dtamind-components
Version:
Apps integration for Dtamind. Contain Nodes and Credentials.
135 lines • 5.43 kB
JavaScript
;
Object.defineProperty(exports, "__esModule", { value: true });
const lodash_1 = require("lodash");
const zep_cloud_1 = require("@getzep/zep-cloud");
const langchain_1 = require("@getzep/zep-cloud/langchain");
const document_1 = require("langchain/document");
const utils_1 = require("../../../src/utils");
const VectorStoreUtils_1 = require("../VectorStoreUtils");
const fake_1 = require("langchain/embeddings/fake");
class Zep_CloudVectorStores {
constructor() {
//@ts-ignore
this.vectorStoreMethods = {
async upsert(nodeData, options) {
const zepCollection = nodeData.inputs?.zepCollection;
const docs = nodeData.inputs?.document;
const credentialData = await (0, utils_1.getCredentialData)(nodeData.credential ?? '', options);
const apiKey = (0, utils_1.getCredentialParam)('apiKey', credentialData, nodeData);
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 document_1.Document(flattenDocs[i]));
}
}
const client = new zep_cloud_1.ZepClient({
apiKey: apiKey
});
const zepConfig = {
apiKey: apiKey,
collectionName: zepCollection,
client
};
try {
await langchain_1.ZepVectorStore.fromDocuments(finalDocs, new fake_1.FakeEmbeddings(), zepConfig);
return { numAdded: finalDocs.length, addedDocs: finalDocs };
}
catch (e) {
throw new Error(e);
}
}
};
this.label = 'Zep Collection - Cloud';
this.name = 'zepCloud';
this.version = 2.0;
this.type = 'Zep';
this.icon = 'zep.svg';
this.category = 'Vector Stores';
this.description =
'Upsert embedded data and perform similarity or mmr search upon query using Zep, a fast and scalable building block for LLM apps';
this.baseClasses = [this.type, 'VectorStoreRetriever', 'BaseRetriever'];
this.credential = {
label: 'Connect Credential',
name: 'credential',
type: 'credential',
optional: false,
description: 'Configure JWT authentication on your Zep instance (Optional)',
credentialNames: ['zepMemoryApi']
};
this.inputs = [
{
label: 'Document',
name: 'document',
type: 'Document',
list: true,
optional: true
},
{
label: 'Zep Collection',
name: 'zepCollection',
type: 'string',
placeholder: 'my-first-collection'
},
{
label: 'Zep Metadata Filter',
name: 'zepMetadataFilter',
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: 'Zep Retriever',
name: 'retriever',
baseClasses: this.baseClasses
},
{
label: 'Zep Vector Store',
name: 'vectorStore',
baseClasses: [this.type, ...(0, utils_1.getBaseClasses)(langchain_1.ZepVectorStore)]
}
];
}
async init(nodeData, _, options) {
const zepCollection = nodeData.inputs?.zepCollection;
const zepMetadataFilter = nodeData.inputs?.zepMetadataFilter;
const credentialData = await (0, utils_1.getCredentialData)(nodeData.credential ?? '', options);
const apiKey = (0, utils_1.getCredentialParam)('apiKey', credentialData, nodeData);
const zepConfig = {
apiKey,
collectionName: zepCollection
};
if (zepMetadataFilter) {
zepConfig.filter = typeof zepMetadataFilter === 'object' ? zepMetadataFilter : JSON.parse(zepMetadataFilter);
}
zepConfig.client = new zep_cloud_1.ZepClient({
apiKey: apiKey
});
const vectorStore = await ZepExistingVS.init(zepConfig);
return (0, VectorStoreUtils_1.resolveVectorStoreOrRetriever)(nodeData, vectorStore, zepConfig.filter);
}
}
class ZepExistingVS extends langchain_1.ZepVectorStore {
constructor(embeddings, args) {
super(embeddings, args);
this.filter = args.filter;
this.args = args;
}
static async fromExistingIndex(embeddings, dbConfig) {
return new this(embeddings, dbConfig);
}
}
module.exports = { nodeClass: Zep_CloudVectorStores };
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