Initialize PromptArch: The Prompt Enhancer (Fork of ClavixDev/Clavix)

lib/services/zai-plan.ts

@@ -0,0 +1,187 @@
+import type { ChatMessage, APIResponse } from "@/types";
+
+export interface ZaiPlanConfig {
+  apiKey?: string;
+  generalEndpoint?: string;
+  codingEndpoint?: string;
+}
+
+export class ZaiPlanService {
+  private config: ZaiPlanConfig;
+
+  constructor(config: ZaiPlanConfig = {}) {
+    this.config = {
+      generalEndpoint: config.generalEndpoint || "https://api.z.ai/api/paas/v4",
+      codingEndpoint: config.codingEndpoint || "https://api.z.ai/api/coding/paas/v4",
+      apiKey: config.apiKey || process.env.ZAI_API_KEY,
+    };
+  }
+
+  private getHeaders(): Record<string, string> {
+    return {
+      "Content-Type": "application/json",
+      "Authorization": `Bearer ${this.config.apiKey}`,
+      "Accept-Language": "en-US,en",
+    };
+  }
+
+  async chatCompletion(
+    messages: ChatMessage[],
+    model: string = "glm-4.7",
+    useCodingEndpoint: boolean = false
+  ): Promise<APIResponse<string>> {
+    try {
+      if (!this.config.apiKey) {
+        throw new Error("API key is required. Please configure your Z.AI API key in settings.");
+      }
+
+      const endpoint = useCodingEndpoint ? this.config.codingEndpoint : this.config.generalEndpoint;
+
+      console.log("[Z.AI] API call:", { endpoint, model, messages });
+
+      const response = await fetch(`${endpoint}/chat/completions`, {
+        method: "POST",
+        headers: this.getHeaders(),
+        body: JSON.stringify({
+          model,
+          messages,
+          stream: false,
+        }),
+      });
+
+      console.log("[Z.AI] Response status:", response.status, response.statusText);
+
+      if (!response.ok) {
+        const errorText = await response.text();
+        console.error("[Z.AI] Error response:", errorText);
+        throw new Error(`Chat completion failed (${response.status}): ${response.statusText} - ${errorText}`);
+      }
+
+      const data = await response.json();
+      console.log("[Z.AI] Response data:", data);
+      
+      if (data.choices && data.choices[0] && data.choices[0].message) {
+        return { success: true, data: data.choices[0].message.content };
+      } else if (data.output && data.output.choices && data.output.choices[0]) {
+        return { success: true, data: data.output.choices[0].message.content };
+      } else {
+        return { success: false, error: "Unexpected response format" };
+      }
+    } catch (error) {
+      console.error("[Z.AI] Chat completion error:", error);
+      return {
+        success: false,
+        error: error instanceof Error ? error.message : "Chat completion failed",
+      };
+    }
+  }
+
+  async enhancePrompt(prompt: string, model?: string): Promise<APIResponse<string>> {
+    const systemMessage: ChatMessage = {
+      role: "system",
+      content: `You are an expert prompt engineer. Your task is to enhance user prompts to make them more precise, actionable, and effective for AI coding agents.
+
+Apply these principles:
+1. Add specific context about project and requirements
+2. Clarify constraints and preferences
+3. Define expected output format clearly
+4. Include edge cases and error handling requirements
+5. Specify testing and validation criteria
+
+Return ONLY the enhanced prompt, no explanations or extra text.`,
+    };
+
+    const userMessage: ChatMessage = {
+      role: "user",
+      content: `Enhance this prompt for an AI coding agent:\n\n${prompt}`,
+    };
+
+    return this.chatCompletion([systemMessage, userMessage], model || "glm-4.7", true);
+  }
+
+  async generatePRD(idea: string, model?: string): Promise<APIResponse<string>> {
+    const systemMessage: ChatMessage = {
+      role: "system",
+      content: `You are an expert product manager and technical architect. Generate a comprehensive Product Requirements Document (PRD) based on user's idea.
+
+Structure your PRD with these sections:
+1. Overview & Objectives
+2. User Personas & Use Cases
+3. Functional Requirements (prioritized by importance)
+4. Non-functional Requirements
+5. Technical Architecture Recommendations
+6. Success Metrics & KPIs
+
+Use clear, specific language suitable for development teams.`,
+    };
+
+    const userMessage: ChatMessage = {
+      role: "user",
+      content: `Generate a PRD for this idea:\n\n${idea}`,
+    };
+
+    return this.chatCompletion([systemMessage, userMessage], model || "glm-4.7");
+  }
+
+  async generateActionPlan(prd: string, model?: string): Promise<APIResponse<string>> {
+    const systemMessage: ChatMessage = {
+      role: "system",
+      content: `You are an expert technical lead and project manager. Generate a detailed action plan based on the PRD.
+
+Structure of action plan with:
+1. Task breakdown with priorities (High/Medium/Low)
+2. Dependencies between tasks
+3. Estimated effort for each task
+4. Recommended frameworks and technologies
+5. Architecture guidelines and best practices
+
+Include specific recommendations for:
+- Frontend frameworks
+- Backend architecture
+- Database choices
+- Authentication/authorization
+- Deployment strategy`,
+    };
+
+    const userMessage: ChatMessage = {
+      role: "user",
+      content: `Generate an action plan based on this PRD:\n\n${prd}`,
+    };
+
+    return this.chatCompletion([systemMessage, userMessage], model || "glm-4.7", true);
+  }
+
+  async listModels(): Promise<APIResponse<string[]>> {
+    try {
+      if (this.config.apiKey) {
+        const response = await fetch(`${this.config.generalEndpoint}/models`, {
+          headers: this.getHeaders(),
+        });
+
+        if (!response.ok) {
+          throw new Error(`Failed to list models: ${response.statusText}`);
+        }
+
+        const data = await response.json();
+        const models = data.data?.map((m: any) => m.id) || [];
+        
+        return { success: true, data: models };
+      } else {
+        console.log("[Z.AI] No API key, using fallback models");
+        return { success: true, data: ["glm-4.7", "glm-4.6", "glm-4.5", "glm-4.5-air", "glm-4-flash", "glm-4-flashx"] };
+      }
+    } catch (error) {
+      console.error("[Z.AI] listModels error:", error);
+      return {
+        success: false,
+        error: error instanceof Error ? error.message : "Failed to list models",
+      };
+    }
+  }
+
+  getAvailableModels(): string[] {
+    return ["glm-4.7", "glm-4.6", "glm-4.5", "glm-4.5-air", "glm-4-flash", "glm-4-flashx"];
+  }
+}
+
+export default ZaiPlanService;