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

lib/services/adapter-instance.ts

@@ -0,0 +1,5 @@
+import ModelAdapter from "./model-adapter";
+
+const adapter = new ModelAdapter();
+
+export default adapter;

lib/services/model-adapter.ts

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+import type { ModelProvider, APIResponse, ChatMessage } from "@/types";
+import QwenOAuthService from "./qwen-oauth";
+import OllamaCloudService from "./ollama-cloud";
+import ZaiPlanService from "./zai-plan";
+
+export interface ModelAdapterConfig {
+  qwen?: {
+    apiKey?: string;
+    endpoint?: string;
+  };
+  ollama?: {
+    apiKey?: string;
+    endpoint?: string;
+  };
+  zai?: {
+    apiKey?: string;
+    generalEndpoint?: string;
+    codingEndpoint?: string;
+  };
+}
+
+export class ModelAdapter {
+  private qwenService: QwenOAuthService;
+  private ollamaService: OllamaCloudService;
+  private zaiService: ZaiPlanService;
+  private preferredProvider: ModelProvider;
+
+  constructor(config: ModelAdapterConfig = {}, preferredProvider: ModelProvider = "qwen") {
+    this.qwenService = new QwenOAuthService(config.qwen);
+    this.ollamaService = new OllamaCloudService(config.ollama);
+    this.zaiService = new ZaiPlanService(config.zai);
+    this.preferredProvider = preferredProvider;
+  }
+
+  setPreferredProvider(provider: ModelProvider): void {
+    this.preferredProvider = provider;
+  }
+
+  updateQwenApiKey(apiKey: string): void {
+    this.qwenService = new QwenOAuthService({ apiKey });
+  }
+
+  setQwenOAuthTokens(accessToken: string, refreshToken?: string, expiresIn?: number): void {
+    this.qwenService.setOAuthTokens(accessToken, refreshToken, expiresIn);
+  }
+
+  getQwenAuthUrl(): string {
+    return this.qwenService.getAuthorizationUrl();
+  }
+
+  updateOllamaApiKey(apiKey: string): void {
+    this.ollamaService = new OllamaCloudService({ apiKey });
+  }
+
+  updateZaiApiKey(apiKey: string): void {
+    this.zaiService = new ZaiPlanService({ apiKey });
+  }
+
+  private async callWithFallback<T>(
+    operation: (service: any) => Promise<APIResponse<T>>,
+    providers: ModelProvider[]
+  ): Promise<APIResponse<T>> {
+    for (const provider of providers) {
+      try {
+        let service: any;
+
+        switch (provider) {
+          case "qwen":
+            service = this.qwenService;
+            break;
+          case "ollama":
+            service = this.ollamaService;
+            break;
+          case "zai":
+            service = this.zaiService;
+            break;
+        }
+
+        const result = await operation(service);
+        if (result.success) {
+          return result;
+        }
+      } catch (error) {
+        console.error(`Error with ${provider}:`, error);
+      }
+    }
+
+    return {
+      success: false,
+      error: "All providers failed",
+    };
+  }
+
+  async enhancePrompt(prompt: string, provider?: ModelProvider, model?: string): Promise<APIResponse<string>> {
+    const providers: ModelProvider[] = provider ? [provider] : [this.preferredProvider, "ollama", "zai"];
+    return this.callWithFallback((service) => service.enhancePrompt(prompt, model), providers);
+  }
+
+  async generatePRD(idea: string, provider?: ModelProvider, model?: string): Promise<APIResponse<string>> {
