using System;
using System.Collections.Generic;
using System.IO;
using System.Net.Http;
using System.Net.Http.Headers;
using System.Text;
using System.Text.Json;
using System.Threading;
using System.Threading.Tasks;
using Gott.Models;
using Microsoft.AspNetCore.Http;

namespace Gott.Services;

/// <summary>
/// OpenAI-kompatibler Service – unterstützt Nvidia (g4f.space Proxy) und andere
/// OpenAI-API-kompatible Endpunkte. Unterstützt Tool-Calling (list_files, read_file,
/// write_file, edit_file, delete_file, execute) und Streaming via SSE.
/// </summary>
public class OpenAiService
{
    private readonly IHttpClientFactory _httpFactory;
    private readonly DatabaseService _db;
    private readonly ToolService _toolService;

    // Provider-Keys (statisch für ChatService.ResolveModelId)
    public static readonly HashSet<string> ProviderKeys = new() { "nvidia", "pollinations", "gemini" };

    // Provider-Definitionen (inkl. API-Keys, instanzgebunden)
    private readonly Dictionary<string, (string BaseUrl, string ApiKey)> Providers;

    // Tools die für OpenAI-kompatible Provider verfügbar sind
    private static readonly List<object> OpenAiTools = new()
    {
        new
        {
            type = "function",
            function = new
            {
                name = "list_files",
                description = "Listet Dateien und Ordner in einem Verzeichnis auf.",
                parameters = new
                {
                    type = "object",
                    properties = new Dictionary<string, object>
                    {
                        ["path"] = new { type = "string", description = "Relativer Verzeichnispfad (leer = Root)" }
                    },
                    required = Array.Empty<string>()
                }
            }
        },
        new
        {
            type = "function",
            function = new
            {
                name = "read_file",
                description = "Liest den Inhalt einer Datei. Standardmäßig werden maximal 2000 Zeilen gelesen. Bei großen Dateien offset und limit verwenden.",
                parameters = new
                {
                    type = "object",
                    properties = new Dictionary<string, object>
                    {
                        ["path"] = new { type = "string", description = "Relativer Dateipfad" },
                        ["offset"] = new { type = "integer", description = "Erste Zeile die gelesen werden soll (1-basiert, Default: 1)" },
                        ["limit"] = new { type = "integer", description = "Maximale Anzahl Zeilen die gelesen werden sollen (Default: 2000)" }
                    },
                    required = new[] { "path" }
                }
            }
        },
        new
        {
            type = "function",
            function = new
            {
                name = "write_file",
                description = "Erstellt oder überschreibt eine Datei mit dem angegebenen Inhalt.",
                parameters = new
                {
                    type = "object",
                    properties = new Dictionary<string, object>
                    {
                        ["path"] = new { type = "string", description = "Relativer Dateipfad" },
                        ["content"] = new { type = "string", description = "Dateiinhalt" }
                    },
                    required = new[] { "path", "content" }
                }
            }
        },
        new
        {
            type = "function",
            function = new
            {
                name = "edit_file",
                description = "Ersetzt einen exakten Textabschnitt in einer Datei. oldText muss exakt matchen.",
                parameters = new
                {
                    type = "object",
                    properties = new Dictionary<string, object>
                    {
                        ["path"] = new { type = "string", description = "Relativer Dateipfad" },
                        ["old_text"] = new { type = "string", description = "Exakter Text der ersetzt werden soll" },
                        ["new_text"] = new { type = "string", description = "Neuer Text" }
                    },
                    required = new[] { "path", "old_text", "new_text" }
                }
            }
        },
        new
        {
            type = "function",
            function = new
            {
                name = "delete_file",
                description = "Löscht eine Datei oder ein leeres Verzeichnis.",
                parameters = new
                {
                    type = "object",
                    properties = new Dictionary<string, object>
                    {
                        ["path"] = new { type = "string", description = "Relativer Pfad zur Datei oder zum Verzeichnis" }
                    },
                    required = new[] { "path" }
                }
            }
        },
        new
        {
            type = "function",
            function = new
            {
                name = "execute",
                description = "Führt einen Shell-Befehl im Workspace-Verzeichnis aus. Gibt stdout und stderr zurück.",
                parameters = new
                {
                    type = "object",
                    properties = new Dictionary<string, object>
                    {
                        ["command"] = new { type = "string", description = "Shell-Befehl zum Ausführen" }
                    },
                    required = new[] { "command" }
                }
            }
        }
    };

