Merge branch 'main' into add-role-to-reflection-with-llm

.github/workflows/lfs-check.yml

@@ -10,6 +10,6 @@ jobs:
         uses: actions/checkout@v4
         with:
           lfs: true
-      - name: Check Git LFS files for consistency
+      - name: "Check Git LFS files for consistency, if you see error like 'pointer: unexpectedGitObject ... should have been a pointer but was not', please install Git LFS locally, delete the problematic file, and then add it back again. This ensures it's properly tracked."
         run: |
           git lfs fsck

autogen/agentchat/conversable_agent.py

@@ -149,7 +149,13 @@ def __init__(
         )
         # Take a copy to avoid modifying the given dict
         if isinstance(llm_config, dict):
-            llm_config = copy.deepcopy(llm_config)
+            try:
+                llm_config = copy.deepcopy(llm_config)
+            except TypeError as e:
+                raise TypeError(
+                    "Please implement __deepcopy__ method for each value class in llm_config to support deepcopy."
+                    " Refer to the docs for more details: https://microsoft.github.io/autogen/docs/topics/llm_configuration#adding-http-client-in-llm_config-for-proxy"
+                ) from e
 
         self._validate_llm_config(llm_config)
 

notebook/agentchat_oai_assistant_function_call.ipynb

@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Chat with OpenAI Assistant using function call in AutoGen: OSS Insights for Advanced GitHub Data Analysis\n",
+    "# Chat with OpenAI Assistant using function call in AutoGen: OSS Insights for Advanced GitHub Data Analysis\n",
     "\n",
     "This Jupyter Notebook demonstrates how to leverage OSS Insight (Open Source Software Insight) for advanced GitHub data analysis by defining `Function calls` in AutoGen for the OpenAI Assistant. \n",
     "\n",
@@ -14,12 +14,19 @@
     "2. Defining an OpenAI Assistant Agent in AutoGen\n",
     "3. Fetching GitHub Insight Data using Function Call\n",
     "\n",
-    "### Requirements\n",
+    "## Requirements\n",
     "\n",
     "AutoGen requires `Python>=3.8`. To run this notebook example, please install:\n",
+    "````{=mdx}\n",
+    ":::info Requirements\n",
+    "Install `pyautogen`:\n",
     "```bash\n",
     "pip install pyautogen\n",
-    "```"
+    "```\n",
+    "\n",
+    "For more information, please refer to the [installation guide](/docs/installation/).\n",
+    ":::\n",
+    "````"
    ]
   },
   {
@@ -36,7 +43,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Function Schema and Implementation\n",
+    "## Function Schema and Implementation\n",
     "\n",
     "This section provides the function schema definition and their implementation details. These functions are tailored to fetch and process data from GitHub, utilizing OSS Insight's capabilities."
    ]
@@ -101,7 +108,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Defining an OpenAI Assistant Agent in AutoGen\n",
+    "## Defining an OpenAI Assistant Agent in AutoGen\n",
     "\n",
     "Here, we explore how to define an OpenAI Assistant Agent within the AutoGen. This includes setting up the agent to make use of the previously defined function calls for data retrieval and analysis."
    ]
@@ -159,7 +166,18 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Fetching GitHub Insight Data using Function Call\n",
+    "````{=mdx}\n",
+    ":::tip\n",
+    "Learn more about configuring LLMs for agents [here](/docs/topics/llm_configuration).\n",
+    ":::\n",
+    "````\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Fetching GitHub Insight Data using Function Call\n",
     "\n",
     "This part of the notebook demonstrates the practical application of the defined functions and the OpenAI Assistant Agent in fetching and interpreting GitHub Insight data."
    ]
@@ -256,6 +274,13 @@
   }
  ],
  "metadata": {
+  "front_matter": {
+   "description": "This Jupyter Notebook demonstrates how to leverage OSS Insight (Open Source Software Insight) for advanced GitHub data analysis by defining `Function calls` in AutoGen for the OpenAI Assistant.",
+   "tags": [
+    "OpenAI Assistant",
+    "function call"
+   ]
+  },
   "kernelspec": {
    "display_name": "autogen",
    "language": "python",

