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opensearch.ts
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import { Client, RequestParams, errors } from "@opensearch-project/opensearch";
import * as uuid from "uuid";
import type { EmbeddingsInterface } from "@langchain/core/embeddings";
import { VectorStore } from "@langchain/core/vectorstores";
import { Document } from "@langchain/core/documents";
type OpenSearchEngine = "nmslib" | "hnsw";
type OpenSearchSpaceType = "l2" | "cosinesimil" | "ip";
/**
* Interface defining the options for vector search in OpenSearch. It
* includes the engine type, space type, and parameters for the HNSW
* algorithm.
*/
interface VectorSearchOptions {
readonly engine?: OpenSearchEngine;
readonly spaceType?: OpenSearchSpaceType;
readonly m?: number;
readonly efConstruction?: number;
readonly efSearch?: number;
}
/**
* Interface defining the arguments required to create an instance of the
* OpenSearchVectorStore class. It includes the OpenSearch client, index
* name, and vector search options.
*/
export interface OpenSearchClientArgs {
readonly client: Client;
readonly service?: "es" | "aoss";
readonly indexName?: string;
readonly vectorSearchOptions?: VectorSearchOptions;
}
/**
* Type alias for an object. It's used to define filters for OpenSearch
* queries.
*/
type OpenSearchFilter = {
[key: string]: FilterTypeValue | (string | number)[] | string | number;
};
/**
* FilterTypeValue for OpenSearch queries.
*/
interface FilterTypeValue {
exists?: boolean;
fuzzy?: string;
ids?: string[];
prefix?: string;
gte?: number;
gt?: number;
lte?: number;
lt?: number;
regexp?: string;
// eslint-disable-next-line @typescript-eslint/no-explicit-any
terms_set?: Record<string, any>;
wildcard?: string;
}
/**
* Class that provides a wrapper around the OpenSearch service for vector
* search. It provides methods for adding documents and vectors to the
* OpenSearch index, searching for similar vectors, and managing the
* OpenSearch index.
*/
export class OpenSearchVectorStore extends VectorStore {
declare FilterType: OpenSearchFilter;
private readonly client: Client;
private readonly indexName: string;
// if true, use the Amazon OpenSearch Serverless service instead of es
private readonly isAoss: boolean;
private readonly engine: OpenSearchEngine;
private readonly spaceType: OpenSearchSpaceType;
private readonly efConstruction: number;
private readonly efSearch: number;
private readonly m: number;
_vectorstoreType(): string {
return "opensearch";
}
constructor(embeddings: EmbeddingsInterface, args: OpenSearchClientArgs) {
super(embeddings, args);
this.spaceType = args.vectorSearchOptions?.spaceType ?? "l2";
this.engine = args.vectorSearchOptions?.engine ?? "nmslib";
this.m = args.vectorSearchOptions?.m ?? 16;
this.efConstruction = args.vectorSearchOptions?.efConstruction ?? 512;
this.efSearch = args.vectorSearchOptions?.efSearch ?? 512;
this.client = args.client;
this.indexName = args.indexName ?? "documents";
this.isAoss = (args.service ?? "es") === "aoss";
}
/**
* Method to add documents to the OpenSearch index. It first converts the
* documents to vectors using the embeddings, then adds the vectors to the
* index.
* @param documents The documents to be added to the OpenSearch index.
* @returns Promise resolving to void.
*/
async addDocuments(documents: Document[]): Promise<void> {
const texts = documents.map(({ pageContent }) => pageContent);
return this.addVectors(
await this.embeddings.embedDocuments(texts),
documents
);
}
/**
* Method to add vectors to the OpenSearch index. It ensures the index
* exists, then adds the vectors and associated documents to the index.
* @param vectors The vectors to be added to the OpenSearch index.
* @param documents The documents associated with the vectors.
* @param options Optional parameter that can contain the IDs for the documents.
* @returns Promise resolving to void.
