult: QueryResult<UpstashQueryMetadata>[] = [];
const namespace = this.index.namespace(this.namespace ?? "");
if (typeof query === "string") {
queryResult = await namespace.query<UpstashQueryMetadata>({
data: query,
topK: k,
includeMetadata: true,
filter,
...options,
});
} else {
queryResult = await namespace.query<UpstashQueryMetadata>({
vector: query,
topK: k,
includeMetadata: true,
filter,
...options,
});
}
return queryResult;
}
/**
* This method performs a similarity search in the Upstash database
* over the existing vectors.
* @param query Query vector for the similarity search.
* @param k The number of similar vectors to return as result.
* @returns Promise that resolves with an array of tuples, each containing
* Document object and similarity score. The length of the result will be
* maximum of 'k' and vectors in the index.
*/
async similaritySearchVectorWithScore(
query: number[] | string,
k: number,
filter?: this["FilterType"]
): Promise<[DocumentInterface, number][]> {
const results = await this._runUpstashQuery(query, k, filter);
const searchResult: [DocumentInterface, number][] = results.map((res) => {
const { _pageContentLC, ...metadata } = (res.metadata ??
{}) as UpstashQueryMetadata;
return [
new Document({
metadata,
pageContent: _pageContentLC,
}),
res.score,
];
});
return searchResult;
}
/**
* This method creates a new UpstashVector instance from an array of texts.
* The texts are initially converted to Document instances and added to Upstash
* database.
* @param texts The texts to create the documents from.
* @param metadatas The metadata values associated with the texts.
* @param embeddings Embedding interface of choice, to create the text embeddings.
* @param dbConfig Object containing the Upstash database configs.
* @returns Promise that resolves with a new UpstashVector instance.
*/
static async fromTexts(
texts: string[],
metadatas: UpstashMetadata | UpstashMetadata[],
embeddings: EmbeddingsInterface,
dbConfig: UpstashVectorLibArgs
): Promise<UpstashVectorStore> {
const docs: DocumentInterface[] = [];
for (let i = 0; i < texts.length; i += 1) {
const metadata = Array.isArray(metadatas) ? metadatas[i] : metadatas;
const newDocument = new Document({
pageContent: texts[i],
metadata,
});
docs.push(newDocument);
}
return this.fromDocuments(docs, embeddings, dbConfig);
}
/**
* This method creates a new UpstashVector instance from an array of Document instances.
* @param docs The docs to be added to Upstash database.
* @param embeddings Embedding interface of choice, to create the embeddings.
* @param dbConfig Object containing the Upstash database configs.
* @returns Promise that resolves with a new UpstashVector instance
*/
static async fromDocuments(
docs: DocumentInterface[],
embeddings: EmbeddingsInterface,
dbConfig: UpstashVectorLibArgs
): Promise<UpstashVectorStore> {
const instance = new this(embeddings, dbConfig);
await instance.addDocuments(docs);
return instance;
}
/**
* This method creates a new UpstashVector instance from an existing index.
* @param embeddings Embedding interface of the choice, to create the embeddings.
* @param dbConfig Object containing the Upstash database configs.
* @returns
*/
static async fromExistingIndex(
embeddings: EmbeddingsInterface,
dbConfig: UpstashVectorLibArgs
): Promise<UpstashVectorStore> {
const instance = new this(embeddings, dbConfig);
return instance;
}
}