notebook/agentchat_oai_assistant_groupchat.ipynb

@@ -14,9 +14,16 @@
     "## Requirements\n",
     "\n",
     "AutoGen requires `Python>=3.8`. To run this notebook example, please install:\n",
+    "````{=mdx}\n",
+    ":::info Requirements\n",
+    "Install `pyautogen`:\n",
     "```bash\n",
-    "pip install \"pyautogen>=0.2.3\"\n",
-    "```"
+    "pip install pyautogen\n",
+    "```\n",
+    "\n",
+    "For more information, please refer to the [installation guide](/docs/installation/).\n",
+    ":::\n",
+    "````"
    ]
   },
   {
@@ -50,19 +57,11 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "It first looks for environment variable \"OAI_CONFIG_LIST\" which needs to be a valid json string. If that variable is not found, it then looks for a json file named \"OAI_CONFIG_LIST\". It filters the configs by models (you can filter by other keys as well).\n",
-    "\n",
-    "The config list looks like the following:\n",
-    "```python\n",
-    "config_list = [\n",
-    "    {\n",
-    "        \"model\": \"gpt-4\",\n",
-    "        \"api_key\": \"<your OpenAI API key>\",\n",
-    "    },  # OpenAI API endpoint for gpt-4\n",
-    "]\n",
-    "```\n",
-    "\n",
-    "Currently Azure OpenAI does not support assistant api. You can set the value of config_list in any way you prefer. Please refer to this [notebook](https://github.com/microsoft/autogen/blob/main/website/docs/topics/llm_configuration.ipynb) for full code examples of the different methods."
+    "````{=mdx}\n",
+    ":::tip\n",
+    "Learn more about configuring LLMs for agents [here](/docs/topics/llm_configuration).\n",
+    ":::\n",
+    "````"
    ]
   },
   {
@@ -482,6 +481,13 @@
   }
  ],
  "metadata": {
+  "front_matter": {
+   "description": "This Jupyter Notebook demonstrates how to use the GPTAssistantAgent in AutoGen's group chat mode, enabling collaborative task performance through automated chat with agents powered by LLMs, tools, or humans.",
+   "tags": [
+    "OpenAI Assistant",
+    "group chat"
+   ]
+  },
   "kernelspec": {
    "display_name": "Python 3",
    "language": "python",

notebook/agentchat_oai_code_interpreter.ipynb

@@ -10,9 +10,16 @@
     "## Requirements\n",
     "\n",
     "AutoGen requires `Python>=3.8`. To run this notebook example, please install:\n",
+    "````{=mdx}\n",
+    ":::info Requirements\n",
+    "Install `pyautogen`:\n",
     "```bash\n",
     "pip install pyautogen\n",
-    "```"
+    "```\n",
+    "\n",
+    "For more information, please refer to the [installation guide](/docs/installation/).\n",
+    ":::\n",
+    "````"
    ]
   },
   {
@@ -52,19 +59,11 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "It first looks for environment variable \"OAI_CONFIG_LIST\" which needs to be a valid json string. If that variable is not found, it then looks for a json file named \"OAI_CONFIG_LIST\". It filters the configs by models (you can filter by other keys as well).\n",
-    "\n",
-    "The config list looks like the following:\n",
-    "```python\n",
-    "config_list = [\n",
-    "    {\n",
-    "        \"model\": \"gpt-4\",\n",
-    "        \"api_key\": \"<your OpenAI API key>\",\n",
-    "    },  # OpenAI API endpoint for gpt-4\n",
-    "]\n",
-    "```\n",
-    "\n",
-    "Currently Azure OpenAi does not support assistant api. You can set the value of config_list in any way you prefer. Please refer to this [notebook](https://github.com/microsoft/autogen/blob/main/website/docs/llm_endpoint_configuration.ipynb) for full code examples of the different methods."
+    "````{=mdx}\n",
+    ":::tip\n",
+    "Learn more about configuring LLMs for agents [here](/docs/topics/llm_configuration).\n",
+    ":::\n",
+    "````"
    ]
   },
   {
@@ -297,6 +296,13 @@
   }
  ],
  "metadata": {
+  "front_matter": {
+   "description": "This Jupyter Notebook showcases the integration of the Code Interpreter tool which executes Python code dynamically within applications.",
+   "tags": [
+    "OpenAI Assistant",
+    "code interpreter"
+   ]
+  },
   "kernelspec": {
    "display_name": "Python 3",
    "language": "python",