416 lines
17 KiB
JavaScript
416 lines
17 KiB
JavaScript
|
"use strict";
|
||
|
|
var __createBinding = (this && this.__createBinding) || (Object.create ? (function(o, m, k, k2) {
|
||
|
|
if (k2 === undefined) k2 = k;
|
||
|
|
var desc = Object.getOwnPropertyDescriptor(m, k);
|
||
|
|
if (!desc || ("get" in desc ? !m.__esModule : desc.writable || desc.configurable)) {
|
||
|
|
desc = { enumerable: true, get: function() { return m[k]; } };
|
||
|
|
}
|
||
|
|
Object.defineProperty(o, k2, desc);
|
||
|
|
}) : (function(o, m, k, k2) {
|
||
|
|
if (k2 === undefined) k2 = k;
|
||
|
|
o[k2] = m[k];
|
||
|
|
}));
|
||
|
|
var __setModuleDefault = (this && this.__setModuleDefault) || (Object.create ? (function(o, v) {
|
||
|
|
Object.defineProperty(o, "default", { enumerable: true, value: v });
|
||
|
|
}) : function(o, v) {
|
||
|
|
o["default"] = v;
|
||
|
|
});
|
||
|
|
var __importStar = (this && this.__importStar) || function (mod) {
|
||
|
|
if (mod && mod.__esModule) return mod;
|
||
|
|
var result = {};
|
||
|
|
if (mod != null) for (var k in mod) if (k !== "default" && Object.prototype.hasOwnProperty.call(mod, k)) __createBinding(result, mod, k);
|
||
|
|
__setModuleDefault(result, mod);
|
||
|
|
return result;
|
||
|
|
};
|
||
|
|
Object.defineProperty(exports, "__esModule", { value: true });
|
||
|
|
exports.OpenSearchVectorStore = void 0;
|
||
|
|
const opensearch_1 = require("@opensearch-project/opensearch");
|
||
|
|
const uuid = __importStar(require("uuid"));
|
||
|
|
const vectorstores_1 = require("@langchain/core/vectorstores");
|
||
|
|
const documents_1 = require("@langchain/core/documents");
|
||
|
|
/**
|
||
|
|
* 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.
|
||
|
|
*/
|
||
|
|
class OpenSearchVectorStore extends vectorstores_1.VectorStore {
|
||
|
|
_vectorstoreType() {
|
||
|
|
return "opensearch";
|
||
|
|
}
|
||
|
|
constructor(embeddings, args) {
|
||
|
|
super(embeddings, args);
|
||
|
|
Object.defineProperty(this, "client", {
|
||
|
|
enumerable: true,
|
||
|
|
configurable: true,
|
||
|
|
writable: true,
|
||
|
|
value: void 0
|
||
|
|
});
|
||
|
|
Object.defineProperty(this, "indexName", {
|
||
|
|
enumerable: true,
|
||
|
|
configurable: true,
|
||
|
|
writable: true,
|
||
|
|
value: void 0
|
||
|
|
});
|
||
|
|
// if true, use the Amazon OpenSearch Serverless service instead of es
|
||
|
|
Object.defineProperty(this, "isAoss", {
|
||
|
|
enumerable: true,
|
||
|
|
configurable: true,
|
||
|
|
writable: true,
|
||
|
|
value: void 0
|
||
|
|
});
|
||
|
|
Object.defineProperty(this, "engine", {
|
||
|
|
enumerable: true,
|
||
|
|
configurable: true,
|
||
|
|
writable: true,
|
||
|
|
value: void 0
|
||
|
|
});
|
||
|
|
Object.defineProperty(this, "spaceType", {
|
||
|
|
enumerable: true,
|
||
|
|
configurable: true,
|
||
|
|
writable: true,
|
||
|
|
value: void 0
|
||
|
|
});
|
||
|
|
Object.defineProperty(this, "efConstruction", {
|
||
|
|
enumerable: true,
|
||
|
|
configurable: true,
|
||
|
|
writable: true,
|
||
|
|
value: void 0
|
||
|
|
});
|
||
|
|
Object.defineProperty(this, "efSearch", {
|
||
|
|
enumerable: true,
|
||
|
|
configurable: true,
|
||
|
|
writable: true,
|
||
|
|
value: void 0
|
||
|
|
});
|
||
|
|
Object.defineProperty(this, "numberOfShards", {
|
||
|
|
enumerable: true,
|
||
|
|
configurable: true,
|
||
|
|
writable: true,
|
||
|
|
value: void 0
|
||
|
|
});
|
||
|
|
Object.defineProperty(this, "numberOfReplicas", {
|
||
|
|
enumerable: true,
|
||
|
|
configurable: true,
|
||
|
|
writable: true,
|
||
|
|
value: void 0
|
||
|
|
});
|
||
|
|
Object.defineProperty(this, "m", {
|
||
|
|
enumerable: true,
|
||
|
|
configurable: true,
|
||
|
|
writable: true,
|
||
|
|
value: void 0
|
||
|
|
});
|
||
|
|
Object.defineProperty(this, "vectorFieldName", {
|
||
|
|
enumerable: true,
|
||
|
|
configurable: true,
|
||
|
|
writable: true,
|
||
|
|
value: void 0
|
||
|
|
});
|
||
|
|
Object.defineProperty(this, "textFieldName", {
|
||
|
|
enumerable: true,
|
||
|
|
configurable: true,
|
||
|
|
writable: true,
|
||
|
|
value: void 0
|
||
|
|
});
|
||
|
|
Object.defineProperty(this, "metadataFieldName", {
|
||
|
|
enumerable: true,
|
||
|
|
configurable: true,
|
||
|
|
writable: true,
