2023-06-17 02:42:35 +00:00
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using Azure;
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using Azure.AI.OpenAI;
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2023-08-18 04:27:07 +00:00
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using BotSharp.Abstraction.Agents.Enums;
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using BotSharp.Abstraction.Agents.Models;
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using BotSharp.Abstraction.Conversations;
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using BotSharp.Abstraction.Conversations.Models;
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using BotSharp.Abstraction.Conversations.Settings;
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using BotSharp.Abstraction.MLTasks;
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using BotSharp.Plugin.AzureOpenAI.Settings;
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using Microsoft.Extensions.DependencyInjection;
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using Microsoft.Extensions.Logging;
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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.Threading.Tasks;
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2023-06-17 13:32:39 +00:00
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namespace BotSharp.Plugin.AzureOpenAI.Providers;
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public class ChatCompletionProvider : IChatCompletion
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{
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private readonly AzureOpenAiSettings _settings;
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private readonly IServiceProvider _services;
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private readonly ILogger _logger;
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private string _model;
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public string Provider => "azure-openai";
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public ChatCompletionProvider(AzureOpenAiSettings settings,
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ILogger<ChatCompletionProvider> logger,
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IServiceProvider services)
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{
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_settings = settings;
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_logger = logger;
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_services = services;
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}
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public RoleDialogModel GetChatCompletions(Agent agent, List<RoleDialogModel> conversations)
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{
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var hooks = _services.GetServices<IContentGeneratingHook>().ToList();
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// Before chat completion hook
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Task.WaitAll(hooks.Select(hook =>
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hook.BeforeGenerating(agent, conversations)).ToArray());
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var client = ProviderHelper.GetClient(_model, _settings);
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var chatCompletionsOptions = PrepareOptions(agent, conversations);
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var response = client.GetChatCompletions(_model, chatCompletionsOptions);
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var choice = response.Value.Choices[0];
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var message = choice.Message;
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var msg = new RoleDialogModel(AgentRole.Assistant, message.Content)
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{
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CurrentAgentId = agent.Id
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};
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if (choice.FinishReason == CompletionsFinishReason.FunctionCall)
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{
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_logger.LogInformation($"[{agent.Name}]: {message.FunctionCall.Name}({message.FunctionCall.Arguments})");
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msg = new RoleDialogModel(AgentRole.Function, message.Content)
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{
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CurrentAgentId = agent.Id,
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FunctionName = message.FunctionCall.Name,
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FunctionArgs = message.FunctionCall.Arguments
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};
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// Somethings LLM will generate a function name with agent name.
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if (!string.IsNullOrEmpty(msg.FunctionName))
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{
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msg.FunctionName = msg.FunctionName.Split('.').Last();
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}
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}
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// After chat completion hook
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Task.WaitAll(hooks.Select(hook =>
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hook.AfterGenerated(msg, new TokenStatsModel
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{
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Model = _model,
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PromptCount = response.Value.Usage.PromptTokens,
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CompletionCount = response.Value.Usage.CompletionTokens
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})).ToArray());
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return msg;
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}
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public async Task<bool> GetChatCompletionsAsync(Agent agent,
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List<RoleDialogModel> conversations,
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Func<RoleDialogModel, Task> onMessageReceived,
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Func<RoleDialogModel, Task> onFunctionExecuting)
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{
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var hooks = _services.GetServices<IContentGeneratingHook>().ToList();
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// Before chat completion hook
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Task.WaitAll(hooks.Select(hook =>
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hook.BeforeGenerating(agent, conversations)).ToArray());
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var client = ProviderHelper.GetClient(_model, _settings);
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var chatCompletionsOptions = PrepareOptions(agent, conversations);
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var response = await client.GetChatCompletionsAsync(_model, chatCompletionsOptions);
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var choice = response.Value.Choices[0];
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var message = choice.Message;
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var msg = new RoleDialogModel(AgentRole.Assistant, message.Content)
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{
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CurrentAgentId = agent.Id
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};
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// After chat completion hook
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Task.WaitAll(hooks.Select(hook =>
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hook.AfterGenerated(msg, new TokenStatsModel
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{
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Model = _model,
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PromptCount = response.Value.Usage.PromptTokens,
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CompletionCount = response.Value.Usage.CompletionTokens
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})).ToArray());
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if (choice.FinishReason == CompletionsFinishReason.FunctionCall)
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{
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_logger.LogInformation($"[{agent.Name}]: {message.FunctionCall.Name}({message.FunctionCall.Arguments})");
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var funcContextIn = new RoleDialogModel(AgentRole.Function, message.Content)
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{
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CurrentAgentId = agent.Id,
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FunctionName = message.FunctionCall.Name,
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FunctionArgs = message.FunctionCall.Arguments
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};
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2023-09-08 22:05:55 +00:00
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// Somethings LLM will generate a function name with agent name.
