dtamind-components
Version:
DTAmindai Components
185 lines • 7.73 kB
JavaScript
;
Object.defineProperty(exports, "__esModule", { value: true });
const lodash_1 = require("lodash");
const documents_1 = require("@langchain/core/documents");
const utils_1 = require("../../../src/utils");
const VectorStoreUtils_1 = require("../VectorStoreUtils");
const core_1 = require("./core");
// TODO: Add ability to specify env variable and use singleton pattern (i.e initialize MongoDB on server and pass to component)
class MongoDBAtlas_VectorStores {
constructor() {
//@ts-ignore
this.vectorStoreMethods = {
async upsert(nodeData, options) {
const credentialData = await (0, utils_1.getCredentialData)(nodeData.credential ?? '', options);
const databaseName = nodeData.inputs?.databaseName;
const collectionName = nodeData.inputs?.collectionName;
const indexName = nodeData.inputs?.indexName;
let textKey = nodeData.inputs?.textKey;
let embeddingKey = nodeData.inputs?.embeddingKey;
const embeddings = nodeData.inputs?.embeddings;
let mongoDBConnectUrl = (0, utils_1.getCredentialParam)('mongoDBConnectUrl', credentialData, nodeData);
const docs = nodeData.inputs?.document;
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) {
const document = new documents_1.Document(flattenDocs[i]);
finalDocs.push(document);
}
}
try {
if (!textKey || textKey === '')
textKey = 'text';
if (!embeddingKey || embeddingKey === '')
embeddingKey = 'embedding';
const mongoDBAtlasVectorSearch = new core_1.MongoDBAtlasVectorSearch(embeddings, {
connectionDetails: { mongoDBConnectUrl, databaseName, collectionName },
indexName,
textKey,
embeddingKey
});
await mongoDBAtlasVectorSearch.addDocuments(finalDocs);
return { numAdded: finalDocs.length, addedDocs: finalDocs };
}
catch (e) {
throw new Error(e);
}
}
};
this.label = 'MongoDB Atlas';
this.name = 'mongoDBAtlas';
this.version = 1.0;
this.description = `Upsert embedded data and perform similarity or mmr search upon query using MongoDB Atlas, a managed cloud mongodb database`;
this.type = 'MongoDB Atlas';
this.icon = 'mongodb.svg';
this.category = 'Vector Stores';
this.baseClasses = [this.type, 'VectorStoreRetriever', 'BaseRetriever'];
this.credential = {
label: 'Connect Credential',
name: 'credential',
type: 'credential',
credentialNames: ['mongoDBUrlApi']
};
this.inputs = [
{
label: 'Document',
name: 'document',
type: 'Document',
list: true,
optional: true
},
{
label: 'Embeddings',
name: 'embeddings',
type: 'Embeddings'
},
{
label: 'Database',
name: 'databaseName',
placeholder: '<DB_NAME>',
type: 'string'
},
{
label: 'Collection Name',
name: 'collectionName',
placeholder: '<COLLECTION_NAME>',
type: 'string'
},
{
label: 'Index Name',
name: 'indexName',
placeholder: '<VECTOR_INDEX_NAME>',
type: 'string'
},
{
label: 'Content Field',
name: 'textKey',
description: 'Name of the field (column) that contains the actual content',
type: 'string',
default: 'text',
additionalParams: true,
optional: true
},
{
label: 'Embedded Field',
name: 'embeddingKey',
description: 'Name of the field (column) that contains the Embedding',
type: 'string',
default: 'embedding',
additionalParams: true,
optional: true
},
{
label: 'Mongodb Metadata Filter',
name: 'mongoMetadataFilter',
type: 'json',
optional: true,
additionalParams: true
},
{
label: 'Top K',
name: 'topK',
description: 'Number of top results to fetch. Default to 4',
placeholder: '4',
type: 'number',
additionalParams: true,
optional: true
}
];
(0, VectorStoreUtils_1.addMMRInputParams)(this.inputs);
this.outputs = [
{
label: 'MongoDB Retriever',
name: 'retriever',
baseClasses: this.baseClasses
},
{
label: 'MongoDB Vector Store',
name: 'vectorStore',
baseClasses: [this.type, ...(0, utils_1.getBaseClasses)(core_1.MongoDBAtlasVectorSearch)]
}
];
}
async init(nodeData, _, options) {
const credentialData = await (0, utils_1.getCredentialData)(nodeData.credential ?? '', options);
const databaseName = nodeData.inputs?.databaseName;
const collectionName = nodeData.inputs?.collectionName;
const indexName = nodeData.inputs?.indexName;
let textKey = nodeData.inputs?.textKey;
let embeddingKey = nodeData.inputs?.embeddingKey;
const embeddings = nodeData.inputs?.embeddings;
const mongoMetadataFilter = nodeData.inputs?.mongoMetadataFilter;
let mongoDBConnectUrl = (0, utils_1.getCredentialParam)('mongoDBConnectUrl', credentialData, nodeData);
const mongoDbFilter = {};
try {
if (!textKey || textKey === '')
textKey = 'text';
if (!embeddingKey || embeddingKey === '')
embeddingKey = 'embedding';
const vectorStore = new core_1.MongoDBAtlasVectorSearch(embeddings, {
connectionDetails: { mongoDBConnectUrl, databaseName, collectionName },
indexName,
textKey,
embeddingKey
});
if (mongoMetadataFilter) {
const metadataFilter = typeof mongoMetadataFilter === 'object' ? mongoMetadataFilter : JSON.parse(mongoMetadataFilter);
for (const key in metadataFilter) {
mongoDbFilter.preFilter = {
...mongoDbFilter.preFilter,
[key]: {
$eq: metadataFilter[key]
}
};
}
}
return (0, VectorStoreUtils_1.resolveVectorStoreOrRetriever)(nodeData, vectorStore, mongoDbFilter);
}
catch (e) {
throw new Error(e);
}
}
}
module.exports = { nodeClass: MongoDBAtlas_VectorStores };
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