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Add knowledgebase and conversation APIs (Azure#46893)
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‎eng/Packages.Data.props‎

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@@ -157,6 +157,7 @@
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<PackageReference Update="Azure.Provisioning.KeyVault" Version="1.0.0" />
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<PackageReference Update="Azure.Provisioning.ServiceBus" Version="1.0.0" />
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<PackageReference Update="Azure.Provisioning.Storage" Version="1.0.0" />
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<PackageReference Update="Microsoft.Bcl.Numerics" Version="8.0.0" />
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<!-- Other approved packages -->
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<PackageReference Update="Microsoft.Azure.Amqp" Version="2.6.7" />

‎sdk/provisioning/Azure.Provisioning.CloudMachine/api/Azure.Provisioning.CloudMachine.netstandard2.0.cs‎

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@@ -138,9 +138,21 @@ namespace Azure.Provisioning.CloudMachine.OpenAI
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{
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public static partial class AzureOpenAIExtensions
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{
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public static Azure.Provisioning.CloudMachine.OpenAI.EmbeddingKnowledgebase CreateEmbeddingKnowledgebase(this Azure.Core.ClientWorkspace workspace) { throw null; }
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public static Azure.Provisioning.CloudMachine.OpenAI.OpenAIConversation CreateOpenAIConversation(this Azure.Core.ClientWorkspace workspace) { throw null; }
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public static OpenAI.Chat.ChatClient GetOpenAIChatClient(this Azure.Core.ClientWorkspace workspace) { throw null; }
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public static OpenAI.Embeddings.EmbeddingClient GetOpenAIEmbeddingsClient(this Azure.Core.ClientWorkspace workspace) { throw null; }
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}
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public partial class EmbeddingKnowledgebase
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{
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internal EmbeddingKnowledgebase() { }
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public void Add(string fact) { }
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}
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public partial class OpenAIConversation
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{
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internal OpenAIConversation() { }
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public string Say(string message) { throw null; }
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}
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public partial class OpenAIFeature : Azure.Provisioning.CloudMachine.CloudMachineFeature
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{
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public OpenAIFeature(AiModel chatDeployment, AiModel? embeddingsDeployment = null) { }

‎sdk/provisioning/Azure.Provisioning.CloudMachine/src/Azure.Provisioning.CloudMachine.csproj‎

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<PackageReference Include="Azure.Provisioning.EventGrid" />
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<PackageReference Include="Azure.Security.KeyVault.Secrets" />
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<PackageReference Include="Microsoft.Extensions.Configuration.Abstractions" VersionOverride="8.0.0" />
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<PackageReference Include="Microsoft.Bcl.Numerics" />
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</ItemGroup>
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</Project>

‎sdk/provisioning/Azure.Provisioning.CloudMachine/src/AzureSdkExtensions/EmbeddingKnowledgebase.cs‎

