NFT & LLM Data Integration
Budget: $250 – $750 USD
I need a unified workflow that collects my text documents, images, and videos, packages each bundle as on-chain metadata, and mints it as an NFT on Ethereum. The resulting token must permanently prove ownership, allow normal transfers, and embed a royalty mechanism.
Alongside the minting process, I want a language model to process the same material—extract keywords, generate a concise summary, possibly produce embeddings—and write those insights back into the token’s metadata or a linked JSON file. Think Solidity (ERC-721 or ERC-1155 with OpenZeppelin), IPFS or Arweave for storage, plus a Python or Node script that calls GPT-4 / Llama via LangChain or similar.
Deliverables
• Ethereum smart contract with ownership, transferability, and royalty logic ready for main-net deployment
• Data ingestion script that uploads assets, builds metadata, and calls the contract’s mint() function
• LLM integration code that analyses each asset bundle and appends the AI-generated output to the metadata
• Simple demo (CLI or lightweight web page) showing the full flow from raw files to a minted, AI-enriched NFT
Acceptance criteria
• Contract passes unit tests and deploys successfully on Goerli before main-net
• Metadata for at least one sample NFT contains the LLM summary and keywords
• Transfer and royalty events fire correctly in a test transaction
If you’ve built similar pipelines—especially on Ethereum—let’s move quickly.
Alongside the minting process, I want a language model to process the same material—extract keywords, generate a concise summary, possibly produce embeddings—and write those insights back into the token’s metadata or a linked JSON file. Think Solidity (ERC-721 or ERC-1155 with OpenZeppelin), IPFS or Arweave for storage, plus a Python or Node script that calls GPT-4 / Llama via LangChain or similar.
Deliverables
• Ethereum smart contract with ownership, transferability, and royalty logic ready for main-net deployment
• Data ingestion script that uploads assets, builds metadata, and calls the contract’s mint() function
• LLM integration code that analyses each asset bundle and appends the AI-generated output to the metadata
• Simple demo (CLI or lightweight web page) showing the full flow from raw files to a minted, AI-enriched NFT
Acceptance criteria
• Contract passes unit tests and deploys successfully on Goerli before main-net
• Metadata for at least one sample NFT contains the LLM summary and keywords
• Transfer and royalty events fire correctly in a test transaction
If you’ve built similar pipelines—especially on Ethereum—let’s move quickly.