Chaining and Streaming with Responses API in Azure
Responses API is an enhancement of the existing Chat Completions API. It is stateful and supports agentic capabilities. As a superset of the Chat Completions class, it continues to support functionality of chat completions. In addition, reasoning models, like GPT-5 result in better model intelligence when compared to Chat Completions. It has input flexibility, supporting a range of input types. It is currently available in the following regions on Azure and can be used with all the models available in the region. The API supports response streaming, chaining and also function calling. In the examples below, we use the gpt-5-nano model for a simple response, a chained response and streaming responses. To get started update the installed openai library. pip install --upgrade openai Simple Message 1) Build the client with the following code from openai import OpenAI client = OpenAI( base_url=endpoint, api_key=api_key, ) 2) The response received is an id which can then be used to retrieve the message. # Non-streaming request resp_id = client.responses.create( model=deployment, input=messages, ) 3) Message is retrieved using the response id from previous step response = client.responses.retrieve(resp_id.id) Chaining For a chained message, an extra step is sharing the context. This is done by sending the response id in the subsequent requests. resp_id = client.responses.create( model=deployment, previous_response_id=resp_id.id, input=[{"role": "user", "content": "Explain this at a level that could be understood by a college freshman"}] ) Streaming A different function call is used for streaming queries. client.responses.stream( model=deployment, input=messages, # structured messages ) In addition, the streaming query response has to be handled appropriately till end of event stream for event in s: # Accumulate only text deltas for clean output if event.type == "response.output_text.delta": delta = event.delta or "" text_out.append(delta) # Echo streaming output to console as it arrives print(delta, end="", flush=True) The code is available in the following github link - https://github.com/arunacarunac/ResponsesAPI Additional details are available in the following links - Azure OpenAI Responses API - Azure OpenAI | Microsoft LearnAzure Cognitive Search AMA: Vector search, Azure OpenAI Service, generative apps, plugins & more
In this session we’ll answer questions about the emerging Retrieval-Augmented Generation pattern and how you can use Azure OpenAI service and Azure Cognitive Search to implement it today in your applications to power ChatGPT-like experiences, generative scenarios, and more. Bring your questions about vector search in Azure Cognitive Search, which is coming to public preview soon, as well as about implementation details, data preparation, integration with large language models, and anything else related to Azure Cognitive Search. An AMA is a live text-based online event similar to an “Ask Me Anything” on Reddit. This AMA gives you the opportunity to connect with Microsoft product experts who will be on hand to answer your questions and listen to feedback. Feel free to post your questions anytime in the comments below beforehand, if it fits your schedule or time zone better, though questions will not be answered until the live hour.Azure OpenAI Add your data - Azure Cognitive Search
Dear community, I have created an Azure Cognitive Search Index. Back in the Azure OpenAI studio under the Chat, I used Add your data and selected the ACS index. As you can see from the screen shot, I cannot proceed to the next step nor can I select "Add vector search". Does anyone know why? Thanks all!
