BotSharp/src/Plugins/BotSharp.Plugin.PythonInterpreter/Functions/PyInterpretationFn.cs

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using BotSharp.Abstraction.Routing;
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using Microsoft.Extensions.Logging;
using Python.Runtime;
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using System.Runtime;
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using System.Text.Json;
using System.Threading.Tasks;
namespace BotSharp.Plugin.PythonInterpreter.Functions;
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public class PyInterpretationFn : IFunctionCallback
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{
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public string Name => "util-code-python_interpreter";
public string Indication => "Executing python code";
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private readonly IServiceProvider _services;
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private readonly ILogger<PyInterpretationFn> _logger;
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private readonly PythonInterpreterSettings _settings;
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public PyInterpretationFn(
IServiceProvider services,
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ILogger<PyInterpretationFn> logger,
PythonInterpreterSettings settings)
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{
_services = services;
_logger = logger;
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_settings = settings;
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}
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public async Task<bool> Execute(RoleDialogModel message)
{
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var agentService = _services.GetRequiredService<IAgentService>();
var convService = _services.GetRequiredService<IConversationService>();
var routingCtx = _services.GetRequiredService<IRoutingContext>();
var args = JsonSerializer.Deserialize<LlmContextIn>(message.FunctionArgs);
var agent = await agentService.GetAgent(message.CurrentAgentId);
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var inst = GetPyCodeInterpreterInstruction(message.CurrentAgentId);
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var innerAgent = new Agent
{
Id = agent.Id,
Name = agent.Name,
Instruction = inst,
LlmConfig = GetLlmConfig(),
TemplateDict = new Dictionary<string, object>
{
{ "user_requirement", args?.UserRquirement ?? string.Empty }
}
};
var dialogs = routingCtx.GetDialogs();
if (dialogs.IsNullOrEmpty())
{
dialogs = convService.GetDialogHistory();
}
dialogs.Add(new RoleDialogModel(AgentRole.User, "Please follow the instruction and chat context to generate valid python code.")
{
CurrentAgentId = message.CurrentAgentId,
MessageId = message.MessageId
});
var response = await GetChatCompletion(innerAgent, dialogs);
var ret = response.JsonContent<LlmContextOut>();
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using (Py.GIL())
{
// Import necessary Python modules
dynamic sys = Py.Import("sys");
dynamic io = Py.Import("io");
// Redirect standard output to capture it
dynamic stringIO = io.StringIO();
sys.stdout = stringIO;
// Execute a simple Python script
using var locals = new PyDict();
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PythonEngine.Exec(ret.PythonCode, null, locals);
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// Console.WriteLine($"Result from Python: {result}");
message.Content = stringIO.getvalue();
// Restore the original stdout
sys.stdout = sys.__stdout__;
}
return true;
}
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private async Task<string> GetChatCompletion(Agent agent, List<RoleDialogModel> dialogs)
{
try
{
var (provider, model) = GetLlmProviderModel();
var completion = CompletionProvider.GetChatCompletion(_services, provider: provider, model: model);
var response = await completion.GetChatCompletions(agent, dialogs);
return response.Content;
}
catch (Exception ex)
{
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var error = $"Error when generating python code. {ex.Message}";
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_logger.LogWarning(ex, error);
return error;
}
}
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private string GetPyCodeInterpreterInstruction(string agentId)
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{
var db = _services.GetRequiredService<IBotSharpRepository>();
var state = _services.GetRequiredService<IConversationStateService>();
var templateContent = string.Empty;
var templateName = state.GetState("python_generate_template");
if (!string.IsNullOrEmpty(templateName))
{
templateContent = db.GetAgentTemplate(agentId, templateName);
}
else
{
templateName = "util-code-python_generate_instruction";
templateContent = db.GetAgentTemplate(BuiltInAgentId.UtilityAssistant, templateName);
}
return templateContent;
}
private (string, string) GetLlmProviderModel()
{
var provider = "openai";
var model = "gpt-5";
var state = _services.GetRequiredService<IConversationStateService>();
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provider = state.GetState("py_intepreter_llm_provider")
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//.IfNullOrEmptyAs(_settings.ChartPlot?.LlmProvider)
.IfNullOrEmptyAs(provider);
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model = state.GetState("py_intepreter_llm_model")
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//.IfNullOrEmptyAs(_settings.ChartPlot?.LlmModel)
.IfNullOrEmptyAs(model);
return (provider, model);
}
private AgentLlmConfig GetLlmConfig()
{
var maxOutputTokens = 8192;
var reasoningEffortLevel = "minimal";
var state = _services.GetRequiredService<IConversationStateService>();
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maxOutputTokens = int.TryParse(state.GetState("py_intepreter_max_output_tokens"), out var tokens) ? tokens : maxOutputTokens;
reasoningEffortLevel = state.GetState("py_intepreter_reasoning_effort_level").IfNullOrEmptyAs(reasoningEffortLevel);
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return new AgentLlmConfig
{
MaxOutputTokens = maxOutputTokens,
ReasoningEffortLevel = reasoningEffortLevel
};
}
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}