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// Copyright (c) Microsoft Corporation. All rights reserved.
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// Licensed under the MIT License.
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using System;
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using System.Collections.Generic;
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using OpenAI.Embeddings;
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namespace Azure.Provisioning.CloudMachine.OpenAI;
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/// <summary>
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/// Represents a knowledgebase of facts represented by embeddings that can be used to find relevant facts based on a given text.
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/// </summary>
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public class EmbeddingKnowledgebase
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{
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private EmbeddingClient _client;
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private List<string> _factsToProcess = new List<string>();
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private List<ReadOnlyMemory<float>> _vectors = new List<ReadOnlyMemory<float>>();
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private List<string> _facts = new List<string>();
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internal EmbeddingKnowledgebase(EmbeddingClient client)
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{
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_client = client;
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}
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/// <summary>
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/// Add a fact to the knowledgebase.
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/// </summary>
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/// <param name="fact">The fact to add.</param>
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public void Add(string fact)
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{
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ChunkAndAddToFactsToProcess(fact, 1000);
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ProcessUnprocessedFacts();
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}
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internal List<Fact> FindRelevantFacts(string text, float threshold = 0.29f, int top = 3)
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{
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if (_factsToProcess.Count > 0)
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ProcessUnprocessedFacts();
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ReadOnlySpan<float> textVector = ProcessFact(text).Span;
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var results = new List<Fact>();
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var distances = new List<(float Distance, int Index)>();
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for (int index = 0; index < _vectors.Count; index++)
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{
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ReadOnlyMemory<float> dbVector = _vectors[index];
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float distance = 1.0f - CosineSimilarity(dbVector.Span, textVector);
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distances.Add((distance, index));
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}
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distances.Sort(((float D1, int I1) v1, (float D2, int I2) v2) => v1.D1.CompareTo(v2.D2));
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top = Math.Min(top, distances.Count);
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for (int i = 0; i < top; i++)
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{
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var distance = distances[i].Distance;
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if (distance > threshold)
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break;
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var index = distances[i].Index;
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results.Add(new Fact(_facts[index], index));
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}
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return results;
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}
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private static float CosineSimilarity(ReadOnlySpan<float> x, ReadOnlySpan<float> y)
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{
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float dot = 0, xSumSquared = 0, ySumSquared = 0;
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for (int i = 0; i < x.Length; i++)
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{
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dot += x[i] * y[i];
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xSumSquared += x[i] * x[i];
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ySumSquared += y[i] * y[i];
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}
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return dot / (MathF.Sqrt(xSumSquared) * MathF.Sqrt(ySumSquared));
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}
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private void ProcessUnprocessedFacts()
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{
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if (_factsToProcess.Count == 0)
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{
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return;
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}
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var embeddings = _client.GenerateEmbeddings(_factsToProcess);
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foreach (var embedding in embeddings.Value)
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{
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_vectors.Add(embedding.ToFloats());
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_facts.Add(_factsToProcess[embedding.Index]);
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}
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_factsToProcess.Clear();
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}
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private ReadOnlyMemory<float> ProcessFact(string fact)
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{
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var embedding = _client.GenerateEmbedding(fact);
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return embedding.Value.ToFloats();
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}
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internal void ChunkAndAddToFactsToProcess(string text, int chunkSize)
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{
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if (chunkSize <= 0)
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{
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throw new ArgumentException("Chunk size must be greater than zero.", nameof(chunkSize));
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}
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int overlapSize = (int)(chunkSize * 0.15);
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int stepSize = chunkSize - overlapSize;
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ReadOnlySpan<char> textSpan = text.AsSpan();
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for (int i = 0; i < text.Length; i += stepSize)
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{
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while (i > 0 && !char.IsWhiteSpace(textSpan[i]))
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{
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i--;
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}
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if (i + chunkSize > text.Length)
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{
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_factsToProcess.Add(textSpan.Slice(i).ToString());
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}
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else
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{
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int end = i + chunkSize;
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if (end > text.Length)
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{
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_factsToProcess.Add(textSpan.Slice(i).ToString());
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}
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else
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{
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while (end < text.Length && !char.IsWhiteSpace(textSpan[end]))
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{
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end++;
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}
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_factsToProcess.Add(textSpan.Slice(i, end - i).ToString());
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}
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}
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}
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}
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internal struct Fact
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{
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public Fact(string text, int id)
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{
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Text = text;
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Id = id;
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}
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public string Text { get; set; }
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public int Id { get; set; }
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}
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}

‎sdk/provisioning/Azure.Provisioning.CloudMachine/src/AzureSdkExtensions/OpenAIConversation.cs‎

