Merge pull request #146 from haileytap/agentic-retrieval

Replace "search agent" with "knowledge agent"

Author: haileytap

Quickstart-Agentic-Retrieval/quickstart-agentic-retrieval.ipynb

@@ -283,7 +283,7 @@
    "source": [
     "## Create an agent in Azure AI Search\n",
     "\n",
-    "This step creates a search agent, which acts as a wrapper for the LLM you deployed to Azure OpenAI. The LLM is used to send queries to an agentic retrieval pipeline."
+    "This step creates a knowledge agent, which acts as a wrapper for the LLM you deployed to Azure OpenAI. The LLM is used to send queries to an agentic retrieval pipeline."
    ]
   },
   {
@@ -689,7 +689,7 @@
    "source": [
     "## Continue the conversation\n",
     "\n",
-    "This step continues the conversation with the search agent, building upon the previous messages and queries to retrieve relevant information from your search index."
+    "This step continues the conversation with the knowledge agent, building upon the previous messages and queries to retrieve relevant information from your search index."
    ]
   },
   {
@@ -1014,7 +1014,7 @@
    "id": "a14f6fe6",
    "metadata": {},
    "source": [
-    "### Delete the search agent"
+    "### Delete the knowledge agent"
    ]
   },
   {
@@ -1027,14 +1027,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Search agent 'earth-search-agent' deleted successfully\n"
+      "Knowledge agent 'earth-search-agent' deleted successfully\n"
      ]
     }
    ],
    "source": [
     "index_client = SearchIndexClient(endpoint=endpoint, credential=credential)\n",
     "index_client.delete_agent(agent_name)\n",
-    "print(f\"Search agent '{agent_name}' deleted successfully\")"
+    "print(f\"Knowledge agent '{agent_name}' deleted successfully\")"
    ]
   },
   {

README.md

@@ -11,7 +11,7 @@ This repository contains Python code samples used in Azure AI Search documentati
 | [Quickstart-RAG](Quickstart-RAG/quickstart-rag.ipynb) | "Day One" introduction to LLM integration with a chat model such as GPT-3.5-turbo or equivalent. We recommend the basic tier or higher for this quickstart.|
 | [Quickstart-Document-Permissions-Pull-API](Quickstart-Document-Permissions-Pull-API/document-permissions-pull-api.ipynb) | Using an indexer "pull API" approach, flow access control lists from a data source to search results and apply permission filters that restrict access to authorized content. Indexer support is limited to Azure Data Lake Storage (ADLS) Gen2 permission metadata. You must have  basic tier or higher for this quickstart.|
 | [Quickstart-Document-Permissions-Push-API](Quickstart-Document-Permissions-Push-API/document-permissions-push-api.ipynb) | Using the push APIs for indexing a JSON payload, flow embedded permission metadata to indexed documents, and to search results that are filtered based on user access to authorized content. We recommend the basic tier or higher for this quickstart.|
-| [Quickstart-Agentic-Retrieval](Quickstart-Agentic-Retrieval/quickstart-agentic-retrieval.ipynb) | Set up a search agent in Azure AI Search to integrate LLM reasoning into query planning. We recommend the basic tier or higher for this quickstart. |
+| [Quickstart-Agentic-Retrieval](Quickstart-Agentic-Retrieval/quickstart-agentic-retrieval.ipynb) | Set up a knowledge agent in Azure AI Search to integrate LLM reasoning into query planning. We recommend the basic tier or higher for this quickstart. |
 |[Tutorial-RAG](Tutorial-RAG/tutorial-rag.ipynb) | A deeper dive into LLM integration with a chat model such as GPT-3.5-turbo or equivalent. We recommend the basic tier or higher for this quickstart. |
 
 ## Deeper dive tutorials

agentic-retrieval-pipeline-example/agent-example.ipynb

@@ -13,7 +13,7 @@
     "\n",
     "+ Create an \"earth_at_night\" search index\n",
     "+ Load it with documents from a GitHub URL\n",
-    "+ Create a search agent on Azure AI Search that points to an LLM for intelligent query planning\n",
+    "+ Create a knowledge agent on Azure AI Search that points to an LLM for intelligent query planning\n",
     "+ Create a Foundry agent in Azure AI Foundry to determine when queries are needed\n",
     "+ Create a Azure AI Agent tool (client) to orchestrate all requests\n",
     "+ Start a chat with the agent\n",
@@ -729,14 +729,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Search agent 'earth-search-agent' deleted successfully\n"
+      "Knowledge agent 'earth-search-agent' deleted successfully\n"
      ]
     }
    ],
    "source": [
     "index_client = SearchIndexClient(endpoint=endpoint, credential=credential)\n",
     "index_client.delete_agent(agent_name)\n",
-    "print(f\"Search agent '{agent_name}' deleted successfully\")"
+    "print(f\"Knowledge agent '{agent_name}' deleted successfully\")"
    ]
   },
   {