Search SharePoint and OneDrive files in natural language with OpenAI function calling and Microsoft Graph Search API

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.

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. 

Finally, lets have a look at our CallOpenAI function which is responsible for talking to the Open AI chat api.

 

This code defines the Open AI function which will be included in our Chat API calls. Also, the user's search query is sent to the API to determine if the function needs to be called. This function is also called again after the response from the Microsoft Graph is fetched. At that time, this function contains the details fetched from the Graph to generate an output in natural language. This way, we can use Open AI function calling together with Microsoft Graph API to search files in SharePoint and OneDrive.

Hope this helps!

Connect an OpenAI chat bot to the internet using Bing Search API

In the previous post, we saw what is OpenAI function calling and how to use it to chat with your organization's user directory using Microsoft Graph. Please have a look at the article here: Chat with your user directory using OpenAI functions and Microsoft Graph

In this post, we will implement function calling for a very common scenario of augmenting the large language model's responses with data fetched from internet search.

Since the Large Language model (LLM) was trained with data only up to a certain date, we cannot talk to it about events which happened after that date. To solve this, we will use OpenAI function calling to call out the Bing Search API and then augment the LLM's responses with the data returned via internet search.

This pattern is called Retrieval Augmented Generation or RAG. 

Let's look at the code now on how to achieve this. In this code sample I have used the following nuget packages:

https://www.nuget.org/packages/Azure.AI.OpenAI/1.0.0-beta.6/

https://www.nuget.org/packages/Azure.Identity/1.10.2/

The very first thing we will look at is our function definition for informing the model that it can call out to external search API to search information:

In this function we are informing the LLM that if it needs to search the internet as part of providing the responses, it can call this function. The function name will be returned in the response and the relevant parameters will be provided as well.

Next, let's see how our orchestrator looks. I have added comments to each line where relevant:

This code is responsible for handling the chat with OpenAI, calling the Bing API and also responding back to the user based on the response from internet search. 

Next, let's have a look at the code which calls the Bing API based on the parameters provided by the LLM. Before executing this code, you will need to have created Bing Web Search API resource in Azure. Here is more information on it: https://learn.microsoft.com/en-us/bing/search-apis/bing-web-search/overview

The Bing Web Search API key can be found in the "Keys and Endpoint" section on the Azure resource:

Here is the code for calling the Bing Search API:

In this code, we are calling the Bing Web Search REST API to get results based on the search query created by the LLM. Once the top 3 results are fetched we are getting the text snippets of those results, combining them and sending it back the LLM. 

We are only getting the search result snippets to keep this demo simple. In production, ideally you will need to get the Url of each search result and then get the content of the page using the Url.

Finally, lets have a look at our CallChatGPT function which is responsible for talking to the Open AI chat API:

This code defines the OpenAI function which will be included in our Chat API calls. Also, the user's question is sent to the Chat API to determine if the function needs to be called. This function is also called again after the response from the Bing Web Search API is fetched. At that time, this function contains the search results and uses them to generate an output in natural language.

This way, we can use Open AI function calling together with Bing Web Search API to connect our chat bot to the internet!