Here is an article about connecting Ethereum to on-chain data using LLM models:
Connecting on-chain data using LLM models: Retrieving token data in Solidity
As a developer building tokens, you are constantly looking for ways to improve the user experience and streamline the functionality of your application. One 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 connect on-chain data using LLM models in Solidity.
What are LLMs?
Large language models, such as Google’s Transformer-based models, have revolutionized natural language processing and have shown great potential in a variety of applications. They consist of a huge dataset of text that they can learn to process and generate human-like responses. These models have several advantages over traditional data retrieval methods:
- Speed: LLMs can analyze huge amounts of data in a fraction of the time it would take with traditional methods.
- Accuracy: By leveraging large data sets, LLMs can provide highly accurate answers to complex queries.
- Scalability: Thanks to the massive training dataset, LLMs can handle large numbers of user queries.
Challenges and Limitations
While LLMs are an excellent solution for in-chain data retrieval, there are several challenges and limitations that need to be considered:
- Data Requirements: Creating and maintaining large data sets for LLMs 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 data, such as balance, owner addresses, or minted tokens, can require custom models tailored to your token.
Connecting on-chain data with LLM models
To address these challenges, we focus on creating a bridge between on-chain data and LLM models. This allows users to query your token data directly in the application using natural language queries.
Here’s an example of how you can implement this in Solidity:
pragma solidity ^0,8,0;
contract TokenInfo {
mapping(address => uint256) public balances;
function getBalance(addressUser) internal view return (uint256) {
return balances[user];
}
function getTokenMinted(uint256 _mintedToken) internal view returns (bool) {
// This is a placeholder for your custom logic
// You can implement this logic according to your token requirements
bool minted = true;
return minted;
}
}
pragma strength ^0,8,0;
contract bridge {
address public tokenAddress; // The address of the token you want to query
struct TokenData {
uint256 balance;
uint256 mintedToken;
}
function getBalance(address user) internal view returns (uint256) {
return bridges[tokenAddress][user].balance;
}
function getTokenMinted(uint256 _mintedToken) internal view returns (bool) {
return bridges[tokenAddress][_mintedToken].minted;
}
}
contract BridgeManager {
Bridge[] public bridges; // Store bridge mappings
constructor() {
bridges.push(Bridge(0x...)); // Replace with your token
}
function getBalance(address user, uint256 _token) internal view returns (uint256) {
TokenData data = bridges[user][_id];
return data.balance;
}
function getTokenMinted(uint256 _token, uint256 _mintedToken) internal view returns (bool) {
TokenData data = bridges[_token][_mintedToken];
return data.minted;
}
}
In this example, we have created a “bridge” contract that acts as a bridge between the data on the chain and the LLM models.
