Introduction

  • Rivalz is not only a storage service but also a powerful AI platform that can help you to extract knowledge from your documents.
  • The Vectorization process is the process of converting a document into a vector representation. This vector representation is a numerical representation of the document that can be used by machine learning models to perform various tasks such as classification, clustering, and similarity search. vectoriz
  • In Rivalz, we provide a simple and easy-to-use API to vectorize your documents end embed them into an AI model Which create a custom ‘knowledge base’ for your organization.
  • For this version we only support PDF documents

Create your first knowledge base

Prerequisites

  • Saving vector data is a costly operation, so you need credits to create a knowledge base. Don’t worry! We provide you with 500 USD free credits when you create an account. Claim your free credits now by visiting the Rivalz console. And click on the Create credit button. In Profile tab.

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  • Prepare your document in PDF format.
  • Also, you need to have an API secret key to authenticate your request. If you don’t have one, please refer to the Authenticate and secret keys section to get one.

Create a knowledge base

  • Knowledge base is a collection of information that the chat agent can use to answer questions and provide assistance to users.
  • Use our SDKs to create a knowledge base.

Setup the SDK

  • Chose your preferred SDK we now support Python and Node.js.
  • Install the SDK using the following command:
    • Python
  pip install rivalz-client
  • Node.js
  npm install rivalz-client

Prepare your document

  • Prepare your document in PDF format.
  • Place your document in the same directory as your script.

Initialize the client

  • Initialize the client with your secret token. You can create .env file with SECRET_TOKEN variable and store your secret token in it. Or you can directly pass the secret token to the client.

  • For loading the secret token from the .env file, you can use the python-dotenv package in Python and dotenv in Node.js. By executing pip install python-dotenv or npm install dotenv

    • Python
  from dotenv import load_dotenv
from rivalz_client.client import RivalzClient 
load_dotenv()


# Get the secret token from environment variables
secret_token = os.getenv('SECRET_TOKEN')
if not secret_token:
  raise ValueError("SECRET_TOKEN is not set in the environment variables.")

# Initialize the RivalzClient with the secret token
client = RivalzClient(secret_token)
  • Node.js
  import RivalzClient from 'rivalz-client';
import dotenv from 'dotenv';

dotenv.config();

const rivalzClient = new RivalzClient(process.env.SECRET_TOKEN);

Create a knowledge base

  • Use the create_rag_knowledge_base method to create a knowledge base. This method takes the path to the PDF document and the name of the knowledge base as arguments.
      # python
    # create knowledge base
    knowledge_base = client.create_rag_knowledge_base('sample.pdf', 'knowledge_base_name')
    print(knowledge_base) # print the knowledge base details
  
  // node.js
// create knowledge base
const knowledgeBase = await rivalzClient.createRagKnowledgeBase('sample.pdf', 'knowledge_base_name');
console.log(knowledgeBase); // print the knowledge base details

This method will return the knowledge base details as a JSON object. Which include the knowledge base id. You can use this id later to query the knowledge base.

Full code example

  • Python
  # main.py

import os
from dotenv import load_dotenv
from rivalz_client.client import RivalzClient


def main():
    # Load environment variables from .env file
    load_dotenv()

    # Get the secret token from environment variables
    secret_token = os.getenv('SECRET_TOKEN')

    if not secret_token:
        raise ValueError("SECRET_TOKEN is not set in the environment variables.")

    # Initialize the RivalzClient with the secret token
    client = RivalzClient(secret_token)

    # create knowledge base
    knowledge_base = client.create_rag_knowledge_base('sample.pdf', 'knowledge_base_name')
    print(knowledge_base) # print the knowledge base details
if __name__ == '__main__':
    main()
  • Node.js
  // main.js
import RivalzClient from 'rivalz-client';
import dotenv from 'dotenv';

dotenv.config();

async function main() {
  const rivalzClient = new RivalzClient(process.env.SECRET_TOKEN);
  const knowledgeBase = await rivalzClient.createRagKnowledgeBase('sample.pdf', 'knowledge_base_name');
  console.log(knowledgeBase); // print the knowledge base details
}

main();

Next Steps

  • Now you have successfully created your first knowledge base. You can use this knowledge base to build a Retrieval Augmented Generation (RAG) App.