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GPTextual

A Terminal-Based Chat Client for various LLMs built with Textual

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Features

Related Projects and Credits

This project is a fork of the Elia project by Darren Burns, with most of the runtime logic and parts of the UI rewritten, especially to decouple the UI from the conversational runtime and to abstract the app from the concrete LLM it should connect to.

This app also integrates TooLong as the in-place log file viewer.

The LLMs are called and streamed via LangChain API clients.

The connection to SAPs Generative AI Hub is provided by the corresponding SDK library.

Installation

Prerequisites:

Steps

  1. To avoid any conflicts with other packages or system-wide Python installations, it is recommended to use virtual environments (venv or conda). Create and activate a virtual environment using the following commands (for conda, see documentation):

Change into directory where you want to create the virtual environment. Then:

python3 -m venv gptextual-env
  1. Activate:
.\gptextual-env\Scripts\activate

source gptextual-env/bin/activate
  1. Install the package:
pip install gptextual[all]

gptextual can be installed with these flavors:

This way you can limit your installation footprint if you only want to use native OpenAI, for example.

Quickstart

1. Create file ~/.gptextual/config.yml and enter your API key for the API provider(s) you want to connect to

api_config:
  # Open AI
  openai:
    api_key: <your key>
  
  # Google Gen AI APIs
  google:
    api_key: <your key>
  
  # Anthropic API
  anthropic:
    api_key: <your key>

  # SAP Gen AI Hub for enterprise scenarios
  gen-ai-hub:
    client_id: str
    client_secret: str
    auth_url: str
    api_base: str
    resource_group: str

If you don't need one or more of the API providers, just remove them from the yaml file. Other API providers (like Huggingface) will be added soon.

2. Start the app by opening a terminal, then:

gptx

Make sure you activated the virtual environment.

Terminals to use

On Mac

It is recommended to run gptextual with iTerm2, because it gives the best user experience. Markdown links in LLM responses will also be fully clickable by holding the Command key and all colors look as intended.

On Windows

It is recommended to use Windows Terminal.

In VS Code

For developers, the VSCode integrated terminal also renders the app well, markdown links are clickable via the Command key. This gives a nice IDE integration of LLMs for development support.

More Info

For more info on general settings and optimizations when using Textual based apps, consult the Textual FAQ.

Using the UI

Textual based apps can be navigated with a mouse or by keyboard only.

It is recommended to learn the key shortcuts and tab orders of the UI elements for the best and most efficient user experience.

Configuration Guide

In order to use gptextual you need to create a YAML file at ~/.gptextual/config.yml and maintain the configuration for at least 1 API provider (OpenAI, Google, etc.).

Note: For any configuration changes in the config.yml to take effect, an application restart is required.

API Providers

The following outlines the mandatory config to get started, the full configuration will be explained further below.

Open AI

api_config:
  openai:
    api_key: <your key> # mandatory

Google

api_config:
  google:
    api_key: <your key> # mandatory

Configuring both, OpenAI and Google would hence look like this:

api_config:
  openai:
    api_key: <your key>
  
  google:
    api_key: <your key>

Anthropic

api_config:
  anthropic:
    api_key: <your key> # mandatory

SAP GenAI Hub

api_config:
  gen-ai-hub:
    client_id: str
    client_secret: str
    auth_url: str
    api_base: str
    resource_group: str

You will get these settings from your SAP GenAI Hub setup.

gptextual will automatically create the required config.json file at ~/.aicore/config.json.

Models

For each API provider you can specify which models should be selectable in the app. This is optional for all API providers except gen-ai-hub. For OpenAI and Google, default models will be selected, reflecting the models available at the time of the gptextual release.

For example:

api_config:
  openai:
    api_key: <your key>

    # specify which models you want to chat with
    # the model name has to exist on the API platform
    models: # Examples
      gpt-3.5-turbo:
        context_window: 4096
      gpt-4:
        context_window: 8192
      gpt-4-0125-preview:
        context_window: 128000

Double check the model names since they will be passed to the LangChain API client as-is.

The contex_window parameter is used to

Function Calling

gptextual supports LLM function calling of functions developed by you or provided as python packages you install.

