Revolutionizing Blockchain Access: Conversational Interfaces with GPT Neo

Haneen Mansoor 13 Jun, 2023 • 6 min read


The article aims to demonstrate how to use GPT Neo, the Hugging Face Transformers module, Python programming language, library, and a blockchain database to create a conversational interface that allows users to interact with the blockchain network more user-friendly and intuitively. By the end of the article, readers should clearly understand how to use these tools to build a chatbot that can retrieve and display data from a blockchain database based on user queries.

How to Create a Conversational Interface Using Blockchain Data?

Learning Objectives

  1. Understanding the basics of blockchain database and its potential use case in practice.
  2. Familiarizing oneself with the limitations of traditional query interfaces for blockchain data.
  3. Learning how to set up a development environment for building a chatbot using the GPT-Neo model and
  4. Understanding the Ethereum network and its data structure and learning how to connect to it using
  5. Learning how to generate text responses using the GPT Neo model and how to combine it with code to create a functional chatbot.

This article was published as a part of the Data Science Blogathon.


Before we begin, you will need to install the following packages:

  • is a Python library that allows us to interact with the Ethereum blockchain.
  • Transformers: Transformers is a library for Natural Language Processing (NLP) tasks, including language modeling, machine translation, and text generation.

You can install these packages using pip, the Python package manager:

pip install web3 transformers

Connecting to Blockchain

Before we can build our chatbot, we need to connect to the Ethereum blockchain using To do so, we will be using the library. First, we will import the necessary modules and initialize a connection to the blockchain:

from web3 import Web3

# Initializing a connection to the blockchain
w3 = Web3(Web3.HTTPProvider(''))

# Checking if the connection is successful
if w3.isConnected():
    print('Connected to the Ethereum network')
    print('Connection to the Ethereum network failed')

With an Infura project ID, we connect to the Ethereum main net in this illustration. You can provide your own Infura project ID or a local node address in place of the placeholder YOUR-PROJECT-ID.

Using to create a chatbot using blockchain data

Querying Blockchain Data

After establishing a link with the blockchain, we can use the library to query data from the blockchain. For instance, we can get the most recent block number:

# Retrieving the latest block number
latest_block = w3.eth.blockNumber

print(f'The latest block number is {latest_block}')

This will output the latest block number to the console.

Setting Up Chatbot

We will use the GPT Neo model from Hugging Face Transformers to provide conversational access to the blockchain data. First, we will import the necessary modules and initialize the model:

from transformers import pipeline

# Initializing the GPT Neo pipeline
chatbot = pipeline('text-generation', model='EleutherAI/gpt-neo-1.3B')

In this example, we use the 1.3B variant of the GPT-Neo model from EleutherAI. You can replace this with a different model if you prefer.

Providing Conversational Access

We can now leverage our chatbot and blockchain connection to offer conversational access to blockchain data. This is an illustration of a dialogue:

# Defining a function to handle user input
def handle_input(input_text):
    # Generating a response using GPT Neo model
    response = chatbot(input_text)[0]['generated_text']
    # Extracting the latest block number from the blockchain
    latest_block = w3.eth.blockNumber
    # If user asks for the latest block number, return it
    if 'latest block number' in input_text.lower():
        return f'The latest block number is {latest_block}.'
    # If user asks for the block hash of a specific block, return it
    if 'block hash' in input_text.lower():
        block_number = int(input_text.split()[-1])
        block_hash = w3.eth.getBlock(block_number)['hash'].hex()
        return f'The hash of block {block_number} is {block_hash}.'
    # Otherwise, return the GPT Neo response
    return response

# Starting a conversation with the user
while True:
    # Prompting the user for input
        user_input = input('You: ')
    # Handling the user input
    response = handle_input(user_input)
    # Printing the response
    print(f'Chatbot: {response}')

This will continuously prompt the user for input, generate a response using GPT Neo, and return the answer to the user. If the user asks for the latest block number or the hash of a specific block, the function handle_input() will query the blockchain using and return the corresponding information. Otherwise, GPT Neo will generate a response based on the user input.

