MCP Puppet Pipeline

import axios from 'axios'; import { CONFIG } from '../config.js'; import { LocalLLMInput, ToolResponse } from '../validation.js'; export async function executeLMStudioLLM(input: LocalLLMInput): Promise<ToolResponse> { const { model, prompt, temperature = 0.7 } = input; try { const url = `${CONFIG.LMSTUDIO_BASE_URL}/v1/chat/completions`; const requestBody = { model, messages: [{ role: 'user', content: prompt }], temperature, max_tokens: 1000 }; const headers: any = { 'Content-Type': 'application/json' }; // Add API key if available in environment if (process.env.LMSTUDIO_API_KEY) { headers['Authorization'] = `Bearer ${process.env.LMSTUDIO_API_KEY}`; } const startTime = Date.now(); const response = await axios.post(url, requestBody, { timeout: 60000, headers }); const latencyMs = Date.now() - startTime; if (!response.data.choices || response.data.choices.length === 0) { throw new Error('No response choices from LM Studio'); } const text = response.data.choices[0].message.content; const tokens = response.data.usage?.total_tokens || 0; const result = { text, model, latencyMs, tokens }; return { content: [{ type: 'text' as const, text: JSON.stringify(result, null, 2) }] }; } catch (error: any) { const errorMessage = error.response?.data?.error?.message || error.message || 'Unknown error'; return { content: [{ type: 'text' as const, text: `Error calling LM Studio: ${errorMessage}` }] }; } }