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llmModule (development version)

llmModule provides a structured and extensible R interface to interact with both remote (e.g., OpenAI, DeepSeek) and local (via Ollama) Large Language Model (LLM) APIs. It simplifies key workflows such as model selection, prompt configuration, and request handling through a consistent object-oriented interface.

llmModule provides a structured R interface for working with Large Language Model (LLM) APIs, including OpenAI and DeepSeek.

It simplifies interactions with chat-based LLMs by offering methods and S3 classes for:

  • API key management and validation
  • Prompt configuration
  • Sending chat prompts
  • Extracting responses

🚀 Features

  • Modular, object-oriented interface using S3 classes:
    • RemoteLlmApi for remote providers (OpenAI, DeepSeek)
    • LocalLlmApi for local Ollama servers
    • LlmPromptConfig to configure prompt messages and parameters
    • LlmResponse for structured handling of responses
  • Comprehensive API validation:
    • Validates API key format, provider match, and key functionality via test request
    • Clear error reporting with automatic suggestion of likely provider mismatches
  • Local model support (via Ollama):
    • Allows to pull new models
    • Allows exclusion of deprecated or irrelevant models via exclude_pattern
  • Unified method interface:
  • Optional Docker integration for local deployment (see below)

🧪 Quick Example 

# Create an LLM API object
api <- new_RemoteLlmApi("~/.secrets/openai.txt", provider = "OpenAI")

# Set up a prompt
prompt <- new_LlmPromptConfig(
      model = "gpt-4.1",
      prompt_content = "What's the capital of Italy?"
)

# Send the prompt
result <- send_prompt(api, prompt)

# Extract the assistant's reply
result$choices[[1]]$message$content

📦 Docker Installation (recommended)

Run this app in your browser with just one command! The Docker setup includes all components — the llmModule Shiny frontend and the ollama backend for local LLM model serving — no manual setup required.

✅ 1. Install the software Docker

Download installation files from one of the links below and follow installation instructions:

After Docker is installed you can pull & run the app manually.

🚀 2. Run the App with Docker Compose

Open a terminal (command line):

  • Windows command line:
    1. Open the Start menu or press the Windows key + R;
    2. Type cmd or cmd.exe in the Run command box;
    3. Press Enter.
  • MacOS: open the Terminal app.
  • Linux: most Linux systems use the same default keyboard shortcut to start the command line: Ctrl-Alt-T or Super-T

To start the app you need the docker-compose.yaml of this Repository. You can either:

Run directly without cloning the repo:

curl -sL https://raw.githubusercontent.com/Pandora-IsoMemo/llmModule/refs/heads/main/docker-compose.yml | docker compose -f - up

OR: Clone the repository and run in the project directory:

git clone https://github.com/Pandora-IsoMemo/llmModule.git
cd llmModule
docker compose up

These commands will:

  1. The first time you run this, it will download the necessary Docker images for
    • ollama (for model serving and its REST API) and
    • the llm-module (the Shiny web frontend that controls Ollama and can also interact with other LLM APIs like OpenAI, Deepseek).
  2. After images are pulled, a Docker network and a Docker volume will be created, and both container will start.
  3. The llm-module container hosts the application, which you can access in your web browser at http://127.0.0.1:3838/.

🚀 3. Run the app with a custom Ollama models path (optional)

To use your own pre-downloaded Ollama models, specify a custom path by setting the OLLAMA_LOCAL_MODELS_PATH environment variable.

This requires cloning the repository and running Docker Compose from the project directory.

OLLAMA_LOCAL_MODELS_PATH=/path/to/your/models docker compose up

Default locations for Ollama models:

  • linux: /usr/share/ollama/.ollama
  • macOS: ~/.ollama
  • windows: C:\\Users\\<username>\\.ollama

This will mount your local models into the container for faster startup and persistent access.

Notes for developers — local testing

To build and run the app locally:

docker compose up --build

Use --build if:

  • You made changes to source code or Dockerfiles,
  • Or you’re testing a fresh environment.

To run with a custom models path:

OLLAMA_LOCAL_MODELS_PATH=</path/to/your/models> docker compose up --build

Tip: Use docker compose down to stop and clean up the containers when done.