Readme: Azure Cognitive Search - Vector search using Azure OpenAI with .NET

This repository contains a .NET console application that demonstrates how to generate text embeddings using Azure OpenAI, insert those embeddings into vector fields in Azure Cognitive Search, and issue vector queries. Queries include vector searches with metadata filtering and hybrid (text + vectors) search. The code uses Azure OpenAI to generate embeddings for titleVector and contentVector fields. You'll need access to Azure OpenAI to run this demo.

The code reads the text-sample.json file, which contains the raw data for which embeddings are generated.

The output is a combination of human-readable text and embeddings that can be pushed into a search index.

Prerequisites

To run this code, you'll need the following:

• An Azure subscription, with access to Azure OpenAI. You must have the Azure OpenAI endpoint and an API key.

• A deployment of the text-embedding-ada-002 embedding model. We use API version 2023-05-15 in this demo. For the deployment name, the deployment name is the same as the model, "text-embedding-ada-002".

• Model capacity should be sufficient to handle the load. We successfully tested this sample on a deployment model having a 33K tokens per minute rate limit.

• Azure SDK for .NET 5.0 or later.

• An Azure Cognitive Search service with room for a new index. You must have full endpoint and an admin API key.

You can use Visual Studio or Visual Studio Code with the C# extension for this demo.

Setup

1. Clone this repository.

2. Create a local.settings.json file in the same directory as the code and include the following variables:

   {
    "AZURE_SEARCH_SERVICE_ENDPOINT": "YOUR-SEARCH-SERVICE-ENDPOINT",
    "AZURE_SEARCH_INDEX_NAME": "YOUR-SEARCH-SERVICE-INDEX-NAME",
    "AZURE_SEARCH_ADMIN_KEY": "YOUR-SEARCH-SERVICE-ADMIN-KEY",
    "AZURE_OPENAI_ENDPOINT": "YOUR-OPENAI-ENDPOINT",
    "AZURE_OPENAI_API_KEY": "YOUR-OPENAI-API-KEY",
    "AZURE_OPENAI_API_VERSION": "YOUR-OPENAI-API-VERSION",
    "AZURE_OPENAI_EMBEDDING_DEPLOYED_MODEL": "YOUR-OPENAI-MODEL-DEPLOYMENT-NAME"
   }

   Here's an example with fictitious values:

   {
    "AZURE_SEARCH_SERVICE_ENDPOINT": "https://demo-srch-eastus.search.windows.net",
    "AZURE_SEARCH_INDEX_NAME": "demo-vector-idx",
    "AZURE_SEARCH_ADMIN_KEY": "000000000000000000000000000000000",
    "AZURE_OPENAI_ENDPOINT": "https://demo-openai-southcentralus.openai.azure.com/",
    "AZURE_OPENAI_API_KEY": "0000000000000000000000000000000000",
    "AZURE_OPENAI_API_VERSION": "2023-05-15",
    "AZURE_OPENAI_EMBEDDING_DEPLOYED_MODEL": "text-embedding-ada-002"
   }

Run the code

Before running the code, ensure you have the .NET SDK installed on your machine.

1. If you're using Visual Studio Code, select Terminal and New Terminal to get a command line prompt.

2. Navigate to the demo-dotnet/code folder in your terminal and execute the following comman to verify .Net 5.0 or later is installed:

   dotnet build

3. Run the program:

   dotnet run

4. When prompted, select "Y" to create and load the index. Wait for the query prompt.

5. Choose a query type, such as single vector query or a hybrid query. The program calls Azure OpenAI to convert your query string into a vector.

   Sample data is 108 descriptions of Azure services, so your query should be about Azure. For example, for a vector query, type in "what Azure services support full text search" or "what product has OCR".

Output

Output is a search index. You can use the Azure portal to explore the index definition or delete the index if you no longer need it.

Troubleshoot errors

If you get error 429 from Azure OpenAI, it means the resource is over capacity:

• Check the Activity Log of the Azure OpenAI service to see what else might be running.

• Check the Tokens Per Minute (TPM) on the deployed model. On a system that isn't running other jobs, a TPM of 33K or higher should be sufficient to generate vectors for the sample data. You can try a model with more capacity if 429 errors persist.

• Review these articles for information on rate limits: Understanding rate limits and A Guide to Azure OpenAI Service's Rate Limits and Monitoring.