SemianiakaVY/BotSharp
1
0
Fork 0
Code Issues Pull requests Actions Packages Projects Releases Wiki Activity

Add Temperature

Browse source
This commit is contained in:
Haiping Chen 2023-11-30 21:59:05 -06:00
parent 9cc9839796
commit 9d4487836c
2 changed files with 25 additions and 18 deletions
Show stats Download patch file Download diff file Expand all files Collapse all files

34
src/Plugins/BotSharp.Plugin.GoogleAI/Providers/ChatCompletionProvider.cs
View file

@ -1,14 +1,12 @@
using BotSharp.Abstraction.Agents;
using BotSharp.Abstraction.Agents.Enums;
using BotSharp.Abstraction.Conversations;
using BotSharp.Abstraction.Loggers;
using BotSharp.Abstraction.Functions.Models;
using BotSharp.Abstraction.Routing;
using BotSharp.Plugin.GoogleAI.Settings;
using LLMSharp.Google.Palm;
using Microsoft.Extensions.Logging;
using System.Diagnostics.Metrics;
using static System.Net.Mime.MediaTypeNames;
using LLMSharp.Google.Palm.DiscussService;
namespace BotSharp.Plugin.GoogleAI.Providers;
@ -39,19 +37,25 @@ public class ChatCompletionProvider : IChatCompletion
var client = new GooglePalmClient(apiKey: _settings.PaLM.ApiKey);
var (prompt, messages) = PrepareOptions(agent, conversations);
var (prompt, messages, hasFunctions) = PrepareOptions(agent, conversations);
RoleDialogModel msg;
if (messages == null)
if (hasFunctions)
{
// use text completion
var response = client.GenerateTextAsync(prompt, null).Result;
// var response = client.GenerateTextAsync(prompt, null).Result;
var response = client.ChatAsync(new PalmChatCompletionRequest
{
Context = prompt,
Messages = messages,
Temperature = 0.1f
}).Result;
var message = response.Candidates.First();
// check if returns function calling
var llmResponse = message.Output.JsonContent<FunctionCallingResponse>();
var llmResponse = message.Content.JsonContent<FunctionCallingResponse>();
msg = new RoleDialogModel(llmResponse.Role, llmResponse.Content)
{
@ -79,13 +83,14 @@ public class ChatCompletionProvider : IChatCompletion
Task.WaitAll(hooks.Select(hook =>
hook.AfterGenerated(msg, new TokenStatsModel
{
Prompt = prompt,
Model = _model
})).ToArray());
return msg;
}
private (string, List<PalmChatMessage>) PrepareOptions(Agent agent, List<RoleDialogModel> conversations)
private (string, List<PalmChatMessage>, bool) PrepareOptions(Agent agent, List<RoleDialogModel> conversations)
{
var prompt = "";
@ -99,6 +104,9 @@ public class ChatCompletionProvider : IChatCompletion
var routing = _services.GetRequiredService<IRoutingService>();
var router = routing.Router;
var messages = conversations.Select(c => new PalmChatMessage(c.Content, c.Role == AgentRole.User ? "user" : "AI"))
.ToList();
if (agent.Functions != null && agent.Functions.Count > 0)
{
prompt += "\r\n\r\n[Functions] defined in JSON Schema:\r\n";
@ -118,13 +126,13 @@ public class ChatCompletionProvider : IChatCompletion
prompt += "\r\n\r\n" + router.Templates.FirstOrDefault(x => x.Name == "response_with_function").Content;
return (prompt, null);
return (prompt, new List<PalmChatMessage>
{
new PalmChatMessage("Which function should be used for the next step based on latest user or function response, output your response in JSON:", AgentRole.User),
}, true);
}
var messages = conversations.Select(c => new PalmChatMessage(c.Content, c.Role == AgentRole.User ? "user" : "AI"))
.ToList();
return (prompt, messages);
return (prompt, messages, false);
}
public Task<bool> GetChatCompletionsAsync(Agent agent, List<RoleDialogModel> conversations, Func<RoleDialogModel, Task> onMessageReceived, Func<RoleDialogModel, Task> onFunctionExecuting)

9
src/WebStarter/data/agents/01fcc3e5-9af7-49e6-ad7a-a760bd12dc4a/templates/response_with_function.liquid
View file

@ -1,10 +1,9 @@
[Output Requirements]
1. Read the [Functions] definition, you can utilize the function to retrieve data or execute actions.
2. Think step by step, check if specific function will provider data to help complete user request based on the [Conversation].
2. Think step by step, check if specific function will provider data to help complete user request based on the conversation.
3. If you need to call a function to decide how to response user,
response in format: {"role": "function", "reason":"why choose this function", "function_name": "", "args": {}},
otherwise response in format: {"role": "assistant", "reason":"why response to user", "content":"next step question"}.
4. If the [Conversation] already contains the function execution result, don't need to call it again.
4. If the conversation already contains the function execution result, don't need to call it again.
5. If user mentioned some specific requirment, don't ask this question in your response.
6. Don't repeat the same question in your response.
Which function should be used for the next step based on latest user's response, output your response in JSON:
6. Don't repeat the same question in your response.