updated text content within responses API (#1756)

examples/responses_api/responses_api_tool_orchestration.ipynb

@@ -12,7 +12,11 @@
    "metadata": {},
    "source": [
     "\n",
-    "This cookbook guides you through building dynamic, multi-tool workflows using OpenAI's Responses API. It demonstrates how to implement a Retrieval-Augmented Generation (RAG) approach that intelligently routes user queries to the appropriate in-built or external tools. Whether your query calls for general knowledge or requires accessing specific internal context from a vector database (like Pinecone), this guide shows you how to integrate function calls, web searches in-built tool, and leverage document retrieval to generate accurate, context-aware responses."
+    "This cookbook guides you through building dynamic, multi-tool workflows using OpenAI's Responses API. It demonstrates how to implement a Retrieval-Augmented Generation (RAG) approach that intelligently routes user queries to the appropriate in-built or external tools. Whether your query calls for general knowledge or requires accessing specific internal context from a vector database (like Pinecone), this guide shows you how to integrate function calls, web searches in-built tool, and leverage document retrieval to generate accurate, context-aware responses.\n",
+    "\n",
+    "For a practical example of performing RAG on PDFs using the Responses API's file search feature, refer to [this](https://cookbook.openai.com/examples/file_search_responses) notebook.\n",
+    "\n",
+    "This example showcases the flexibility of the Responses API, illustrating that beyond the internal `file_search` tool—which connects to an internal vector store—there is also the capability to easily connect to external vector databases. This allows for the implementation of a RAG approach in conjunction with hosted tooling, providing a versatile solution for various retrieval and generation tasks."
    ]
   },
   {
@@ -1166,7 +1170,11 @@
    "metadata": {},
    "source": [
     "\n",
-    "Here, we have seen  how to utilize OpenAI's Responses API to implement a Retrieval-Augmented Generation (RAG) approach with multi-tool calling capabilities. It showcases an example where the model selects the appropriate tool based on the input query: general questions may be handled by built-in tools such as web-search, while specific medical inquiries related to internal knowledge are addressed by retrieving context from a vector database (such as Pinecone) via function calls. Additonally, we have showcased how multiple tool calls can be sequentially combined to generate a final response based on our instructions provided to responses API. Happy coding! "
+    "Here, we have seen  how to utilize OpenAI's Responses API to implement a Retrieval-Augmented Generation (RAG) approach with multi-tool calling capabilities. It showcases an example where the model selects the appropriate tool based on the input query: general questions may be handled by built-in tools such as web-search, while specific medical inquiries related to internal knowledge are addressed by retrieving context from a vector database (such as Pinecone) via function calls. Additonally, we have showcased how multiple tool calls can be sequentially combined to generate a final response based on our instructions provided to responses API. \n",
+    "\n",
+    "As you continue to experiment and build upon these concepts, consider exploring additional resources and examples to further enhance your understanding and applications\n",
+    "\n",
+    "Happy coding! "
    ]
   }
  ],