fixes and improvements

Author: Pablo Marin

01-Load-Data-ACogSearch.ipynb

@@ -6,7 +6,7 @@
    "source": [
     "# Introduction\n",
     "\n",
-    "Welcome to this repository. We will be walking you to a series of notebooks in which you will understand how RAG works (Retrieval Augmented Generation, a technique that combines the power of search and generation of AI to answer user queries). We will work with different sources (Azure Cog Search, Files, SQL Server, etc) and at the end of the notebooks you will understand why the magic happens with the combination of:\n",
+    "Welcome to this repository. We will be walking you to a series of notebooks in which you will understand how RAG works (Retrieval Augmented Generation, a technique that combines the power of search and generation of AI to answer user queries). We will work with different sources (Azure Cog Search, Files, SQL Server, Websites, etc) and at the end of the notebooks you will understand why the magic happens with the combination of:\n",
     "\n",
     "1) Multi-Agents: Agents talking to each other\n",
     "2) GPT-4-32k: The best model available\n",