    public record ProviderModel(string Id, string DisplayName);

    private List<ProviderModel> _nvidiaModels      = new List<ProviderModel>();
    private List<ProviderModel> _pollinationsModels = new List<ProviderModel>();
    private List<ProviderModel> _geminiModels       = new List<ProviderModel>();
    private readonly string _cacheFile;

    public OpenAiService(IHttpClientFactory httpFactory, DatabaseService db, ToolService toolService, Microsoft.Extensions.Configuration.IConfiguration configuration, string dataDir)
    {
        _httpFactory = httpFactory;
        _db = db;
        _toolService = toolService;
        _cacheFile = Path.Combine(dataDir, "models_cache.json");
        Providers = new Dictionary<string, (string BaseUrl, string ApiKey)>
        {
            ["nvidia"]      = ("https://g4f.space/api/nvidia/v1",                                    ""),
            ["pollinations"] = ("https://text.pollinations.ai/openai",                               ""),
            ["gemini"]      = ("https://generativelanguage.googleapis.com/v1beta/openai", configuration["GeminiApiKey"] ?? "")
        };
    }

    /// <summary>
    /// Lädt verfügbare Modelle – aus lokalem Cache (models_cache.json) oder von den APIs.
    /// </summary>
    public async Task LoadModelsAsync(bool forceRefresh = false)
    {
        if (!forceRefresh && File.Exists(_cacheFile))
        {
            try
            {
                var json = await File.ReadAllTextAsync(_cacheFile);
                var cache = JsonSerializer.Deserialize<Dictionary<string, List<ProviderModel>>>(json);
                if (cache != null)
                {
                    _nvidiaModels       = cache.ContainsKey("nvidia")       ? cache["nvidia"]       : new List<ProviderModel>();
                    _pollinationsModels = cache.ContainsKey("pollinations") ? cache["pollinations"] : new List<ProviderModel>();
                    _geminiModels       = cache.ContainsKey("gemini")       ? cache["gemini"]       : new List<ProviderModel>();
                    Console.WriteLine($"[OpenAiService] Modelle aus Cache geladen: {_nvidiaModels.Count} Nvidia, {_pollinationsModels.Count} Pollinations, {_geminiModels.Count} Gemini");
                    return;
                }
            }
            catch (Exception ex)
            {
                Console.WriteLine($"[OpenAiService] Cache-Datei fehlerhaft, lade von APIs: {ex.Message}");
            }
        }

        await RefreshModelsFromApis();
    }

    /// <summary>
    /// Lädt Modelle von allen Provider-APIs und speichert sie im Cache.
    /// </summary>
    public async Task RefreshModelsFromApis()
    {
        _nvidiaModels       = await FetchModels("https://g4f.space/api/nvidia/models", "Nvidia");
        _pollinationsModels = await FetchModels("https://text.pollinations.ai/models", "Pollinations");

        var geminiApiKey = Providers["gemini"].ApiKey;
        if (!string.IsNullOrEmpty(geminiApiKey))
            _geminiModels = await FetchModels("https://generativelanguage.googleapis.com/v1beta/openai/models", "Gemini", geminiApiKey);
        else
            Console.WriteLine("[OpenAiService] GeminiApiKey nicht konfiguriert – Gemini-Modelle nicht geladen.");