GPT Neo | conversational interface | chatbot

Advantages of Using Conversational Interfaces to Access Blockchain Databases

  1. Natural and Intuitive: Compared to conventional query interfaces, conversational interfaces offer a more natural and intuitive approach to retrieving blockchain data. Users may find getting the information they require simpler without a deep understanding of technology.
  2. Accessibility: Chatbots can reach a comprehensive range of people who can access through messaging services.
  3. Faster Response Times: Compared to standard query interfaces, conversational interfaces can offer shorter response times. Thus, enabling users to find the information they require more quickly.
  4. Reduced Complexity: Conversational interfaces can help make engaging with blockchain data less complicated, which can be especially helpful for users who may not have a technical background.
  5. Improved User Engagement: Conversational interfaces can help improve user engagement by providing a more personalized and interactive experience. Designers can create chatbots that guide users through complex queries, offer relevant suggestions, and even provide educational resources to help users better understand blockchain technology. Designers can create chatbots that guide users through complex queries, offer relevant suggestions, and even provide educational resources to help users better understand blockchain technology. This can make accessing and interacting with blockchain databases a more enjoyable and engaging experience, leading to increased adoption and usage.

Overall, conversational interfaces provide a promising solution for accessing blockchain databases, offering a more accessible, convenient, and secure way to interact with blockchain technology.

Also Read: Ensuring Secure Data Management With Blockchain Technology

Restrictions to Consider When Using Conversational Interfaces to Access Blockchain Databases

  1. Language Limitations: Chatbots are only as good as the language models they are trained on. While GPT Neo is one of the most advanced language models available, it may not understand specific language nuances or regional dialects.
  2. Limited Functionality: Chatbots can only perform tasks they have been programmed to do. This means their functionality may be limited compared to traditional query interfaces, which can be customized to perform more complex tasks.
  3. Security Concerns: While blockchain databases are generally considered secure, security breaches or hacking are still risks. Additionally, inadequate security measures in conversational interfaces may introduce additional security concerns.
  4. Dependence on Internet Connection: Conversational interfaces require a stable Internet connection to function correctly. This can be a limitation in areas with poor internet connectivity.

Overall, while conversational interfaces can offer some benefits in accessing blockchain databases, they may not be suitable for all use cases. Considering the advantages and restrictions of conversational interfaces before implementing them in a blockchain application is essential.


In summary, the concepts we went through in this article include the following:

  • Access a blockchain database via conversation using the GPT Neo model.
  • Interaction with a blockchain network using the Python programming language and the library.
  • Interaction with GPT Neo model using the Hugging Face Transformers package.
  • Building a straightforward chatbot that can respond to inquiries regarding blockchain data by integrating these techniques.

We can design more approachable and accessible interfaces for interacting with complex data. We can achieve this by combining the strength of blockchain technology and conversational AI. This method might be used in several use cases, including supply chain management and finance, to assure openness, correctness, and security.

Therefore with the ongoing development of blockchain and conversational AI, we can expect to see even more innovative and user-friendly solutions emerge. Overall, integrating these technologies can unlock new possibilities for creating more accessible and secure interfaces for interacting with complex data.

The media shown in this article is not owned by Analytics Vidhya and is used at the Author’s discretion. 

Frequently Asked Questions

Q1. What is the GPT-Neo model?

A. GPT-Neo is an open-source language model developed by EleutherAI. It is based on the GPT (Generative Pre-trained Transformer) architecture and is designed to generate human-like text by predicting the next word given the context. GPT-Neo is a community-driven project aiming to provide accessible and powerful language models.

Q2. What can GPT-Neo be used for?

A. GPT-Neo can be used for various natural language processing tasks such as text generation, language translation, question answering, and sentiment analysis. It can assist in content creation, chatbot development, and language understanding applications.

Q3. Is GPT-Neo free to use?

A. Yes, GPT-Neo is free to use. It is an open-source project that allows users to access the model’s code and utilize it for their own purposes. However, resource requirements and potential usage limitations may vary depending on the specific implementation and infrastructure.

Q4. How accurate is GPT-Neo?

A. GPT-Neo’s accuracy depends on the quality and diversity of the data it has been trained on and the task at hand. It can generate coherent and contextually relevant text but may also produce errors, nonsensical outputs, or biased content.

Q5. Who created GPT-Neo?

A. GPT-Neo was created by a community-driven organization called EleutherAI. It is a collaborative effort of researchers and enthusiasts passionate about democratizing access to state-of-the-art language models. GPT-Neo builds upon the foundation laid by OpenAI’s GPT models and aims to advance the field of natural language processing.

Haneen Mansoor 13 Jun 2023

Frequently Asked Questions

Lorem ipsum dolor sit amet, consectetur adipiscing elit,

Responses From Readers

  • [tta_listen_btn class="listen"]