PythonSDK

Rivalz Python SDK for developers

https://pypi.org/project/rivalz-client/0.2.2/

from mimetypes import knownfiles

Rivalz Python Client

rivalz-client is a Python client designed for interacting with the Rivalz API. It enables developers to upload, download, and manage files on the Rivalz platform using IPFS (InterPlanetary File System).

Features

• Upload Files: Upload any file to the Rivalz platform and get an IPFS hash.

• Upload Passport Images: Upload passport images to the Rivalz platform.

• Download Files: Download files from the Rivalz platform using an IPFS hash.v

• Delete Files: Delete files from the Rivalz platform using an IPFS hash.

• Vectorize Documents: Vectorize documents to create a RAG (Retrieval-Augmented Generation) based on the document uploaded.

• Create conversations: Create conversations based on the document uploaded.

Installation

You can install the rivalz-client package via pip using the following command:

pip install rivalz-client

Usage

This guide provides detailed instructions on how to use the rivalz-client to interact with the Rivalz API.

Initialization

First, import the RivalzClient class and initialize it with your secret token. If you don’t provide a token, it will use a default example token.

from rivalz_client.client import RivalzClient

# Initialize the client with your secret token
client = RivalzClient('your_secret_token')

1. Uploading a File

To upload a file to the Rivalz, you can use the upload_file method. Simply provide the path to the file you want to upload as an argument

response = client.upload_file('path/to/your/file.txt')
print(response)

2. Uploading a Passport Image

To upload a passport image, use the upload_passport method. Provide the path to the passport image file.

response = client.upload_passport('path/to/your/passport_image.jpg')
print(response)

3. Downloading a File

To download a file, use the download_file method with the IPFS hash of the file and the directory where you want to save the file.

file_path = client.download_file('QmSampleHash', 'save/directory')
print(f"File downloaded to: {file_path}")

4. Deleting a File

To delete a file, use the delete_file method with the IPFS hash of the file you want to delete.

response = client.delete_file('QmSampleHash')
print(response)

5. Getting all uploaded files

To get your uploaded files, use the get_upload_history method. This method accept page and size for pagination. Page start from 0. And default is page 0 (first page) and size is 10.

total_files_uploaded, upload_histories = client.get_upload_history(0, 10)
print(f"Total files uploaded: {total_files_uploaded} ")
print(f"Upload histories: {upload_histories}")

6. RAG (Retrieval-Augmented Generation) API

Prerequisites

Before using the RAG API, you need api key and some rivalz credits. Claim for free now here

Creating a knowledge base from a document

To vectorize a document and create a knowledge base for Retrieval-Augmented Generation (RAG), use the create_rag_knowledge_base method, which takes the document's file path as input. This method generates a vectorized embedding of the document, assigns it a knowledge base ID, and stores it for future use in RAG-based conversations. Currently, this process supports only PDF files.

Click here to learn How to create a knowledge base

response = client.create_rag_knowledge_base('path/to/your/document.pdf', 'knowledge_base_name')
print(response)
# {'id': '66fa5bf022e73c17073768f0', 'name': 'test', 'files': '1727683567711_sample.pdf', 'userId': '66c4151c98bd0d3d47de682a'}

Adding documents to an existing knowledge base

To add a document to an existing knowledge base, use the add_document_to_knowledge_base method. This method requires the knowledge base ID (from the knowledge base you’ve already created) and the file path of the new document.

response = client.add_document_to_knowledge_base('path/to/your/document.pdf', 'knowledge_base_id')
print(response)

Deleting documents from an existing knowledge base

To delete a document from an existing knowledge base, use the delete_document_from_knowledge_base method with the knowledge base id and the document name.

response = client.delete_document_from_knowledge_base('document_id','knowledge_base_id',)
print(response)

Getting all knowledge bases

To get all knowledge bases, use the get_knowledge_bases method.

response = client.get_knowledge_bases()
print(response)

Getting details of a knowledge base

To get details of a knowledge base, use the get_knowledge_base method with the knowledge base id.

response = client.get_knowledge_base('knowledge_base_id')
print(response)

7. Conversations

Creating a conversation

To initiate a conversation in the RAG (Retrieval Augmented Generation) system, use the create_chat_session method. This method requires the knowledge base ID (from your existing knowledge base) and the question you want to ask. The AI will return a response based on the context provided by the knowledge base, along with a chat session ID to continue the conversation if needed.

response = client.create_chat_session('knowledge_base_id', 'question')
print(response)
# {'answer': 'Hello! How can I help you today? \n', 'session_id': '66fa625fb58f5a4b9a30b983', 'userId': '66c4151c98bd0d3d47de682a'}

Adding a message to a conversation

To add a message to a conversation, use the same method create_chat_session with the chat session id and the message.

response = client.create_chat_session('knowledge_base_id', 'message', 'chat_session_id')
print(response)

Getting all conversations

To get all conversations, use the get_chat_sessions method.

response = client.get_chat_sessions()
print(response)

Getting details of a conversation

To get details of a conversation (which contains chat history for this conversation), use the get_chat_session method with the chat session id.

response = client.get_chat_session('chat_session_id')
print(response)

Get uploaded documents

To get all uploaded documents, use the get_uploaded_documents method.

response = client.get_uploaded_documents()
print(response)

Examples

Here is a complete example demonstrating how to use the rivalz-client to create a simple RAG conversation based on a PDF document:

# main.py

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

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')
    knowledge_base_id = knowledge_base['id']
    if knowledge_base['status'] == 'processing':
        print('Knowledge base is processing')
        #sleep for 5 seconds
        time.sleep(5)
    # create conversation
    conversation = client.create_chat_session(knowledge_base_id, 'what is the document about?')
    conversation_id = conversation['session_id']
    # add message to conversation
    conversation = client.create_chat_session(knowledge_base_id, 'what is the document about?', conversation_id)
    print(conversation['answer'])


if __name__ == '__main__':
    main()