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Vector storage and retrieval in C#
Using Azure.Search.Documents, index and query vectors in a RAG pattern or a traditional search solution.
azure
azure-cognitive-search
csharp-vector-search

Vector search in C# (Azure AI Search)

In this .NET console application for Azure AI Search, DotNetVectorDemo provides raw data for which embeddings are generated externally and then pushed into a search index for queries. First, it calls Azure OpenAI resource and a deployment of the text-embedding-ada-002 model to create embeddings for text in a local text-sample.json file. Next, it pushes the embeddings and other textual content to a search index. The searchable output is a combination of human-readable text and embeddings that can be queried from your code.

Prerequisites

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

  • A deployment of the text-embedding-ada-002 embedding model. We use API version 2023-05-15 in these demos. 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 these samples on a deployment model having a 33K tokens per minute rate limit.

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

  • Azure SDK for .NET 5.0 or later.

You can use Visual Studio or Visual Studio Code with the C# extension for these demos.

Setup

  1. Clone this repository.

  2. Create a local.settings.json file in the same directory as the code for each project 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-index",  
 "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. For each project, navigate to the project folder (e.g., demo-dotnet/DotNetVectorDemo or demo-dotnet/DotNetIntegratedVectorizationDemo) in your terminal and execute the following command 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 service to convert your query string into a vector.

    For DotNetVectorDemo, 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 service, 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.