HNSWLib is an high performance in-memory vectorstore. It is great for testing since you do not need access to the file system or a cloud service.

​
Install HNSWLib addon

npm install @llm-tools/embedjs-hnswlib

​
Usage

import { RAGApplicationBuilder } from '@llm-tools/embedjs';

import { OpenAiEmbeddings } from '@llm-tools/embedjs-openai';

import { HNSWDb } from '@llm-tools/embedjs-hnswlib';

import { WebLoader } from '@llm-tools/embedjs-loader-web';



// set OPENAI_API_KEY in your env

process.env.OPENAI_API_KEY = "sk-xxx";



const app = await new RAGApplicationBuilder()

.setEmbeddingModel(new OpenAiEmbeddings())

.setModel(SIMPLE_MODELS.OPENAI_GPT4_O)

.setVectorDatabase(new HNSWDb())

.build();



//add data source and start query it

await app.addLoader(new WebLoader({ urlOrContent: 'https://www.forbes.com/profile/elon-musk' }));

await app.query('Tell me about Elon Musk');

If you can't find specific feature or run into issues, please feel free to reach out through the following channels.

GitHub

Open an issue on our GitHub

Was this page helpful?