+    const providers: ModelProvider[] = provider ? [provider] : ["ollama", "zai", this.preferredProvider];
+    return this.callWithFallback((service) => service.generatePRD(idea, model), providers);
+  }
+
+  async generateActionPlan(prd: string, provider?: ModelProvider, model?: string): Promise<APIResponse<string>> {
+    const providers: ModelProvider[] = provider ? [provider] : ["zai", "ollama", this.preferredProvider];
+    return this.callWithFallback((service) => service.generateActionPlan(prd, model), providers);
+  }
+
+  async chatCompletion(
+    messages: ChatMessage[],
+    model: string,
+    provider: ModelProvider = this.preferredProvider
+  ): Promise<APIResponse<string>> {
+    try {
+      let service: any;
+
+      switch (provider) {
+        case "qwen":
+          service = this.qwenService;
+          break;
+        case "ollama":
+          service = this.ollamaService;
+          break;
+        case "zai":
+          service = this.zaiService;
+          break;
+      }
+
+      return await service.chatCompletion(messages, model);
+    } catch (error) {
+      return {
+        success: false,
+        error: error instanceof Error ? error.message : "Chat completion failed",
+      };
+    }
+  }
+
+  async listModels(provider?: ModelProvider): Promise<APIResponse<Record<ModelProvider, string[]>>> {
+    const fallbackModels: Record<ModelProvider, string[]> = {
+      qwen: ["qwen-coder-plus", "qwen-coder-turbo", "qwen-coder-lite"],
+      ollama: ["gpt-oss:120b", "llama3.1", "gemma3", "deepseek-r1", "qwen3"],
+      zai: ["glm-4.7", "glm-4.5", "glm-4.5-air", "glm-4-flash", "glm-4-flashx"],
+    };
+    const models: Record<ModelProvider, string[]> = { ...fallbackModels };
+    
+    if (provider === "ollama" || !provider) {
+      try {
+        const ollamaModels = await this.ollamaService.listModels();
+        if (ollamaModels.success && ollamaModels.data && ollamaModels.data.length > 0) {
+          models.ollama = ollamaModels.data;
+        }
+      } catch (error) {
+        console.error("[ModelAdapter] Failed to load Ollama models, using fallback:", error);
+      }
+    }
+    if (provider === "zai" || !provider) {
+      try {
+        const zaiModels = await this.zaiService.listModels();
+        if (zaiModels.success && zaiModels.data && zaiModels.data.length > 0) {
+          models.zai = zaiModels.data;
+        }
+      } catch (error) {
+        console.error("[ModelAdapter] Failed to load Z.AI models, using fallback:", error);
+      }
+    }
+    if (provider === "qwen" || !provider) {
+      try {
+        const qwenModels = await this.qwenService.listModels();
+        if (qwenModels.success && qwenModels.data && qwenModels.data.length > 0) {
+          models.qwen = qwenModels.data;
+        }
+      } catch (error) {
+        console.error("[ModelAdapter] Failed to load Qwen models, using fallback:", error);
+      }
+    }
+
+    return { success: true, data: models };
+  }
+
+  getAvailableModels(provider: ModelProvider): string[] {
+    switch (provider) {
+      case "qwen":
+        return this.qwenService.getAvailableModels();
+      case "ollama":
+        return this.ollamaService.getAvailableModels();
+      case "zai":
+        return this.zaiService.getAvailableModels();
+      default:
+        return [];
+    }
+  }
+}
+
+export default ModelAdapter;

