Get structured, fully typed, and validated JSON outputs from OpenAI and Anthropic models.
Under the hood, zod-gpt uses functions to coerce the model to always respond as function calls. Add self-reflection for reliability and zod for parsing & typing.
ZodGPT is a library for
- Receiving structured outputs from models with complete type safety. All responses are fully validated & typed, works with zod as a peer dep.
- Schema definition, serialization / parsing, and automatically asking the model to correct outputs.
- Handle rate limit and any other API errors as gracefully as possible (e.g. exponential backoff for rate-limit) via llm-api.
With zod-gpt, you can simply query OpenAI's ChatGPT model like so:
import { OpenAIChatApi } from 'llm-api';
import { completion } from 'zod-gpt';
const openai = new OpenAIChatApi({ apiKey: 'YOUR_OPENAI_KEY' });
const response = await completion(openai, 'Generate a startup idea', {
schema: z.object({
name: z.string().describe('The name of the startup'),
description: z.string().describe('What does this startup do?'),
}),
});
// data will be typed as { name: string; description: string }
console.log(response.data);Anthropic is also supported via llm-api:
import { AnthropicChatApi } from 'llm-api';
import { completion } from 'zod-gpt';
const client = new AnthropicChatApi({ apiKey: 'YOUR_ANTHROPIC_KEY' });
const response = await completion(client, ...);This package is hosted on npm:
npm i zod-gpt
yarn add zod-gpt
To setup in your codebase, initialize a new instance with the model you want via the llm-api peer dep. Note that zod-gpt is designed to work with any models that implements the CompletionApi interface, so you can also import your own API wrapper.
import { OpenAIChatApi } from 'llm-api';
const openai = new OpenAIChatApi(
{ apiKey: 'YOUR_OPENAI_KEY' },
{ model: 'gpt-4-0613' },
);To send a standard completion request with a given model, simply call the completion method.
const response = await completion(openai, 'hello');
// data will be typed as string
console.log(response.data);To add schema parsing and typing, simply add a schema key in the options argument. Make sure to add a description to each key via the describe method. The descriptions will be fed into the model to ensure that it understand exactly what data is requested for each key. Try to error on the side of being over descriptive to ensure the model understands exactly.
const response = await completion(
openai,
'Generate a step by step plan on how to run a hackathon',
{
schema: z.object({
plan: z.array(
z.object({
reason: z.string().describe('Name the reasoning for this step'),
id: z.string().describe('Unique step id'),
task: z
.string()
.describe('What is the task to be done for this step?'),
}),
),
}),
},
);
// data will be typed as { plan: { reason: string; id: string; task: string }[] }
console.info('Response:', response.data);NOTE: the schema key ONLY takes object type schemas - this is a limitation of the functions API. If you need to generate arrays or other type of reponses, simply wrap them in an object like the above example.
By default, zod-gpt has logic to automatically detect and heal any schema errors via self-reflection (e.g. if the function api is not being used correctly, if the schema has parse errors.. etc). This means whenever these types of errors happen, zod-gpt will send a new message to re-ask the model to correct its own output, together with any error messages it gathered from parsing.
The logic is simple but incredabily powerful, and adds a layer of reliability to model outputs. I suggest leaving this flag set to true (its default setting), unless if token usage or response time becomes a real issue.
A common way to handle token limit issues is to split your content. zod-gpt provides an autoSlice option to automatically split your text when a token overflow error from llm-api is detected. It's smart enough to only split your text if it determines that it is above the token limit, and will try to preserve as much of the original text as possible.
const openai = new OpenAIChatApi(
{ apiKey: 'YOUR_OPENAI_KEY' },
// make sure `contextSize` is set to enable throwing TokenErrors
{ model: 'gpt-4-0613', contextSize: 8129 },
);
const response = await completion(
openai,
'hello world, testing overflow logic',
{ autoSlice: true },
);zod-gpt uses the debug module for logging & error messages. To run in debug mode, set the DEBUG env variable:
DEBUG=zod-gpt:* yarn playground
You can also specify different logging types via:
DEBUG=zod-gpt:error yarn playground
DEBUG=zod-gpt:log yarn playground
zod-gpt currently users the llm-api library to support multiple LLM providers. Check the llm-api documentation on how to configure model parameters.
To send a completion request to a model:
const res: Response = await completion(model, prompt, options: RequestOptions);options
You can override the default request options via this parameter. The RequestOptions object extends the request options defined in llm-api.
type RequestOptions = {
// set a zod schema to enable JSON output
schema?: T;
// set to enable automatically slicing the prompt on token overflow. prompt will be sliced starting from the last character
// default: false
autoSlice?: boolean;
// attempt to auto heal the output via reflection
// default: true
autoHeal?: boolean;
// set message history, useful if you want to continue an existing conversation
messageHistory?: ChatRequestMessage[];
// the number of time to retry this request due to rate limit or recoverable API errors
// default: 3
retries?: number;
// default: 30s
retryInterval?: number;
// default: 60s
timeout?: number;
// the minimum amount of tokens to allocate for the response. if the request is predicted to not have enough tokens, it will automatically throw a 'TokenError' without sending the request
// default: 200
minimumResponseTokens?: number;
};Completion responses extends the model responses from llm-api, specifically adding a data field for the pased JSON that's automatically typed according to the input zod schema.
interface Response<T extends z.ZodType> {
// parsed and typecasted data from the model
data: z.infer<T>;
// raw response from the completion API
content?: string;
name?: string;
arguments?: JsonValue;
usage?: {
promptTokens: number;
completionTokens: number;
totalTokens: number;
};
}If you need to split long text into multiple chunks before calling the llm, few text splitters are also exported in text-spitter.ts. Try to default to RecursiveTextSplitter unless if there is a specific reason to use the other text splitters, as it is the most widely used text splitter.