Ethereum: Linking on-chain data with LLM models: Retrieving token information (Solidity + GPT)

Here is an article about Ethereum that combines on-chain data with LLM models:

On-chain Data Connection with LLM Models: Retrieving Token Information in Solidity

As a developer building a token, you are constantly looking for ways to improve the user experience and streamline the functionality of your application. An innovative approach that can significantly improve the efficiency of your project is to integrate large language models (LLMs) such as GPT into on-chain data retrieval. In this article, we will explore how to combine on-chain data with LLM models in Solidity.

What is LLM?

Large language models, such as Google Transformer models, have revolutionized natural language processing and have shown great potential in a variety of applications. They consist of a huge set of textual data that they can learn to process and generate human-like responses. These models have several advantages over traditional data retrieval methods:

• Speed

  

  : LLM can analyze large amounts of data in a fraction of the time it would take traditional methods.

• Accuracy: Using large datasets, LLM can provide highly accurate answers to complex queries.

• Scalability

  : With a large training dataset, LLM can handle large volumes of user queries.

Challenges and Limitations

While LLM is a great solution for on-chain data retrieval, there are several challenges and limitations to consider:

• Data Requirements: Creating and managing large LLM datasets is time-consuming and expensive.

• Data Quality: High-quality training data is required to ensure the accuracy and relevance of the generated answers.

• Token-specific data: Retrieving token information such as balance, owner addresses, or minted tokens may require custom templates tailored to your specific token.

Connecting on-chain data to LLM models

To address these challenges, we will focus on building a bridge between on-chain data and LLM models. This will allow users to query token data directly in your application using natural language queries.

Here is an example of how you can implement this in Solidity:

pragma solidity ^0.8.0;
contractTokenInfo {
mapping(address => uint256) public balances;
function getBalance(user address) internalview returns (uint256) {
return balances[user];
}
function getTokenMinted(uint256 _mintedToken) internalview returns (bool) {
// This is a placeholder for your custom logic
// You can implement this logic based on your token requirements
bool minted = true;
return minted;
}
}

pragma strength ^0.8.0;
contractBridge {
public address tokenAddress; // The address of the token you want to request
struct TokenData {
uint256 balance;
uint256 mintedToken;
}
function getBalance(userAddress) internalview returns (uint256) {
return bridges[tokenAddress][user].balance;
}
function getTokenMinted(uint256 _mintedToken) internal return (bool) {
return bridges[tokenAddress][_mintedToken].minted;
}
}
contractBridgeManager {
Bridge[] public bridges; // Stores bridge maps
constructor () {
bridges.push(Bridge(0x...)); // Replace with token address
}
function getBalance(address user, uint256 _token) internal return (uint256) {
TokenData data = bridges[user][_token];
return data.balance;
}
function getTokenMinted(uint256 _token, uint256 _mintedToken) internal return (bool) {
TokenData data = bridges[_token][_mintedToken];
return data.minted;
}
}

In this example, we created a “bridge” contract that acts as a bridge between the chain data and the LLM models.