        // Cache speichern
        try
        {
            var cache = new Dictionary<string, List<ProviderModel>>
            {
                ["nvidia"]       = _nvidiaModels,
                ["pollinations"] = _pollinationsModels,
                ["gemini"]       = _geminiModels
            };
            var json = JsonSerializer.Serialize(cache, new JsonSerializerOptions { WriteIndented = true });
            await File.WriteAllTextAsync(_cacheFile, json);
            Console.WriteLine($"[OpenAiService] Modelle-Cache gespeichert: {_cacheFile}");
        }
        catch (Exception ex)
        {
            Console.WriteLine($"[OpenAiService] Fehler beim Speichern des Modelle-Cache: {ex.Message}");
        }
    }

    private async Task<List<ProviderModel>> FetchModels(string url, string providerName, string apiKey = "")
    {
        try
        {
            var client = CreateClient(apiKey);
            var resp = await client.GetAsync(url);
            if (!resp.IsSuccessStatusCode) return new List<ProviderModel>();

            var json = await resp.Content.ReadAsStringAsync();
            var root = JsonSerializer.Deserialize<JsonElement>(json);

            // Pollinations: root is array [{ name, ... }]
            // Nvidia/OpenAI: root is { data: [{ id, ... }] }
            JsonElement arr;
            if (root.ValueKind == JsonValueKind.Array)
                arr = root;
            else if (root.TryGetProperty("data", out var data))
                arr = data;
            else
                return new List<ProviderModel>();

            var models = new List<ProviderModel>();
            foreach (var item in arr.EnumerateArray())
            {
                // try "id" first (Nvidia), fallback to "name" (Pollinations)
                string id = null;
                if (item.TryGetProperty("id",   out var idProp)   && idProp.ValueKind   == JsonValueKind.String) id = idProp.GetString();
                if (id == null && item.TryGetProperty("name", out var nameProp) && nameProp.ValueKind == JsonValueKind.String) id = nameProp.GetString();
                if (string.IsNullOrEmpty(id)) continue;
                var display = id.Contains('/') ? id[(id.LastIndexOf('/') + 1)..] : id;
                models.Add(new ProviderModel(id, display));
            }

            Console.WriteLine($"[OpenAiService] {models.Count} {providerName}-Modelle geladen.");
            return models;
        }
        catch (Exception ex)
        {
            Console.WriteLine($"[OpenAiService] Fehler beim Laden der {providerName}-Modelle: {ex.Message}");
            return new List<ProviderModel>();
        }
    }

    public List<ProviderModel> GetNvidiaModels()       => _nvidiaModels;
    public List<ProviderModel> GetPollinationsModels() => _pollinationsModels;
    public List<ProviderModel> GetGeminiModels()       => _geminiModels;

    /// <summary>
    /// Gibt den Provider und das eigentliche Modell-ID aus einem kombinierten Model-String zurück.
    /// Format: "nvidia:moonshotai/kimi-k2.5" → ("nvidia", "moonshotai/kimi-k2.5")
    /// </summary>
    public static (string provider, string modelId) ParseModelString(string model)
    {
        if (string.IsNullOrEmpty(model)) return ("", model ?? "");
        var idx = model.IndexOf(':');
        if (idx < 0) return ("", model);
        return (model[..idx], model[(idx + 1)..]);
    }

    /// <summary>
    /// Prüft ob ein Model-String einen externen OpenAI-kompatiblen Provider referenziert.
    /// </summary>
    public static bool IsOpenAiProvider(string model)
    {
        var (provider, _) = ParseModelString(model);
        return ProviderKeys.Contains(provider);
    }

    public async Task StreamChat(
        HttpResponse response,
        string modelString,
        string systemPromptText,
        List<object> messages,
        string chainId,
        string workspace = null,
        string sessionId = null,
        string agentId = null,
        int maxIterations = 10,
        CancellationToken workCt = default,
        CancellationToken sseCt = default)
    {
        var sw = System.Diagnostics.Stopwatch.StartNew();
        var usage = new TokenUsage();
        var iteration = 0;

        var chain = new ChainRecord
        {
            ChainId = chainId,
            SessionId = sessionId,
            Model = modelString
        };

