257 lines
8.6 KiB
TypeScript
257 lines
8.6 KiB
TypeScript
import type { ChatMessage, APIResponse } from "@/types";
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export interface ZaiPlanConfig {
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apiKey?: string;
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generalEndpoint?: string;
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codingEndpoint?: string;
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}
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export class ZaiPlanService {
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private config: ZaiPlanConfig;
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constructor(config: ZaiPlanConfig = {}) {
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this.config = {
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generalEndpoint: config.generalEndpoint || "https://api.z.ai/api/paas/v4",
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codingEndpoint: config.codingEndpoint || "https://api.z.ai/api/coding/paas/v4",
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apiKey: config.apiKey || process.env.ZAI_API_KEY,
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};
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}
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hasAuth(): boolean {
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return !!this.config.apiKey;
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}
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private getHeaders(): Record<string, string> {
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return {
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"Content-Type": "application/json",
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"Authorization": `Bearer ${this.config.apiKey}`,
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"Accept-Language": "en-US,en",
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};
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}
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async chatCompletion(
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messages: ChatMessage[],
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model: string = "glm-4.7",
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useCodingEndpoint: boolean = false
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): Promise<APIResponse<string>> {
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try {
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if (!this.config.apiKey) {
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throw new Error("API key is required. Please configure your Z.AI API key in settings.");
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}
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const endpoint = useCodingEndpoint ? this.config.codingEndpoint : this.config.generalEndpoint;
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console.log("[Z.AI] API call:", { endpoint, model, messages });
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const response = await fetch(`${endpoint}/chat/completions`, {
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method: "POST",
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headers: this.getHeaders(),
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body: JSON.stringify({
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model,
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messages,
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stream: false,
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}),
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});
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console.log("[Z.AI] Response status:", response.status, response.statusText);
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if (!response.ok) {
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const errorText = await response.text();
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console.error("[Z.AI] Error response:", errorText);
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throw new Error(`Chat completion failed (${response.status}): ${response.statusText} - ${errorText}`);
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}
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const data = await response.json();
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console.log("[Z.AI] Response data:", data);
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if (data.choices && data.choices[0] && data.choices[0].message) {
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return { success: true, data: data.choices[0].message.content };
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} else if (data.output && data.output.choices && data.output.choices[0]) {
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return { success: true, data: data.output.choices[0].message.content };
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} else {
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return { success: false, error: "Unexpected response format" };
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}
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} catch (error) {
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console.error("[Z.AI] Chat completion error:", error);
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return {
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success: false,
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error: error instanceof Error ? error.message : "Chat completion failed",
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};
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}
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}
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async enhancePrompt(prompt: string, model?: string): Promise<APIResponse<string>> {
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const systemMessage: ChatMessage = {
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role: "system",
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content: `You are an expert prompt engineer. Your task is to enhance user prompts to make them more precise, actionable, and effective for AI coding agents.
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Apply these principles:
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1. Add specific context about project and requirements
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2. Clarify constraints and preferences
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3. Define expected output format clearly
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4. Include edge cases and error handling requirements
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5. Specify testing and validation criteria
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Return ONLY the enhanced prompt, no explanations or extra text.`,
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};
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const userMessage: ChatMessage = {
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role: "user",
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content: `Enhance this prompt for an AI coding agent:\n\n${prompt}`,
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};
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return this.chatCompletion([systemMessage, userMessage], model || "glm-4.7", true);
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}
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async generatePRD(idea: string, model?: string): Promise<APIResponse<string>> {
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const systemMessage: ChatMessage = {
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role: "system",
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content: `You are an expert product manager and technical architect. Generate a comprehensive Product Requirements Document (PRD) based on user's idea.
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Structure your PRD with these sections:
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1. Overview & Objectives
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2. User Personas & Use Cases
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3. Functional Requirements (prioritized by importance)
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4. Non-functional Requirements
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5. Technical Architecture Recommendations
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6. Success Metrics & KPIs
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Use clear, specific language suitable for development teams.`,
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};
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const userMessage: ChatMessage = {
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role: "user",
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content: `Generate a PRD for this idea:\n\n${idea}`,
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};
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return this.chatCompletion([systemMessage, userMessage], model || "glm-4.7");
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}
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async generateActionPlan(prd: string, model?: string): Promise<APIResponse<string>> {
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const systemMessage: ChatMessage = {
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role: "system",
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content: `You are an expert technical lead and project manager. Generate a detailed action plan based on the PRD.
