feat: add Semantic Kernel Adapter documentation and usage examples in…

… user guides (#5256) Partially address #5205 and #5226
microsoft · Jan 30, 2025 · 403844e · 403844e
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diff --git a/python/packages/autogen-core/docs/src/user-guide/agentchat-user-guide/tutorial/models.ipynb b/python/packages/autogen-core/docs/src/user-guide/agentchat-user-guide/tutorial/models.ipynb
@@ -327,6 +327,101 @@
     "response = await model_client.create([UserMessage(content=\"What is the capital of France?\", source=\"user\")])\n",
     "print(response)"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Semantic Kernel Adapter\n",
+    "\n",
+    "The {py:class}`~autogen_ext.models.semantic_kernel.SKChatCompletionAdapter`\n",
+    "allows you to use Semantic kernel model clients as a\n",
+    "{py:class}`~autogen_core.models.ChatCompletionClient` by adapting them to the required interface.\n",
+    "\n",
+    "You need to install the relevant provider extras to use this adapter. \n",
+    "\n",
+    "The list of extras that can be installed:\n",
+    "\n",
+    "- `semantic-kernel-anthropic`: Install this extra to use Anthropic models.\n",
+    "- `semantic-kernel-google`: Install this extra to use Google Gemini models.\n",
+    "- `semantic-kernel-ollama`: Install this extra to use Ollama models.\n",
+    "- `semantic-kernel-mistralai`: Install this extra to use MistralAI models.\n",
+    "- `semantic-kernel-aws`: Install this extra to use AWS models.\n",
+    "- `semantic-kernel-hugging-face`: Install this extra to use Hugging Face models.\n",
+    "\n",
+    "For example, to use Anthropic models, you need to install `semantic-kernel-anthropic`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "shellscript"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# pip install \"autogen-ext[semantic-kernel-anthropic]\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To use this adapter, you need create a Semantic Kernel model client and pass it to the adapter.\n",
+    "\n",
+    "For example, to use the Anthropic model:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "finish_reason='stop' content='The capital of France is Paris. It is also the largest city in France and one of the most populous metropolitan areas in Europe.' usage=RequestUsage(prompt_tokens=0, completion_tokens=0) cached=False logprobs=None\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "\n",
+    "from autogen_core.models import UserMessage\n",
+    "from autogen_ext.models.semantic_kernel import SKChatCompletionAdapter\n",
+    "from semantic_kernel import Kernel\n",
+    "from semantic_kernel.connectors.ai.anthropic import AnthropicChatCompletion, AnthropicChatPromptExecutionSettings\n",
+    "from semantic_kernel.memory.null_memory import NullMemory\n",
+    "\n",
+    "sk_client = AnthropicChatCompletion(\n",
+    "    ai_model_id=\"claude-3-5-sonnet-20241022\",\n",
+    "    api_key=os.environ[\"ANTHROPIC_API_KEY\"],\n",
+    "    service_id=\"my-service-id\",  # Optional; for targeting specific services within Semantic Kernel\n",
+    ")\n",
+    "settings = AnthropicChatPromptExecutionSettings(\n",
+    "    temperature=0.2,\n",
+    ")\n",
+    "\n",
+    "anthropic_model_client = SKChatCompletionAdapter(\n",
+    "    sk_client, kernel=Kernel(memory=NullMemory()), prompt_settings=settings\n",
+    ")\n",
+    "\n",
+    "# Call the model directly.\n",
+    "model_result = await anthropic_model_client.create(\n",
+    "    messages=[UserMessage(content=\"What is the capital of France?\", source=\"User\")]\n",
+    ")\n",
+    "print(model_result)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Read more about the [Semantic Kernel Adapter](../../../reference/python/autogen_ext.models.semantic_kernel.rst)."
+   ]
   }
  ],
  "metadata": {

diff --git a/.../packages/autogen-core/docs/src/user-guide/core-user-guide/components/model-clients.ipynb b/.../packages/autogen-core/docs/src/user-guide/core-user-guide/components/model-clients.ipynb
@@ -336,6 +336,101 @@
     "print(response)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Semantic Kernel Adapter\n",
+    "\n",
+    "The {py:class}`~autogen_ext.models.semantic_kernel.SKChatCompletionAdapter`\n",
+    "allows you to use Semantic kernel model clients as a\n",
+    "{py:class}`~autogen_core.models.ChatCompletionClient` by adapting them to the required interface.\n",
+    "\n",
+    "You need to install the relevant provider extras to use this adapter. \n",
+    "\n",
+    "The list of extras that can be installed:\n",
+    "\n",
+    "- `semantic-kernel-anthropic`: Install this extra to use Anthropic models.\n",
+    "- `semantic-kernel-google`: Install this extra to use Google Gemini models.\n",
+    "- `semantic-kernel-ollama`: Install this extra to use Ollama models.\n",
+    "- `semantic-kernel-mistralai`: Install this extra to use MistralAI models.\n",
+    "- `semantic-kernel-aws`: Install this extra to use AWS models.\n",
+    "- `semantic-kernel-hugging-face`: Install this extra to use Hugging Face models.\n",
+    "\n",
+    "For example, to use Anthropic models, you need to install `semantic-kernel-anthropic`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "shellscript"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# pip install \"autogen-ext[semantic-kernel-anthropic]\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To use this adapter, you need create a Semantic Kernel model client and pass it to the adapter.\n",
+    "\n",
+    "For example, to use the Anthropic model:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "finish_reason='stop' content='The capital of France is Paris. It is also the largest city in France and one of the most populous metropolitan areas in Europe.' usage=RequestUsage(prompt_tokens=0, completion_tokens=0) cached=False logprobs=None\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "\n",
+    "from autogen_core.models import UserMessage\n",
+    "from autogen_ext.models.semantic_kernel import SKChatCompletionAdapter\n",
+    "from semantic_kernel import Kernel\n",
+    "from semantic_kernel.connectors.ai.anthropic import AnthropicChatCompletion, AnthropicChatPromptExecutionSettings\n",
+    "from semantic_kernel.memory.null_memory import NullMemory\n",
+    "\n",
+    "sk_client = AnthropicChatCompletion(\n",
+    "    ai_model_id=\"claude-3-5-sonnet-20241022\",\n",
+    "    api_key=os.environ[\"ANTHROPIC_API_KEY\"],\n",
+    "    service_id=\"my-service-id\",  # Optional; for targeting specific services within Semantic Kernel\n",
+    ")\n",
+    "settings = AnthropicChatPromptExecutionSettings(\n",
+    "    temperature=0.2,\n",
+    ")\n",
+    "\n",
+    "anthropic_model_client = SKChatCompletionAdapter(\n",
+    "    sk_client, kernel=Kernel(memory=NullMemory()), prompt_settings=settings\n",
+    ")\n",
+    "\n",
+    "# Call the model directly.\n",
+    "model_result = await anthropic_model_client.create(\n",
+    "    messages=[UserMessage(content=\"What is the capital of France?\", source=\"User\")]\n",
+    ")\n",
+    "print(model_result)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Read more about the [Semantic Kernel Adapter](../../../reference/python/autogen_ext.models.semantic_kernel.rst)."
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},