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if (!string.IsNullOrEmpty(funcContextIn.FunctionName))
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{
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funcContextIn.FunctionName = funcContextIn.FunctionName.Split('.').Last();
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}
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// Execute functions
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await onFunctionExecuting(funcContextIn);
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}
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else
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{
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// Text response received
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await onMessageReceived(msg);
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}
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return true;
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}
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public async Task<bool> GetChatCompletionsStreamingAsync(Agent agent, List<RoleDialogModel> conversations, Func<RoleDialogModel, Task> onMessageReceived)
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{
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var client = ProviderHelper.GetClient(_model, _settings);
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var chatCompletionsOptions = PrepareOptions(agent, conversations);
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var response = await client.GetChatCompletionsStreamingAsync(_model, chatCompletionsOptions);
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using StreamingChatCompletions streaming = response.Value;
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string output = "";
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await foreach (var choice in streaming.GetChoicesStreaming())
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{
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if (choice.FinishReason == CompletionsFinishReason.FunctionCall)
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{
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var args = "";
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await foreach (var message in choice.GetMessageStreaming())
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{
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if (message.FunctionCall == null || message.FunctionCall.Arguments == null)
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continue;
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Console.Write(message.FunctionCall.Arguments);
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args += message.FunctionCall.Arguments;
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}
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await onMessageReceived(new RoleDialogModel(ChatRole.Assistant.ToString(), args));
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continue;
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}
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await foreach (var message in choice.GetMessageStreaming())
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{
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if (message.Content == null)
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continue;
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Console.Write(message.Content);
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output += message.Content;
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_logger.LogInformation(message.Content);
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await onMessageReceived(new RoleDialogModel(message.Role.ToString(), message.Content));
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}
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output = "";
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}
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return true;
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}
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2023-08-17 04:04:23 +00:00
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2023-09-09 15:37:38 +00:00
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protected ChatCompletionsOptions PrepareOptions(Agent agent, List<RoleDialogModel> conversations)
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{
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var chatCompletionsOptions = new ChatCompletionsOptions();
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if (!string.IsNullOrEmpty(agent.Instruction))
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{
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chatCompletionsOptions.Messages.Add(new ChatMessage(ChatRole.System, agent.Instruction));
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}
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2023-06-29 23:14:57 +00:00
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if (!string.IsNullOrEmpty(agent.Knowledges))
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{
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chatCompletionsOptions.Messages.Add(new ChatMessage(ChatRole.System, agent.Knowledges));
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}
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2023-07-21 01:55:40 +00:00
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2023-10-09 22:28:17 +00:00
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var samples = ProviderHelper.GetChatSamples(agent.Samples);
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foreach (var message in samples)
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{
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chatCompletionsOptions.Messages.Add(new ChatMessage(message.Role, message.Content));
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}
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2023-09-27 20:49:44 +00:00
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foreach (var function in agent.Functions)
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{
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chatCompletionsOptions.Functions.Add(new FunctionDefinition
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{
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Name = function.Name,
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Description = function.Description,
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Parameters = BinaryData.FromObjectAsJson(function.Parameters)
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});
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}
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foreach (var message in conversations)
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{
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if (message.Role == ChatRole.Function)
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{
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chatCompletionsOptions.Messages.Add(new ChatMessage(message.Role, message.Content)
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{
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Name = message.FunctionName
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});
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}
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else
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{
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chatCompletionsOptions.Messages.Add(new ChatMessage(message.Role, message.Content));
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}
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}
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2023-08-17 04:04:23 +00:00
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// https://community.openai.com/t/cheat-sheet-mastering-temperature-and-top-p-in-chatgpt-api-a-few-tips-and-tricks-on-controlling-the-creativity-deterministic-output-of-prompt-responses/172683
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var state = _services.GetRequiredService<IConversationStateService>();
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var temperature = float.Parse(state.GetState("temperature", "0.5"));
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var samplingFactor = float.Parse(state.GetState("sampling_factor", "0.5"));
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chatCompletionsOptions.Temperature = temperature;
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chatCompletionsOptions.NucleusSamplingFactor = samplingFactor;
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2023-09-23 21:33:05 +00:00
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// chatCompletionsOptions.FrequencyPenalty = 0;
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// chatCompletionsOptions.PresencePenalty = 0;
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2023-08-17 04:04:23 +00:00
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2023-08-20 20:33:35 +00:00
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var convSetting = _services.GetRequiredService<ConversationSetting>();
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if (convSetting.ShowVerboseLog)
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{
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if (chatCompletionsOptions.Messages.Count > 0)
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2023-08-20 20:20:16 +00:00
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{
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2023-10-20 03:47:14 +00:00
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_logger.LogInformation("VERBOSE COMPLETION MESSAGES");
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var verbose = string.Join("\r\n", chatCompletionsOptions.Messages.Select(x =>
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{
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return x.Role == ChatRole.Function ?
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$"{x.Role}: {x.Name} => {x.Content}" :
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$"{x.Role}: {x.Content}";
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}));
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_logger.LogInformation(verbose);
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}
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2023-09-28 03:31:58 +00:00
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2023-10-20 03:47:14 +00:00
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if (chatCompletionsOptions.Functions.Count > 0)
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2023-09-28 03:31:58 +00:00
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{
|
2023-10-20 03:47:14 +00:00
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_logger.LogInformation("VERBOSE FUNCTIONS");
|
|
|
|
|
var verbose = string.Join("\r\n", chatCompletionsOptions.Functions.Select(x =>
|
|
|
|
|
{
|
|
|
|
|
return $"{x.Name}: {x.Description}\r\n{x.Parameters}";
|
|
|
|
|
}));
|
|
|
|
|
_logger.LogInformation(verbose);
|
|
|
|
|
}
|
2023-08-20 20:33:35 +00:00
|
|
|
}
|
|
|
|
|
|
2023-06-17 02:42:35 +00:00
|
|
|
return chatCompletionsOptions;
|
|
|
|
|
}
|
2023-09-19 16:29:25 +00:00
|
|
|
|
|
|
|
|
public void SetModelName(string model)
|
|
|
|
|
{
|
|
|
|
|
_model = model;
|
|
|
|
|
}
|
2023-06-17 02:42:35 +00:00
|
|
|
}
|