added lesson summary in each notebook and README

recipes/3p_integrations/llamaindex/dlai_agentic_rag/Building_Agentic_RAG_with_Llamaindex_L2_Tool_Calling.ipynb

@@ -6,7 +6,7 @@
    "source": [
     "<a href=\"https://colab.research.google.com/github/meta-llama/llama-recipes/blob/main/recipes/3p_integrations/llamaindex/dlai_agentic_rag/Building_Agentic_RAG_with_Llamaindex_L2_Tool_Calling.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
     "\n",
-    "This notebook ports the DeepLearning.AI short course [Building Agentic RAG with Llamaindex Lesson 2 Tool Calling](https://learn.deeplearning.ai/courses/building-agentic-rag-with-llamaindex/lesson/3/tool-calling) to using Llama 3. \n",
+    "This notebook ports the DeepLearning.AI short course [Building Agentic RAG with Llamaindex Lesson 2 Tool Calling](https://learn.deeplearning.ai/courses/building-agentic-rag-with-llamaindex/lesson/3/tool-calling) to using Llama 3. It shows how to use Llama 3 to not only pick a function to execute, but also infer an argument to pass through the function.\n",
     "\n",
     "You should take the course before or after going through this notebook to have a deeper understanding.\n",
     "\n",

recipes/3p_integrations/llamaindex/dlai_agentic_rag/Building_Agentic_RAG_with_Llamaindex_L3_Building_an_Agent_Reasoning_Loop.ipynb

@@ -6,7 +6,7 @@
    "source": [
     "<a href=\"https://colab.research.google.com/github/meta-llama/llama-recipes/blob/main/recipes/3p_integrations/llamaindex/dlai_agentic_rag/Building_Agentic_RAG_with_Llamaindex_L3_Building_an_Agent_Reasoning_Loop.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
     "\n",
-    "This notebook ports the DeepLearning.AI short course [Building Agentic RAG with Llamaindex Lesson 3 Building an Agent Reasoning Loop](https://learn.deeplearning.ai/courses/building-agentic-rag-with-llamaindex/lesson/4/building-an-agent-reasoning-loop) to using Llama 3. \n",
+    "This notebook ports the DeepLearning.AI short course [Building Agentic RAG with Llamaindex Lesson 3 Building an Agent Reasoning Loop](https://learn.deeplearning.ai/courses/building-agentic-rag-with-llamaindex/lesson/4/building-an-agent-reasoning-loop) to using Llama 3. It shows how to define a complete agent reasoning loop to reason over tools and multiple steps on a complex question the user asks about a single document while maintaining memory.\n",
     "\n",
     "You should take the course before or after going through this notebook to have a deeper understanding."
    ]

recipes/3p_integrations/llamaindex/dlai_agentic_rag/Building_Agentic_RAG_with_Llamaindex_L4_Building_a_Multi-Document_Agent.ipynb

@@ -6,7 +6,7 @@
    "source": [
     "<a href=\"https://colab.research.google.com/github/meta-llama/llama-recipes/blob/main/recipes/3p_integrations/llamaindex/dlai_agentic_rag/Building_Agentic_RAG_with_Llamaindex_L4_Building_a_Multi-Document_Agent.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
     "\n",
-    "This notebook ports the DeepLearning.AI short course [Building Agentic RAG with Llamaindex Lesson 4 Building a Multi-Document Agent](https://learn.deeplearning.ai/courses/building-agentic-rag-with-llamaindex/lesson/5/building-a-multi-document-agent) to using Llama 3. \n",
+    "This notebook ports the DeepLearning.AI short course [Building Agentic RAG with Llamaindex Lesson 4 Building a Multi-Document Agent](https://learn.deeplearning.ai/courses/building-agentic-rag-with-llamaindex/lesson/5/building-a-multi-document-agent) to using Llama 3. It shows how to use an agent to handle multiple documents and increasing degrees of complexity.\n",
     "\n",
     "You should take the course before or after going through this notebook to have a deeper understanding.\n",
     "\n",

recipes/3p_integrations/llamaindex/dlai_agentic_rag/README.md

@@ -2,10 +2,10 @@
 
 The folder here containts the Llama 3 ported notebooks of the DLAI short course [Building Agentic RAG with Llamaindex](https://www.deeplearning.ai/short-courses/building-agentic-rag-with-llamaindex/).
 
-1. [Building Agentic RAG with Llamaindex L1 Router Engine](../../../quickstart/agents/dlai/Building_Agentic_RAG_with_Llamaindex_L1_Router_Engine.ipynb) Note this lesson 1 notebook is located in the `quickstart` folder and only notebooks for lessons 2-4 are located here.
+1. [Building Agentic RAG with Llamaindex L1 Router Engine](../../../quickstart/agents/dlai/Building_Agentic_RAG_with_Llamaindex_L1_Router_Engine.ipynb) shows how to implement a simple agentic RAG, a router that will pick up one of several query tools (question answering or summarization) to execute a query on a single document. Note this notebook is located in the `quickstart` folder.
 
-2. [Building Agentic RAG with Llamaindex L2 Tool Calling](Building_Agentic_RAG_with_Llamaindex_L2_Tool_Calling.ipynb)
+2. [Building Agentic RAG with Llamaindex L2 Tool Calling](Building_Agentic_RAG_with_Llamaindex_L2_Tool_Calling.ipynb) shows how to use Llama 3 to not only pick a function to execute, but also infer an argument to pass through the function.
 
-3. [Building Agentic RAG with Llamaindex L3 Building an Agent Reasoning Loop](Building_Agentic_RAG_with_Llamaindex_L3_Building_an_Agent_Reasoning_Loop.ipynb)
+3. [Building Agentic RAG with Llamaindex L3 Building an Agent Reasoning Loop](Building_Agentic_RAG_with_Llamaindex_L3_Building_an_Agent_Reasoning_Loop.ipynb) shows how to define a complete agent reasoning loop to reason over tools and multiple steps on a complex question the user asks about a single document while maintaining memory.
 
-3. [Building Agentic RAG with Llamaindex L4 Building a Multi-Document Agent](Building_Agentic_RAG_with_Llamaindex_L4_Building_a_Multi-Document_Agent.ipynb)
+3. [Building Agentic RAG with Llamaindex L4 Building a Multi-Document Agent](Building_Agentic_RAG_with_Llamaindex_L4_Building_a_Multi-Document_Agent.ipynb) shows how to use an agent to handle multiple documents and increasing degrees of complexity.