feat: OpenAI-kompatibler Provider (Nvidia/g4f.space) mit Tool-Calling und SSE-Streaming
- OpenAiService.cs: Neuer Service für OpenAI-kompatible APIs - SSE-Streaming mit text_start/text_delta/text_end Events - Tool-Calling: list_files, read_file, write_file, edit_file, delete_file, execute - Agentic Loop (max 10 Iterationen) - Tool-Use + Tool-Result in DB gespeichert - History-Rebuild: Anthropic-Format → OpenAI-Format (tool_use/tool_result korrekt konvertiert) - ChatContext.cs: Provider-Feld hinzugefügt - ChatService.cs: OpenAI-Provider-Erkennung in ResolveModelId + PrepareContext - ChatEndpoint.cs: Provider-Dispatch (nvidia → OpenAiService, sonst → AnthropicService) - Program.cs: OpenAiService als Singleton registriert - wwwroot/index.html: nvidia:moonshotai/kimi-k2.5 in Modell-Auswahl
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@@ -30,6 +30,9 @@ public class ChatService
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public static string ResolveModelId(string model)
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{
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// OpenAI-kompatible Provider werden unverändert durchgereicht (Format: "provider:model")
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if (OpenAiService.IsOpenAiProvider(model)) return model;
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return model switch
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{
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"sonnet" => "claude-sonnet-4-6",
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@@ -160,11 +163,14 @@ Für welche Plattform soll die App entwickelt werden? Web, Mobile oder Desktop?
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reviewContext.MaxReviewIterations = session.MaxReviewIterations > 0 ? session.MaxReviewIterations : 3;
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var (provider, _) = OpenAiService.ParseModelString(modelId);
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return new ChatContext
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{
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ChainId = chainId,
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SessionId = sessionId,
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ModelId = modelId,
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Provider = provider,
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Workspace = workspace,
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AgentId = agent.AgentId,
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SystemPromptText = systemPromptText,
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@@ -0,0 +1,640 @@
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using System;
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using System.Collections.Generic;
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using System.Net.Http;
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using System.Net.Http.Headers;
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using System.Text;
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using System.Text.Json;
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using System.Threading;
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using System.Threading.Tasks;
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using Gott.Models;
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using Microsoft.AspNetCore.Http;
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namespace Gott.Services;
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/// <summary>
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/// OpenAI-kompatibler Service – unterstützt Nvidia (g4f.space Proxy) und andere
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/// OpenAI-API-kompatible Endpunkte. Unterstützt Tool-Calling (list_files, read_file,
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/// write_file, edit_file, delete_file, execute) und Streaming via SSE.
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/// </summary>
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public class OpenAiService
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{
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private readonly IHttpClientFactory _httpFactory;
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private readonly DatabaseService _db;
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private readonly ToolService _toolService;
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// Provider-Definitionen
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private static readonly Dictionary<string, (string BaseUrl, string ApiKey)> Providers = new()
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{
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["nvidia"] = ("https://g4f.space/api/nvidia/v1", "")
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};
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// Tools die für OpenAI-kompatible Provider verfügbar sind
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private static readonly List<object> OpenAiTools = new()
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{
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new
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{
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type = "function",
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function = new
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{
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name = "list_files",
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description = "Listet Dateien und Ordner in einem Verzeichnis auf.",
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parameters = new
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{
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type = "object",
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properties = new Dictionary<string, object>
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{
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["path"] = new { type = "string", description = "Relativer Verzeichnispfad (leer = Root)" }
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},
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required = Array.Empty<string>()
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}
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}
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},
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new
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{
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type = "function",
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function = new
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{
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name = "read_file",
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description = "Liest den Inhalt einer Datei. Standardmäßig werden maximal 2000 Zeilen gelesen. Bei großen Dateien offset und limit verwenden.",
