Python: feat: Add ModelsLab chat completion connector

[adhikjoshi]

feat: Add ModelsLab chat completion connector

Summary

Adds ModelsLabChatCompletion — a new connector in
semantic_kernel/connectors/ai/modelslab/ that makes
ModelsLab's uncensored Llama models available to
any Semantic Kernel kernel.

Zero new dependencies. ModelsLab exposes an OpenAI-compatible chat
endpoint, so ModelsLabChatCompletion simply subclasses the existing
OpenAIChatCompletion service and wires a pre-configured AsyncOpenAI
client to the ModelsLab base URL — exactly the same pattern used internally
for other OpenAI-compatible providers.

---

What this PR adds

File	Description
semantic_kernel/connectors/ai/modelslab/__init__.py	Package init & public exports
semantic_kernel/connectors/ai/modelslab/modelslab_chat_completion.py	ModelsLabChatCompletion service (main implementation)
semantic_kernel/connectors/ai/modelslab/modelslab_settings.py	Constants: endpoint URL, model list, default model
tests/unit/connectors/ai/modelslab/test_modelslab_chat_completion.py	17 unit tests covering init, env vars, from_dict, client wiring, error paths

---

Usage

from semantic_kernel import Kernel
from semantic_kernel.connectors.ai.modelslab import ModelsLabChatCompletion
from semantic_kernel.connectors.ai.open_ai import OpenAIChatPromptExecutionSettings
from semantic_kernel.contents import ChatHistory

kernel = Kernel()
kernel.add_service(
    ModelsLabChatCompletion(
        ai_model_id="llama-3.1-70b-uncensored",  # optional, default: 8b
        # api_key="..." or set MODELSLAB_API_KEY env var
    )
)

history = ChatHistory()
history.add_user_message("Explain quantum entanglement simply.")
settings = OpenAIChatPromptExecutionSettings(max_tokens=512, temperature=0.7)

service = kernel.get_service(type=ModelsLabChatCompletion)
result = await service.get_chat_message_contents(history, settings)
print(result[0].content)

Streaming, function calling, prompt templates, planners, and multi-service
kernels all work without any changes — ModelsLabChatCompletion inherits
them from OpenAIChatCompletion.

---

Design rationale

Why subclass OpenAIChatCompletion?

ModelsLab's /uncensored-chat/v1/chat/completions endpoint is 100%
OpenAI Chat Completions compatible. Subclassing lets us:

• Reuse all existing request/response logic, streaming, function-calling, and
  retry handling — nothing duplicated.
• Inherit all future improvements to OpenAIChatCompletion automatically.
• Keep the diff minimal and reviewable.

This is consistent with how other providers expose OpenAI-compatible
endpoints in the SK ecosystem.

---

Supported models

Model	Context	Notes
llama-3.1-8b-uncensored (default)	128 K	Fast
llama-3.1-70b-uncensored	128 K	Higher quality

---

Configuration

Parameter	Env var	Default
api_key	MODELSLAB_API_KEY	(required)
ai_model_id	MODELSLAB_CHAT_MODEL_ID	llama-3.1-8b-uncensored
base_url	MODELSLAB_CHAT_BASE_URL	https://modelslab.com/api/uncensored-chat/v1

Get an API key at modelslab.com/api-keys.

---

Testing

pip install pytest pytest-asyncio pytest-mock
pytest tests/unit/connectors/ai/modelslab/ -v
# 17 tests — all pass

---

Checklist

• Follows the existing connector file structure and naming conventions
• No new third-party dependencies introduced
• All configuration via environment variables (matches other connectors)
• from_dict factory implemented (required by SK serialization)
• Unit tests cover: init (happy + error paths), env var resolution,
  from_dict, client wiring, and constants
• Docstrings and type hints throughout
• __all__ defined in __init__.py

---

About ModelsLab

ModelsLab is an AI API platform providing access to
Llama, Flux, SDXL, video generation, voice synthesis, and many other models.
Documentation: docs.modelslab.com

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