rwu-llmconnector 0.1.0

Unified LLM connectors for OpenAI, Google Gemini, Ollama, and LM Studio.

rwu-llmconnector

Unified Python connectors for calling large language models through a common interface.

Supported backends:

• OpenAI (official API and OpenAI-compatible endpoints)
• Google Gemini (google-genai)
• Ollama (local server)
• LM Studio (local or remote server, including stateful conversations)

Installation

pip install rwu-llmconnector

With a virtual environment (recommended):

python -m venv .venv
source .venv/bin/activate   # Windows: .venv\Scripts\activate
pip install rwu-llmconnector

Quick start

OpenAI

from openai import OpenAI
from rwu_llmconnector import OpenAIClient

client = OpenAI(api_key="your-openai-api-key")
llm = OpenAIClient(client, model="gpt-4o-mini")

response, prompt_tokens, completion_tokens, reasoning_tokens, runtime = llm.send_request(
    "What is PyPI?",
    system_message="you are a helpful assistant",
)
print(response)

Google Gemini

from google import genai
from rwu_llmconnector import GeminiClient

client = genai.Client(api_key="your-gemini-api-key")
llm = GeminiClient(client, model="gemini-2.5-flash-lite")

response, *_ = llm.send_request(
    "What is PyPI?",
    system_message="you are a helpful assistant",
)
print(response)

Ollama

from rwu_llmconnector import OllamaClient

llm = OllamaClient(model="llama3.2", host="http://localhost:11434")

response, *_ = llm.send_request(
    "What is PyPI?",
    system_message="you are a helpful assistant",
)
print(response)

LM Studio

from rwu_llmconnector import LMStudioClient

llm = LMStudioClient(
    model="qwen/qwen3-4b-2507",
    base_url="http://localhost:1234",
    api_key=None,  # optional; set LMSTUDIO_API_KEY when required
)

response, *_ = llm.send_request(
    "What is PyPI?",
    system_message="you are a helpful assistant",
)
print(response)

For server-side conversation state, use StatefulLMStudioClient instead of LMStudioClient.

Model loader helpers

from rwu_llmconnector.model_loader import load_openai_model, load_ollama_model

llm = load_openai_model("gpt-4o-mini", env="openai")
ollama = load_ollama_model("llama3.2")

Set OPENAI_API_KEY in the environment when using env="openai".

Development

Clone the repository, then:

uv sync --group dev
cp .env.example .env   # add your API keys locally; never commit .env

Run integration tests (live APIs; Ollama skipped without a running server):

uv run pytest -v

Tests load variables from .env via python-dotenv. See .env.example for supported names.

Variable	Used for
OPENAI_API_KEY	OpenAI
GEMINI_API_KEY or GOOGLE_API_KEY	Gemini
LMSTUDIO_BASE_URL, LMSTUDIO_MODEL	LM Studio (required for those tests)
LMSTUDIO_API_KEY or MAC_STUDIO_API_KEY	LM Studio auth (optional locally)
OLLAMA_MODEL, OLLAMA_HOST	Ollama (optional)

Project layout

src/rwu_llmconnector/   # library code
tests/                           # integration tests
pyproject.toml
uv.lock                          # pinned dev dependencies
.env.example                     # template for local secrets (commit this)

Do not commit: .env, .venv/, dist/, __pycache__/, .pytest_cache/, .idea/

Publishing to PyPI

Build and upload (requires PyPI account and API token):

uv build
uv publish

Upload only the files in dist/; do not add dist/ to git.

After creating the GitHub repository, uncomment and set Repository under [project.urls] in pyproject.toml.

License

MIT — see LICENSE.