lib/services/ollama-cloud.ts

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+import type { ChatMessage, APIResponse } from "@/types";
+
+export interface OllamaCloudConfig {
+  apiKey?: string;
+  endpoint?: string;
+}
+
+export interface OllamaModel {
+  name: string;
+  size?: number;
+  digest?: string;
+}
+
+export class OllamaCloudService {
+  private config: OllamaCloudConfig;
+  private availableModels: string[] = [];
+
+  constructor(config: OllamaCloudConfig = {}) {
+    this.config = {
+      endpoint: config.endpoint || "https://ollama.com/api",
+      apiKey: config.apiKey || process.env.OLLAMA_API_KEY,
+    };
+  }
+
+  private getHeaders(): Record<string, string> {
+    const headers: Record<string, string> = {
+      "Content-Type": "application/json",
+    };
+
+    if (this.config.apiKey) {
+      headers["Authorization"] = `Bearer ${this.config.apiKey}`;
+    }
+
+    return headers;
+  }
+
+  async chatCompletion(
+    messages: ChatMessage[],
+    model: string = "gpt-oss:120b",
+    stream: boolean = false
+  ): Promise<APIResponse<string>> {
+    try {
+      if (!this.config.apiKey) {
+        throw new Error("API key is required. Please configure your Ollama API key in settings.");
+      }
+
+      console.log("[Ollama] API call:", { endpoint: this.config.endpoint, model, messages });
+
+      const response = await fetch(`${this.config.endpoint}/chat`, {
+        method: "POST",
+        headers: this.getHeaders(),
+        body: JSON.stringify({
+          model,
+          messages,
+          stream,
+        }),
+      });
+
+      console.log("[Ollama] Response status:", response.status, response.statusText);
+
+      if (!response.ok) {
+        const errorText = await response.text();
+        console.error("[Ollama] Error response:", errorText);
+        throw new Error(`Chat completion failed (${response.status}): ${response.statusText} - ${errorText}`);
+      }
+
+      const data = await response.json();
+      console.log("[Ollama] Response data:", data);
+      
+      if (data.message && data.message.content) {
+        return { success: true, data: data.message.content };
+      } else if (data.choices && data.choices[0]) {
+        return { success: true, data: data.choices[0].message.content };
+      } else {
+        return { success: false, error: "Unexpected response format" };
+      }
+    } catch (error) {
+      console.error("[Ollama] Chat completion error:", error);
+      return {
+        success: false,
+        error: error instanceof Error ? error.message : "Chat completion failed",
+      };
+    }
+  }
+
+  async listModels(): Promise<APIResponse<string[]>> {
+    try {
+      if (this.config.apiKey) {
+        console.log("[Ollama] Listing models from:", `${this.config.endpoint}/tags`);
+
+        const response = await fetch(`${this.config.endpoint}/tags`, {
+          headers: this.getHeaders(),
+        });
+
+        console.log("[Ollama] List models response status:", response.status, response.statusText);
+
+        if (!response.ok) {
+          throw new Error(`Failed to list models: ${response.statusText}`);
+        }
+
+        const data = await response.json();
+        console.log("[Ollama] Models data:", data);
+        const models = data.models?.map((m: OllamaModel) => m.name) || [];
+        
+        this.availableModels = models;
+        
+        return { success: true, data: models };
+      } else {
+        console.log("[Ollama] No API key, using fallback models");
+        return { success: true, data: ["gpt-oss:120b", "llama3.1", "gemma3", "deepseek-r1", "qwen3"] };
+      }
+    } catch (error) {
+      console.error("[Ollama] listModels error:", error);
+      return {
+        success: false,
+        error: error instanceof Error ? error.message : "Failed to list models",
+      };
+    }
+  }
+
+  getAvailableModels(): string[] {
+    return this.availableModels.length > 0 
+      ? this.availableModels 
+      : ["gpt-oss:120b", "llama3.1", "gemma3", "deepseek-r1", "qwen3"];
+  }
+
+  async enhancePrompt(prompt: string, model?: string): Promise<APIResponse<string>> {
+    const systemMessage: ChatMessage = {
+      role: "system",
+      content: `You are an expert prompt engineer. Your task is to enhance user prompts to make them more precise, actionable, and effective for AI coding agents.
+
+Apply these principles:
+1. Add specific context about project and requirements
+2. Clarify constraints and preferences
+3. Define expected output format clearly
+4. Include edge cases and error handling requirements
+5. Specify testing and validation criteria
+
+Return ONLY the enhanced prompt, no explanations.`,
+    };
+
+    const userMessage: ChatMessage = {
+      role: "user",
+      content: `Enhance this prompt for an AI coding agent:\n\n${prompt}`,
+    };
+
+    return this.chatCompletion([systemMessage, userMessage], model || "gpt-oss:120b");
+  }
+
+  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)
+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${idea}`,
+    };
+
+    return this.chatCompletion([systemMessage, userMessage], model || "gpt-oss:120b");
+  }
+
+  async generateActionPlan(prd: string, model?: string): Promise<APIResponse<string>> {
+    const systemMessage: ChatMessage = {
+      role: "system",
+      content: `You are an expert technical lead and project manager. Generate a detailed action plan based on PRD.
+
+Structure of action plan with:
+1. Task breakdown with priorities (High/Medium/Low)
+2. Dependencies between tasks
+3. Estimated effort for each task
+4. Recommended frameworks and technologies
+5. Architecture guidelines and best practices
+
+Include specific recommendations for:
+- Frontend frameworks
+- Backend architecture
+- Database choices
+- Authentication/authorization
+- Deployment strategy`,
+    };
+
+    const userMessage: ChatMessage = {
+      role: "user",
+      content: `Generate an action plan based on this PRD:\n\n${prd}`,
+    };
+
+    return this.chatCompletion([systemMessage, userMessage], model || "gpt-oss:120b");
+  }
+}
+
+export default OllamaCloudService;

