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SergeyMenshykh
04b662543c
* move catalog samples to the HostedAgents folder * move the catalog samples' projects to the HostedAgents folder
42 lines
1.7 KiB
Markdown
42 lines
1.7 KiB
Markdown
# What this sample demonstrates
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This sample demonstrates how to use TextSearchProvider to add retrieval augmented generation (RAG) capabilities to an AI agent. The provider runs a search against an external knowledge base before each model invocation and injects the results into the model context.
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Key features:
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- Configuring TextSearchProvider with custom search behavior
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- Running searches before AI invocations to provide relevant context
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- Managing conversation memory with a rolling window approach
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- Citing source documents in AI responses
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## Prerequisites
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Before running this sample, ensure you have:
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1. An Azure OpenAI endpoint configured
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2. A deployment of a chat model (e.g., gpt-4o-mini)
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3. Azure CLI installed and authenticated
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## Environment Variables
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Set the following environment variables:
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```powershell
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# Replace with your Azure OpenAI endpoint
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$env:AZURE_OPENAI_ENDPOINT="https://your-openai-resource.openai.azure.com/"
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# Optional, defaults to gpt-4o-mini
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$env:AZURE_OPENAI_DEPLOYMENT_NAME="gpt-4o-mini"
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```
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## How It Works
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The sample uses a mock search function that demonstrates the RAG pattern:
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1. When the user asks a question, the TextSearchProvider intercepts it
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2. The search function looks for relevant documents based on the query
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3. Retrieved documents are injected into the model's context
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4. The AI responds using both its training and the provided context
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5. The agent can cite specific source documents in its answers
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The mock search function returns pre-defined snippets for demonstration purposes. In a production scenario, you would replace this with actual searches against your knowledge base (e.g., Azure AI Search, vector database, etc.).
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