✨ Make your LLM prompts executable and version controlled. ✨

In your Express server:

yarn add spellbook-forge

import { spellbookForge } from "spellbook-forge";

const app = express()
  .use(spellbookForge({
    gitHost: 'https://github.com'
  }))

and then:

GET http://localhost:3000/your/repository/prompt?execute

<-- HTTP 200
{
  "prompt-content": "Complete this phrase in coders’ language: Hello …",
  "model": "gpt3.5",
  "result": "Hello, World!"
}

See live examples to try it out!

This is an ExpressJS middleware that allows you to create an API interface for your LLM prompts. It will automatically generate a server for your prompts stored in a git repository. Using Spellbook, you can:

• Store & manage LLM prompts in a familiar tool: a git repository
• Execute prompts with chosen model and get results using a simple API
• Plug into LangChain templating system
• Perform basic CRUD operations on prompts

💡 Note: It's an early version. Expect bugs, breaking changes and poor performance.

This prompt repository: https://github.com/rafalzawadzki/spellbook-prompts/hello-world

Can be executed like this: https://book.spell.so/rafalzawadzki/spellbook-prompts/hello-world?execute

➡️ Spellbook server: https://book.spell.so

The server uses spellbook-forge and is currently hooked up to Github as a git host. You can use any public repository with prompts in it (as long as they adhere to the accepted format).

For example, using a repository rafalzawadzki/spellbook-prompts, you can form an endpoint (and many more):

https://book.spell.so/rafalzawadzki/spellbook-prompts/hello-world

💡 Full documentation coming soon!

If you want to use the execute query on your own spellbook-forge instance, you need to provide an OpenAI API key in .env file or env variables:

OPENAI_API_KEY=your-key

1. 🦜🔗 LangChain.js
2. simple-git

Prompt files must adhere to a specific format (JSON/YAML). See examples here here.

├── prompt1
│   ├── prompt.json
│   └── readme.md
└── collection
    └── prompt2
        ├── prompt.yaml
        └── readme.md

The above file structure will result in the following API endpoints being generated:

{host}/prompt1

{host}/collection/prompt2

1. prompt.json the main file with the prompt content and configuration.
2. readme.md additional information about prompt usage, examples etc.

• GET {host}/path/to/prompt - get prompt content

• POST {host}/path/to/prompt - upsert prompt content

• DELETE {host}/path/to/prompt - delete prompt (along with readme and metadata!)

• GET {host}/path/to/prompt?execute - for simple prompts without templating

• POST {host}/path/to/prompt?execute - for prompts with templating (recommended)

// request body
{
  "variables": [
    "name": "World"
  ]
}

• GET {host}/path/to/prompt?execute=gpt4 - with different model (not implemented yet)

You can fetch the prompt content and execute it using LangChain:

import { PromptTemplate } from "langchain/prompts";

export const run = async () => {
  const template = await fetch(
          "https://book.spell.so/rafalzawadzki/spellbook-prompts/hello-world"
  ).then((res) => res.text());
  const prompt = new PromptTemplate({template, inputVariables: ["product"]})
  // do something with the prompt ...
}

The presented solution ofc makes sense mostly in chaining, for simple prompts it's best to just use Spellbook directly!

In the future I may contribute to extend the LangChain prompt/load function to support loading prompts from Spellbook, eg:

import { loadPrompt } from "langchain/prompts/load";
const prompt = await loadPrompt("{spellbook-host}/hello-world/prompt");

• Documentation
  • OpenAI API key
  • Generated API
  • Templating
  • Using with LangChain
  • Prompt format
  • Available models
• Add missing functionality
  • POST /prompt?execute with body
  • Support for different models, eg. execute=gpt4
  • Graceful error handling
  • Response formatting