        var (provider, modelId) = ParseModelString(modelString);
        if (!Providers.TryGetValue(provider, out var providerConfig))
        {
            var errMsg = $"Unbekannter Provider: '{provider}'";
            await _db.SaveMessage(new MessageRecord
            {
                ChainId = chainId, Iteration = 1,
                Role = "system", Type = "error",
                Content = errMsg, IsError = true
            });
            await TryWriteSse(response, "error", new { error = errMsg }, sseCt);
            await TryWriteSse(response, "done", new { chainId }, sseCt);
            return;
        }

        try
        {
            // OpenAI-kompatibles Messages-Format aufbauen
            var apiMessages = new List<object>();

            // System-Prompt als erste System-Message
            if (!string.IsNullOrEmpty(systemPromptText))
                apiMessages.Add(new { role = "system", content = systemPromptText });

            // History-Messages konvertieren (Anthropic-Format → OpenAI-Format)
            foreach (var msg in messages)
            {
                var converted = ConvertMessage(msg);
                if (converted == null) continue;

                // Tool-Results kommen als List<object> zurück → einzeln expandieren
                if (converted is List<object> toolResultList)
                {
                    foreach (var tr in toolResultList)
                        apiMessages.Add(tr);
                    continue;
                }

                apiMessages.Add(converted);
            }

            // Agentic Loop
            while (iteration < maxIterations)
            {
                workCt.ThrowIfCancellationRequested();
                iteration++;

                var client = CreateClient(providerConfig.ApiKey);
                var payload = new Dictionary<string, object>
                {
                    ["model"] = modelId,
                    ["messages"] = apiMessages,
                    ["stream"] = true,
                    ["max_tokens"] = 16384,
                    ["tools"] = OpenAiTools,
                    ["tool_choice"] = "auto"
                };

                var jsonPayload = JsonSerializer.Serialize(payload);
                var httpContent = new StringContent(jsonPayload, Encoding.UTF8, "application/json");

                using var httpResp = await client.SendAsync(
                    new HttpRequestMessage(HttpMethod.Post, $"{providerConfig.BaseUrl}/chat/completions") { Content = httpContent },
                    HttpCompletionOption.ResponseHeadersRead,
                    workCt
                );

                if (!httpResp.IsSuccessStatusCode)
                {
                    var errBody = await httpResp.Content.ReadAsStringAsync(workCt);
                    var errMsg = $"API {(int)httpResp.StatusCode}: {errBody}";
                    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);
                    return;
                }

                // SSE-Stream lesen
                using var stream = await httpResp.Content.ReadAsStreamAsync(workCt);
                using var reader = new System.IO.StreamReader(stream);

                var fullText = new StringBuilder();
                // Tool-Call Akkumulatoren: id → (name, arguments)
                var toolCallAccumulators = new Dictionary<int, (string Id, string Name, StringBuilder Args, string ThoughtSignature)>();
                string thoughtSignature = null;
                var textStarted = false;
                var stopReason = "end_turn";

                string line;
                while ((line = await reader.ReadLineAsync(workCt)) != null)
                {
                    workCt.ThrowIfCancellationRequested();
                    if (!line.StartsWith("data:")) continue;

                    var data = line[5..].Trim();
                    if (data == "[DONE]") break;

                    JsonElement parsed;
                    try { parsed = JsonSerializer.Deserialize<JsonElement>(data); }
                    catch { continue; }

                    if (!parsed.TryGetProperty("choices", out var choices)) continue;


                    foreach (var choice in choices.EnumerateArray())
                    {
                        // Finish reason
                        if (choice.TryGetProperty("finish_reason", out var finishProp) && finishProp.ValueKind != JsonValueKind.Null)
                        {
                            var fr = (finishProp.GetString() ?? "").ToLowerInvariant();
                            if (fr == "tool_calls" || fr == "function_call") stopReason = "tool_use";
                            else if (fr == "stop") stopReason = "end_turn";
                        }

                        if (!choice.TryGetProperty("delta", out var delta)) continue;

