Ethereum: Connecting On-Chain Data with LLM Models: Token Information Extraction (Solidity + GPT)

ارسال شده توسط

در ۱۵ بهمن ۱۴۰۳

Here is an article on bridging Ethereum on-chain data with LLM models:

Bridging On-Chain Data with LLM Models: Getting Token Information in Solid State

As a developer building a token, 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 mining. In this article, we will look at how to connect on-chain data with LLM models in Solidity.

What are LLMs?

Large language models, such as those based on Google’s Transformer, have revolutionized natural language processing and have shown great potential in a variety of applications. They consist of a massive dataset of text that can be learned to process and generate human-like responses. These models have a number of advantages over traditional data mining methods:

Speed

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

Accuracy: By leveraging large datasets, LLMs can provide highly accurate answers to complex questions.

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

Challenges and Limitations

While LLMs are an excellent solution for data mining in the chain, there are several challenges and limitations to consider:

Data Requirements: Creating and maintaining large datasets for LLMs is time-consuming and expensive.

Data Quality: Ensuring the accuracy and relevance of the answers generated requires high-quality training data.

Token-Specific Data: Retrieving token information such as balance, owner addresses, or minted tokens may require custom models tailored to your specific token.

Bridging On-Chain Data with LLM Models

To address these challenges, we’ll focus on building a bridge between on-chain data and LLM models. This will allow users to query your token data directly in your 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(user address) internal view returns (uint256) {
return balances[user];
}
function getTokenMinted(uint256 _mintedToken) internal view returns (bool) {
// This is a placeholder for your own logic
// You can implement this logic according to your token requirements
bool minted = true;
return minted;
}
}


pragma strength ^ 0,8,0;
contract bridge {
public token addressAddress; // The address of the token you want to query
struct TokenData {
balance uint256;
uint256 mintedToken;
}
function getBalance(userAddress) 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 the mapping of bridges
constructor() {
bridges.push(Bridge(0x...)); // Replace with token address
}
function getBalance(userAddress, uint256 _token) internal view returns (uint256) {
TokenData data = bridges[user][_token];
return data.balance;
}
function getTokenMinted(uint256 _token, uint256 _mintedToken) internal view returns (bool) {
TokenData data = bridges[_token][_mintedToken];
return data.minted;
}
}