dtamind-components
Version:
DTAmindai Components
131 lines • 5.41 kB
JavaScript
;
var __importDefault = (this && this.__importDefault) || function (mod) {
return (mod && mod.__esModule) ? mod : { "default": mod };
};
Object.defineProperty(exports, "__esModule", { value: true });
const path_1 = __importDefault(require("path"));
const lodash_1 = require("lodash");
const llamaindex_1 = require("llamaindex");
const document_1 = require("langchain/document");
const src_1 = require("../../../src");
class SimpleStoreUpsert_LlamaIndex_VectorStores {
constructor() {
//@ts-ignore
this.vectorStoreMethods = {
async upsert(nodeData) {
const basePath = nodeData.inputs?.basePath;
const docs = nodeData.inputs?.document;
const embeddings = nodeData.inputs?.embeddings;
const model = nodeData.inputs?.model;
let filePath = '';
if (!basePath)
filePath = path_1.default.join((0, src_1.getUserHome)(), '.flowise', 'llamaindex');
else
filePath = basePath;
const flattenDocs = docs && docs.length ? (0, lodash_1.flatten)(docs) : [];
const finalDocs = [];
for (let i = 0; i < flattenDocs.length; i += 1) {
finalDocs.push(new document_1.Document(flattenDocs[i]));
}
const llamadocs = [];
for (const doc of finalDocs) {
llamadocs.push(new llamaindex_1.Document({ text: doc.pageContent, metadata: doc.metadata }));
}
const serviceContext = (0, llamaindex_1.serviceContextFromDefaults)({ llm: model, embedModel: embeddings });
const storageContext = await (0, llamaindex_1.storageContextFromDefaults)({ persistDir: filePath });
try {
await llamaindex_1.VectorStoreIndex.fromDocuments(llamadocs, { serviceContext, storageContext });
return { numAdded: finalDocs.length, addedDocs: finalDocs };
}
catch (e) {
throw new Error(e);
}
}
};
this.label = 'SimpleStore';
this.name = 'simpleStoreLlamaIndex';
this.version = 1.0;
this.type = 'SimpleVectorStore';
this.icon = 'simplevs.svg';
this.category = 'Vector Stores';
this.description = 'Upsert embedded data to local path and perform similarity search';
this.baseClasses = [this.type, 'VectorIndexRetriever'];
this.tags = ['LlamaIndex'];
this.inputs = [
{
label: 'Document',
name: 'document',
type: 'Document',
list: true,
optional: true
},
{
label: 'Chat Model',
name: 'model',
type: 'BaseChatModel_LlamaIndex'
},
{
label: 'Embeddings',
name: 'embeddings',
type: 'BaseEmbedding_LlamaIndex'
},
{
label: 'Base Path to store',
name: 'basePath',
description: 'Path to store persist embeddings indexes with persistence. If not specified, default to same path where database is stored',
type: 'string',
optional: true
},
{
label: 'Top K',
name: 'topK',
description: 'Number of top results to fetch. Default to 4',
placeholder: '4',
type: 'number',
optional: true
}
];
this.outputs = [
{
label: 'SimpleStore Retriever',
name: 'retriever',
baseClasses: this.baseClasses
},
{
label: 'SimpleStore Vector Store Index',
name: 'vectorStore',
baseClasses: [this.type, 'VectorStoreIndex']
}
];
}
async init(nodeData) {
const basePath = nodeData.inputs?.basePath;
const embeddings = nodeData.inputs?.embeddings;
const model = nodeData.inputs?.model;
const topK = nodeData.inputs?.topK;
const k = topK ? parseFloat(topK) : 4;
let filePath = '';
if (!basePath)
filePath = path_1.default.join((0, src_1.getUserHome)(), '.flowise', 'llamaindex');
else
filePath = basePath;
const serviceContext = (0, llamaindex_1.serviceContextFromDefaults)({ llm: model, embedModel: embeddings });
const storageContext = await (0, llamaindex_1.storageContextFromDefaults)({ persistDir: filePath });
const index = await llamaindex_1.VectorStoreIndex.init({ storageContext, serviceContext });
const output = nodeData.outputs?.output;
if (output === 'retriever') {
const retriever = index.asRetriever();
retriever.similarityTopK = k;
retriever.serviceContext = serviceContext;
return retriever;
}
else if (output === 'vectorStore') {
;
index.k = k;
return index;
}
return index;
}
}
module.exports = { nodeClass: SimpleStoreUpsert_LlamaIndex_VectorStores };
//# sourceMappingURL=SimpleStore.js.map