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parameters = new
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{
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type = "object",
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properties = new Dictionary<string, object>
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{
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["path"] = new { type = "string", description = "Relativer Dateipfad" },
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["offset"] = new { type = "integer", description = "Erste Zeile die gelesen werden soll (1-basiert, Default: 1)" },
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["limit"] = new { type = "integer", description = "Maximale Anzahl Zeilen die gelesen werden sollen (Default: 2000)" }
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},
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required = new[] { "path" }
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}
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}
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},
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new
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{
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type = "function",
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function = new
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{
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name = "write_file",
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description = "Erstellt oder überschreibt eine Datei mit dem angegebenen Inhalt.",
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parameters = new
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{
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type = "object",
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properties = new Dictionary<string, object>
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{
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["path"] = new { type = "string", description = "Relativer Dateipfad" },
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["content"] = new { type = "string", description = "Dateiinhalt" }
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},
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required = new[] { "path", "content" }
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}
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}
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},
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new
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{
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type = "function",
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function = new
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{
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name = "edit_file",
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description = "Ersetzt einen exakten Textabschnitt in einer Datei. oldText muss exakt matchen.",
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parameters = new
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{
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type = "object",
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properties = new Dictionary<string, object>
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{
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["path"] = new { type = "string", description = "Relativer Dateipfad" },
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["old_text"] = new { type = "string", description = "Exakter Text der ersetzt werden soll" },
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["new_text"] = new { type = "string", description = "Neuer Text" }
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},
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required = new[] { "path", "old_text", "new_text" }
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}
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}
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},
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new
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{
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type = "function",
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function = new
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{
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name = "delete_file",
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description = "Löscht eine Datei oder ein leeres Verzeichnis.",
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parameters = new
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{
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type = "object",
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properties = new Dictionary<string, object>
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{
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["path"] = new { type = "string", description = "Relativer Pfad zur Datei oder zum Verzeichnis" }
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},
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required = new[] { "path" }
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}
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}
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},
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new
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{
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type = "function",
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function = new
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{
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name = "execute",
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description = "Führt einen Shell-Befehl im Workspace-Verzeichnis aus. Gibt stdout und stderr zurück.",
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parameters = new
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{
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type = "object",
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properties = new Dictionary<string, object>
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{
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["command"] = new { type = "string", description = "Shell-Befehl zum Ausführen" }
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},
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required = new[] { "command" }
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}
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}
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}
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};
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public OpenAiService(IHttpClientFactory httpFactory, DatabaseService db, ToolService toolService)
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{
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_httpFactory = httpFactory;
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_db = db;