lib/services/qwen-oauth.ts

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+import type { ChatMessage, APIResponse } from "@/types";
+
+export interface QwenOAuthConfig {
+  apiKey?: string;
+  accessToken?: string;
+  refreshToken?: string;
+  expiresAt?: number;
+  endpoint?: string;
+  clientId?: string;
+  redirectUri?: string;
+}
+
+export class QwenOAuthService {
+  private config: QwenOAuthConfig;
+
+  constructor(config: QwenOAuthConfig = {}) {
+    this.config = {
+      endpoint: config.endpoint || "https://dashscope-intl.aliyuncs.com/compatible-mode/v1",
+      apiKey: config.apiKey || process.env.QWEN_API_KEY,
+      accessToken: config.accessToken,
+      refreshToken: config.refreshToken,
+      expiresAt: config.expiresAt,
+      clientId: config.clientId || process.env.NEXT_PUBLIC_QWEN_CLIENT_ID,
+      redirectUri: config.redirectUri || (typeof window !== "undefined" ? window.location.origin : ""),
+    };
+  }
+
+  private getHeaders(): Record<string, string> {
+    const authHeader = this.config.accessToken 
+      ? `Bearer ${this.config.accessToken}` 
+      : `Bearer ${this.config.apiKey}`;
+
+    return {
+      "Content-Type": "application/json",
+      "Authorization": authHeader,
+    };
+  }
+
+  isAuthenticated(): boolean {
+    return !!(this.config.apiKey || (this.config.accessToken && (!this.config.expiresAt || this.config.expiresAt > Date.now())));
+  }
+
+  getAccessToken(): string | null {
+    return this.config.accessToken || this.config.apiKey || null;
+  }
+
+  async authenticate(apiKey: string): Promise<APIResponse<string>> {
+    try {
+      this.config.apiKey = apiKey;
+      this.config.accessToken = undefined; // Clear OAuth token if API key is provided
+      return { success: true, data: "Authenticated successfully" };
+    } catch (error) {
+      console.error("Qwen authentication error:", error);
+      return {
+        success: false,
+        error: error instanceof Error ? error.message : "Authentication failed",
+      };
+    }
+  }
+
+  setOAuthTokens(accessToken: string, refreshToken?: string, expiresIn?: number): void {
+    this.config.accessToken = accessToken;
+    if (refreshToken) this.config.refreshToken = refreshToken;
+    if (expiresIn) this.config.expiresAt = Date.now() + expiresIn * 1000;
+  }
+
+  getAuthorizationUrl(): string {
+    const baseUrl = "https://dashscope.console.aliyun.com/oauth/authorize"; // Placeholder URL
+    const params = new URLSearchParams({
+      client_id: this.config.clientId || "",
+      redirect_uri: this.config.redirectUri || "",
+      response_type: "code",
+      scope: "dashscope:chat",
+    });
+    return `${baseUrl}?${params.toString()}`;
+  }
+
+  async logout(): Promise<void> {
+    this.config.apiKey = undefined;
+    this.config.accessToken = undefined;
+    this.config.refreshToken = undefined;
+    this.config.expiresAt = undefined;
+  }
+
+  async chatCompletion(
+    messages: ChatMessage[],
+    model: string = "qwen-coder-plus",
+    stream: boolean = false
+  ): Promise<APIResponse<string>> {
+    try {
+      if (!this.config.apiKey) {
+        throw new Error("API key is required. Please configure your Qwen API key in settings.");
+      }
+
+      console.log("[Qwen] API call:", { endpoint: this.config.endpoint, model, messages });
+
+      const response = await fetch(`${this.config.endpoint}/chat/completions`, {
+        method: "POST",
+        headers: this.getHeaders(),
+        body: JSON.stringify({
+          model,
