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I am trying to replicate some functionality I saw while demo-ing SK using the AzureChatDataSource parameter in AzureOpenAIPromptExecutionSettings, and that is restricting responses strictly to only what's found in the data source provided (as well as adding citations).
I have multiple indexes in Azure Search that I would like to be able to query, and AzureOpenAIPromptExecutionSettings only supports one datasource, so I chose to use VectorStores instead, and inject the results of the query into a system message within the chat history of a call to a chat completion service (as opposed to creating a plugin and invoking a prompt directly from the kernel).
I would like to know how exactly setting the InScope parameter for AzureChatDataSource instructs the chat completion service to only respond with information found in the data source provided, as well as how exactly citations are added to the response, and how I can replicate those functionalities using multiple vector stores instead.
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I am trying to replicate some functionality I saw while demo-ing SK using the AzureChatDataSource parameter in AzureOpenAIPromptExecutionSettings, and that is restricting responses strictly to only what's found in the data source provided (as well as adding citations).
I have multiple indexes in Azure Search that I would like to be able to query, and AzureOpenAIPromptExecutionSettings only supports one datasource, so I chose to use VectorStores instead, and inject the results of the query into a system message within the chat history of a call to a chat completion service (as opposed to creating a plugin and invoking a prompt directly from the kernel).
I would like to know how exactly setting the InScope parameter for AzureChatDataSource instructs the chat completion service to only respond with information found in the data source provided, as well as how exactly citations are added to the response, and how I can replicate those functionalities using multiple vector stores instead.
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