Python: Add Kelly Intelligence chat completion sample (related to #13694)

Adds a new concept sample showing how to use Semantic Kernel with Kelly
Intelligence (https://api.thedailylesson.com), a hosted OpenAI-compatible
API with a built-in 162,000-word vocabulary RAG layer and an AI tutor
persona, built on top of Claude. Operated by Lesson of the Day, PBC, a
public benefit corporation.

The sample mirrors the existing `lm_studio_chat_completion.py` and
`foundry_local_chatbot.py` patterns exactly: an `AsyncOpenAI` client
pointed at a custom `base_url`, wrapped in `OpenAIChatCompletion`, with
a `ChatHistory` and chat loop. No new connector, no new abstraction —
this is a pure example of the existing OpenAI-compatible pattern applied
to a third-party hosted endpoint.

The sample uses the free `kelly-haiku` model id and reads `KELLY_API_KEY`
from the environment. A free key (no credit card) is available at
https://api.thedailylesson.com. Reviewers can also smoke-test the API
without any signup using the public `/v1/demo` endpoint, which is
rate-limited at 5 requests per hour per IP and uses the same wire format.

Related to #13694.

Author: nicoletterankin

python/samples/concepts/local_models/kelly_intelligence_chat_completion.py

@@ -0,0 +1,121 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+
+import asyncio
+import os
+
+from openai import AsyncOpenAI
+
+from semantic_kernel.connectors.ai.open_ai import OpenAIChatCompletion
+from semantic_kernel.contents.chat_history import ChatHistory
+from semantic_kernel.functions.kernel_arguments import KernelArguments
+from semantic_kernel.kernel import Kernel
+
+# This concept sample shows how to use the OpenAI connector to create a chat
+# experience with Kelly Intelligence (https://api.thedailylesson.com), a hosted
+# OpenAI-compatible API with a built-in 162,000-word vocabulary RAG layer and an
+# AI tutor persona, built on top of Claude. It is operated by Lesson of the Day,
+# PBC, a public benefit corporation. The free tier requires no credit card.
+#
+# Although this file lives under "local_models", the same `AsyncOpenAI` +
+# `base_url` pattern that powers `lm_studio_chat_completion.py` and
+# `foundry_local_chatbot.py` works for any OpenAI-compatible endpoint, local or
+# hosted. Kelly Intelligence is a hosted instance of that pattern.
+#
+# Get a free API key (no credit card) at https://api.thedailylesson.com and
+# export it before running this sample:
+#
+#     export KELLY_API_KEY=...
+#     python kelly_intelligence_chat_completion.py
+#
+# You can also try the API with no signup at all using its public `/v1/demo`
+# endpoint, which is rate-limited at 5 requests per hour per IP:
+#
+#     curl -X POST https://api.thedailylesson.com/v1/demo \
+#       -H "Content-Type: application/json" \
+#       -d '{"messages":[{"role":"user","content":"What does ephemeral mean?"}]}'
+
+system_message = """
+You are Kelly, a friendly vocabulary tutor. When the user gives you a word,
+teach it in one short paragraph: definition, a memorable example sentence,
+and one related word. Keep the tone warm and encouraging.
+"""
+
+kernel = Kernel()
+
+service_id = "kelly-intelligence"
+
+# Kelly Intelligence is OpenAI wire-format compatible, so we point a standard
+# AsyncOpenAI client at its `/v1` base URL and let `OpenAIChatCompletion`
+# handle the rest. The free model id is `kelly-haiku`; `kelly-sonnet` and
+# `kelly-opus` are also available on paid tiers.
+openAIClient: AsyncOpenAI = AsyncOpenAI(
+    api_key=os.environ.get("KELLY_API_KEY", "fake-key"),
+    base_url="https://api.thedailylesson.com/v1",
+)
+kernel.add_service(
+    OpenAIChatCompletion(
+        service_id=service_id,
+        ai_model_id="kelly-haiku",
+        async_client=openAIClient,
+    )
+)
+
+settings = kernel.get_prompt_execution_settings_from_service_id(service_id)
+settings.max_tokens = 800
+settings.temperature = 0.7
+settings.top_p = 0.9
+
+chat_function = kernel.add_function(
+    plugin_name="VocabularyTutor",
+    function_name="Chat",
+    prompt="{{$chat_history}}{{$user_input}}",
+    template_format="semantic-kernel",
+    prompt_execution_settings=settings,
+)
+
+chat_history = ChatHistory(system_message=system_message)
+chat_history.add_user_message("Hi! I'd like to learn some new English words today.")
+chat_history.add_assistant_message(
+    "Wonderful! Give me any word you're curious about and I'll teach it to you in one short paragraph."
+)
+
+
+async def chat() -> bool:
+    try:
+        user_input = input("User:> ")
+    except KeyboardInterrupt:
+        print("\n\nExiting chat...")
+        return False
+    except EOFError:
+        print("\n\nExiting chat...")
+        return False
+
+    if user_input == "exit":
+        print("\n\nExiting chat...")
+        return False
+
+    answer = await kernel.invoke(
+        chat_function,
+        KernelArguments(user_input=user_input, chat_history=chat_history),
+    )
+    chat_history.add_user_message(user_input)
+    chat_history.add_assistant_message(str(answer))
+    print(f"Kelly:> {answer}")
+    return True
+
+
+async def main() -> None:
+    chatting = True
+    while chatting:
+        chatting = await chat()
+
+    # Sample output:
+    # User:> serendipity
+    # Kelly:> "Serendipity" is the happy accident of finding something good without looking for it -
+    #         like discovering a favorite cafe while you were just trying to get out of the rain.
+    #         A close cousin is "fortuitous", which describes lucky timing more generally.
+
+
+if __name__ == "__main__":
+    asyncio.run(main())