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

lib/services/ollama-cloud.ts

@@ -0,0 +1,203 @@
+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;