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Structure of action plan with:
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1. Task breakdown with priorities (High/Medium/Low)
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2. Dependencies between tasks
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3. Estimated effort for each task
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4. Recommended frameworks and technologies
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5. Architecture guidelines and best practices
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Include specific recommendations for:
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- Frontend frameworks
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- Backend architecture
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- Database choices
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- Authentication/authorization
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- Deployment strategy`,
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};
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const userMessage: ChatMessage = {
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role: "user",
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content: `Generate an action plan based on this PRD:\n\n${prd}`,
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};
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return this.chatCompletion([systemMessage, userMessage], model || "glm-4.7", true);
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}
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async listModels(): Promise<APIResponse<string[]>> {
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try {
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if (this.config.apiKey) {
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const response = await fetch(`${this.config.generalEndpoint}/models`, {
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headers: this.getHeaders(),
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});
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if (!response.ok) {
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throw new Error(`Failed to list models: ${response.statusText}`);
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}
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const data = await response.json();
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const models = data.data?.map((m: any) => m.id) || [];
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return { success: true, data: models };
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} else {
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console.log("[Z.AI] No API key, using fallback models");
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return { success: true, data: ["glm-4.7", "glm-4.6", "glm-4.5", "glm-4.5-air", "glm-4-flash", "glm-4-flashx"] };
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}
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} catch (error) {
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console.error("[Z.AI] listModels error:", error);
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return {
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success: false,
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error: error instanceof Error ? error.message : "Failed to list models",
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};
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}
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}
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getAvailableModels(): string[] {
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return ["glm-4.7", "glm-4.6", "glm-4.5", "glm-4.5-air", "glm-4-flash", "glm-4-flashx"];
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}
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async generateUXDesignerPrompt(appDescription: string, model?: string): Promise<APIResponse<string>> {
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const systemMessage: ChatMessage = {
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role: "system",
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content: `You are a world-class UX/UI designer with deep expertise in human-centered design principles, user research, interaction design, visual design systems, and modern design tools (Figma, Sketch, Adobe XD).
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Your task is to create an exceptional, detailed prompt for generating the best possible UX design for a given app description.
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Generate a comprehensive UX design prompt that includes:
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1. USER RESEARCH & PERSONAS
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- Primary target users and their motivations
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- User pain points and needs
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- User journey maps
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- Persona archetypes with demographics and goals
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2. INFORMATION ARCHITECTURE
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- Content hierarchy and organization
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- Navigation structure and patterns
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- User flows and key pathways
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- Site map or app structure
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3. VISUAL DESIGN SYSTEM
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- Color palette recommendations (primary, secondary, accent, neutral)
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- Typography hierarchy and font pairings
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- Component library approach
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- Spacing, sizing, and layout grids
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- Iconography style and set
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4. INTERACTION DESIGN
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- Micro-interactions and animations
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- Gesture patterns for touch interfaces
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- Loading states and empty states
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- Error handling and feedback mechanisms
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- Accessibility considerations (WCAG compliance)
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5. KEY SCREENS & COMPONENTS
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- Core screens that need detailed design
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- Critical components (buttons, forms, cards, navigation)
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- Data visualization needs
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- Responsive design requirements (mobile, tablet, desktop)
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6. DESIGN DELIVERABLES
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- Wireframes vs. high-fidelity mockups
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- Design system documentation needs
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- Prototyping requirements
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- Handoff specifications for developers
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7. COMPETITIVE INSIGHTS
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- Design patterns from successful apps in this category
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- Opportunities to differentiate
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- Modern design trends to consider
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The output should be a detailed, actionable prompt that a designer or AI image generator can use to create world-class UX designs.
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Make the prompt specific, inspiring, and comprehensive. Use professional UX terminology.`,
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};
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const userMessage: ChatMessage = {
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role: "user",
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content: `Create the BEST EVER UX design prompt for this app:\n\n${appDescription}`,
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};
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return this.chatCompletion([systemMessage, userMessage], model || "glm-4.7", true);
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}
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}
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export default ZaiPlanService;
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