zclaw_openfang/desktop/src/lib/llm-service.ts

/**
 * LLM Service Adapter - Unified LLM interface for L4 self-evolution engines
 *
 * Provides a unified interface for:
 * - ReflectionEngine: Semantic analysis + deep reflection
 * - ContextCompactor: High-quality summarization
 * - MemoryExtractor: Semantic importance scoring
 *
 * Supports multiple backends:
 * - OpenAI (GPT-4, GPT-3.5)
 * - Volcengine (Doubao)
 * - ZCLAW Gateway (passthrough)
 *
 * Part of ZCLAW L4 Self-Evolution capability.
 */

import { DEFAULT_MODEL_ID, DEFAULT_OPENAI_BASE_URL } from '../constants/models';
import { createLogger } from './logger';
import { recordLLMUsage } from './telemetry-collector';

const log = createLogger('LLMService');

// === Types ===

export type LLMProvider = 'openai' | 'volcengine' | 'gateway' | 'saas' | 'mock';

export interface LLMConfig {
  provider: LLMProvider;
  model?: string;
  apiKey?: string;
  apiBase?: string;
  maxTokens?: number;
  temperature?: number;
  timeout?: number;
}

export interface LLMMessage {
  role: 'system' | 'user' | 'assistant';
  content: string;
}

export interface LLMResponse {
  content: string;
  tokensUsed?: {
    input: number;
    output: number;
  };
  model?: string;
  latencyMs?: number;
}

export interface LLMServiceAdapter {
  complete(messages: LLMMessage[], options?: Partial<LLMConfig>): Promise<LLMResponse>;
  isAvailable(): boolean;
  getProvider(): LLMProvider;
}

// === Default Configs ===

const DEFAULT_CONFIGS: Record<LLMProvider, LLMConfig> = {
  openai: {
    provider: 'openai',
    model: DEFAULT_MODEL_ID,
    apiBase: DEFAULT_OPENAI_BASE_URL,
    maxTokens: 2000,
    temperature: 0.7,
    timeout: 30000,
  },
  volcengine: {
    provider: 'volcengine',
    model: 'doubao-pro-32k',
    apiBase: 'https://ark.cn-beijing.volces.com/api/v3',
    maxTokens: 2000,
    temperature: 0.7,
    timeout: 30000,
  },
  gateway: {
    provider: 'gateway',
    apiBase: '/api/llm',
    maxTokens: 2000,
    temperature: 0.7,
    timeout: 60000,
  },
  saas: {
    provider: 'saas',
    maxTokens: 4096,
    temperature: 0.7,
    timeout: 300000, // 5 min for streaming
  },
  mock: {
    provider: 'mock',
    maxTokens: 100,
    temperature: 0,
    timeout: 100,
  },
};

// === Storage ===

const LLM_CONFIG_KEY = 'zclaw-llm-config';

// === Mock Adapter (for testing) ===

class MockLLMAdapter implements LLMServiceAdapter {
  constructor(_config: LLMConfig) {
    // Config is stored for future use (e.g., custom mock behavior based on config)
  }

  async complete(messages: LLMMessage[]): Promise<LLMResponse> {
    // Simulate latency
    await new Promise((resolve) => setTimeout(resolve, 50));

    const lastMessage = messages[messages.length - 1];
    const content = lastMessage?.content || '';

    // Generate mock response based on content type
    let response = '[Mock LLM Response] ';

    if (content.includes('reflect') || content.includes('反思')) {
      response += JSON.stringify({
        patterns: [
          {
            observation: '用户经常询问代码优化相关问题',
            frequency: 5,
            sentiment: 'positive',
            evidence: ['多次讨论性能优化', '关注代码质量'],
          },
        ],
        improvements: [
          {
            area: '代码解释',
            suggestion: '可以提供更详细的代码注释',
            priority: 'medium',
          },
        ],
        identityProposals: [],
      });
    } else if (content.includes('summarize') || content.includes('摘要')) {
      response += '这是一个关于对话内容的摘要，包含了主要讨论的要点和结论。';
    } else if (content.includes('importance') || content.includes('重要性')) {
      response += JSON.stringify({
        memories: [
          { content: '用户偏好简洁的回答', importance: 7, type: 'preference' },
        ],
      });
    } else {
      response += 'Processed: ' + content.slice(0, 50);
    }

