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examples/responses_api/responses_api_tool_orchestration.ipynb
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@ -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! "
]
}
],