dtamind-components
Version:
DTAmindai Components
100 lines • 3.86 kB
JavaScript
;
Object.defineProperty(exports, "__esModule", { value: true });
const lodash_1 = require("lodash");
const memory_1 = require("langchain/vectorstores/memory");
const documents_1 = require("@langchain/core/documents");
const utils_1 = require("../../../src/utils");
class InMemoryVectorStore_VectorStores {
constructor() {
//@ts-ignore
this.vectorStoreMethods = {
async upsert(nodeData) {
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 {
await memory_1.MemoryVectorStore.fromDocuments(finalDocs, embeddings);
return { numAdded: finalDocs.length, addedDocs: finalDocs };
}
catch (e) {
throw new Error(e);
}
}
};
this.label = 'In-Memory Vector Store';
this.name = 'memoryVectorStore';
this.version = 1.0;
this.type = 'Memory';
this.icon = 'memory.svg';
this.category = 'Vector Stores';
this.description = 'In-memory vectorstore that stores embeddings and does an exact, linear search for the most similar embeddings.';
this.baseClasses = [this.type, 'VectorStoreRetriever', 'BaseRetriever'];
this.inputs = [
{
label: 'Document',
name: 'document',
type: 'Document',
list: true,
optional: true
},
{
label: 'Embeddings',
name: 'embeddings',
type: 'Embeddings'
},
{
label: 'Top K',
name: 'topK',
description: 'Number of top results to fetch. Default to 4',
placeholder: '4',
type: 'number',
optional: true
}
];
this.outputs = [
{
label: 'Memory Retriever',
name: 'retriever',
baseClasses: this.baseClasses
},
{
label: 'Memory Vector Store',
name: 'vectorStore',
baseClasses: [this.type, ...(0, utils_1.getBaseClasses)(memory_1.MemoryVectorStore)]
}
];
}
async init(nodeData) {
const docs = nodeData.inputs?.document;
const embeddings = nodeData.inputs?.embeddings;
const output = nodeData.outputs?.output;
const topK = nodeData.inputs?.topK;
const k = topK ? parseFloat(topK) : 4;
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]));
}
}
const vectorStore = await memory_1.MemoryVectorStore.fromDocuments(finalDocs, embeddings);
if (output === 'retriever') {
const retriever = vectorStore.asRetriever(k);
return retriever;
}
else if (output === 'vectorStore') {
;
vectorStore.k = k;
return vectorStore;
}
return vectorStore;
}
}
module.exports = { nodeClass: InMemoryVectorStore_VectorStores };
//# sourceMappingURL=InMemoryVectorStore.js.map