dtamind-components
Version:
Apps integration for Dtamind. Contain Nodes and Credentials.
163 lines • 7.05 kB
JavaScript
;
Object.defineProperty(exports, "__esModule", { value: true });
const lodash_1 = require("lodash");
const documents_1 = require("@langchain/core/documents");
const astradb_1 = require("@langchain/community/vectorstores/astradb");
const utils_1 = require("../../../src/utils");
const VectorStoreUtils_1 = require("../VectorStoreUtils");
class Astra_VectorStores {
constructor() {
//@ts-ignore
this.vectorStoreMethods = {
async upsert(nodeData, options) {
const docs = nodeData.inputs?.document;
const embeddings = nodeData.inputs?.embeddings;
const vectorDimension = nodeData.inputs?.vectorDimension;
const astraNamespace = nodeData.inputs?.astraNamespace;
const astraCollection = nodeData.inputs?.astraCollection;
const similarityMetric = nodeData.inputs?.similarityMetric;
const credentialData = await (0, utils_1.getCredentialData)(nodeData.credential ?? '', options);
const expectedSimilarityMetric = ['cosine', 'euclidean', 'dot_product'];
if (similarityMetric && !expectedSimilarityMetric.includes(similarityMetric)) {
throw new Error(`Invalid Similarity Metric should be one of 'cosine' | 'euclidean' | 'dot_product'`);
}
const clientConfig = {
token: credentialData?.applicationToken,
endpoint: credentialData?.dbEndPoint
};
const astraConfig = {
...clientConfig,
namespace: astraNamespace ?? 'default_keyspace',
collection: astraCollection ?? credentialData.collectionName ?? 'dtamind_test',
collectionOptions: {
vector: {
dimension: vectorDimension ?? 1536,
metric: similarityMetric ?? 'cosine'
}
}
};
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 astradb_1.AstraDBVectorStore.fromDocuments(finalDocs, embeddings, astraConfig);
return { numAdded: finalDocs.length, addedDocs: finalDocs };
}
catch (e) {
throw new Error(e);
}
}
};
this.label = 'Astra';
this.name = 'Astra';
this.version = 2.0;
this.type = 'Astra';
this.icon = 'astra.svg';
this.category = 'Vector Stores';
this.description = `Upsert embedded data and perform similarity or mmr search upon query using DataStax Astra DB, a serverless vector database that’s perfect for managing mission-critical AI workloads`;
this.baseClasses = [this.type, 'VectorStoreRetriever', 'BaseRetriever'];
this.credential = {
label: 'Connect Credential',
name: 'credential',
type: 'credential',
credentialNames: ['AstraDBApi']
};
this.inputs = [
{
label: 'Document',
name: 'document',
type: 'Document',
list: true,
optional: true
},
{
label: 'Embeddings',
name: 'embeddings',
type: 'Embeddings'
},
{
label: 'Namespace',
name: 'astraNamespace',
type: 'string'
},
{
label: 'Collection',
name: 'astraCollection',
type: 'string'
},
{
label: 'Vector Dimension',
name: 'vectorDimension',
type: 'number',
placeholder: '1536',
optional: true,
description: 'Dimension used for storing vector embedding'
},
{
label: 'Similarity Metric',
name: 'similarityMetric',
type: 'string',
placeholder: 'cosine',
optional: true,
description: 'cosine | euclidean | dot_product'
},
{
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: 'Astra Retriever',
name: 'retriever',
baseClasses: this.baseClasses
},
{
label: 'Astra Vector Store',
name: 'vectorStore',
baseClasses: [this.type, ...(0, utils_1.getBaseClasses)(astradb_1.AstraDBVectorStore)]
}
];
}
async init(nodeData, _, options) {
const embeddings = nodeData.inputs?.embeddings;
const vectorDimension = nodeData.inputs?.vectorDimension;
const similarityMetric = nodeData.inputs?.similarityMetric;
const astraNamespace = nodeData.inputs?.astraNamespace;
const astraCollection = nodeData.inputs?.astraCollection;
const credentialData = await (0, utils_1.getCredentialData)(nodeData.credential ?? '', options);
const expectedSimilarityMetric = ['cosine', 'euclidean', 'dot_product'];
if (similarityMetric && !expectedSimilarityMetric.includes(similarityMetric)) {
throw new Error(`Invalid Similarity Metric should be one of 'cosine' | 'euclidean' | 'dot_product'`);
}
const clientConfig = {
token: credentialData?.applicationToken,
endpoint: credentialData?.dbEndPoint
};
const astraConfig = {
...clientConfig,
namespace: astraNamespace ?? 'default_keyspace',
collection: astraCollection ?? credentialData.collectionName ?? 'dtamind_test',
collectionOptions: {
vector: {
dimension: vectorDimension ?? 1536,
metric: similarityMetric ?? 'cosine'
}
}
};
const vectorStore = await astradb_1.AstraDBVectorStore.fromExistingIndex(embeddings, astraConfig);
return (0, VectorStoreUtils_1.resolveVectorStoreOrRetriever)(nodeData, vectorStore);
}
}
module.exports = { nodeClass: Astra_VectorStores };
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