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_toolService = toolService;
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}
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/// <summary>
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/// Gibt den Provider und das eigentliche Modell-ID aus einem kombinierten Model-String zurück.
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/// Format: "nvidia:moonshotai/kimi-k2.5" → ("nvidia", "moonshotai/kimi-k2.5")
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/// </summary>
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public static (string provider, string modelId) ParseModelString(string model)
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{
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if (string.IsNullOrEmpty(model)) return ("", model ?? "");
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var idx = model.IndexOf(':');
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if (idx < 0) return ("", model);
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return (model[..idx], model[(idx + 1)..]);
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}
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/// <summary>
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/// Prüft ob ein Model-String einen externen OpenAI-kompatiblen Provider referenziert.
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/// </summary>
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public static bool IsOpenAiProvider(string model)
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{
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var (provider, _) = ParseModelString(model);
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return Providers.ContainsKey(provider);
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}
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public async Task StreamChat(
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HttpResponse response,
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string modelString,
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string systemPromptText,
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List<object> messages,
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string chainId,
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string workspace = null,
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string sessionId = null,
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string agentId = null,
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int maxIterations = 10,
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CancellationToken workCt = default,
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CancellationToken sseCt = default)
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{
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var sw = System.Diagnostics.Stopwatch.StartNew();
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var usage = new TokenUsage();
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var iteration = 0;
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var chain = new ChainRecord
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{
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ChainId = chainId,
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SessionId = sessionId,
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Model = modelString
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};
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var (provider, modelId) = ParseModelString(modelString);
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if (!Providers.TryGetValue(provider, out var providerConfig))
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{
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var errMsg = $"Unbekannter Provider: '{provider}'";
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await _db.SaveMessage(new MessageRecord
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{
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ChainId = chainId, Iteration = 1,
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Role = "system", Type = "error",
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Content = errMsg, IsError = true
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});
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await TryWriteSse(response, "error", new { error = errMsg }, sseCt);
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await TryWriteSse(response, "done", new { chainId }, sseCt);
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return;
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}
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try
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{
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// OpenAI-kompatibles Messages-Format aufbauen
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var apiMessages = new List<object>();
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// System-Prompt als erste System-Message
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if (!string.IsNullOrEmpty(systemPromptText))
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apiMessages.Add(new { role = "system", content = systemPromptText });
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// History-Messages konvertieren (Anthropic-Format → OpenAI-Format)
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foreach (var msg in messages)
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{
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var converted = ConvertMessage(msg);
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if (converted == null) continue;
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// Tool-Results kommen als List<object> zurück → einzeln expandieren
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if (converted is List<object> toolResultList)
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{
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foreach (var tr in toolResultList)
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apiMessages.Add(tr);
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continue;
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}
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apiMessages.Add(converted);
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}
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// Agentic Loop
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while (iteration < maxIterations)
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{
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workCt.ThrowIfCancellationRequested();
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iteration++;
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var client = CreateClient(providerConfig.ApiKey);
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var payload = new Dictionary<string, object>
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{
