dtamind-components
Version:
DTAmindai Components
176 lines • 7.34 kB
JavaScript
;
Object.defineProperty(exports, "__esModule", { value: true });
const lodash_1 = require("lodash");
const singlestore_1 = require("@langchain/community/vectorstores/singlestore");
const documents_1 = require("@langchain/core/documents");
const utils_1 = require("../../../src/utils");
class SingleStore_VectorStores {
constructor() {
//@ts-ignore
this.vectorStoreMethods = {
async upsert(nodeData, options) {
const credentialData = await (0, utils_1.getCredentialData)(nodeData.credential ?? '', options);
const user = (0, utils_1.getCredentialParam)('user', credentialData, nodeData);
const password = (0, utils_1.getCredentialParam)('password', credentialData, nodeData);
const singleStoreConnectionConfig = {
connectionOptions: {
host: nodeData.inputs?.host,
port: 3306,
user,
password,
database: nodeData.inputs?.database
},
...(nodeData.inputs?.tableName ? { tableName: nodeData.inputs.tableName } : {}),
...(nodeData.inputs?.contentColumnName ? { contentColumnName: nodeData.inputs.contentColumnName } : {}),
...(nodeData.inputs?.vectorColumnName ? { vectorColumnName: nodeData.inputs.vectorColumnName } : {}),
...(nodeData.inputs?.metadataColumnName ? { metadataColumnName: nodeData.inputs.metadataColumnName } : {})
};
const docs = nodeData.inputs?.document;
const embeddings = nodeData.inputs?.embeddings;
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 {
const vectorStore = new singlestore_1.SingleStoreVectorStore(embeddings, singleStoreConnectionConfig);
vectorStore.addDocuments.bind(vectorStore)(finalDocs);
return { numAdded: finalDocs.length, addedDocs: finalDocs };
}
catch (e) {
throw new Error(e);
}
}
};
this.label = 'SingleStore';
this.name = 'singlestore';
this.version = 1.0;
this.type = 'SingleStore';
this.icon = 'singlestore.svg';
this.category = 'Vector Stores';
this.description =
'Upsert embedded data and perform similarity search upon query using SingleStore, a fast and distributed cloud relational database';
this.baseClasses = [this.type, 'VectorStoreRetriever', 'BaseRetriever'];
this.credential = {
label: 'Connect Credential',
name: 'credential',
type: 'credential',
description: 'Needed when using SingleStore cloud hosted',
optional: true,
credentialNames: ['singleStoreApi']
};
this.inputs = [
{
label: 'Document',
name: 'document',
type: 'Document',
list: true,
optional: true
},
{
label: 'Embeddings',
name: 'embeddings',
type: 'Embeddings'
},
{
label: 'Host',
name: 'host',
type: 'string'
},
{
label: 'Database',
name: 'database',
type: 'string'
},
{
label: 'Table Name',
name: 'tableName',
type: 'string',
placeholder: 'embeddings',
additionalParams: true,
optional: true
},
{
label: 'Content Column Name',
name: 'contentColumnName',
type: 'string',
placeholder: 'content',
additionalParams: true,
optional: true
},
{
label: 'Vector Column Name',
name: 'vectorColumnName',
type: 'string',
placeholder: 'vector',
additionalParams: true,
optional: true
},
{
label: 'Metadata Column Name',
name: 'metadataColumnName',
type: 'string',
placeholder: 'metadata',
additionalParams: true,
optional: true
},
{
label: 'Top K',
name: 'topK',
placeholder: '4',
type: 'number',
additionalParams: true,
optional: true
}
];
this.outputs = [
{
label: 'SingleStore Retriever',
name: 'retriever',
baseClasses: this.baseClasses
},
{
label: 'SingleStore Vector Store',
name: 'vectorStore',
baseClasses: [this.type, ...(0, utils_1.getBaseClasses)(singlestore_1.SingleStoreVectorStore)]
}
];
}
async init(nodeData, _, options) {
const credentialData = await (0, utils_1.getCredentialData)(nodeData.credential ?? '', options);
const user = (0, utils_1.getCredentialParam)('user', credentialData, nodeData);
const password = (0, utils_1.getCredentialParam)('password', credentialData, nodeData);
const singleStoreConnectionConfig = {
connectionOptions: {
host: nodeData.inputs?.host,
port: 3306,
user,
password,
database: nodeData.inputs?.database
},
...(nodeData.inputs?.tableName ? { tableName: nodeData.inputs.tableName } : {}),
...(nodeData.inputs?.contentColumnName ? { contentColumnName: nodeData.inputs.contentColumnName } : {}),
...(nodeData.inputs?.vectorColumnName ? { vectorColumnName: nodeData.inputs.vectorColumnName } : {}),
...(nodeData.inputs?.metadataColumnName ? { metadataColumnName: nodeData.inputs.metadataColumnName } : {})
};
const embeddings = nodeData.inputs?.embeddings;
const output = nodeData.outputs?.output;
const topK = nodeData.inputs?.topK;
const k = topK ? parseFloat(topK) : 4;
const vectorStore = new singlestore_1.SingleStoreVectorStore(embeddings, singleStoreConnectionConfig);
if (output === 'retriever') {
const retriever = vectorStore.asRetriever(k);
return retriever;
}
else if (output === 'vectorStore') {
;
vectorStore.k = k;
return vectorStore;
}
return vectorStore;
}
}
module.exports = { nodeClass: SingleStore_VectorStores };
//# sourceMappingURL=Singlestore.js.map