                        // Text-Delta
                        if (delta.TryGetProperty("content", out var contentProp) && contentProp.ValueKind == JsonValueKind.String)
                        {
                            var chunk = contentProp.GetString();
                            if (!string.IsNullOrEmpty(chunk))
                            {
                                if (!textStarted)
                                {
                                    await TryWriteSse(response, "text_start", new { index = 0 }, sseCt);
                                    textStarted = true;
                                }
                                fullText.Append(chunk);
                                await TryWriteSse(response, "text_delta", new { text = chunk }, sseCt);
                            }
                        }



                        // Tool-Call Deltas akkumulieren
                        if (delta.TryGetProperty("tool_calls", out var toolCallsProp))
                        {
                            foreach (var tc in toolCallsProp.EnumerateArray())
                            {
                                // thought_signature aus tc.extra_content.google.thought_signature (Gemini-spezifisch)
                                if (tc.TryGetProperty("extra_content", out var extraContent)
                                    && extraContent.TryGetProperty("google", out var googleContent)
                                    && googleContent.TryGetProperty("thought_signature", out var tcTsProp)
                                    && tcTsProp.ValueKind == JsonValueKind.String)
                                {
                                    var ts = tcTsProp.GetString();
                                    if (!string.IsNullOrEmpty(ts)) thoughtSignature = ts;
                                }

                                // ID aus diesem Chunk lesen (kann leer sein bei Folge-Deltas)
                                var chunkId = tc.TryGetProperty("id", out var idProp2) ? (idProp2.GetString() ?? "") : "";

                                // Schlüssel bestimmen: zuerst per id, dann per index
                                // → verhindert Kollisionen bei parallelen Tool-Calls ohne index (z.B. Gemini)
                                int idx;
                                if (!string.IsNullOrEmpty(chunkId))
                                {
                                    // Prüfen ob diese ID schon einem Index zugeordnet ist
                                    idx = -1;
                                    foreach (var kv in toolCallAccumulators)
                                    {
                                        if (kv.Value.Id == chunkId) { idx = kv.Key; break; }
                                    }
                                    if (idx == -1)
                                    {
                                        // Neue ID → nächsten freien Index vergeben
                                        idx = toolCallAccumulators.Count;
                                        while (toolCallAccumulators.ContainsKey(idx)) idx++;
                                    }
                                }
                                else
                                {
                                    idx = tc.TryGetProperty("index", out var idxProp) ? idxProp.GetInt32() : 0;
                                }

                                if (!toolCallAccumulators.ContainsKey(idx))
                                    toolCallAccumulators[idx] = ("", "", new StringBuilder(), null);

                                var (accId, accName, accArgs, accTs) = toolCallAccumulators[idx];

                                if (!string.IsNullOrEmpty(chunkId))
                                    accId = chunkId;

                                if (tc.TryGetProperty("function", out var funcProp))
                                {
                                    if (funcProp.TryGetProperty("name", out var nameProp2))
                                    {
                                        var n = nameProp2.GetString() ?? "";
                                        if (!string.IsNullOrEmpty(n)) accName = n;
                                    }
                                    if (funcProp.TryGetProperty("arguments", out var argsProp))
                                        accArgs.Append(argsProp.GetString() ?? "");

                                }

                                // Globalen thoughtSignature-Wert auch in accTs des ersten Slots übernehmen
                                if (!string.IsNullOrEmpty(thoughtSignature) && accTs == null && idx == 0)
                                    accTs = thoughtSignature;

                                toolCallAccumulators[idx] = (accId, accName, accArgs, accTs);
                            }
                        }
                    }

                    // Usage aus letztem Chunk
                    if (parsed.TryGetProperty("usage", out var usageProp))
                    {
                        if (usageProp.TryGetProperty("prompt_tokens", out var pt))
                            usage.InputTokens = pt.GetInt64();
                        if (usageProp.TryGetProperty("completion_tokens", out var ct2))
                            usage.OutputTokens = ct2.GetInt64();
                    }
                }

