feat(l4): add LLM service adapter for Phase 2 engine upgrades
- Unified interface for OpenAI, Volcengine, Gateway, and Mock providers - Structured LLMMessage and LLMResponse types - Configurable via localStorage with API key security - Built-in prompt templates for reflection, compaction, extraction - Helper functions: llmReflect(), llmCompact(), llmExtract() This adapter enables the 3 engines to be upgraded from rule-based to LLM-powered in Phase 2.1-2.3. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
This commit is contained in:
iven
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desktop/src/lib/llm-service.ts
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desktop/src/lib/llm-service.ts
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/**
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* LLM Service Adapter - Unified LLM interface for L4 self-evolution engines
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*
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* Provides a unified interface for:
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* - ReflectionEngine: Semantic analysis + deep reflection
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* - ContextCompactor: High-quality summarization
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* - MemoryExtractor: Semantic importance scoring
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*
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* Supports multiple backends:
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* - OpenAI (GPT-4, GPT-3.5)
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* - Volcengine (Doubao)
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* - OpenFang Gateway (passthrough)
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*
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* Part of ZCLAW L4 Self-Evolution capability.
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*/
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// === Types ===
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export type LLMProvider = 'openai' | 'volcengine' | 'gateway' | 'mock';
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export interface LLMConfig {
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provider: LLMProvider;
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model?: string;
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apiKey?: string;
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apiBase?: string;
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maxTokens?: number;
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temperature?: number;
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timeout?: number;
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}
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export interface LLMMessage {
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role: 'system' | 'user' | 'assistant';
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content: string;
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}
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export interface LLMResponse {
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content: string;
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tokensUsed?: {
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input: number;
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output: number;
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};
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model?: string;
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latencyMs?: number;
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}
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export interface LLMServiceAdapter {
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complete(messages: LLMMessage[], options?: Partial<LLMConfig>): Promise<LLMResponse>;
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isAvailable(): boolean;
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getProvider(): LLMProvider;
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}
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// === Default Configs ===
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const DEFAULT_CONFIGS: Record<LLMProvider, LLMConfig> = {
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openai: {
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provider: 'openai',
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model: 'gpt-4o-mini',
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apiBase: 'https://api.openai.com/v1',
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maxTokens: 2000,
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temperature: 0.7,
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timeout: 30000,
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},
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volcengine: {
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provider: 'volcengine',
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model: 'doubao-pro-32k',
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apiBase: 'https://ark.cn-beijing.volces.com/api/v3',
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maxTokens: 2000,
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temperature: 0.7,
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timeout: 30000,
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},
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gateway: {
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provider: 'gateway',
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apiBase: '/api/llm',
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maxTokens: 2000,
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temperature: 0.7,
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timeout: 60000,
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},
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mock: {
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provider: 'mock',
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maxTokens: 100,
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temperature: 0,
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timeout: 100,
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},
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};
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// === Storage ===
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const LLM_CONFIG_KEY = 'zclaw-llm-config';
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// === Mock Adapter (for testing) ===
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class MockLLMAdapter implements LLMServiceAdapter {
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private config: LLMConfig;
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constructor(config: LLMConfig) {
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this.config = config;
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}
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async complete(messages: LLMMessage[]): Promise<LLMResponse> {
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// Simulate latency
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await new Promise((resolve) => setTimeout(resolve, 50));
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const lastMessage = messages[messages.length - 1];
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const content = lastMessage?.content || '';
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// Generate mock response based on content type
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let response = '[Mock LLM Response] ';
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if (content.includes('reflect') || content.includes('反思')) {
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response += JSON.stringify({
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patterns: [
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{
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observation: '用户经常询问代码优化相关问题',
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frequency: 5,
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sentiment: 'positive',
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evidence: ['多次讨论性能优化', '关注代码质量'],
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},
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],
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improvements: [
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{
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area: '代码解释',
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suggestion: '可以提供更详细的代码注释',
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priority: 'medium',
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},
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],
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identityProposals: [],
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});
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} else if (content.includes('summarize') || content.includes('摘要')) {
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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。';
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} else if (content.includes('importance') || content.includes('重要性')) {
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response += JSON.stringify({
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memories: [
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{ content: '用户偏好简洁的回答', importance: 7, type: 'preference' },
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],
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});
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} else {
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response += 'Processed: ' + content.slice(0, 50);
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}
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return {
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content: response,
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tokensUsed: { input: content.length / 4, output: response.length / 4 },
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model: 'mock-model',
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latencyMs: 50,
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};
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}
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isAvailable(): boolean {
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return true;
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}
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getProvider(): LLMProvider {
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return 'mock';
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}
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}