*/
async addVectors(
vectors: number[][],
documents: Document[],
options?: { ids?: string[] }
): Promise<void> {
await this.ensureIndexExists(
vectors[0].length,
this.engine,
this.spaceType,
this.efSearch,
this.efConstruction,
this.m
);
const documentIds =
options?.ids ?? Array.from({ length: vectors.length }, () => uuid.v4());
const operations = vectors.flatMap((embedding, idx) => {
// eslint-disable-next-line @typescript-eslint/no-explicit-any
const document: Record<string, any> = [
{
index: {
_index: this.indexName,
_id: documentIds[idx],
},
},
{
embedding,
metadata: documents[idx].metadata,
text: documents[idx].pageContent,
},
];
// aoss does not support document id
if (this.isAoss) {
delete document[0].index?._id;
}
return document;
});
await this.client.bulk({ body: operations });
// aoss does not support refresh
if (!this.isAoss) {
await this.client.indices.refresh({ index: this.indexName });
}
}
/**
* Method to perform a similarity search on the OpenSearch index using a
* query vector. It returns the k most similar documents and their scores.
* @param query The query vector.
* @param k The number of similar documents to return.
* @param filter Optional filter for the OpenSearch query.
* @returns Promise resolving to an array of tuples, each containing a Document and its score.
*/
async similaritySearchVectorWithScore(
query: number[],
k: number,
filter?: OpenSearchFilter | undefined
): Promise<[Document, number][]> {
const search: RequestParams.Search = {
index: this.indexName,
body: {
query: {
bool: {
filter: { bool: this.buildMetadataTerms(filter) },
must: [
{
knn: {
embedding: { vector: query, k },
},
},
],
},
},
size: k,
},
};
const { body } = await this.client.search(search);
// eslint-disable-next-line @typescript-eslint/no-explicit-any
return body.hits.hits.map((hit: any) => [
new Document({
pageContent: hit._source.text,
metadata: hit._source.metadata,
}),
hit._score,
]);
}
/**
* Static method to create a new OpenSearchVectorStore from an array of
* texts, their metadata, embeddings, and OpenSearch client arguments.
* @param texts The texts to be converted into documents and added to the OpenSearch index.
* @param metadatas The metadata associated with the texts. Can be an array of objects or a single object.
* @param embeddings The embeddings used to convert the texts into vectors.
* @param args The OpenSearch client arguments.
* @returns Promise resolving to a new instance of OpenSearchVectorStore.
*/
static fromTexts(
texts: string[],
metadatas: object[] | object,
embeddings: EmbeddingsInterface,
args: OpenSearchClientArgs
): Promise<OpenSearchVectorStore> {
const documents = texts.map((text, idx) => {
const metadata = Array.isArray(metadatas) ? metadatas[idx] : metadatas;
return new Document({ pageContent: text, metadata });
});
return OpenSearchVectorStore.fromDocuments(documents, embeddings, args);
}
/**
* Static method to create a new OpenSearchVectorStore from an array of
* Documents, embeddings, and OpenSearch client arguments.
* @param docs The documents to be added to the OpenSearch index.
* @param embeddings The embeddings used to convert the documents into vectors.
* @param dbConfig The OpenSearch client arguments.
* @returns Promise resolving to a new instance of OpenSearchVectorStore.
*/
static async fromDocuments(
docs: Document[],
embeddings: EmbeddingsInterface,
dbConfig: OpenSearchClientArgs
): Promise<OpenSearchVectorStore> {
const store = new OpenSearchVectorStore(embeddings, dbConfig);
await store.addDocuments(docs).then(() => store);
return store;
}
/**
* Static method to create a new OpenSearchVectorStore from an existing
* OpenSearch index, embeddings, and OpenSearch client arguments.
* @param embeddings The embeddings used to convert the documents into vectors.
* @param dbConfig The OpenSearch client arguments.
* @returns Promise resolving to a new instance of OpenSearchVectorStore.