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["model"] = modelId,
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["messages"] = apiMessages,
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["stream"] = true,
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["max_tokens"] = 16384,
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["tools"] = OpenAiTools,
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["tool_choice"] = "auto"
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};
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var jsonPayload = JsonSerializer.Serialize(payload);
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var httpContent = new StringContent(jsonPayload, Encoding.UTF8, "application/json");
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using var httpResp = await client.SendAsync(
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new HttpRequestMessage(HttpMethod.Post, $"{providerConfig.BaseUrl}/chat/completions") { Content = httpContent },
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HttpCompletionOption.ResponseHeadersRead,
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workCt
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);
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if (!httpResp.IsSuccessStatusCode)
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{
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var errBody = await httpResp.Content.ReadAsStringAsync(workCt);
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var errMsg = $"API {(int)httpResp.StatusCode}: {errBody}";
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await _db.SaveMessage(new MessageRecord
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{
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ChainId = chainId, Iteration = iteration,
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Role = "system", Type = "error",
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Content = errMsg, IsError = true
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});
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await TryWriteSse(response, "error", new { error = errMsg }, sseCt);
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await FinalizeChain(chain, usage, iteration, "error", sw);
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await TryWriteSse(response, "done", new { chainId }, sseCt);
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return;
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}
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// SSE-Stream lesen
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using var stream = await httpResp.Content.ReadAsStreamAsync(workCt);
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using var reader = new System.IO.StreamReader(stream);
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var fullText = new StringBuilder();
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// Tool-Call Akkumulatoren: id → (name, arguments)
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var toolCallAccumulators = new Dictionary<int, (string Id, string Name, StringBuilder Args)>();
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var textStarted = false;
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var stopReason = "end_turn";
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string line;
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while ((line = await reader.ReadLineAsync(workCt)) != null)
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{
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workCt.ThrowIfCancellationRequested();
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if (!line.StartsWith("data:")) continue;
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var data = line[5..].Trim();
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if (data == "[DONE]") break;
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JsonElement parsed;
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try { parsed = JsonSerializer.Deserialize<JsonElement>(data); }
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catch { continue; }
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if (!parsed.TryGetProperty("choices", out var choices)) continue;
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foreach (var choice in choices.EnumerateArray())
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{
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// Finish reason
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if (choice.TryGetProperty("finish_reason", out var finishProp) && finishProp.ValueKind != JsonValueKind.Null)
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{
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var fr = finishProp.GetString() ?? "";
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if (fr == "tool_calls") stopReason = "tool_use";
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else if (fr == "stop") stopReason = "end_turn";
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}
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if (!choice.TryGetProperty("delta", out var delta)) continue;
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// Text-Delta
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if (delta.TryGetProperty("content", out var contentProp) && contentProp.ValueKind == JsonValueKind.String)
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{
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var chunk = contentProp.GetString();
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if (!string.IsNullOrEmpty(chunk))
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{
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if (!textStarted)
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{
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await TryWriteSse(response, "text_start", new { index = 0 }, sseCt);
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textStarted = true;
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}
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fullText.Append(chunk);
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await TryWriteSse(response, "text_delta", new { text = chunk }, sseCt);
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}
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}
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// Tool-Call Deltas akkumulieren
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if (delta.TryGetProperty("tool_calls", out var toolCallsProp))
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{
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foreach (var tc in toolCallsProp.EnumerateArray())
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{
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var idx = tc.TryGetProperty("index", out var idxProp) ? idxProp.GetInt32() : 0;
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if (!toolCallAccumulators.ContainsKey(idx))
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toolCallAccumulators[idx] = ("", "", new StringBuilder());
