OpenAI Function library with Zod and TypeScript type safety. Built to facilitate interactions with the OpenAI API in a type-safe manner. This package includes a variety of tools for communicating with the OpenAI API using type-safe requests and responses.
To install the package, run the following command in your terminal:
npm install zod-mindzodMind() is the primary function for interacting with the Zod Mind library,
which provides a structured interface to the OpenAI API.
// Step 1: Create a Zod Mind instance
const client = zodMind( {
openai: {
model: "gpt-3.5-turbo",
temperature: 0.5,
},
// Replace this with your actual API key
api_key: "sk-WUBbaLuBbAdUBDUBmEanSIAMInGREaTPAInp1she1PME",
} );
// Step 2: Send a message to OpenAI
async function fetchAnswer() {
try {
const prompt = "What is the meaning of life?";
const result = await client.chat( prompt );
console.log( result );
} catch ( error ) {
console.error( error );
}
}You can provide OpenAI's API options
using the openai of the zodMind() function.
Remember to replace 'sk-WUBbaLuBbAdUBDUBmEanSIAMInGREaTPAInp1she1PME' with
your actual API key. Never expose this key publicly. It is generally recommended
to store it in an environment variable or a secure secret storage.
Environment Variable
Zod Mind is going to attempt to read OPENAI_API_KEY from your environment if no key is provided to the zodMind() function.
Structured chat refers to an organized and formatted way of communicating with the AI with a predefined schema to guide AI's responses. It allows you to use a chat interface that handles both the instruction and the desired response format.
The structured_chat method takes two parameters:
message: A string that serves as the instruction to the AI.zod_schema: A Zod schema that defines the format of the AI's response.
Note: The schema should always be at the very least a Zod Object, like so:
const results = await client.structured_chat( "What is the meaning of life?", z.object( {
answer: z.string()
} ) );The method returns a structured response from the AI that matches the given Zod schema or it will throw a validation error.
The invoke method allows the AI to call a function from a predefined list of
functions based on a given message.
The invoke method takes three parameters:
message: A string that serves as the instruction to the AI.functions: An object mapping function names to GPT_Function definitions.function_call: An optional parameter that specifies which function to call. If it's not provided, the AI will decide which function to call based on the message.
const functions = {
"random_number": {
description: "Generate a random number between two numbers.",
schema: z.object( {
from: z.number(),
to: z.number()
} )
},
"random_quote": {
description: "Generate a random quote.",
schema: z.object( {
quote: z.string()
} )
}
};
const result = await client.invoke( "Random number between 1 and 42", functions );
if ( result.name === "random_number" ) {
const random_number = random_number_generator( result.arguments.from, result.arguments.to );
app.debug( `GPT is calling function "${ result.name }"` )
.debug( "With Arguments:", result.arguments )
.info( `The random number is ${ random_number }` );
} else {
app.error( `GPT is calling function "${ result.name }"` ).error( "With Arguments:", result.arguments );
}The invoke method returns an object that includes the name of the function
called and its arguments, formatted according to the appropriate Zod schema.
Force Function Call
If you want to force the AI to call a specific function, you can do so by passing the third argument to the invoke method:
const result = await client.invoke( "Random number between 1 and 42", functions, "random_number" );Even though this library is designed with type-safety in mind, you can just call
simple chat methods without type safety if you need to.
const result = await client.chat( "What is the answer to life?" );If you want to customize the system message, you can do so using the set_system_message()
method:
client.set_system_message( "This is a custom system message." );