By now, we have seen "Chat with your documents" functionality being introduced in many Microsoft 365 applications. It is typically built by combining Large Language Models (LLMs) and vector databases.
To make the documents "chat ready", they have to be converted to embeddings and stored in vector databases like Azure AI Search. However, indexing the documents and keeping the index in sync are not trivial tasks. There are many moving pieces involved. Also, many times there is no need for "similarity search" or "vector search" where the search is made based on meaning of the query.
In such cases, a simple "keyword" search can do the trick. The advantage of using keyword search in Microsoft 365 applications is that the Microsoft Search indexes are already available as part of the service. APIs like the Microsoft Graph Search API and the SharePoint Search REST API give us "ready to consume" endpoints which can be used to query documents across SharePoint and OneDrive. Keeping these search indexes in sync with the changes in the documents is also handled by the Microsoft 365 service itself.
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So in this post, let's have a look at how we can combine OpenAI's gpt-4o Large Language Model with Microsoft Graph Search API to query SharePoint and OneDrive documents in natural language.
On a high level we will be using OpenAI function calling to achieve this. Our steps are going to be:
1. Define an OpenAI function and make it available to the LLM.
2. During the course of the chat, if the LLM thinks that to respond
to the user, it needs to call our function, it will respond with the
function name along with the parameters.
3. Call the Microsoft Graph Search API based on the parameters provided by
the LLM.
4. Send the results returned from the Microsoft Graph back to the LLM to generate a response in natural language.
So let's see how to achieve this. In this code I have used the following nuget packages:
https://www.nuget.org/packages/Azure.AI.OpenAI/2.1.0
https://www.nuget.org/packages/Microsoft.Graph/5.64.0
The first thing we will look at is our OpenAI function definition:
In this function we are informing the LLM that if needs to search any files as part of providing the responses, it can call this function. The function name will be returned in the response and the relevant parameter will be provided as well. Now let's see how our orchestrator function looks:There is a lot to unpack here as this function is the one which does the heavy lifting. This code is responsible for handling the chat with OpenAI, calling the MS Graph and also responding back to the user based on the response from the Graph.
Next, let's have a look at the code which calls the Microsoft Graph based on the parameters provided by the LLM.
Before executing this code, you will need to have created an App registration. Here is how to do that: https://learn.microsoft.com/en-us/azure/active-directory/develop/quickstart-register-app
Since we are calling the Microsoft Graph /search endpoint with delegated permissions, the app registration will need a minimum of the User.Read and Files.Read.All permissions granted. https://learn.microsoft.com/en-us/graph/api/search-query?view=graph-rest-1.0&tabs=http
This code get the parameters sent from the LLM and uses the Microsoft Graph .NET SDK to call the /search endpoint and fetch the files based on the searchQuery properties. Once the files are returned, their summary value is concatenated into a string and returned to the orchestrator function so that it can be sent again to the LLM.
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