                // Text-End senden falls Text gestreamt wurde
                if (textStarted)
                    await TryWriteSse(response, "text_end", new { }, sseCt);

                // Text in DB speichern
                var finalText = fullText.ToString();
                if (!string.IsNullOrEmpty(finalText))
                {
                    await _db.SaveMessage(new MessageRecord
                    {
                        ChainId = chainId, Iteration = iteration,
                        Role = "assistant", Type = "text",
                        Content = finalText
                    });
                }

                // Keine Tool-Calls → fertig (stopReason als Fallback, primär Count prüfen)
                if (toolCallAccumulators.Count == 0)
                {
                    await FinalizeChain(chain, usage, iteration, stopReason, sw);
                    await TryWriteSse(response, "done", new
                    {
                        model = modelString,
                        inputTokens = usage.InputTokens,
                        outputTokens = usage.OutputTokens,
                        iterations = iteration,
                        chainId,
                        durationMs = sw.ElapsedMilliseconds,
                        stopReason
                    }, sseCt);
                    return;
                }

                // Tool-Calls ausführen
                // Assistant-Message mit tool_calls in die History einfügen
                var toolCallsForHistory = new List<object>();
                foreach (var (_, (tcId, tcName, tcArgs, tcTs)) in toolCallAccumulators)
                {
                    // thought_signature pro Tool-Call mitschicken falls vorhanden
                    // Gemini erwartet sie in extra_content.google.thought_signature (nicht in function.thought_signature)
                    if (!string.IsNullOrEmpty(tcTs))
                    {
                        toolCallsForHistory.Add(new
                        {
                            id = tcId,
                            type = "function",
                            function = new { name = tcName, arguments = tcArgs.ToString() },
                            extra_content = new { google = new { thought_signature = tcTs } }
                        });
                    }
                    else
                    {
                        toolCallsForHistory.Add(new
                        {
                            id = tcId,
                            type = "function",
                            function = new { name = tcName, arguments = tcArgs.ToString() }
                        });
                    }

                    // In DB speichern – thought_signature als eigenes Feld
                    await _db.SaveMessage(new MessageRecord
                    {
                        ChainId = chainId, Iteration = iteration,
                        Role = "assistant", Type = "tool_use",
                        Content = tcArgs.ToString(),
                        ToolCallId = tcId, ToolName = tcName,
                        ThoughtSignature = tcTs
                    });
                }

                apiMessages.Add(new
                {
                    role = "assistant",
                    content = string.IsNullOrEmpty(finalText) ? "" : finalText,
                    tool_calls = toolCallsForHistory
                });

                // Tool-Results sammeln
                var toolResults = new List<object>();
                foreach (var (_, (tcId, tcName, tcArgs, _)) in toolCallAccumulators)
                {
                    // Tool-Start SSE
                    await TryWriteSse(response, "tool_start", new { name = tcName, id = tcId }, sseCt);

                    // Tool ausführen
                    JsonElement inputEl;
                    try { inputEl = JsonSerializer.Deserialize<JsonElement>(tcArgs.ToString()); }
                    catch { inputEl = JsonSerializer.Deserialize<JsonElement>("{}"); }

                    var toolResult = await _toolService.ExecuteTool(tcName, inputEl, workspace);
                    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)
                        : "{}";
                    // thought_signature als eigenes Feld (vom DB-Record direkt in den Block übernommen)
                    var tcTs = block.TryGetProperty("thought_signature", out var tsProp2) && tsProp2.ValueKind == JsonValueKind.String
                        ? tsProp2.GetString() : null;

                    // Gemini erwartet thought_signature in extra_content.google.thought_signature
                    if (!string.IsNullOrEmpty(tcTs))
                        toolCalls.Add(new { id = tcId, type = "function", function = new { name = tcName, arguments = tcInput }, extra_content = new { google = new { thought_signature = tcTs } } });
                    else
                        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) ? "" : 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 { }
    }
}