SlashGPT

SlashGPT is a playground for develeopers to make quick prototypes of LLM agents (or apps with Natural Language UI).

Here are the design goals:

Extremely easy to create a new LLM agent. You just need to add a new manifest file (in Json).
Instantly switch amang agents, by just typing "/{agent_name}"
Extensible enough so that it is possible to implement most of LLM agents without writing any code.
It is possible to integrate ChatGPT plugins as agents without writing any code.
It enables broker agent (or dispatcher), which routes user's messgae to an appropraite agent.
It is able to run generated Python code like Code Interpreter (see "jupyter" agent).

Initialization

Install the required packages:

pip install -r requirements.txt
Create .env file, and specify your OpenAI key as follows:

OPENAI_API_KEY=...
You need to specify other variables to use following features (optional).
- PINECONE_API_KEY, PINECONE_ENVIRONMENT: requred to use embedding vector db.
- GOOGLE_PALM_KEY: required to switch to PaLM LLC using /palm command.
- WOLFRAM_API_KEY: required to use "walfram" agent.
- OPENWEATHER_API_KEY: required to use "weather" agent.
- NOTEABLE_API_KEY: required to use "noteable" agent.
- ALCHEMY_API_KEY: required to use "web3" agent.
- WEBPILOT_UID: required to use "webpilot" agent (any unique UUID is fine)
- REPLICATE_API_TOKEN: required to use "llama2" model.
- CODEBOX_API_KEY: set this to "local" to use CodeBox's LocalBox instead of IPython

Execution

Type ./SlashGPT.py
When you see "You({agent_name}):", type a message to the agent OR type a slash command starting with "/".
It activate "dispatcher" agent first, which is able to dispatch queries to appropriate agents.
Type "/help" to see the list of system commands and available agents.

Outputs

Each conversation will be store as a json file under the "output/{context}" folder, where the context is "GTP" for general chat, and the app id for a specialized chat.
Please notice that the "output" folder is ignored by git.
Code Interpreter agents will generate Jupyter notebook in "output/notebooks" folder.

Code Interpreter Agents

Some of agents are built to mimic the behaviors of ChatGPT code intepreter (or Noteable plugin) with various LLMs.

jupyter: GPT3.5
jupyterp: PaLM2 (GOOGLE_PALM_KEY key is required)
juypter2: LlaMA (REPLICATE_API_TOKEN is required)

jupyter (GPT3.5) works just like Code Interpreter. It is able to respond to the output of nenerated code appropriately.

jupyterp (PaLM2) and jupyter2 (LlaMA) are not able to respond to the output of generated code (they often enter into an infinit loop). Therefore, we stop the conversation after the output, and the user needs to explicitly ask it to analize the result.

For the runtime, it uses IPython by default, but it uses CodeBox if you specify CODEBOX_API_KEY key. IPython displays images as popups, but does not write them into the notebook. CodeBox is able to write them into the notebook.

Sample queries.

Draw sine curve
List first 50 prime numbers
graph common moving average
Draw histogram
Graph 4 year stock price of apple and tesla using yfinance

Manifest files

Create a new manifest file, {agent_name}.json in "manifests" folder with following properties:

title (string, required): Title for the user to see
source (string, optional): Source of the prompt (URL, email, github id, or twitter id)
promt (array of strings, required): The system prompts which define the agent (required)
form (string): format string to extend user's query (e.g. "Write python code to {question}").
result_form (string): format string to extend function call result.
skip_function_result (boolean): skip the chat completion right after the function call.
notepad (boolean): create a new notebook at the beginning of each session (for jupyter2)
bot (string, optional): Agent name
you (string, optional): User name. The default is You({agent_name}).
sample (string, optional): Sample question (type "/sample" to send it)
intro (array of strings, optional): Introduction statements (will be randomly selected)
model (string, optional): LLM model (such as "gpt-4-613", the default is "gpt-3-turbo")
temperature (string, optional): Temperature (the default is 0.7)
data (array of string, optional): {random} will put one of them randamly into the prompt
embeddings (object, optional):
- name (string, optional): index name of the embedding vector database
resource (string, optional): location of the resource file. Use {resource} to paste it into the prompt
functions (string, optional): location of the function definitions
module (string, optional): location of the pytoh script to be loaded for function calls
actions (object, optional): Template-based function processor (see details below)

Name of that file becomes the slash command. (the slash command of "foo.json" is "/foo")

Actions

It defines template-based function implementations (including mockups), alternative to writing Python code using the "module" property.

It supports three different methods.

1. Formatted string.

Use this method to develop the front-end of a system before the backend become ready.

message (format string, required): chat messgae to be added
metafile (format string, optional): metafile name to be loaded for chained action

Here is an example (home2).

  "actions": {
    "fill_bath": { "message":"Success. I started filling the bath tab." },
    "set_temperature": { "message":"Success. I set the teperature to {temperature} for {location}" },
    "start_sprinkler": { "message":"Success. I started the sprinkler for {location}" },
    "take_picture": { "message":"Success. I took a picture of {location}" },
    "play_music": { "message":"Success. I started playing {music} in {location}" },
    "control_light": { "message":"Success. The light switch of {location} is now {switch}." }
  }

2. REST calls

Use this method to call REST APIs (equivalent to ChatGPT's plugin system).

url (string, required): Python-style format string, which references to function arguments.
method (string, optional): Specify "POST" if we need to use HTTP-POST. The body will contain a JSON representation of function parameters.

Here is an example (currency).

  "actions": {
    "convert": {
      "url": "https://today-currency-converter.oiconma.repl.co/currency-converter?from={from}&to={to}&amount={amount}"
    }
  }

3. data URL

This mechod allows a developer to generate a text data (typically in JSON, but not limited to), and turn it into a data URL.

template (string, required): The location of the template file.
mime_type (string, required): The mime type of the data.
message (string, required): Python-style format string, which references to the data-URL as {url}.

Here is an example for "make_event" function (cal).

  "actions": {
    "make_event": {
      "template": "./resources/calendar.ics",
      "mime_type": "text/calendar",
      "message": "The event was scheduled. Here is the invitation link: '{url}'"
    }
  }

The contents of calendar.ics file.

BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//My Calendar//NONSGML v1.0//EN
BEGIN:VEVENT
DTSTART:{DTSTART}
DTEND:{DTEND}
SUMMARY:{SUMMARY}
DESCRIPTION:{DESCRIPTION}
LOCATION:{LOCATION}
END:VEVENT
END:VCALENDAR

The definition of "make_event" function.

{
  "name": "make_event",
  "description": "Create a calendar event in iCalendar format",
  "parameters": {
    "type": "object",
    "properties": {
      "SUMMARY": {
        "type": "string",
        "description": "a short, one-line description of the event"
      },
      "DESCRIPTION": {
        "type": "string",
        "description": "a more complete description of the calendar",
        "maxLength": 400
      },
      "DTSTART": {
        "type": "string",
        "format": "date-time",
        "description": "the date and time in UTC that the event begins such as 19980119T020000Z"
      },
      "DTEND": {
        "type": "string",
        "format": "date-time",
        "description": "the date and time in UTC that the event ends such as 19980119T030000Z"
      },
      "LOCATION": {
        "type": "string",
        "description": "the intended venue with address for the event."
      }
    },
    "required": ["SUMMARY", "DTSTART", "DTEND", "DESCRIPTION", "LOCATION"]
  }
}

BabyBlue26 / SlashGPT