The .query() method empowers developers to ask questions and receive relevant answers through a user-friendly query API. Function signature is given below:

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Parameters

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userQuery
string
required
Question to ask
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conversationId
string
The conversation ID to which the query belongs in case this is a chatbot scenario
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customContext
Chunk[]
In case you want to pass along your own context information. Passing this parameter disables the default context generation.

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Returns

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id
string
Unique identifier for the query response in a conversation
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timestamp
Date
Unique identifier for the query response in a conversation
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content
string
Unique identifier for the query response in a conversation
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sources
SourceDetail[]
The individual sources that were used to generate the answer
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tokenUse
object
The number of tokens used to answer the query

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Usage

If you want to get the answer to question and return both answer and citations, use the following code snippet:
Code Example
import { RAGApplicationBuilder } from '@llm-tools/embedjs';
import { OllamaEmbeddings } from '@llm-tools/embedjs-ollama';
import { WebLoader } from '@llm-tools/embedjs-loader-web';
import { HNSWDb } from '@llm-tools/embedjs-hnswlib';

const ragApplication = await new RAGApplicationBuilder()
.setModel(new Ollama({ modelName: "llama3.2", baseUrl: 'http://localhost:11434' }))
.setEmbeddingModel(new OllamaEmbeddings({ model: 'nomic-embed-text', baseUrl: 'http://localhost:11434' }))
.setVectorDatabase(new HNSWDb())
.build();

ragApplication.addLoader({ urlOrContent: 'https://www.forbes.com/profile/elon-musk' })
ragApplication.addLoader({ urlOrContent: 'https://en.wikipedia.org/wiki/Elon_Musk' })

await ragApplication.query('What is the net worth of Elon Musk today?')
await ragApplication.query('Who is Elon Musk?' { conversationId: '1' })
/*

*/