    return {
      content: response,
      tokensUsed: { input: content.length / 4, output: response.length / 4 },
      model: 'mock-model',
      latencyMs: 50,
    };
  }

  isAvailable(): boolean {
    return true;
  }

  getProvider(): LLMProvider {
    return 'mock';
  }
}

// === OpenAI Adapter ===

class OpenAILLMAdapter implements LLMServiceAdapter {
  private config: LLMConfig;

  constructor(config: LLMConfig) {
    this.config = { ...DEFAULT_CONFIGS.openai, ...config };
  }

  async complete(messages: LLMMessage[], options?: Partial<LLMConfig>): Promise<LLMResponse> {
    const config = { ...this.config, ...options };
    const startTime = Date.now();

    if (!config.apiKey) {
      throw new Error('[OpenAI] API key not configured');
    }

    const response = await fetch(`${config.apiBase}/chat/completions`, {
      method: 'POST',
      headers: {
        'Content-Type': 'application/json',
        Authorization: `Bearer ${config.apiKey}`,
      },
      body: JSON.stringify({
        model: config.model,
        messages,
        max_tokens: config.maxTokens,
        temperature: config.temperature,
      }),
      signal: AbortSignal.timeout(config.timeout || 30000),
    });

    if (!response.ok) {
      const errorBody = await response.text();
      // Log full error in development only
      if (import.meta.env.DEV) {
        console.error('[OpenAI] API error:', errorBody);
      }
      // Return sanitized error to caller
      throw new Error(`[OpenAI] API error: ${response.status} - Request failed`);
    }

    const data = await response.json();
    const latencyMs = Date.now() - startTime;

    return {
      content: data.choices[0]?.message?.content || '',
      tokensUsed: {
        input: data.usage?.prompt_tokens || 0,
        output: data.usage?.completion_tokens || 0,
      },
      model: data.model,
      latencyMs,
    };
  }

  isAvailable(): boolean {
    return !!this.config.apiKey;
  }

  getProvider(): LLMProvider {
    return 'openai';
  }
}

// === Volcengine Adapter ===

class VolcengineLLMAdapter implements LLMServiceAdapter {
  private config: LLMConfig;

  constructor(config: LLMConfig) {
    this.config = { ...DEFAULT_CONFIGS.volcengine, ...config };
  }

  async complete(messages: LLMMessage[], options?: Partial<LLMConfig>): Promise<LLMResponse> {
    const config = { ...this.config, ...options };
    const startTime = Date.now();

    if (!config.apiKey) {
      throw new Error('[Volcengine] API key not configured');
    }

    const response = await fetch(`${config.apiBase}/chat/completions`, {
      method: 'POST',
      headers: {
        'Content-Type': 'application/json',
        Authorization: `Bearer ${config.apiKey}`,
      },
      body: JSON.stringify({
        model: config.model,
        messages,
        max_tokens: config.maxTokens,
        temperature: config.temperature,
      }),
      signal: AbortSignal.timeout(config.timeout || 30000),
    });

    if (!response.ok) {
      const errorBody = await response.text();
      // Log full error in development only
      if (import.meta.env.DEV) {
        console.error('[Volcengine] API error:', errorBody);
      }
      // Return sanitized error to caller
      throw new Error(`[Volcengine] API error: ${response.status} - Request failed`);
    }

    const data = await response.json();
    const latencyMs = Date.now() - startTime;

    return {
      content: data.choices[0]?.message?.content || '',
      tokensUsed: {
        input: data.usage?.prompt_tokens || 0,
        output: data.usage?.completion_tokens || 0,
      },
      model: data.model,
      latencyMs,
    };
  }

  isAvailable(): boolean {
    return !!this.config.apiKey;
  }

  getProvider(): LLMProvider {
    return 'volcengine';
  }
}

// === Gateway Adapter (pass through to ZCLAW or internal Kernel) ===

class GatewayLLMAdapter implements LLMServiceAdapter {
  private config: LLMConfig;

  constructor(config: LLMConfig) {
    this.config = { ...DEFAULT_CONFIGS.gateway, ...config };
  }

  async complete(messages: LLMMessage[], options?: Partial<LLMConfig>): Promise<LLMResponse> {
    const config = { ...this.config, ...options };
    const startTime = Date.now();

    // Build a single prompt from messages
    const systemMessage = messages.find(m => m.role === 'system')?.content || '';
    const userMessage = messages.find(m => m.role === 'user')?.content || '';