+          messages,
+          stream,
+        }),
+      });
+
+      console.log("[Qwen] Response status:", response.status, response.statusText);
+
+      if (!response.ok) {
+        const errorText = await response.text();
+        console.error("[Qwen] Error response:", errorText);
+        throw new Error(`Chat completion failed (${response.status}): ${response.statusText} - ${errorText}`);
+      }
+
+      const data = await response.json();
+      console.log("[Qwen] Response data:", data);
+      
+      if (data.choices && data.choices[0] && data.choices[0].message) {
+        return { success: true, data: data.choices[0].message.content };
+      } else {
+        return { success: false, error: "Unexpected response format" };
+      }
+    } catch (error) {
+      console.error("[Qwen] Chat completion error:", error);
+      return {
+        success: false,
+        error: error instanceof Error ? error.message : "Chat completion failed",
+      };
+    }
+  }
+
+  async enhancePrompt(prompt: string, model?: string): Promise<APIResponse<string>> {
+    const systemMessage: ChatMessage = {
+      role: "system",
+      content: `You are an expert prompt engineer. Your task is to enhance user prompts to make them more precise, actionable, and effective for AI coding agents.
+
+Apply these principles:
+1. Add specific context about project and requirements
+2. Clarify constraints and preferences
+3. Define expected output format clearly
+4. Include edge cases and error handling requirements
+5. Specify testing and validation criteria
+
+Return ONLY the enhanced prompt, no explanations or extra text.`,
+    };
+
+    const userMessage: ChatMessage = {
+      role: "user",
+      content: `Enhance this prompt for an AI coding agent:\n\n${prompt}`,
+    };
+
+    return this.chatCompletion([systemMessage, userMessage], model || "qwen-coder-plus");
+  }
+
+  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)
+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${idea}`,
+    };
+
+    return this.chatCompletion([systemMessage, userMessage], model || "qwen-coder-plus");
+  }
+
+  async generateActionPlan(prd: string, model?: string): Promise<APIResponse<string>> {
+    const systemMessage: ChatMessage = {
+      role: "system",
+      content: `You are an expert technical lead and project manager. Generate a detailed action plan based on PRD.
+
+Structure of action plan with:
+1. Task breakdown with priorities (High/Medium/Low)
+2. Dependencies between tasks
+3. Estimated effort for each task
+4. Recommended frameworks and technologies
+5. Architecture guidelines and best practices
+
+Include specific recommendations for:
+- Frontend frameworks
+- Backend architecture
+- Database choices
+- Authentication/authorization
+- Deployment strategy`,
+    };
+
+    const userMessage: ChatMessage = {
+      role: "user",
+      content: `Generate an action plan based on this PRD:\n\n${prd}`,
+    };
+
+    return this.chatCompletion([systemMessage, userMessage], model || "qwen-coder-plus");
+  }
+
+  async listModels(): Promise<APIResponse<string[]>> {
+    const models = ["qwen-coder-plus", "qwen-coder-turbo", "qwen-coder-lite", "qwen-plus", "qwen-turbo", "qwen-max"];
+    return { success: true, data: models };
+  }
+
+  getAvailableModels(): string[] {
+    return ["qwen-coder-plus", "qwen-coder-turbo", "qwen-coder-lite", "qwen-plus", "qwen-turbo", "qwen-max"];
+  }
+}
+
+export default QwenOAuthService;

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;