dtamind-components
Version:
DTAmindai Components
134 lines • 5.48 kB
JavaScript
;
Object.defineProperty(exports, "__esModule", { value: true });
const lodash_1 = require("lodash");
const opensearch_1 = require("@opensearch-project/opensearch");
const documents_1 = require("@langchain/core/documents");
const opensearch_2 = require("@langchain/community/vectorstores/opensearch");
const utils_1 = require("../../../src/utils");
class OpenSearch_VectorStores {
constructor() {
//@ts-ignore
this.vectorStoreMethods = {
async upsert(nodeData, options) {
const docs = nodeData.inputs?.document;
const embeddings = nodeData.inputs?.embeddings;
const indexName = nodeData.inputs?.indexName;
const credentialData = await (0, utils_1.getCredentialData)(nodeData.credential ?? '', options);
const opensearchURL = (0, utils_1.getCredentialParam)('openSearchUrl', credentialData, nodeData);
const user = (0, utils_1.getCredentialParam)('user', credentialData, nodeData);
const password = (0, utils_1.getCredentialParam)('password', credentialData, nodeData);
const client = getOpenSearchClient(opensearchURL, user, password);
const flattenDocs = docs && docs.length ? (0, lodash_1.flatten)(docs) : [];
const finalDocs = [];
for (let i = 0; i < flattenDocs.length; i += 1) {
if (flattenDocs[i] && flattenDocs[i].pageContent) {
finalDocs.push(new documents_1.Document(flattenDocs[i]));
}
}
try {
await opensearch_2.OpenSearchVectorStore.fromDocuments(finalDocs, embeddings, {
client,
indexName: indexName
});
return { numAdded: finalDocs.length, addedDocs: finalDocs };
}
catch (e) {
throw new Error(e);
}
}
};
this.label = 'OpenSearch';
this.name = 'openSearch';
this.version = 3.0;
this.type = 'OpenSearch';
this.icon = 'opensearch.svg';
this.category = 'Vector Stores';
this.description = `Upsert embedded data and perform similarity search upon query using OpenSearch, an open-source, all-in-one vector database`;
this.baseClasses = [this.type, 'VectorStoreRetriever', 'BaseRetriever'];
this.credential = {
label: 'Connect Credential',
name: 'credential',
type: 'credential',
credentialNames: ['openSearchUrl']
};
this.inputs = [
{
label: 'Document',
name: 'document',
type: 'Document',
list: true,
optional: true
},
{
label: 'Embeddings',
name: 'embeddings',
type: 'Embeddings'
},
{
label: 'Index Name',
name: 'indexName',
type: 'string'
},
{
label: 'Top K',
name: 'topK',
description: 'Number of top results to fetch. Default to 4',
placeholder: '4',
type: 'number',
additionalParams: true,
optional: true
}
];
this.outputs = [
{
label: 'OpenSearch Retriever',
name: 'retriever',
baseClasses: this.baseClasses
},
{
label: 'OpenSearch Vector Store',
name: 'vectorStore',
baseClasses: [this.type, ...(0, utils_1.getBaseClasses)(opensearch_2.OpenSearchVectorStore)]
}
];
}
async init(nodeData, _, options) {
const embeddings = nodeData.inputs?.embeddings;
const indexName = nodeData.inputs?.indexName;
const output = nodeData.outputs?.output;
const topK = nodeData.inputs?.topK;
const k = topK ? parseFloat(topK) : 4;
const credentialData = await (0, utils_1.getCredentialData)(nodeData.credential ?? '', options);
const opensearchURL = (0, utils_1.getCredentialParam)('openSearchUrl', credentialData, nodeData);
const user = (0, utils_1.getCredentialParam)('user', credentialData, nodeData);
const password = (0, utils_1.getCredentialParam)('password', credentialData, nodeData);
const client = getOpenSearchClient(opensearchURL, user, password);
const vectorStore = new opensearch_2.OpenSearchVectorStore(embeddings, {
client,
indexName
});
if (output === 'retriever') {
const retriever = vectorStore.asRetriever(k);
return retriever;
}
else if (output === 'vectorStore') {
;
vectorStore.k = k;
return vectorStore;
}
return vectorStore;
}
}
const getOpenSearchClient = (url, user, password) => {
if (user && password) {
const urlObj = new URL(url);
urlObj.username = user;
urlObj.password = password;
url = urlObj.toString();
}
return new opensearch_1.Client({
nodes: [url]
});
};
module.exports = { nodeClass: OpenSearch_VectorStores };
//# sourceMappingURL=OpenSearch.js.map