    // Combine system and user messages into a single prompt
    const fullPrompt = systemMessage
      ? `${systemMessage}\n\n${userMessage}`
      : userMessage;

    // Check if running in Tauri with internal kernel
    // Use the same detection as kernel-client.ts
    const isTauri = typeof window !== 'undefined' &&
      '__TAURI_INTERNALS__' in window;

    if (isTauri) {
      // Use internal Kernel via Tauri invoke
      try {
        const { invoke } = await import('@tauri-apps/api/core');

        // Get the default agent ID from connectionStore or use the first agent
        const agentId = localStorage.getItem('zclaw-default-agent-id');

        const response = await invoke<{ content: string; input_tokens: number; output_tokens: number }>('agent_chat', {
          request: {
            agentId: agentId || null,  // null will use default agent
            message: fullPrompt,
          },
        });

        const latencyMs = Date.now() - startTime;
        return {
          content: response.content || '',
          tokensUsed: {
            input: response.input_tokens || 0,
            output: response.output_tokens || 0,
          },
          latencyMs,
        };
      } catch (err) {
        console.error('[LLMService] Kernel chat failed:', err);
        const message = err instanceof Error ? err.message : String(err);
        throw new Error(`[Gateway] Kernel chat failed: ${message}`);
      }
    }

    // External Gateway mode: Use ZCLAW's chat endpoint
    const agentId = localStorage.getItem('zclaw-default-agent-id') || 'default';

    const response = await fetch(`/api/agents/${agentId}/message`, {
      method: 'POST',
      headers: {
        'Content-Type': 'application/json',
      },
      body: JSON.stringify({
        message: fullPrompt,
        max_tokens: config.maxTokens,
        temperature: config.temperature ?? 0.3, // Lower temperature for extraction tasks
      }),
      signal: AbortSignal.timeout(config.timeout || 60000),
    });

    if (!response.ok) {
      const error = await response.text();
      // If agent not found, try without agent ID (direct /api/chat)
      if (response.status === 404) {
        const fallbackResponse = await fetch('/api/chat', {
          method: 'POST',
          headers: { 'Content-Type': 'application/json' },
          body: JSON.stringify({
            message: fullPrompt,
            max_tokens: config.maxTokens,
            temperature: config.temperature ?? 0.3,
          }),
          signal: AbortSignal.timeout(config.timeout || 60000),
        });

        if (!fallbackResponse.ok) {
          throw new Error(`[Gateway] Both endpoints failed: ${fallbackResponse.status}`);
        }

        const data = await fallbackResponse.json();
        const latencyMs = Date.now() - startTime;
        return {
          content: data.response || data.content || '',
          tokensUsed: { input: data.input_tokens || 0, output: data.output_tokens || 0 },
          latencyMs,
        };
      }
      throw new Error(`[Gateway] API error: ${response.status} - ${error}`);
    }

    const data = await response.json();
    const latencyMs = Date.now() - startTime;

    return {
      content: data.response || data.content || '',
      tokensUsed: { input: data.input_tokens || 0, output: data.output_tokens || 0 },
      latencyMs,
    };
  }

  isAvailable(): boolean {
    // Gateway is available if we're in browser (can connect to ZCLAW)
    return typeof window !== 'undefined';
  }

  getProvider(): LLMProvider {
    return 'gateway';
  }
}

// === SaaS Relay Adapter (via SaaS backend) ===

class SaasLLMAdapter implements LLMServiceAdapter {
  private config: LLMConfig;

  constructor(config: LLMConfig) {
    this.config = { ...DEFAULT_CONFIGS.saas, ...config };
  }

  async complete(messages: LLMMessage[], options?: Partial<LLMConfig>): Promise<LLMResponse> {
    const config = { ...this.config, ...options };
    const startTime = Date.now();

    // Dynamic import to avoid circular dependency
    const { useSaaSStore } = await import('../store/saasStore');
    const { saasUrl, authToken } = useSaaSStore.getState();

    if (!saasUrl || !authToken) {
      throw new Error('[SaaS] 未登录 SaaS 平台，请先在设置中登录');
    }

