128 lines
4.1 KiB
C#
128 lines
4.1 KiB
C#
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.LLamaSharp.Settings;
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using BotSharp.Plugins.LLamaSharp;
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using LLama;
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using LLama.Common;
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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.Text;
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using System.Threading.Tasks;
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namespace BotSharp.Plugin.LLamaSharp.Providers;
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public class ChatCompletionProvider : IChatCompletion
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{
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private readonly IServiceProvider _services;
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private readonly ILogger _logger;
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private readonly LlamaSharpSettings _settings;
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private string _model;
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public ChatCompletionProvider(IServiceProvider services,
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ILogger<ChatCompletionProvider> logger,
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LlamaSharpSettings settings)
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{
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_services = services;
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_logger = logger;
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_settings = settings;
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}
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public string Provider => "llama-sharp";
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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 content = string.Join("\r\n", conversations.Select(x => $"{x.Role}: {x.Content}")).Trim();
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content += $"\r\n{AgentRole.Assistant}: ";
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var state = _services.GetRequiredService<IConversationStateService>();
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var model = state.GetState("model", _settings.DefaultModel);
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var llama = _services.GetRequiredService<LlamaAiModel>();
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llama.LoadModel(model);
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var executor = llama.GetStatelessExecutor();
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var inferenceParams = new InferenceParams()
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{
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Temperature = 0.1f,
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AntiPrompts = new List<string> { $"{AgentRole.User}:", "[/INST]" },
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MaxTokens = 64
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};
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string totalResponse = "";
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var prompt = agent.Instruction + "\r\n" + content;
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var convSetting = _services.GetRequiredService<ConversationSetting>();
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if (convSetting.ShowVerboseLog)
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{
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_logger.LogInformation(prompt);
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}
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foreach (var response in executor.Infer(prompt, inferenceParams))
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{
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Console.Write(response);
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totalResponse += response;
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}
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foreach (var anti in inferenceParams.AntiPrompts)
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{
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totalResponse = totalResponse.Replace(anti, "").Trim();
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}
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var msg = new RoleDialogModel(AgentRole.Assistant, totalResponse)
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{
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CurrentAgentId = agent.Id
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};
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// Text response received
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await onMessageReceived(msg);
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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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string totalResponse = "";
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var content = string.Join("\r\n", conversations.Select(x => $"{x.Role}: {x.Content}")).Trim();
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content += $"\r\n{AgentRole.Assistant}: ";
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var state = _services.GetRequiredService<IConversationStateService>();
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var model = state.GetState("model", "llama-2-7b-chat.Q8_0");
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var llama = _services.GetRequiredService<LlamaAiModel>();
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llama.LoadModel(model);
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var executor = new StatelessExecutor(llama.Model, llama.Params);
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var inferenceParams = new InferenceParams() { Temperature = 1.0f, AntiPrompts = new List<string> { $"{AgentRole.User}:" }, MaxTokens = 64 };
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var convSetting = _services.GetRequiredService<ConversationSetting>();
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if (convSetting.ShowVerboseLog)
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{
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_logger.LogInformation(agent.Instruction);
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}
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foreach (var response in executor.Infer(agent.Instruction, inferenceParams))
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{
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Console.Write(response);
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totalResponse += response;
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}
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return true;
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}
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public void SetModelName(string model)
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{
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_model = model;
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}
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}
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