*/
static async fromExistingIndex(
embeddings: EmbeddingsInterface,
dbConfig: OpenSearchClientArgs
): Promise<OpenSearchVectorStore> {
const store = new OpenSearchVectorStore(embeddings, dbConfig);
await store.client.cat.indices({ index: store.indexName });
return store;
}
private async ensureIndexExists(
dimension: number,
engine = "nmslib",
spaceType = "l2",
efSearch = 512,
efConstruction = 512,
m = 16
): Promise<void> {
const body = {
settings: {
index: {
number_of_shards: 5,
number_of_replicas: 1,
knn: true,
"knn.algo_param.ef_search": efSearch,
},
},
mappings: {
dynamic_templates: [
{
// map all metadata properties to be keyword
"metadata.*": {
match_mapping_type: "string",
mapping: { type: "keyword" },
},
},
{
"metadata.loc": {
match_mapping_type: "object",
mapping: { type: "object" },
},
},
],
properties: {
text: { type: "text" },
metadata: { type: "object" },
embedding: {
type: "knn_vector",
dimension,
method: {
name: "hnsw",
engine,
space_type: spaceType,
parameters: { ef_construction: efConstruction, m },
},
},
},
},
};
const indexExists = await this.doesIndexExist();
if (indexExists) return;
await this.client.indices.create({ index: this.indexName, body });
}
/**
* Builds metadata terms for OpenSearch queries.
*
* This function takes a filter object and constructs an array of query terms
* compatible with OpenSearch 2.x. It supports a variety of query types including
* term, terms, terms_set, ids, range, prefix, exists, fuzzy, wildcard, and regexp.
* Reference: https://opensearch.org/docs/latest/query-dsl/term/index/
*
* @param {Filter | null} filter - The filter object used to construct query terms.
* Each key represents a field, and the value specifies the type of query and its parameters.
*
* @returns {Array<Record<string, any>>} An array of OpenSearch query terms.
*
* @example
* // Example filter:
* const filter = {
* status: { "exists": true },
* age: { "gte": 30, "lte": 40 },
* tags: ["tag1", "tag2"],
* description: { "wildcard": "*test*" },
*
* };
*
* // Resulting query terms:
* const queryTerms = buildMetadataTerms(filter);
* // queryTerms would be an array of OpenSearch query objects.
*/
buildMetadataTerms(filter: OpenSearchFilter | undefined): object {
if (!filter) return {};
const must = [];
const must_not = [];
for (const [key, value] of Object.entries(filter)) {
const metadataKey = `metadata.${key}`;
if (value) {
if (typeof value === "object" && !Array.isArray(value)) {
if ("exists" in value) {
if (value.exists) {
must.push({ exists: { field: metadataKey } });
} else {
must_not.push({ exists: { field: metadataKey } });
}
} else if ("fuzzy" in value) {
must.push({ fuzzy: { [metadataKey]: value.fuzzy } });
} else if ("ids" in value) {
must.push({ ids: { values: value.ids } });
} else if ("prefix" in value) {
must.push({ prefix: { [metadataKey]: value.prefix } });
} else if (
"gte" in value ||
"gt" in value ||
"lte" in value ||
"lt" in value
) {
must.push({ range: { [metadataKey]: value } });
} else if ("regexp" in value) {
must.push({ regexp: { [metadataKey]: value.regexp } });
} else if ("terms_set" in value) {
must.push({ terms_set: { [metadataKey]: value.terms_set } });
} else if ("wildcard" in value) {
must.push({ wildcard: { [metadataKey]: value.wildcard } });
}
} else {
const aggregatorKey = Array.isArray(value) ? "terms" : "term";
must.push({ [aggregatorKey]: { [metadataKey]: value } });
}
}
}
return { must, must_not };
}
/**
* Method to check if the OpenSearch index exists.
* @returns Promise resolving to a boolean indicating whether the index exists.
*/
async doesIndexExist(): Promise<boolean> {
try {
await this.client.cat.indices({ index: this.indexName });
return true;
} catch (err: unknown) {
// eslint-disable-next-line no-instanceof/no-instanceof
if (err instanceof errors.ResponseError && err.statusCode === 404) {
return false;
}
throw err;
}
}
/**
* Method to delete the OpenSearch index if it exists.
* @returns Promise resolving to void.
*/
async deleteIfExists(): Promise<void> {
const indexExists = await this.doesIndexExist();
if (!indexExists) return;
await this.client.indices.delete({ index: this.indexName });
}
}