AI Marketplace is a platform that allows users to share and sell their AI models. The platform is built with a payment system handled on chain. Service providers offer to host a LangChain Runtime Environment where a Python workload can be executed using LangChain. Use a model for a chat and pay per use.

The Chat demo uses the AI Marketplace on the back end to answer user prompts. User's pay for each reponse using ETH.

graph TB
    UM["Users"] -- "Submit Request" --> AM["AI Marketplace"]
    UM -- "Payment" --> AM
    AM -- "Tip with Request" --> H["TellorFlex"]
    AM -- "Payment" --> H["TellorFlex"]
    H -- "Receives Request" --> R["Reporter"]
    R -- "Invokes LLM" --> LRE["LangChain Runtime Environment"]
    LRE -- "Utilizes" --> LC["LangChain"]
    LC -- "Runs Python Script" --> PA["Python"]
    PA -- "Submits Response" --> H
    style AM fill:#09f,stroke:#333,stroke-width:4px
    style H fill:#0b0,stroke:#333,stroke-width:2px
    style R fill:#0b0,stroke:#333,stroke-width:2px
    style LRE fill:#dfd,stroke:#333,stroke-width:2px

A workload is a Python script that defines a LangChain model.

It MUST contain:

• prompt - a string that is the prompt for the model
• input - a string that is the input for the model

It MAY contain:

• model - a string that is the model to use
• temperature - a float that is the temperature to use

Example:

prompt = "What is the meaning of life?"
input = "The meaning of life is to be happy."

These workloads are executed using a function that may look like this:

function work(prompt: str, input: str) -> str:
    model = ChatOpenAI(openai_api_key=os.getenv("OPENAI_APY_KEY", ""), temperature=0.1)
    full_prompt = ChatPromptTemplate.from_messages(
        [
            ("system", prompt),
            ("human", "{input}"),
        ]
    )
    chain = (
        {"input": RunnablePassthrough()}
        | full_prompt
        | model
        | StrOutputParser()
    )

    return chain.invoke(input)

There's a lot more that can be put in as arguments for the work function but this is the minimum required. The example above assumes the workload executor has setup OpenAI and LangChain properly.

Payments are made to a Host for the execution of a workload. The Host is responsible for executing the workload and returning the result. The payment is made using the LangChain token.

Publish your prompt and input into a transaction onchain along with an amount of ETH to be paid to the host for running the workload. The host will execute the workload and return the result.

Hosts listen to a onchain index of workloads and execute them. They are paid for their services.

• Better Model Execution Runtime Environment - The current runtime environment is very basic and could be improved.
• Homomophic Encryption - Encrypt the input and output of the workload so that the host cannot see the data. This would allow for more sensitive data to be used in the workloads.
• More Models - Add more models to the platform to allow for more diverse workloads.
• More Payment Options - Add more payment options to the platform to allow for more diverse payment options.