westey
ada5b83c80
* Port store for adding text to a vector store to AF * Fix typo. * Change TextSearchStore to sample, and add sample to use it and do rag with a custom schema * Add more tests and fix broken ones * Fix merge issue * Fix sample after merge. * Convert TextSearchStore to use Dynamic mode to be AOT compatible. * Add some more clarification on when to use assistant messages in rag searches.
Agent Framework Retrieval Augmented Generation (RAG) with an external Vector Store with a custom schema
This sample demonstrates how to create and run an agent that uses Retrieval Augmented Generation (RAG) with an external vector store. It also uses a custom schema for the documents stored in the vector store. This sample uses Qdrant for the vector store, but this can easily be swapped out for any vector store that has a Microsoft.Extensions.VectorStore implementation.
Prerequisites
- .NET 8.0 SDK or later
- Azure OpenAI service endpoint
- Both a chat completion and embedding deployment configured in the Azure OpenAI resource
- Azure CLI installed and authenticated (for Azure credential authentication)
- User has the
Cognitive Services OpenAI Contributorrole for the Azure OpenAI resource. - An existing Qdrant instance. You can use a managed service or run a local instance using Docker, but the sample assumes the instance is running locally.
Note: These samples use Azure OpenAI models. For more information, see how to deploy Azure OpenAI models with Azure AI Foundry.
Note: These samples use Azure CLI credentials for authentication. Make sure you're logged in with az login and have access to the Azure OpenAI resource and have the Cognitive Services OpenAI Contributor role. For more information, see the Azure CLI documentation.
Running the sample from the console
Set the following environment variables:
$env:AZURE_OPENAI_ENDPOINT="https://your-resource.openai.azure.com/" # Replace with your Azure OpenAI resource endpoint
$env:AZURE_OPENAI_DEPLOYMENT_NAME="gpt-4o-mini" # Optional, defaults to gpt-4o-mini
$env:AZURE_OPENAI_EMBEDDING_DEPLOYMENT_NAME="text-embedding-3-large" # Optional, defaults to text-embedding-3-large
If the variables are not set, you will be prompted for the values when running the samples.
To use Qdrant in docker locally, start your Qdrant instance using the default port mappings.
docker run -d --name qdrant -p 6333:6333 -p 6334:6334 qdrant/qdrant:latest
Execute the following command to build the sample:
dotnet build
Execute the following command to run the sample:
dotnet run --no-build
Or just build and run in one step:
dotnet run
Running the sample from Visual Studio
Open the solution in Visual Studio and set the sample project as the startup project. Then, run the project using the built-in debugger or by pressing F5.
You will be prompted for any required environment variables if they are not already set.