LLMClient - A new way to build with LLMs.

JS/TS library based on the Stanford DSP paper. Create and compose efficient prompts using prompt signatures. Reasoning + Function Calling, RAG and more.

🌵 🦙 🔥 ❤️ 🖖🏼

Build LLM workflows with prompt signatures

LLMClient is an easy to use library build around "Prompt Signatures" from the Stanford DSP paper. This library will automatically generate efficient and typed prompts from prompt signatures like question:string -> answer:string.

Build powerful workflows using components like RAG, ReAcT, Chain of Thought, Function calling, Agents, etc all built on prompt signatures and easy to compose together to build whatever you want.

Why use LLMClient?

• Support for various LLMs and Vector DBs
• Prompts auto-generated from simple signatures
• Multi-Hop RAG, ReAcT, CoT, Function Calling, Agents and more
• Almost zero-dependencies

Whats a prompt signature?

"Write a simple search query that will help answer a complex question."
context?:string[] "may contain relevant facts", question -> query

Efficient type-safe prompts are auto-generated from a simple signature. A prompt signature is made of a "description" inputField:type -> outputField:type". The idea behind prompt signatures is based off work done in the "Demonstrate-Search-Predict" paper.

LLM's Supported

Example: Using chain-of-thought to summarize text

import { AI, ChainOfThought, OpenAIArgs } from 'llmclient';

const textToSummarize = `
The technological singularity—or simply the singularity[1]—is a hypothetical future point in time at which technological growth becomes uncontrollable and irreversible, resulting in unforeseeable changes to human civilization.[2][3] ...`;

const ai = AI('openai', { apiKey: process.env.OPENAI_APIKEY } as OpenAIArgs);
const gen = new ChainOfThought(
  ai,
  `textToSummarize -> shortSummary "summarize in 5 to 10 words"`
);
const res = await gen.forward({ textToSummarize });

console.log('>', res);

Example: Building an agent

Use the agent prompt (framework) to build agents that work with other agents to complete tasks. Agents are easy to build with prompt signatures. Try out the agent example.

# npm run tsx ./src/examples/agent.ts

const agent = new Agent(ai, {
  name: 'agent',
  description: 'A an agent to research complex topics',
  signature: `question -> answer`,
  agents: [researcher, summarizer]
});

agent.forward({ questions: "How many atoms are there in the universe" })

Checkout more examples

Use the tsx command to run the examples it makes node run typescript code. It also support using a .env file to pass the AI API Keys as opposed to putting them in the commandline.

OPENAI_APIKEY=openai_key npm run tsx ./src/examples/marketing.ts

Reasoning + Function Calling

Often you need the LLM to reason through a task and fetch and update external data related to this task. This is where reasoning meets function (API) calling. It's built-in so you get all of the magic automatically. Just define the functions you wish to you, a schema for the response object and thats it.

There are even some useful built-in functions like a Code Interpreter that the LLM can use to write and execute JS code.

We support providers like OpenAI that offer multiple parallel function calling and the standard single function calling.

Built-in Functions

Our Goal

Large language models (LLMs) are getting really powerful and have reached a point where they can work as the backend for your entire product. However there is still a lot of manage a lot of complexity to manage from using the right prompts, models, etc. Our goal is to package all this complexity into a well maintained easy to use library that can work with all the LLMs out there. Additionally we are using the latest research to add useful new capabilities like DSP to the library.

How to use this library?

1. Pick an AI to work with

// Pick a LLM
const ai = new OpenAI({ apiKey: process.env.OPENAI_APIKEY } as OpenAIArgs);

2. Pick a memory for storing context (optional)

// Can be sub classed to build you own memory backends
const mem = new Memory();

3. Pick a prompt based on your usecase

const cot = new ChainOfThought(ai, `question:string -> answer:string`, { mem });

4. Use the prompt

const res = await cot.forward({ question: 'Are we in a simulation?' });

5. Alternatively use the LLM directly.

const res = await ai.chat([
  { role: "system", content: "Help the customer with his questions" }
  { role: "user", content: "I'm looking for a Macbook Pro M2 With 96GB RAM?" }
]);

How do you use function calling

1. Define the functions

// define one or more functions and a function handler
const functions = [
  {
    name: 'getCurrentWeather',
    description: 'get the current weather for a location',
    parameters: {
      type: 'object',
      properties: {
        location: {
          type: 'string',
          description: 'location to get weather for'
        },
        units: {
          type: 'string',
          enum: ['imperial', 'metric'],
          default: 'imperial',
          description: 'units to use'
        }
      },
      required: ['location']
    },
    func: async (args: Readonly<{ location: string; units: string }>) => {
      return `The weather in ${args.location} is 72 degrees`;
    }
  }
];

2. Pass the functions to a prompt

const cot = new ReAct(ai, `question:string -> answer:string`, { functions });

Enable debug logs

const ai = new OpenAI({ apiKey: process.env.OPENAI_APIKEY } as OpenAIArgs);
ai.setOptions({ debug: true });

Reach out

We're happy to help reach out if you have questions or join the Discord twitter/dosco

FAQ

1. The LLM can't find the right function to use

Improve the function naming and description be very clear on what the function does. Also ensure the function parameter's also have good descriptions. The descriptions don't have to be very long but need to be clear.

2. How do I change the configuration of the LLM used

You can pass a configuration object as the second parameter when creating a new LLM object

const apiKey = process.env.OPENAI_APIKEY;
const conf = OpenAIBestConfig();
const ai = new OpenAI({ apiKey, conf } as OpenAIArgs);

3. My prompt is too long and can I change the max tokens

const conf = OpenAIDefaultConfig(); // or OpenAIBestOptions()
conf.maxTokens = 2000;

4. How do I change the model say I want to use GPT4

const conf = OpenAIDefaultConfig(); // or OpenAIBestOptions()
conf.model = OpenAIModel.GPT4Turbo;