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var (accId, accName, accArgs) = toolCallAccumulators[idx];
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if (tc.TryGetProperty("id", out var idProp))
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accId = idProp.GetString() ?? accId;
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if (tc.TryGetProperty("function", out var funcProp))
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{
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if (funcProp.TryGetProperty("name", out var nameProp2))
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accName = nameProp2.GetString() ?? accName;
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if (funcProp.TryGetProperty("arguments", out var argsProp))
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accArgs.Append(argsProp.GetString() ?? "");
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}
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toolCallAccumulators[idx] = (accId, accName, accArgs);
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}
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}
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}
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// Usage aus letztem Chunk
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if (parsed.TryGetProperty("usage", out var usageProp))
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{
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if (usageProp.TryGetProperty("prompt_tokens", out var pt))
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usage.InputTokens = pt.GetInt64();
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if (usageProp.TryGetProperty("completion_tokens", out var ct2))
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usage.OutputTokens = ct2.GetInt64();
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}
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}
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// Text-End senden falls Text gestreamt wurde
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if (textStarted)
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await TryWriteSse(response, "text_end", new { }, sseCt);
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// Text in DB speichern
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var finalText = fullText.ToString();
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if (!string.IsNullOrEmpty(finalText))
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{
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await _db.SaveMessage(new MessageRecord
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{
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ChainId = chainId, Iteration = iteration,
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Role = "assistant", Type = "text",
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Content = finalText
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});
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}
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// Keine Tool-Calls → fertig
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if (toolCallAccumulators.Count == 0 || stopReason != "tool_use")
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{
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await FinalizeChain(chain, usage, iteration, stopReason, sw);
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await TryWriteSse(response, "done", new
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{
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model = modelString,
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inputTokens = usage.InputTokens,
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outputTokens = usage.OutputTokens,
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iterations = iteration,
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chainId,
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durationMs = sw.ElapsedMilliseconds,
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stopReason
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}, sseCt);
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return;
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}
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// Tool-Calls ausführen
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// Assistant-Message mit tool_calls in die History einfügen
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var toolCallsForHistory = new List<object>();
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foreach (var (_, (tcId, tcName, tcArgs)) in toolCallAccumulators)
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{
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toolCallsForHistory.Add(new
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{
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id = tcId,
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type = "function",
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function = new { name = tcName, arguments = tcArgs.ToString() }
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});
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// Tool-Use in DB speichern
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await _db.SaveMessage(new MessageRecord
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{
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ChainId = chainId, Iteration = iteration,
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Role = "assistant", Type = "tool_use",
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Content = tcArgs.ToString(),
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ToolCallId = tcId, ToolName = tcName
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});
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}
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apiMessages.Add(new
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{
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role = "assistant",
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content = finalText,
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tool_calls = toolCallsForHistory
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});
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// Tool-Results sammeln
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var toolResults = new List<object>();
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foreach (var (_, (tcId, tcName, tcArgs)) in toolCallAccumulators)
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{
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// Tool-Start SSE
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await TryWriteSse(response, "tool_start", new { name = tcName, id = tcId }, sseCt);
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// Tool ausführen
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JsonElement inputEl;
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try { inputEl = JsonSerializer.Deserialize<JsonElement>(tcArgs.ToString()); }