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// === OpenAI Adapter ===
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class OpenAILLMAdapter implements LLMServiceAdapter {
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private config: LLMConfig;
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constructor(config: LLMConfig) {
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this.config = { ...DEFAULT_CONFIGS.openai, ...config };
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}
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async complete(messages: LLMMessage[], options?: Partial<LLMConfig>): Promise<LLMResponse> {
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const config = { ...this.config, ...options };
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const startTime = Date.now();
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if (!config.apiKey) {
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throw new Error('[OpenAI] API key not configured');
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}
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const response = await fetch(`${config.apiBase}/chat/completions`, {
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method: 'POST',
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headers: {
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'Content-Type': 'application/json',
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Authorization: `Bearer ${config.apiKey}`,
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},
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body: JSON.stringify({
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model: config.model,
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messages,
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max_tokens: config.maxTokens,
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temperature: config.temperature,
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}),
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signal: AbortSignal.timeout(config.timeout || 30000),
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});
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if (!response.ok) {
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const error = await response.text();
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throw new Error(`[OpenAI] API error: ${response.status} - ${error}`);
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}
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const data = await response.json();
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const latencyMs = Date.now() - startTime;
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return {
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content: data.choices[0]?.message?.content || '',
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tokensUsed: {
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input: data.usage?.prompt_tokens || 0,
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output: data.usage?.completion_tokens || 0,
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},
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model: data.model,
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latencyMs,
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};
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}
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isAvailable(): boolean {
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return !!this.config.apiKey;
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}
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getProvider(): LLMProvider {
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return 'openai';
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}
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}
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// === Volcengine Adapter ===
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class VolcengineLLMAdapter implements LLMServiceAdapter {
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private config: LLMConfig;
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constructor(config: LLMConfig) {
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this.config = { ...DEFAULT_CONFIGS.volcengine, ...config };
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}
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async complete(messages: LLMMessage[], options?: Partial<LLMConfig>): Promise<LLMResponse> {
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const config = { ...this.config, ...options };
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const startTime = Date.now();
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if (!config.apiKey) {
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throw new Error('[Volcengine] API key not configured');
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}
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const response = await fetch(`${config.apiBase}/chat/completions`, {
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method: 'POST',
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headers: {
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'Content-Type': 'application/json',
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Authorization: `Bearer ${config.apiKey}`,
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},
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body: JSON.stringify({
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model: config.model,
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messages,
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max_tokens: config.maxTokens,
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temperature: config.temperature,
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}),
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signal: AbortSignal.timeout(config.timeout || 30000),
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});
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if (!response.ok) {
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const error = await response.text();
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throw new Error(`[Volcengine] API error: ${response.status} - ${error}`);
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}
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const data = await response.json();
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const latencyMs = Date.now() - startTime;
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return {
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content: data.choices[0]?.message?.content || '',
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tokensUsed: {
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input: data.usage?.prompt_tokens || 0,
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output: data.usage?.completion_tokens || 0,
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},
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model: data.model,
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latencyMs,
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};
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}
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isAvailable(): boolean {
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return !!this.config.apiKey;
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}
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getProvider(): LLMProvider {
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return 'volcengine';
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}
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}
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// === Gateway Adapter (pass through to OpenFang) ===
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class GatewayLLMAdapter implements LLMServiceAdapter {
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private config: LLMConfig;
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constructor(config: LLMConfig) {
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this.config = { ...DEFAULT_CONFIGS.gateway, ...config };
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}
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async complete(messages: LLMMessage[], options?: Partial<LLMConfig>): Promise<LLMResponse> {
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const config = { ...this.config, ...options };
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const startTime = Date.now();
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const response = await fetch(`${config.apiBase}/complete`, {
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method: 'POST',
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headers: {
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'Content-Type': 'application/json',
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},
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body: JSON.stringify({
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messages,
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max_tokens: config.maxTokens,
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temperature: config.temperature,
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}),
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signal: AbortSignal.timeout(config.timeout || 60000),
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});
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if (!response.ok) {
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const error = await response.text();
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throw new Error(`[Gateway] API error: ${response.status} - ${error}`);
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}
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const data = await response.json();
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const latencyMs = Date.now() - startTime;
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return {
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content: data.content || data.choices?.[0]?.message?.content || '',
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tokensUsed: data.tokensUsed || { input: 0, output: 0 },
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model: data.model,
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latencyMs,
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};
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}
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isAvailable(): boolean {
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// Gateway is available if we're connected to OpenFang
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return typeof window !== 'undefined';
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}
