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https://github.com/terrence-ou/ai-fetcher
A NPM package that provides integration with popular language models. It is designed to facilitate easy and efficient ai-fetching tasks for your application.
https://github.com/terrence-ou/ai-fetcher
msw npm-package typescript vitest
Last synced: 7 days ago
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A NPM package that provides integration with popular language models. It is designed to facilitate easy and efficient ai-fetching tasks for your application.
- Host: GitHub
- URL: https://github.com/terrence-ou/ai-fetcher
- Owner: terrence-ou
- License: mit
- Created: 2024-08-23T01:54:57.000Z (about 1 month ago)
- Default Branch: main
- Last Pushed: 2024-09-22T22:55:57.000Z (13 days ago)
- Last Synced: 2024-09-25T19:13:35.424Z (10 days ago)
- Topics: msw, npm-package, typescript, vitest
- Language: TypeScript
- Homepage: https://www.npmjs.com/package/ai-fetcher
- Size: 115 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# ai-fetcher
[](https://github.com/terrence-ou/ai-fetcher/actions/workflows/CI.yml)
## Overview
`ai-fetcher` is a Node.js package that provides integration with popular language models. It is designed to facilitate easy and efficient ai-fetching tasks for your application. Currently the package supports Claude and DeepL models, and the supported models/services is keep expanding.
## Installation
```bash
$ npm install ai-fetcher@latest
```
## Supported AI Services
- DeepL - Supports both free and pro API keys;
- Claude - Supports regular text generation for now;
- OpenAI
- Chat - Supports chat generation;
- Text-to-Speech - Convery given texts to audio;
## Examples:
### DeepL:
```javascript
import { DeepL } from "ai-fetcher";
// Initialize the DeepL agent with your API key
const deepLAgent = new DeepL(YOUR_DEEPL_API_KEY, true); // Change `true` to `false` if not using Pro API key
// Example tranlation parameters
const translationParams = {
from: "EN", // Source language code (optional, DeepL can also auto-detect the input language)
to: "DE", // Target language code
text: ["Hello world!", "This is a test translation"], // Text(s) to translate
};
// Call the translate method
async function translateText() {
try {
const result = await deepLAgent.translate(translationParams);
console.log(result.translations);
return result.translations;
} catch (error) {
console.error("Translation Error:", error);
}
}
// Execute the function to translate the text
translateText();
```
### Claude:
```javascript
import { Claude } from "ai-fetcher";
// Initialize the Claude agent with your API key and preferred model
const claudeAgent = new Claude(YOUR_CLAUDE_API_KEY, "claude-3-haiku-20240307"); // You can also specify the model
// example system prompt and message for generating a response
const systemPrompt = "Write a haiku about the sea.";
const conversationHistory = [
{ role: "user", content: [{ type: "text", text: "Randomly generate 10 words."] } }
];
// Call the generate method
async function generateResponse() {
try {
const result = await claudeAgent.generate(
systemPrompt,
conversationHistory,
);
console.log(result.content);
return result.content;
} catch (error) {
console.error(error);
}
}
// execute the function and get the generated result
generateResponse();
```
### OpenAI:
#### *Chat Model*
```javascript
import { OpenAI } from "ai-fetcher";
// Initialize the OpenAI chat agent with your API key and preferred model
const openaiChatAgent = OpenAI.chat(YOUR_OPENAI_API_KEY, "gpt-4o-mini");
// Call the generate method
async function generateResponse() {
try {
const result = await openaiChatAgent.generate([
{ role: "user", content: "Randomly generate 20 words" },
]);
console.log(result.choices[0].message.content);
} catch (error) {
console.error(error);
}
}
// execute the function and get the generated result
generateResponse();
```
#### *Text-to-Speech(TTS) Model*
```javascript
import { OpenAI } from "ai-fetcher";
// Initialize the OpenAI tts agent with your API key
const openaiTTSAgent = OpenAI.textToSpeech(YOUR_OPENAI_API_KEY);
async function convertTextToSpeech() {
try {
const result = await openaiTTSAgent.convert(
"Read this text.",
"filename",
"speech.mp3",
);
console.log(result); // The result is a filename
} catch (error) {
console.error(error);
}
}
// execute the function and get the generated result
convertTextToSpeech();
```