|
||
|
|
value: void 0
|
||
|
|
});
|
||
|
|
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.numberOfShards = args.vectorSearchOptions?.numberOfShards ?? 5;
|
||
|
|
this.numberOfReplicas = args.vectorSearchOptions?.numberOfReplicas ?? 1;
|
||
|
|
this.vectorFieldName = args.vectorFieldName ?? "embedding";
|
||
|
|
this.textFieldName = args.textFieldName ?? "text";
|
||
|
|
this.metadataFieldName = args.metadataFieldName ?? "metadata";
|
||
|
|
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) {
|
||
|
|
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, documents, options) {
|
||
|
|
await this.ensureIndexExists(vectors[0].length, this.engine, this.spaceType, this.efSearch, this.efConstruction, this.numberOfShards, this.numberOfReplicas, 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 = [
|
||
|
|
{
|
||
|
|
index: {
|
||
|
|
_index: this.indexName,
|
||
|
|
_id: documentIds[idx],
|
||
|
|
},
|
||
|
|
},
|
||
|
|
{
|
||
|
|
[this.vectorFieldName]: embedding,
|
||
|
|
[this.textFieldName]: documents[idx].pageContent,
|
||
|
|
[this.metadataFieldName]: documents[idx].metadata,
|
||
|
|
},
|
||
|
|
];
|
||
|
|
// 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, k, filter) {
|
||
|
|
const search = {
|
||
|
|
index: this.indexName,
|
||
|
|
body: {
|
||
|
|
query: {
|
||
|
|
bool: {
|
||
|
|
filter: { bool: this.buildMetadataTerms(filter) },
|
||
|
|
must: [
|
||
|
|
{
|
||
|
|
knn: {
|
||
|
|
[this.vectorFieldName]: { 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) => [
|
||
|
|
new documents_1.Document({
|
||
|
|
pageContent: hit._source[this.textFieldName],
|
||
|
|
metadata: hit._source[this.metadataFieldName],
|
||
|
|
}),
|
||
|
|
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, metadatas, embeddings, args) {
|
||
|
|
const documents = texts.map((text, idx) => {
|
||
|
|
const metadata = Array.isArray(metadatas) ? metadatas[idx] : metadatas;
|
||
|
|
return new documents_1.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, embeddings, dbConfig) {
|
||
|
|
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, dbConfig) {
|
||
|
|
const store = new OpenSearchVectorStore(embeddings, dbConfig);
|
||
|
|
await store.client.cat.indices({ index: store.indexName });
|
||
|
|
return store;
|
||
|
|
}
|
||
|
|
async ensureIndexExists(dimension, engine = "nmslib", spaceType = "l2", efSearch = 512, efConstruction = 512, numberOfShards = 5, numberOfReplicas = 1, m = 16) {
|
||
|
|
const body = {
|
||
|
|
settings: {
|
||
|
|
index: {
|
||
|
|
number_of_shards: numberOfShards,
|
||
|
|
number_of_replicas: numberOfReplicas,
|
||
|
|
knn: true,
|
||
|
|
"knn.algo_param.ef_search": efSearch,
|
||
|
|
},
|
||
|
|
},
|
||
|
|
mappings: {
|
||
|
|
dynamic_templates: [
|
||
|
|
{
|
||
|
|
// map all metadata properties to be keyword
|
||
|
|
[`${this.metadataFieldName}.*`]: {
|
||
|
|
match_mapping_type: "string",
|
||
|
|
mapping: { type: "keyword" },
|
||
|
|
},
|
||
|
|
},
|
||
|
|
{
|
||
|
|
[`${this.metadataFieldName}.loc`]: {
|
||
|
|
match_mapping_type: "object",
|
||
|
|
mapping: { type: "object" },
|
||
|
|
},
|
||
|
|
},
|
||
|
|
],
|
||
|
|
properties: {
|
||
|
|
[this.textFieldName]: { type: "text" },
|
||
|
|
[this.metadataFieldName]: { type: "object" },
|
||
|
|
[this.vectorFieldName]: {
|
||
|
|
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) {
|
||
|
|
if (!filter)
|
||
|
|
return {};
|
||
|
|
const must = [];
|
||
|
|
const must_not = [];
|
||
|
|
for (const [key, value] of Object.entries(filter)) {
|
||
|
|
const metadataKey = `${this.metadataFieldName}.${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() {
|
||
|
|
try {
|
||
|
|
await this.client.cat.indices({ index: this.indexName });
|
||
|
|
return true;
|
||
|
|
}
|
||
|
|
catch (err) {
|
||
|
|
// eslint-disable-next-line no-instanceof/no-instanceof
|
||
|
|
if (err instanceof opensearch_1.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() {
|
||
|
|
const indexExists = await this.doesIndexExist();
|
||
|
|
if (!indexExists)
|
||
|
|
return;
|
||
|
|
await this.client.indices.delete({ index: this.indexName });
|
||
|
|
}
|
||
|
|
}
|
||
|
|
exports.OpenSearchVectorStore = OpenSearchVectorStore;
|