Currently, the following function calling protocols are supported:

Configuring Function Calling

Function Call Support

In the config.yml you can configure the type of function call support for each model. For example, from the Open AI Website we see that only a few models support the parallel tool calling API, most of the rest will support the legacy function calling. So we could add the following to the config.yml

api_config:
  openai:
    api_key: ...
    function_call_support:
      # either listing the models one by one
      gpt-4-turbo-preview: openai_tool
      gpt-4-0125-preview: openai_tool
      # Or using a comma separated list of model names
      'gpt-4-1106-preview,gpt-3.5-turbo-0125,gpt-3.5-turbo-1106': 'openai_tool'

      # or also specify a wildcard meaning "all other models not specified by name'
      '*': openai_function

Registering functions

1. Develop your function in a new pip package and decorate it with the provided function decorator register_for_function_calling:

For example:

from gptextual.runtime.function_calling import register_for_function_calling, get_function_config

@register_for_function_calling
def multiply(a: int, a: int):
  """Multiply two integers together.

    Args:
        a: First integer
        b: Second integer
  """
  return a * b


@register_for_function_calling
async def google_web_search(query: str) -> str:
    """
    Executes a Google search with the specified search string and returns the top 10 search results.
    For each result, a title, URL and preview snippet is returned.

    Args:
        query: the query string
    """
    # Functions can also access an optional config from the config.yml
    config = get_function_config(google_web_search)
    api_key, cx_id = None, None
    if config:
        api_key = config.get("api_key", None)
        cx_id = config.get("cx_id", None)
    ...

When developing the function, following the guidelines by LangChain.

Specifically:

2. Define an entry point with name gptextual_function in the setup.py/pyproject.toml of your project:


entry_points={
        "gptextual_function": [
            "module_name = your.package:your_function"
        ],
},

Note: gptextual will only load your entry point, and expect each function to have the decorator above. So specifying one function per module in the entry point is enough, because all functions will be loaded when the module loads.

3. Configure which functions should be used at runtime

Each function in your python environment that is registered via the decorator and entry point can be used for LLM function calling. In order to actually pick the functions you want to use, you need to list them in the config.yml:


functions:
  # In this example the function 'google_web_search' will be exposed to LLMs that support function calling
  google_web_search:
    # Functions can also have an optional configuration, in this case Google Cloud API keys
    api_key: ... # API Key
    cx_id: ... # Custom search engine ID

  multiply: {} # empty config

Functions Included with gptextual

Currently gptextual comes with the following example functions:

More functions are planned in the future.

UI Configuration

There is a config section directly related to the textual UI:

textual:
  # When LLMs support streaming, this number specifies after how many response chunks
  # a refresh and re-layouting of the chat message will be triggered in the textual framework.
  # A smaller number will give a smoother streaming experience but will lead to more refresh calls.
  refresh_no_stream_chunks: 3

  # gptextual comes with a light and dark theme.
  theme: light|dark

Logging

gptextual writes into a JSON-lines log file located at ~/.gptextual/logging/gptextual.jsonl

The log file can be opened in app with the Ctrl+L keyboard shortcut. This will open TooLong, which is a very fast, real-time tailing log file viewer.

Inspecting the log can be useful to check if the LLM is called like you expect or to inspect function calling results, for example.

Within TooLong, in addition to navigating line by line and searching, you can also navigate via log entry timestamps:

Press:

Configuring the log level

By default, gptextual sets the log level to INFO.

You can change this in the config.yml with:

# other config...

log_level: DEBUG|INFO|WARNING|ERROR|CRITICAL

config.yml template

A complete config.yml for your reference:

api_config:
  openai:
    api_key: ... 

    models: # Example: Only want to chat with these 3 models from OpenAI:
      gpt-3.5-turbo:
        context_window: 4096
      gpt-4:
        context_window: 8192
      gpt-4-0125-preview:
        context_window: 128000
      
    function_call_support:
      # onlu activate function calling for GPT-4
      'gpt-4,gpt-4-0125-preview': 'openai_tool'
     
  google:
    api_key: ...
    # No models are specified, gptextual will select 'gemini-pro' by default
  
  # No SAP API specified, so you will not see that option in the UI

textual:
  refresh_no_stream_chunks: 3
  theme: light
  # theme: dark
functions:
  # The gpt-4 models above will be able to call this function!
  google_web_search:
    api_key: ... 
    cx_id: ...

log_level: INFO

Conversation Storage

The conversations are stored in folder

~/.gptextual/conversations

For each conversation, there will be

The parquet file can be read with any library that supports it. gptextual uses polars internally.

Markdown Export

When you export a conversation to markdown in the app, they are stored in folder

~/.gptextual/exports

Copying Messages to clipboard

Messages in the chatview are focusable. You see the selected message marked. When a message is selected, you can

Bug Reports

Submit bugs via GitHub issues in this repo.