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Secure Your AI-Generated Code with Socket MCP
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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.
QueryEngine
function. Examplepnpm install @ai-d/aid
First, import the necessary modules and set up your OpenAI instance:
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" });
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 }),
);
For example, Cohere's Command.
import { Aid, CohereQuery } from "@ai-d/aid";
const aid = Aid.chat(
CohereQuery(COHERE_TOKEN, { model: "command" }),
);
You can implement your own
QueryEngine
function.
Define a custom task with expected output types:
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),
}),
);
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),
}),
);
Execute the task and handle the output:
const { result } = await analyze("Your input here, e.g. a news article");
console.log(result); // { summary: "...", keywords: ["...", "..."] }
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", ... }
For more complex scenarios, you can use few-shot examples:
const run_advanced_task = aid.task(
"Some Advanced Task",
z.object({
// Define your output schema here
}),
{
examples: [
// Provide few-shot examples here
],
}
);
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.
Contributions are welcome! Please submit pull requests with any bug fixes or feature enhancements.
FAQs
Aid provides a structured and type-safe way to interact with LLMs.
The npm package @ai-d/aid receives a total of 48 weekly downloads. As such, @ai-d/aid popularity was classified as not popular.
We found that @ai-d/aid demonstrated a not healthy version release cadence and project activity because the last version was released a year ago. It has 1 open source maintainer collaborating on the project.
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Product
Socket MCP brings real-time security checks to AI-generated code, helping developers catch risky dependencies before they enter the codebase.
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