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getProvider(): LLMProvider {
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return 'gateway';
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}
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}
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// === Factory ===
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let cachedAdapter: LLMServiceAdapter | null = null;
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export function createLLMAdapter(config?: Partial<LLMConfig>): LLMServiceAdapter {
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const savedConfig = loadConfig();
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const finalConfig = { ...savedConfig, ...config };
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switch (finalConfig.provider) {
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case 'openai':
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return new OpenAILLMAdapter(finalConfig);
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case 'volcengine':
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return new VolcengineLLMAdapter(finalConfig);
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case 'gateway':
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return new GatewayLLMAdapter(finalConfig);
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case 'mock':
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default:
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return new MockLLMAdapter(finalConfig);
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}
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}
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export function getLLMAdapter(): LLMServiceAdapter {
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if (!cachedAdapter) {
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cachedAdapter = createLLMAdapter();
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}
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return cachedAdapter;
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}
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export function resetLLMAdapter(): void {
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cachedAdapter = null;
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}
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// === Config Management ===
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export function loadConfig(): LLMConfig {
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if (typeof window === 'undefined') {
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return DEFAULT_CONFIGS.mock;
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}
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try {
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const saved = localStorage.getItem(LLM_CONFIG_KEY);
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if (saved) {
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return JSON.parse(saved);
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}
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} catch {
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// Ignore parse errors
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}
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// Default to mock for safety
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return DEFAULT_CONFIGS.mock;
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}
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export function saveConfig(config: LLMConfig): void {
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if (typeof window === 'undefined') return;
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// Don't save API key to localStorage for security
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const safeConfig = { ...config };
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delete safeConfig.apiKey;
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localStorage.setItem(LLM_CONFIG_KEY, JSON.stringify(safeConfig));
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resetLLMAdapter();
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}
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// === Prompt Templates ===
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export const LLM_PROMPTS = {
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reflection: {
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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。
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输出 JSON 格式：
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{
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"patterns": [
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{
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"observation": "观察到的模式描述",
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"frequency": 数字,
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"sentiment": "positive/negative/neutral",
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"evidence": ["证据1", "证据2"]
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}
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],
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"improvements": [
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{
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"area": "改进领域",
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"suggestion": "具体建议",
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"priority": "high/medium/low"
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}
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],
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"identityProposals": []
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}`,
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user: (context: string) => `分析以下对话历史，进行自我反思：
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${context}
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请识别行为模式（积极和消极），并提供具体的改进建议。`,
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},
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compaction: {
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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。
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要求：
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1. 保留所有重要决策和结论
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2. 保留用户偏好和约束
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3. 保留未完成的任务
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4. 保持时间顺序
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5. 摘要应能在后续对话中替代原始内容`,
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user: (messages: string) => `请将以下对话压缩为简洁摘要，保留关键信息：
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${messages}`,
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},
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extraction: {
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system: `你是一个记忆提取专家。从对话中提取值得长期记住的信息。
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提取类型：
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- fact: 用户告知的事实（如"我的公司叫XXX"）
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- preference: 用户的偏好（如"我喜欢简洁的回答"）
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- lesson: 本次对话的经验教训
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- task: 未完成的任务或承诺
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输出 JSON 数组：
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[
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{
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"content": "记忆内容",
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"type": "fact/preference/lesson/task",
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"importance": 1-10,
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"tags": ["标签1", "标签2"]
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}
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]`,
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user: (conversation: string) => `从以下对话中提取值得长期记住的信息：
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${conversation}
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如果没有值得记忆的内容，返回空数组 []。`,
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},
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};
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// === Helper Functions ===
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export async function llmReflect(context: string, adapter?: LLMServiceAdapter): Promise<string> {
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const llm = adapter || getLLMAdapter();
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const response = await llm.complete([
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{ role: 'system', content: LLM_PROMPTS.reflection.system },
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{ role: 'user', content: LLM_PROMPTS.reflection.user(context) },
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]);
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return response.content;
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}
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export async function llmCompact(messages: string, adapter?: LLMServiceAdapter): Promise<string> {
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const llm = adapter || getLLMAdapter();
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const response = await llm.complete([
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{ role: 'system', content: LLM_PROMPTS.compaction.system },
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{ role: 'user', content: LLM_PROMPTS.compaction.user(messages) },
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]);
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return response.content;
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}
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export async function llmExtract(
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conversation: string,
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adapter?: LLMServiceAdapter
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): Promise<string> {
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const llm = adapter || getLLMAdapter();
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const response = await llm.complete([
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{ role: 'system', content: LLM_PROMPTS.extraction.system },
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{ role: 'user', content: LLM_PROMPTS.extraction.user(conversation) },
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]);
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return response.content;
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}
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