EasyRAG

EasyRAG makes it simple to integrate tools and documents into your AI chats with ease. It's the typescript AI library you have been looking for!

Getting Started

npm install --save easyrag

import EasyRAG, { OpenAIModelAdapter, Tool, Model } from 'easyrag';

// 1. Setup the model adapter, model, and any tools.

const modelAdapter = new OpenAIModelAdapter({
  apiKey: process.env.OPENAI_API_KEY
});

const chatGpt35Turbo = new Model("gpt-3.5-turbo", "chat");

const weatherToolParams = [
  {
    "name": "zipCode",
    "description": "Zip code",
    "type": "string",
    "required": true
  }
];

const weatherTool = new Tool(
  "weather",
  "Looks up the weather by zip code",
  weatherToolParams,
  async (params) => {
    return "It's a crisp 30f in " + params.zipCode;
  }
);

// 2. Initalize the EasyRAG client.

const ragClient = new EasyRAG({ modelAdapter });

// 3. Register your models and tools

ragClient.register(chatGpt35Turbo);
ragClient.register(weatherTool);

// 4. Query the client
await ragClient.query("What is the weather in zip 92021?");

// 5. Get the built-in conversation history
console.log(
  ragClient
    .conversation
    .getMessages()
    .map(m => `${JSON.stringify(m, null, 2)}`)
    .join('\n')
);

Documentation

Documentation is currently under development. Check back soon for updates.

Why & Ethos

I created this library as an alternative to existing tools due to increasing frustration with them. EasyRAG aims to:

• Provide a minimal API abstraction to easily build a variety of AI-powered applications.
• Be extendable out-of-the-box. Create your own adapters and integrate with any inference engine (Ollama, LlamaIndex, etc.)
• Throw errors instead of falling back to defaults.

Feature Roadmap

I am committed to releasing the following features:

• [x] Model Adapter
  • [x] Support for parameters (temperature, top_p, etc.)
  • [x] Chat completion
  • [x] Embedding
  • [ ] Streaming
• [x] Conversation
  • [x] Hot swap conversation
  • [x] Hot swap tools
  • [x] Hot swap models
  • [x] System prompt
  • [x] History limit for generation context
  • [ ] Context - define variables for the AI to use as a "memory"
• [x] Tool Support
  • [ ] Type support for parameters
• [ ] Document Storage Adapter
  • [ ] Search nearest K
• [ ] Out-of-the-box Adapters
  • [x] OpenAI Model Adapter
    • [x] Chat
    • [x] Embeddings
    • [x] Tool Support
    • [ ] Images (base64)
  • [ ] Ollama Model Adapter
    • [x] Chat
    • [ ] Embeddings
    • [ ] Images (base64)
    • [ ] Tool Support
  • [ ] PGVector Document Storage Adapter
• [ ] Improved Documentation
  • [ ] Official docs/wiki
  • [ ] Documented code with descriptions + hints
• [ ] Better testing

Ideas

• Assign the model to the adapter instead of the client.
• Swap adapter in query/embedding options

Contributing

Contributions and suggestions for improvement are welcome! Feel free to open an issue or pull request.

License

This project is licensed under the MIT License - see the LICENSE file for details.