Create a vector RAG chatbot

The flows are ready to use. Continue the tutorial to learn how to use the loading flow to load data into your vector store and then call the chat flow in a chatbot application.

Load data and generate embeddings

To load data and generate embeddings, you can use the visual editor or the /v2/files endpoint.

The visual editor option is simpler, but it is only recommended for scenarios where the user who created the flow is the same user who loads data into the database.

In situations where many users load data or you need to load data programmatically, use the Robility flow API option.

a. Visual editor

b. Robility flow API

1. In your RAG chatbot flow, click the File component, and then click File.
2. Select the local file you want to upload, and then click Open. The file is loaded to your Robility flow server.
3. To load the data into your vector store, click the Vector Store component, and then click Run component to run the selected component and all prior dependent components.

When the flow runs, the flow ingests the selected file, chunks the data, loads the data into the vector store database, and then generates embeddings for the chunks, which are also stored in the vector store.

Your database now contains data with vector embeddings that an LLM can use as context to respond to queries, as demonstrated in the next section of the tutorial.

Chat with your flow from a JavaScript application

To chat with the data in your vector database, create a chatbot application that runs the Retriever Flow programmatically.

This tutorial uses JavaScript for demonstration purposes.

1. To construct the chatbot, gather the following information:

a. ROBILITY FLOW _SERVER_ADDRESS: Your Robility flow server’s domain. The default value is 127.0.0.1:7860. You can get this value from the code snippets on your flow’s API access pane. 
b. FLOW_ID: Your flow’s UUID or custom endpoint name. You can get this value from the code snippets on your flow’s API access pane. 
c. ROBILITY FLOW _API_KEY: A valid Robility flow API key.

2. Copy the following script into a JavaScript file, and then replace the placeholders with the information you gathered in the previous step:

const readline = require(‘readline’);

const { Robility flow Client } = require(‘@datastax/Robility flow -client’);

const API_KEY = ‘ROBILITY FLOW _API_KEY’;

const SERVER = ‘ROBILITY FLOW _SERVER_ADDRESS’;

const FLOW_ID = ‘FLOW_ID’;

const rl = readline.createInterface({ input: process.stdin, output: process.stdout });

// Initialize the Robility flow  client

const client = new Robility flow Client({

    baseUrl: SERVER,

    apiKey: API_KEY

});

async function sendMessage(message) {

    try {

        const response = await client.flow(FLOW_ID).run(message, {

            session_id: ‘user_1’

        });

        // Use the convenience method to get the chat output text

        return response.chatOutputText() || ‘No response’;

    } catch (error) {

        return `Error: ${error.message}`;

    }

}

function chat() {

    console.log(‘🤖 Robility flow  RAG Chatbot (type “quit” to exit)\n’);

    const ask = () => {

        rl.question(‘👤 You: ‘, async (input) => {

            if ([‘quit’, ‘exit’, ‘bye’].includes(input.trim().toLowerCase())) {

                console.log(‘👋 Goodbye!’);

                rl.close();

                return;

            }

            const response = await sendMessage(input.trim());

            console.log(`🤖 Assistant: ${response}\n`);

            ask();

        });

    };

    ask();

}

chat();

The script creates a Node application that chats with the content in your vector database, using the chat input and output types to communicate with your flow. Chat maintains ongoing conversation context across multiple messages. If you used text type inputs and outputs, each request is a standalone text string.