    // Dynamic import of SaaSClient singleton
    const { saasClient } = await import('./saas-client');
    saasClient.setBaseUrl(saasUrl);
    saasClient.setToken(authToken);

    const openaiBody = {
      model: config.model || 'default',
      messages,
      max_tokens: config.maxTokens || 4096,
      temperature: config.temperature ?? 0.7,
      stream: false,
    };

    const response = await saasClient.chatCompletion(
      openaiBody,
      AbortSignal.timeout(config.timeout || 300000),
    );

    if (!response.ok) {
      const errorData = await response.json().catch(() => ({
        error: 'unknown',
        message: `SaaS relay 请求失败 (${response.status})`,
      }));
      throw new Error(
        `[SaaS] ${errorData.message || errorData.error || `请求失败: ${response.status}`}`,
      );
    }

    const data = await response.json();
    const latencyMs = Date.now() - startTime;

    const result = {
      content: data.choices?.[0]?.message?.content || '',
      tokensUsed: {
        input: data.usage?.prompt_tokens || 0,
        output: data.usage?.completion_tokens || 0,
      },
      model: data.model,
      latencyMs,
    };

    // Record telemetry for SaaS relay usage
    try {
      const { recordLLMUsage } = await import('./telemetry-collector');
      recordLLMUsage(
        result.model || 'saas-relay',
        result.tokensUsed.input,
        result.tokensUsed.output,
        { latencyMs, success: true, connectionMode: 'saas' },
      );
    } catch (e) {
      log.debug('Failed to record LLM telemetry', { error: e });
    }

    return result;
  }

  isAvailable(): boolean {
    // Check synchronously via localStorage for availability check.
    // Auth is cookie-based — check connection mode + URL presence.
    try {
      const mode = localStorage.getItem('zclaw-connection-mode');
      const saasUrl = localStorage.getItem('zclaw-saas-url');
      return mode === 'saas' && !!saasUrl;
    } catch (e) {
      log.debug('Failed to check SaaS adapter availability', { error: e });
      return false;
    }
  }

  getProvider(): LLMProvider {
    return 'saas';
  }
}

// === Factory ===

let cachedAdapter: LLMServiceAdapter | null = null;

export function createLLMAdapter(config?: Partial<LLMConfig>): LLMServiceAdapter {
  const savedConfig = loadConfig();
  const finalConfig = { ...savedConfig, ...config };

  switch (finalConfig.provider) {
    case 'openai':
      return new OpenAILLMAdapter(finalConfig);
    case 'volcengine':
      return new VolcengineLLMAdapter(finalConfig);
    case 'gateway':
      return new GatewayLLMAdapter(finalConfig);
    case 'saas':
      return new SaasLLMAdapter(finalConfig);
    case 'mock':
    default:
      return new MockLLMAdapter(finalConfig);
  }
}

export function getLLMAdapter(): LLMServiceAdapter {
  if (!cachedAdapter) {
    cachedAdapter = createLLMAdapter();
  }
  return cachedAdapter;
}

export function resetLLMAdapter(): void {
  cachedAdapter = null;
}

// === Config Management ===

export function loadConfig(): LLMConfig {
  if (typeof window === 'undefined') {
    return DEFAULT_CONFIGS.mock;
  }

  try {
    const saved = localStorage.getItem(LLM_CONFIG_KEY);
    if (saved) {
      return JSON.parse(saved);
    }
  } catch (e) {
    log.debug('Failed to parse LLM config', { error: e });
  }

  // Default to gateway (ZCLAW passthrough) for L4 self-evolution
  return DEFAULT_CONFIGS.gateway;
}

export function saveConfig(config: LLMConfig): void {
  if (typeof window === 'undefined') return;

  // Don't save API key to localStorage for security
  const safeConfig = { ...config };
  delete safeConfig.apiKey;

  localStorage.setItem(LLM_CONFIG_KEY, JSON.stringify(safeConfig));

  // Mark config as dirty for SaaS push sync
  localStorage.setItem('zclaw-config-dirty.llm.default', '1');

  resetLLMAdapter();
}

// === Prompt Templates ===

// 硬编码默认值 — 当 SaaS 不可用且本地无缓存时的终极兜底
const HARDCODED_PROMPTS: Record<string, { system: string; user: (arg: string) => string }> = {
  reflection: {
    system: `你是一个 AI Agent 的自我反思引擎。分析最近的对话历史，识别行为模式，并生成改进建议。

