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Jul 6, 2025
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Corrections for instructions #184

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Corrections for instructions
flcdrg Jul 6, 2025
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Merge branch 'main' into fixes
elbruno Jul 6, 2025
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16 changes: 6 additions & 10 deletions 03-CoreGenerativeAITechniques/02-retrieval-augmented-generation.md
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Expand Up @@ -36,10 +36,11 @@ You may have heard of vector databases. These are databases that store data in a
We'll use the Microsoft.Extension.AI along with the [Microsoft.Extensions.VectorData](https://www.nuget.org/packages/Microsoft.Extensions.VectorData.Abstractions/) and [Microsoft.SemanticKernel.Connectors.InMemory](https://www.nuget.org/packages/Microsoft.SemanticKernel.Connectors.InMemory) libraries to implement RAG below.

> 🧑‍💻**Sample code:** You can follow along with the [sample code here](./src/RAGSimple-02MEAIVectorsMemory/).
>
>
> You can also see how to implement a RAG app [using Semantic Kernel by itself in our sample source code here](./src/RAGSimple-01SK/).
>
> We have additional RAG examples for different vector stores and models:
>
> - [RAGSimple-03MEAIVectorsAISearch](./src/RAGSimple-03MEAIVectorsAISearch/) - Using Azure AI Search as a vector store
> - [RAGSimple-04MEAIVectorsQdrant](./src/RAGSimple-04MEAIVectorsQdrant/) - Using Qdrant as a vector store
> - [RAGSimple-10SKOllama](./src/RAGSimple-10SKOllama/) - Using Semantic Kernel with Ollama
Expand Down Expand Up @@ -75,7 +76,7 @@ We'll use the Microsoft.Extension.AI along with the [Microsoft.Extensions.Vector

// get movie list
var movies = vectorStore.GetCollection<int, MovieVector<int>>("movies");
await movies.CreateCollectionIfNotExistsAsync();
await movies.EnsureCollectionExistsAsync();
var movieData = MovieFactory<int>.GetMovieVectorList();

```
Expand Down Expand Up @@ -107,14 +108,9 @@ We'll use the Microsoft.Extension.AI along with the [Microsoft.Extensions.Vector
// generate the embedding vector for the user's prompt
var query = "A family friendly movie that includes ogres and dragons";
var queryEmbedding = await generator.GenerateVectorAsync(query);
var searchOptions = new VectorSearchOptions()
{
Top = 2,
VectorPropertyName = "Vector"
};

// search the knowledge store based on the user's prompt
var results = await movies.VectorizedSearchAsync(queryEmbedding, searchOptions);
var results = movies.SearchAsync(queryEmbedding, 2, new VectorSearchOptions<MovieVector<int>>());

// let's see the results just so we know what they look like
await foreach (var result in results.Results)
Expand All @@ -136,7 +132,7 @@ So we could do something like the following while looping through the results of

```csharp

// assuming chatClient is instatiated as before to a language model
// assuming chatClient is instantiated as before to a language model
// assuming the vector search is done as above
// assuming List<ChatMessage> conversation object is already instantiated and has a system prompt

Expand All @@ -157,7 +153,7 @@ var response = await chatClient.GetResponseAsync(conversation);
conversation.Add(new ChatMessage(ChatRole.Assistant, response.Message));

//display the conversation
Console.WriteLine($"Bot:> {response.Message.Text});
Console.WriteLine($"Bot:> {response.Message.Text}");
```

> 🙋 **Need help?**: If you encounter any issues, [open an issue in the repository](https://github.com/microsoft/Generative-AI-for-beginners-dotnet/issues/new).
Expand Down
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