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// Copyright (c) Microsoft Corporation. All rights reserved.
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// Licensed under the MIT License.
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using System;
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using System.Collections.Generic;
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using System.Linq;
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using System.Text;
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using OpenAI.Chat;
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using static Azure.Provisioning.CloudMachine.OpenAI.EmbeddingKnowledgebase;
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namespace Azure.Provisioning.CloudMachine.OpenAI;
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/// <summary>
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/// Represents a conversation with the OpenAI chat model, incorporating a knowledgebase of embeddings data.
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/// </summary>
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public class OpenAIConversation
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{
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private readonly ChatClient _client;
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private readonly Prompt _prompt;
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private readonly Dictionary<string, ChatTool> _tools = new();
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private readonly EmbeddingKnowledgebase _knowledgebase;
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private readonly ChatCompletionOptions _options = new ChatCompletionOptions();
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/// <summary>
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/// Initializes a new instance of the <see cref="OpenAIConversation"/> class.
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/// </summary>
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/// <param name="client">The ChatClient.</param>
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/// <param name="tools">Any ChatTools to be used by the conversation.</param>
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/// <param name="knowledgebase">The knowledgebase.</param>
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internal OpenAIConversation(ChatClient client, IEnumerable<ChatTool> tools, EmbeddingKnowledgebase knowledgebase)
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{
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foreach (var tool in tools)
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{
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_options.Tools.Add(tool);
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_tools.Add(tool.FunctionName, tool);
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}
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_client = client;
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_knowledgebase = knowledgebase;
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_prompt = new Prompt();
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_prompt.AddTools(tools);
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}
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/// <summary>
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/// Sends a message to the OpenAI chat model and returns the response, incorporating any relevant knowledge from the <see cref="EmbeddingKnowledgebase"/>.
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/// </summary>
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/// <param name="message"></param>
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/// <returns></returns>
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public string Say(string message)
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{
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List<Fact> facts = _knowledgebase.FindRelevantFacts(message);
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_prompt.AddFacts(facts);
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_prompt.AddUserMessage(message);
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var response = CallOpenAI();
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return response;
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}
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private string CallOpenAI()
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{
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bool requiresAction;
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do
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{
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requiresAction = false;
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var completion = _client.CompleteChat(_prompt.Messages).Value;
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switch (completion.FinishReason)
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{
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case ChatFinishReason.ToolCalls:
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// TODO: Implement tool calls
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requiresAction = true;
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break;
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case ChatFinishReason.Length:
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return "Incomplete model output due to MaxTokens parameter or token limit exceeded.";
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case ChatFinishReason.ContentFilter:
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return "Omitted content due to a content filter flag.";
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case ChatFinishReason.Stop:
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_prompt.AddAssistantMessage(new AssistantChatMessage(completion));
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break;
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default:
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throw new NotImplementedException("Unknown finish reason.");
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}
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return _prompt.GetSayResult();
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} while (requiresAction);
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}
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internal class Prompt
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{
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internal readonly List<UserChatMessage> userChatMessages = new();
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internal readonly List<SystemChatMessage> systemChatMessages = new();
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internal readonly List<AssistantChatMessage> assistantChatMessages = new();
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internal readonly List<ToolChatMessage> toolChatMessages = new();
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internal readonly List<ChatCompletion> chatCompletions = new();
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internal readonly List<ChatTool> _tools = new();
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internal readonly List<int> _factsAlreadyInPrompt = new List<int>();
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public Prompt()
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{ }
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public IEnumerable<ChatMessage> Messages
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{
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get
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{
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foreach (var message in systemChatMessages)
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{
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yield return message;
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}
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foreach (var message in userChatMessages)
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{
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yield return message;
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}
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foreach (var message in assistantChatMessages)
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{
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yield return message;
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}
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foreach (var message in toolChatMessages)
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{
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yield return message;
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}
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}
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}
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//public ChatCompletionOptions Current => _prompt;
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public void AddTools(IEnumerable<ChatTool> tools)
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{
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foreach (var tool in tools)
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{
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_tools.Add(tool);
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}
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}
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public void AddFacts(IEnumerable<Fact> facts)
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{
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var sb = new StringBuilder();
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foreach (var fact in facts)
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{
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if (_factsAlreadyInPrompt.Contains(fact.Id))
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continue;
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sb.AppendLine(fact.Text);
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_factsAlreadyInPrompt.Add(fact.Id);
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}
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if (sb.Length > 0)
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{
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systemChatMessages.Add(ChatMessage.CreateSystemMessage(sb.ToString()));
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}
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}
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public void AddUserMessage(string message)
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{
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userChatMessages.Add(ChatMessage.CreateUserMessage(message));
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}
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public void AddAssistantMessage(string message)
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{
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assistantChatMessages.Add(ChatMessage.CreateAssistantMessage(message));
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}
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public void AddAssistantMessage(AssistantChatMessage message)
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{
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assistantChatMessages.Add(message);
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}
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public void AddToolMessage(ToolChatMessage message)
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{
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toolChatMessages.Add(message);
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}
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internal string GetSayResult()
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{
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var result = string.Join("\n", assistantChatMessages.Select(m => m.Content[0].Text));
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assistantChatMessages.Clear();
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userChatMessages.Clear();
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systemChatMessages.Clear();
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return result;
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}
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}
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}

‎sdk/provisioning/Azure.Provisioning.CloudMachine/src/AzureSdkExtensions/OpenAIFeature.cs‎

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using System;
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using System.ClientModel;
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using System.Linq;
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using Azure.AI.OpenAI;
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using Azure.Core;
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using Azure.Provisioning.Authorization;
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return embeddingsClient;
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}
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public static EmbeddingKnowledgebase CreateEmbeddingKnowledgebase(this ClientWorkspace workspace)
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{
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EmbeddingClient embeddingsClient = workspace.GetOpenAIEmbeddingsClient();
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return new EmbeddingKnowledgebase(embeddingsClient);
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}
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public static OpenAIConversation CreateOpenAIConversation(this ClientWorkspace workspace)
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{
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ChatClient chatClient = workspace.GetOpenAIChatClient();
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EmbeddingKnowledgebase knowledgebase = workspace.CreateEmbeddingKnowledgebase();
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return new OpenAIConversation(chatClient, [], knowledgebase);
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}
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private static AzureOpenAIClient CreateAzureOpenAIClient(this ClientWorkspace workspace)
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{
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ClientConnectionOptions connection = workspace.GetConnectionOptions(typeof(AzureOpenAIClient));

‎sdk/provisioning/Azure.Provisioning.CloudMachine/tests/CloudMachineTests.cs‎

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CloudMachineWorkspace cm = new();
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Console.WriteLine(cm.Id);
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var embeddings = cm.GetOpenAIEmbeddingsClient();
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var kb = cm.CreateEmbeddingKnowledgebase();
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var conversation = cm.CreateOpenAIConversation();
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}
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[Ignore("no recordings yet")]

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