输出 JSON 格式：
{
  "patterns": [
    {
      "observation": "观察到的模式描述",
      "frequency": 数字,
      "sentiment": "positive/negative/neutral",
      "evidence": ["证据1", "证据2"]
    }
  ],
  "improvements": [
    {
      "area": "改进领域",
      "suggestion": "具体建议",
      "priority": "high/medium/low"
    }
  ],
  "identityProposals": []
}`,
    user: (context: string) => `分析以下对话历史，进行自我反思：\n\n${context}\n\n请识别行为模式（积极和消极），并提供具体的改进建议。`,
  },

  compaction: {
    system: `你是一个对话摘要专家。将长对话压缩为简洁的摘要，保留关键信息。

要求：
1. 保留所有重要决策和结论
2. 保留用户偏好和约束
3. 保留未完成的任务
4. 保持时间顺序
5. 摘要应能在后续对话中替代原始内容`,
    user: (messages: string) => `请将以下对话压缩为简洁摘要，保留关键信息：\n\n${messages}`,
  },

  extraction: {
    system: `你是一个记忆提取专家。从对话中提取值得长期记住的信息。

提取类型：
- fact: 用户告知的事实（如"我的公司叫XXX"）
- preference: 用户的偏好（如"我喜欢简洁的回答"）
- lesson: 本次对话的经验教训
- task: 未完成的任务或承诺

输出 JSON 数组：
[
  {
    "content": "记忆内容",
    "type": "fact/preference/lesson/task",
    "importance": 1-10,
    "tags": ["标签1", "标签2"]
  }
]`,
    user: (conversation: string) => `从以下对话中提取值得长期记住的信息：\n\n${conversation}\n\n如果没有值得记忆的内容，返回空数组 []。`,
  },
};

// === Prompt Cache (SaaS OTA) ===

const PROMPT_CACHE_KEY = 'zclaw-prompt-cache';

interface CachedPrompt {
  name: string;
  version: number;
  source: string;
  system: string;
  userTemplate: string | null;
  syncedAt: string;
}

/** 获取本地缓存的提示词版本号映射 */
function loadPromptCache(): Record<string, CachedPrompt> {
  if (typeof window === 'undefined') return {};
  try {
    const raw = localStorage.getItem(PROMPT_CACHE_KEY);
    return raw ? JSON.parse(raw) : {};
  } catch (e) {
    log.debug('Failed to parse prompt cache', { error: e });
    return {};
  }
}

/** 保存提示词缓存到 localStorage */
function savePromptCache(cache: Record<string, CachedPrompt>): void {
  if (typeof window === 'undefined') return;
  localStorage.setItem(PROMPT_CACHE_KEY, JSON.stringify(cache));
}

/**
 * 获取指定提示词的系统提示词
 * 优先级：本地缓存 → 硬编码默认值
 */
export function getSystemPrompt(name: string): string {
  const cache = loadPromptCache();
  if (cache[name]?.system) {
    return cache[name].system;
  }
  return HARDCODED_PROMPTS[name]?.system ?? '';
}

/**
 * 获取指定提示词的用户提示词模板
 * 优先级：本地缓存 → 硬编码默认值
 */
export function getUserPromptTemplate(name: string): string | ((arg: string) => string) | null {
  const cache = loadPromptCache();
  if (cache[name]) {
    const tmpl = cache[name].userTemplate;
    if (tmpl) return tmpl;
  }
  return HARDCODED_PROMPTS[name]?.user ?? null;
}

/** 获取提示词当前版本号（本地缓存） */
export function getPromptVersion(name: string): number {
  const cache = loadPromptCache();
  return cache[name]?.version ?? 0;
}

/** 获取所有本地缓存的提示词版本（用于 OTA 检查） */
export function getAllPromptVersions(): Record<string, number> {
  const cache = loadPromptCache();
  const versions: Record<string, number> = {};
  for (const [name, entry] of Object.entries(cache)) {
    versions[name] = entry.version;
  }
  return versions;
}

/**
 * 应用 SaaS OTA 更新到本地缓存
 * @param updates 从 SaaS 拉取的更新列表
 */
export function applyPromptUpdates(updates: Array<{
  name: string;
  version: number;
  system_prompt: string;
  user_prompt_template: string | null;
  source: string;
  changelog?: string | null;
}>): number {
  const cache = loadPromptCache();
  let applied = 0;

  for (const update of updates) {
    cache[update.name] = {
      name: update.name,
      version: update.version,
      source: update.source,
      system: update.system_prompt,
      userTemplate: update.user_prompt_template,
      syncedAt: new Date().toISOString(),
    };
    applied++;
  }