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catch { inputEl = JsonSerializer.Deserialize<JsonElement>("{}"); }
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var toolResult = await _toolService.ExecuteTool(tcName, inputEl, workspace);
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var isError = toolResult.StartsWith("Fehler:") || toolResult.StartsWith("Error:");
|
||||
|
||||
// Tool-End SSE
|
||||
await TryWriteSse(response, "tool_end", new
|
||||
{
|
||||
name = tcName,
|
||||
id = tcId,
|
||||
result = toolResult,
|
||||
isError
|
||||
}, sseCt);
|
||||
|
||||
toolResults.Add(new
|
||||
{
|
||||
role = "tool",
|
||||
tool_call_id = tcId,
|
||||
content = toolResult
|
||||
});
|
||||
|
||||
// Tool-Result in DB speichern
|
||||
await _db.SaveMessage(new MessageRecord
|
||||
{
|
||||
ChainId = chainId, Iteration = iteration,
|
||||
Role = "tool", Type = "tool_result",
|
||||
Content = toolResult,
|
||||
ToolCallId = tcId, ToolName = tcName,
|
||||
IsError = isError
|
||||
});
|
||||
}
|
||||
|
||||
// Tool-Results in History einfügen
|
||||
foreach (var tr in toolResults)
|
||||
apiMessages.Add(tr);
|
||||
}
|
||||
|
||||
// Max Iterations erreicht
|
||||
await TryWriteSse(response, "error", new { error = "Max iterations erreicht" }, sseCt);
|
||||
await FinalizeChain(chain, usage, iteration, "max_iterations", sw);
|
||||
await TryWriteSse(response, "done", new { chainId }, sseCt);
|
||||
}
|
||||
catch (OperationCanceledException)
|
||||
{
|
||||
await FinalizeChain(chain, usage, iteration, "stopped", sw);
|
||||
await TryWriteSse(response, "done", new
|
||||
{
|
||||
model = modelString,
|
||||
iterations = iteration,
|
||||
chainId,
|
||||
durationMs = sw.ElapsedMilliseconds,
|
||||
stopReason = "stopped"
|
||||
}, CancellationToken.None);
|
||||
}
|
||||
catch (Exception ex)
|
||||
{
|
||||
var errMsg = $"Fehler: {ex.Message}";
|
||||
await _db.SaveMessage(new MessageRecord
|
||||
{
|
||||
ChainId = chainId, Iteration = iteration,
|
||||
Role = "system", Type = "error",
|
||||
Content = errMsg, IsError = true
|
||||
});
|
||||
await TryWriteSse(response, "error", new { error = errMsg }, sseCt);
|
||||
await FinalizeChain(chain, usage, iteration, "error", sw);
|
||||
await TryWriteSse(response, "done", new { chainId }, sseCt);
|
||||
}
|
||||
}
|
||||
|
||||
/// <summary>
|
||||
/// Konvertiert eine Anthropic-format Message in das OpenAI-format.
|
||||
/// </summary>
|
||||
private static object ConvertMessage(object msg)
|
||||
{
|
||||
var json = JsonSerializer.Serialize(msg);
|
||||
var el = JsonSerializer.Deserialize<JsonElement>(json);
|
||||
|
||||
if (!el.TryGetProperty("role", out var roleProp)) return null;
|
||||
var role = roleProp.GetString() ?? "";
|
||||
|
||||
if (!el.TryGetProperty("content", out var contentProp)) return null;
|
||||
|
||||
// Content als String
|
||||
if (contentProp.ValueKind == JsonValueKind.String)
|
||||
{
|
||||
var text = contentProp.GetString() ?? "";
|
||||
if (string.IsNullOrEmpty(text)) return null;
|
||||
return new { role, content = text };
|
||||
}
|
||||
|
||||
// Content als Array (Anthropic-Format mit Blöcken)
|
||||
if (contentProp.ValueKind == JsonValueKind.Array)
|
||||
{
|
||||
var textParts = new StringBuilder();
|
||||
var toolCalls = new List<object>();
|
||||
var toolResults = new List<object>();
|
||||
|
||||
foreach (var block in contentProp.EnumerateArray())
|
||||
{
|
||||
if (!block.TryGetProperty("type", out var typeProp)) continue;
|
||||
var blockType = typeProp.GetString() ?? "";
|
||||
|
||||
if (blockType == "text" && block.TryGetProperty("text", out var textProp))
|
||||
{
|
||||
textParts.Append(textProp.GetString());
|
||||
}
|
||||
else if (blockType == "tool_use")
|
||||
{
|
||||
// Anthropic tool_use → OpenAI tool_calls
|
||||
var tcId = block.TryGetProperty("id", out var idProp) ? idProp.GetString() : "";
|
||||
var tcName = block.TryGetProperty("name", out var nameProp) ? nameProp.GetString() : "";
|
||||
var tcInput = block.TryGetProperty("input", out var inputProp)
|
||||
? JsonSerializer.Serialize(inputProp)
|
||||
: "{}";
|
||||
toolCalls.Add(new
|
||||
{
|
||||
id = tcId,
|
||||
type = "function",
|
||||
function = new { name = tcName, arguments = tcInput }
|
||||
});
|
||||
}
|
||||
else if (blockType == "tool_result")
|
||||
{
|
||||
// Anthropic tool_result → OpenAI role: "tool"
|
||||
var trId = block.TryGetProperty("tool_use_id", out var idProp) ? idProp.GetString() : "";
|
||||
var trContent = "";
|
||||
if (block.TryGetProperty("content", out var cProp))
|
||||
{
|
||||
if (cProp.ValueKind == JsonValueKind.String)
|
||||
trContent = cProp.GetString() ?? "";
|
||||
else if (cProp.ValueKind == JsonValueKind.Array)
|
||||
{
|
||||
foreach (var item in cProp.EnumerateArray())
|
||||
if (item.TryGetProperty("text", out var t)) trContent += t.GetString();
|
||||
}
|
||||
}
|
||||
toolResults.Add(new { role = "tool", tool_call_id = trId, content = trContent });
|
||||
}
|
||||
}
|
||||
|
||||
// Assistant-Message mit tool_calls
|
||||
if (toolCalls.Count > 0)
|
||||
{
|
||||
var text = textParts.ToString().Trim();
|
||||
return new
|
||||
{
|
||||
role = "assistant",
|
||||
content = string.IsNullOrEmpty(text) ? (object)null : text,
|
||||
tool_calls = toolCalls
|
||||
};
|
||||
}
|
||||
|
||||
// Tool-Results → als separate Objekte zurückgeben (werden im Caller verarbeitet)
|
||||
if (toolResults.Count > 0)
|
||||
return toolResults; // List<object> wird im Aufrufer per OfType<List<object>> erkannt
|
||||
|
||||
var combined = textParts.ToString().Trim();
|
||||
if (string.IsNullOrEmpty(combined)) return null;
|
||||
return new { role, content = combined };
|
||||
}
|
||||
|
||||
return null;
|
||||
}
|
||||
|
||||
private async Task FinalizeChain(ChainRecord chain, TokenUsage usage, int iterations, string stopReason, System.Diagnostics.Stopwatch sw)
|
||||
{
|
||||
chain.InputTokens = usage.InputTokens;
|
||||
chain.OutputTokens = usage.OutputTokens;
|
||||
chain.CacheCreationTokens = usage.CacheCreationTokens;
|
||||
chain.CacheReadTokens = usage.CacheReadTokens;
|
||||
chain.Iterations = iterations;
|
||||
chain.StopReason = stopReason;
|
||||
chain.DurationMs = sw.ElapsedMilliseconds;
|
||||
await _db.UpdateChain(chain);
|
||||
}
|
||||
|
||||
private HttpClient CreateClient(string apiKey)
|
||||
{
|
||||
var client = _httpFactory.CreateClient();
|
||||
if (!string.IsNullOrEmpty(apiKey))
|
||||
client.DefaultRequestHeaders.Authorization = new AuthenticationHeaderValue("Bearer", apiKey);
|
||||
client.DefaultRequestHeaders.Accept.Add(new MediaTypeWithQualityHeaderValue("application/json"));
|
||||
client.Timeout = TimeSpan.FromMinutes(5);
|
||||
return client;
|
||||
}
|
||||
|
||||
private static async Task TryWriteSse(HttpResponse response, string eventType, object data, CancellationToken ct)
|
||||
{
|
||||
try
|
||||
{
|
||||
if (!ct.IsCancellationRequested)
|
||||
await AnthropicService.WriteSseEvent(response, eventType, data, ct);
|
||||
}
|
||||
catch { }
|
||||
}
|
||||
}
|
||||
Reference in new issue
Block a user