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https://github.com/jacoblincool/aid
A.I. :D | Aid provides a structured and type-safe way to interact with LLMs.
https://github.com/jacoblincool/aid
gpt json json-schema large-language-models openai typescript
Last synced: 8 days ago
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A.I. :D | Aid provides a structured and type-safe way to interact with LLMs.
- Host: GitHub
- URL: https://github.com/jacoblincool/aid
- Owner: JacobLinCool
- License: mit
- Created: 2023-11-22T17:42:06.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2024-07-18T05:52:26.000Z (5 months ago)
- Last Synced: 2024-12-19T20:06:37.601Z (9 days ago)
- Topics: gpt, json, json-schema, large-language-models, openai, typescript
- Language: TypeScript
- Homepage: http://jacoblin.cool/aid/
- Size: 1.3 MB
- Stars: 1
- Watchers: 2
- Forks: 0
- Open Issues: 11
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Aid: TypeScript Library for Typed LLM Interactions
A.I. :D
**Aid** is a TypeScript library designed for developers working with Large Language Models (LLMs) such as OpenAI's GPT-4 (including Vision) and GPT-3.5. The library focuses on ensuring **consistent, typed outputs** from LLM queries, enhancing the reliability and usability of LLM responses. Advanced users can leverage few-shot examples for more sophisticated use cases. It provides a structured and type-safe way to interact with LLMs.
## Features
- **Typed Response**: Aid leverages TypeScript and JSON Schema to ensure consistent, reliable outputs from LLMs, adheres to the predefined schema.
- **Task Based**: Easily define custom tasks with specific input and output types, streamlining the process of LLM interactions.
- **Few-Shot Learning Support**: Allows for the provision of few-shot prompt examples to guide the LLM in producing the desired output.
- **Visual Task Support**: Includes support for visual tasks with image inputs, harnessing the power of OpenAI's GPT-4 Vision. [Example](https://github.com/JacobLinCool/aid/blob/main/packages/aid/tests/vision.test.ts)
- **OpenAI Integration**: Integrates with OpenAI's official library to provide a seamless experience.
- **Customizable**: Allows for customization LLM models, just implement the `QueryEngine` function. [Example](https://github.com/JacobLinCool/aid/blob/main/packages/aid/tests/cohere.test.ts)
## Installation
```sh
pnpm install @ai-d/aid
```
## Usage
### Basic Setup
First, import the necessary modules and set up your OpenAI instance:
```ts
import { OpenAI } from "openai";
import { Aid } from "@ai-d/aid";
const openai = new OpenAI({ apiKey: "your-api-key" });
const aid = Aid.from(openai, { model: "gpt-4-1106-preview" });
```
Using GPT-4 Vision
```ts
import { OpenAI } from "openai";
import { Aid, OpenAIQuery } from "@ai-d/aid";
const openai = new OpenAI({ apiKey: "your-api-key" });
const aid = Aid.vision(
OpenAIQuery(openai, { model: "gpt-4-vision-preview", max_tokens: 2048 }),
);
```
Using Other LLM
For example, [Cohere](https://cohere.ai/)'s Command.
```ts
import { Aid, CohereQuery } from "@ai-d/aid";
const aid = Aid.chat(
CohereQuery(COHERE_TOKEN, { model: "command" }),
);
```
> You can implement your own `QueryEngine` function.
### Creating a Custom Task
Define a custom task with expected output types:
```ts
import { z } from "zod";
const analyze = aid.task(
"Summarize and extract keywords",
z.object({
summary: z.string().max(300),
keywords: z.array(z.string().max(30)).max(10),
}),
);
```
Visual Task Example
```ts
const analyze = aid.task(
"Analyze the person in the image",
z.object({
gender: z.enum(["boy", "girl", "other"]),
age: z.enum(["child", "teen", "adult", "elderly"]),
emotion: z.enum(["happy", "sad", "angry", "surprised", "neutral"]),
clothing: z.string().max(100),
background: z.string().max(100),
}),
);
```
### Executing a Task
Execute the task and handle the output:
```ts
const { result } = await analyze("Your input here, e.g. a news article");
console.log(result); // { summary: "...", keywords: ["...", "..."] }
```
Visual Task Example
```ts
const datauri = `data:image/png;base64,${fs.readFileSync("path/to/image.png" "base64")}`;
const { result } = await analyze({ images: [{ url: datauri }] });
console.log(result); // { "gender": "boy", "age": "teen", ... }
```
### Advanced Usage with Few-Shot Examples
For more complex scenarios, you can use few-shot examples:
```ts
const run_advanced_task = aid.task(
"Some Advanced Task",
z.object({
// Define your output schema here
}),
{
examples: [
// Provide few-shot examples here
],
}
);
```
## Formulation
Case Parameter -> (join) Task Defination -> (join) Format Constraint -> (perform) Query
`Query` and `Format Constraint` are defined and implemented by the `QueryEngine` and `FormatEngine`.
`Task Defination` is defined by the user with `task` method. Task Goal, Expected Schema, Examples, etc.
`Case Parameter` is defined by the user on each single call. Text, Image, etc.
## Contributing
Contributions are welcome! Please submit pull requests with any bug fixes or feature enhancements.