DotNet в Аризе для наблюдения за степенью LLM ⇐ Python

[Anonymous]

Я попытался реализовать простое наблюдение LLM с помощью Arize.ai и dotnet, используя этот код, и хотя я могу получить результаты на своей консоли, но они не отражаются на странице трассировки Arize, что делать? Я попробовал реализовать на Python, все прошло гладко, и, просмотрев официальную документацию Arize, я не смог найти ничего, связанного с .net, только Python и машинописный текст.

Arize.ai не поддерживает .net или я что-то упускаю?
using System;
using System.Collections.Generic;
using System.Diagnostics;
using System.Net.Http;
using System.Text;
using System.Text.Json;
using System.Threading.Tasks;
using OpenTelemetry;
using OpenTelemetry.Trace;
using OpenTelemetry.Resources;
using OpenTelemetry.Exporter;

public class Program
{
private static readonly HttpClient httpClient = new HttpClient();

public static async Task Main(string[] args)
{
// Configuration
string space_id = "********************";
string api_key = Environment.GetEnvironmentVariable("ARIZE_API_KEY");
string openaiApiKey = Environment.GetEnvironmentVariable("OPENAI_API_KEY");
string serviceName = "kovai-llm-observability";
string projectName = "llm-deotnet-app";
string endpoint = "https://otlp.arize.com";

Console.WriteLine("Starting LLM Observability Demo");
Console.WriteLine($"Project: {projectName}");
Console.WriteLine($"Endpoint: {endpoint}\n");

// Initialize OpenTelemetry
var tracerProvider = Sdk.CreateTracerProviderBuilder()
.AddSource(serviceName)
.SetResourceBuilder(ResourceBuilder.CreateDefault()
.AddService(projectName)
.AddTelemetrySdk()
.AddAttributes(new Dictionary
{
["openinference.project.name"] = projectName,
["model_version"] = "v1"
}))
.AddOtlpExporter("trace", opt =>
{
opt.Endpoint = new Uri(endpoint);
opt.Protocol = OtlpExportProtocol.Grpc;
opt.Headers = $"space_id={space_id},api_key={api_key},te=trailers";
opt.ExportProcessorType = ExportProcessorType.Batch;
})
.Build();

var tracer = tracerProvider.GetTracer(serviceName);

// Run workflow
await RunLLMWorkflow(tracer, openaiApiKey);

// Flush traces
Console.WriteLine("\nFlushing traces to Arize...");
var flushResult = tracerProvider.ForceFlush(10000);
Console.WriteLine($"Flush result: {flushResult}");

tracerProvider.Dispose();
Console.WriteLine($"\nView traces at: https://app.arize.com\n");
}

static async Task RunLLMWorkflow(Tracer tracer, string openaiApiKey)
{
// Start workflow span
using var workflowSpan = tracer.StartActiveSpan("llm_workflow");

var traceId = Activity.Current?.TraceId.ToString();
var spanId = Activity.Current?.SpanId.ToString();

Console.WriteLine("=== Workflow Started ===");
Console.WriteLine($"Trace ID: {traceId}");
Console.WriteLine($"Span ID: {spanId}");
Console.WriteLine($"Span Type: CHAIN\n");

workflowSpan.SetAttribute("openinference.span.kind", "CHAIN");
workflowSpan.SetAttribute("user.id", "demo_user");

// Step 1: Preprocessing
Console.WriteLine("Step 1: Preprocessing");
using (var prepSpan = tracer.StartActiveSpan("preprocessing"))
{
Console.WriteLine($" Span ID: {Activity.Current?.SpanId}");
Console.WriteLine($" Input: Who is the 2030 President of UK?");
prepSpan.SetAttribute("input.value", "Who is the 2030 President of UK?");
await Task.Delay(50);
prepSpan.SetStatus(Status.Ok);
Console.WriteLine($" Status: OK\n");
}

// Step 2: OpenAI Call
Console.WriteLine("Step 2: OpenAI LLM Call");
await CallOpenAI(tracer, openaiApiKey);

// Step 3: Postprocessing
Console.WriteLine("\nStep 3: Postprocessing");
using (var postSpan = tracer.StartActiveSpan("postprocessing"))
{
Console.WriteLine($" Span ID: {Activity.Current?.SpanId}");
Console.WriteLine($" Output: Processed response");
postSpan.SetAttribute("output.value", "Processed response");
await Task.Delay(50);
postSpan.SetStatus(Status.Ok);
Console.WriteLine($" Status: OK\n");
}

workflowSpan.SetStatus(Status.Ok);
Console.WriteLine("=== Workflow Complete ===\n");
}

static async Task CallOpenAI(Tracer tracer, string apiKey)
{
var prompt = "Who is the 2030 President of UK?";

// Start LLM span
using var llmSpan = tracer.StartActiveSpan("openai_chat_completion");

Console.WriteLine($" Span ID: {Activity.Current?.SpanId}");
Console.WriteLine($" Span Type: LLM");
Console.WriteLine($" Model: gpt-4");
Console.WriteLine($" Provider: openai");
Console.WriteLine($" Prompt: {prompt}");

// Set LLM attributes
llmSpan.SetAttribute("openinference.span.kind", "LLM");
llmSpan.SetAttribute("llm.model_name", "gpt-4");
llmSpan.SetAttribute("llm.provider", "openai");
llmSpan.SetAttribute("llm.input_messages.0.message.role", "user");
llmSpan.SetAttribute("llm.input_messages.0.message.content", prompt);

// Prepare request
var request = new
{
model = "gpt-4",
messages = new[] { new { role = "user", content = prompt } },
temperature = 0.7,
max_tokens = 150
};

var json = JsonSerializer.Serialize(request);
var content = new StringContent(json, Encoding.UTF8, "application/json");
httpClient.DefaultRequestHeaders.Clear();
httpClient.DefaultRequestHeaders.Add("Authorization", $"Bearer {apiKey}");

// Call API
Console.WriteLine(" Calling API...");
var startTime = DateTime.UtcNow;
var response = await httpClient.PostAsync("https://api.openai.com/v1/chat/completions", content);
var latency = (DateTime.UtcNow - startTime).TotalMilliseconds;

// Parse response
var responseBody = await response.Content.ReadAsStringAsync();
var result = JsonSerializer.Deserialize(responseBody);

var completion = result.GetProperty("choices")[0].GetProperty("message").GetProperty("content").GetString();
var totalTokens = result.GetProperty("usage").GetProperty("total_tokens").GetInt32();
var promptTokens = result.GetProperty("usage").GetProperty("prompt_tokens").GetInt32();
var completionTokens = result.GetProperty("usage").GetProperty("completion_tokens").GetInt32();

// Set output attributes
llmSpan.SetAttribute("llm.output_messages.0.message.content", completion);
llmSpan.SetAttribute("llm.token_count.total", totalTokens);
llmSpan.SetAttribute("llm.token_count.prompt", promptTokens);
llmSpan.SetAttribute("llm.token_count.completion", completionTokens);
llmSpan.SetAttribute("llm.response.latency_ms", latency);
llmSpan.SetStatus(Status.Ok);

// Print results
Console.WriteLine($"\n Response: {completion}");
Console.WriteLine($" Latency: {latency:F0}ms");
Console.WriteLine($" Total Tokens: {totalTokens}");
Console.WriteLine($" Prompt Tokens: {promptTokens}");
Console.WriteLine($" Completion Tokens: {completionTokens}");
Console.WriteLine($" Status: OK");
}
}


Подробнее здесь: https://stackoverflow.com/questions/798 ... ervability