  if (applied > 0) {
    savePromptCache(cache);
  }

  return applied;
}

/**
 * 后台异步检查 SaaS 提示词更新
 * 启动时和每 30 分钟调用一次
 */
let promptSyncTimer: ReturnType<typeof setInterval> | null = null;

export function startPromptOTASync(deviceId: string): void {
  if (promptSyncTimer) return; // 已启动
  if (typeof window === 'undefined') return;

  const doSync = async () => {
    try {
      const { saasClient } = await import('./saas-client');
      const { useSaaSStore } = await import('../store/saasStore');
      const { saasUrl, authToken } = useSaaSStore.getState();

      if (!saasUrl || !authToken) return;

      saasClient.setBaseUrl(saasUrl);
      saasClient.setToken(authToken);

      const versions = getAllPromptVersions();
      const result = await saasClient.checkPromptUpdates(deviceId, versions);

      if (result.updates.length > 0) {
        const applied = applyPromptUpdates(result.updates);
        if (applied > 0) {
          log.debug(`已更新 ${applied} 个提示词模板`);
        }
      }
    } catch (err) {
      // 静默失败，不影响正常使用
      log.debug('检查更新失败:', err);
    }
  };

  // 立即执行一次
  doSync();
  // 每 30 分钟检查一次
  promptSyncTimer = setInterval(doSync, 30 * 60 * 1000);
}

export function stopPromptOTASync(): void {
  if (promptSyncTimer) {
    clearInterval(promptSyncTimer);
    promptSyncTimer = null;
  }
}

// 保留向后兼容的 LLM_PROMPTS 导出（读取走 PromptCache）
export const LLM_PROMPTS = {
  get reflection() { return { system: getSystemPrompt('reflection'), user: getUserPromptTemplate('reflection')! }; },
  get compaction() { return { system: getSystemPrompt('compaction'), user: getUserPromptTemplate('compaction')! }; },
  get extraction() { return { system: getSystemPrompt('extraction'), user: getUserPromptTemplate('extraction')! }; },
};

// === Telemetry Integration ===

/**
 * 记录一次 LLM 调用结果到遥测收集器。
 * 所有 adapter 的 complete() 返回后应调用此函数。
 */
function trackLLMCall(
  adapter: LLMServiceAdapter,
  response: LLMResponse,
  error?: unknown,
): void {
  try {
    recordLLMUsage(
      response.model || adapter.getProvider(),
      response.tokensUsed?.input ?? 0,
      response.tokensUsed?.output ?? 0,
      {
        latencyMs: response.latencyMs,
        success: !error,
        errorType: error instanceof Error ? error.message.slice(0, 80) : undefined,
        connectionMode: adapter.getProvider() === 'saas' ? 'saas' : 'tauri',
      },
    );
  } catch (e) {
    log.debug('Telemetry recording failed', { error: e });
  }
}

// === Helper Functions ===

export async function llmReflect(context: string, adapter?: LLMServiceAdapter): Promise<string> {
  const llm = adapter || getLLMAdapter();

  const response = await llm.complete([
    { role: 'system', content: LLM_PROMPTS.reflection.system },
    { role: 'user', content: typeof LLM_PROMPTS.reflection.user === 'function' ? LLM_PROMPTS.reflection.user(context) : LLM_PROMPTS.reflection.user },
  ]);

  trackLLMCall(llm, response);
  return response.content;
}

export async function llmCompact(messages: string, adapter?: LLMServiceAdapter): Promise<string> {
  const llm = adapter || getLLMAdapter();

  const response = await llm.complete([
    { role: 'system', content: LLM_PROMPTS.compaction.system },
    { role: 'user', content: typeof LLM_PROMPTS.compaction.user === 'function' ? LLM_PROMPTS.compaction.user(messages) : LLM_PROMPTS.compaction.user },
  ]);

  trackLLMCall(llm, response);
  return response.content;
}

export async function llmExtract(
  conversation: string,
  adapter?: LLMServiceAdapter
): Promise<string> {
  const llm = adapter || getLLMAdapter();

  const response = await llm.complete([
    { role: 'system', content: LLM_PROMPTS.extraction.system },
    { role: 'user', content: typeof LLM_PROMPTS.extraction.user === 'function' ? LLM_PROMPTS.extraction.user(conversation) : LLM_PROMPTS.extraction.user },
  ]);

  trackLLMCall(llm, response);
  return response.content;
}