ellma

ellma

Easy LLM Assistants

Overview

• Concepts
  • Interfaces and adapters
    • Example: Mapping api.openai.com/v1/chat/completions to ChatIntegration
  • Peripherals
    • Example: Getting input from or displaying output to a user
  • Models
    • Embedding models
  • Integrations
• How to use ellma
• How to contribute to ellma
  • Set up your development environment

Concepts

To get the best out of ellma, there are some concepts that you should understand.

Interfaces and adapters

In order to keep this library flexible, while also maintaining reasonable defaults, features that relate to external runtime functionality should be implemented with the adapter pattern. In this library, the adapter pattern consists of 2 main concepts: the interface and the adapter. The interface refers to the internal interface that we will use throughout the codebase. The adapter maps that internal interface to the external interface that a given implementation provides. Some examples of this are:

• Mapping the openai endpoint for chat completions to the ChatIntegration interface used by chat models.
• Mapping the node:readline terminal IO utilities to the IoPeripheral interface used by features that deal with user input and output.

Example: Mapping api.openai.com/v1/chat/completions to ChatIntegration

Take a look at the following interface for ChatIntegration.

export type ChatIntegration = {
  chat: (messages: ChatMessage[]) => Promise<ChatMessage>,
}

The interface is meant to be simple for the generic chat model to consume, so it has a single chat property. The chat property is a function that takes an array of ChatMessage objects (the conversation so far) and returns a single ChatMessage object (the reply). The interface for the function that calls /v1/chat/completions, however, is a bit more complicated.

export type OpenAiChatApi = (config: {
  apiKey: string,
  messages: OpenAiChatMessage[],
  model?: string,
  organizationId?: string,
  peripherals?: Partial<Peripherals>,
}) => Promise<OpenAiChatApiResponse>

Not only do we need the messages, but we also need the API key, the preferred model, and more. The function is essentially a raw implementation of the corresponding openai endpoint, and an adapter must be used to map it to the ChatIntegration interface.

Peripherals

Peripherals wrap environment-specific functionality that we use in our models, integrations, or even other peripherals. Some examples of this are:

• Making HTTP requests
• Getting input from or displaying output to a user
• Storing data temporarily or permanently

Example: Getting input from or displaying output to a user

To better understand this concept, take a look at the io implementation under ./peripherals. The adapter interface is defined by IoAdapter as an object that has two async function properties: read and write. The terminal adapter conforms to that interface, and the useIo peripheral maps the terminal adapter to the IoPeripheral interface. This allows us to use the IoPeripheral interface throughout the codebase without knowledge about specific implementations that end-users might choose to use. Additionally, we can expose helper functions in the peripheral that utilize the underlying adapter interface without requiring adapters to implement the function directly. The prompt function is one example that uses the write function to output something to a user followed by the read function to receive user input.

Models

These are the various types of AI models that we can use in our agents. Models are responsible for taking input and producing output. They can be used in a variety of ways, but they are typically used as the higher-level building blocks of an agent. For example, a model might be used to generate a response to a user's input, or it might be used to generate a new piece of content based on a given prompt. Some examples of this are:

• Chat-based LLMs
• Completion-based LLMs
• Text-to-embedding transformers

Embedding models

The embedding model is a special type of model that is used to convert text into a vector representation. This is useful for a variety of tasks, including Q&A on a specific dataset. For example, we can generate vector representations of text with the embedding model and then store those vectors in a database alongside the text they represent. Then, we can generate a vector representation of a given input, query the database for the most similar pieces of text, and include one or more of those results in the prompt for the model.

Integrations

These are the interfaces that allow us to communicate with third-party services. Integrations wrap the functionality of third-party services for use by models or peripherals. Integrations are organized by provider (e.g. openai) and may expose multiple models or peripherals. The interface for an integration is defined by the consumer of the specific implementation. For example, the openai integration exposes a chat function that conforms to the ChatIntegration interface defined by the chat model in ./models.

Example model integrations

• OpenAI
• PaLM 2

Integrations can also be used to wrap peripherals. An integration for firebase, for example, could be used by a custom adapter for the storage peripheral.

Example storage integrations

• AWS S3
• Google Cloud Storage
• Supabase

How to use ellma

Install it with your preferred package manager.

# npm
npm i ellma

# pnpm
pnpm add ellma

# yarn
yarn add ellma

Import (or create) an integration, and use it to initialize a model. Use the model to generate output.

import { useChat } from 'ellma'
import { openai } from 'ellma/integrations'

const integration = openai({ apiKey: 'your-private-api-key' })
const { factory, model } = useChat({ integration })

const greeting = factory.human({ text: 'Good morning!' })
const reply = await model.generate(greeting)

console.log(reply.text) // 'Good morning! How may I assist you today?'

For more examples, check out the playground directory.

How to contribute to ellma

Things are still changing, but I recommend you read through the "Concepts" section above before you get started.

Set up your development environment

Clone the repo to your machine.

git clone [e-mail]:davidmyersdev/ellma.git

Install dependencies with pnpm.

# ~/path/to/ellma
pnpm i

Create your .env file.

# ~/path/to/ellma
cp .env.example .env

Add your OpenAI API key and (optionally) add your organization and user keys if you have them.

# ~/path/to/ellma/.env
VITE_OPENAI_API_KEY=your-api-key
# The rest are optional.
VITE_OPENAI_ORGANIZATION_ID=
VITE_OPENAI_USER_ID=

Run a playground example pnpm vite-node ./playground/<example>.ts. To try out the basic chat implementation, run the following.

# ~/path/to/ellma
pnpm vite-node ./playground/chat-basic.ts