Update the auto_feedback_from_code_execution notebook to use code executor (#2366)

* Use code executors in notebook.

* Add custom code executor guide

* Add to gitignore

* Update

* fix links

* link

* typo

---------

Co-authored-by: Chi Wang <wang.chi@microsoft.com>

.gitignore

@@ -188,3 +188,5 @@ local_cache
 
 notebook/result.png
 samples/apps/autogen-studio/autogenstudio/models/test/
+
+notebook/coding

.pre-commit-config.yaml

@@ -43,6 +43,7 @@ repos:
               website/static/img/ag.svg |
               website/yarn.lock |
               website/docs/tutorial/code-executors.ipynb |
+              website/docs/topics/code-execution/custom-executor.ipynb |
               website/docs/topics/non-openai-models/cloud-gemini.ipynb |
               notebook/.*
             )$

notebook/agentchat_agentoptimizer.ipynb

@@ -2,15 +2,7 @@
   "cells": [
     {
       "cell_type": "markdown",
-   "metadata": {
-    "front_matter": {
-     "description": "AgentOptimizer is able to prompt LLMs to iteratively optimize function/skills of AutoGen agents according to the historical conversation and performance.",
-     "tags": [
-      "optimization",
-      "tool/function"
-     ]
-    }
-   },
+      "metadata": {},
       "source": [
         "# AgentOptimizer: An Agentic Way to Train Your LLM Agent\n",
         "\n",
@@ -24,7 +16,7 @@
         "Specifically, given a set of training data, AgentOptimizer would iteratively prompt the LLM to optimize the existing function list of the AssistantAgent and UserProxyAgent with code implementation if necessary. It also includes two strategies, roll-back, and early-stop, to streamline the training process.\n",
         "In the example scenario, we test the proposed AgentOptimizer in solving problems from the [MATH dataset](https://github.com/hendrycks/math). \n",
         "\n",
-    "![AgentEval](../website/blog/2023-12-23-AgentOptimizer/img/agentoptimizer.png)\n",
+        "![AgentOptimizer](../website/blog/2023-12-23-AgentOptimizer/img/agentoptimizer.png)\n",
         "\n",
         "More information could be found in the [paper](https://arxiv.org/abs/2402.11359).\n",
         "\n",
@@ -444,6 +436,13 @@
     }
   ],
   "metadata": {
+    "front_matter": {
+      "description": "AgentOptimizer is able to prompt LLMs to iteratively optimize function/skills of AutoGen agents according to the historical conversation and performance.",
+      "tags": [
+        "optimization",
+        "tool/function"
+      ]
+    },
     "kernelspec": {
       "display_name": "py3.9",
       "language": "python",
@@ -459,7 +458,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-   "version": "3.9.13"
+      "version": "3.9.18"
     }
   },
   "nbformat": 4,

website/.gitignore

@@ -21,6 +21,9 @@ docs/topics/prompting-and-reasoning/*.mdx
 docs/topics/non-openai-models/*.mdx
 docs/topics/non-openai-models/**/*.py
 docs/topics/non-openai-models/**/*.svg
+docs/topics/code-execution/*.mdx
+docs/topics/groupchat/customized_speaker_selection.mdx
+cell-*-output-*.png
 
 # Misc
 .DS_Store