Consult LLM MCP

import { getExecutorForModel } from './llm.js' import { type SupportedChatModel } from './schema.js' import { calculateCost } from './llm-cost.js' import { config } from './config.js' import { getSystemPrompt } from './system-prompt.js' export async function queryLlm( prompt: string, model: SupportedChatModel, filePaths?: string[], ): Promise<{ response: string costInfo: string }> { const executor = getExecutorForModel(model) // Get system prompt (with CLI suffix if needed) const isCliMode = (model.startsWith('gemini-') && config.geminiMode === 'cli') || ((model.startsWith('gpt-') || model === 'o3') && config.openaiMode === 'cli') const systemPrompt = getSystemPrompt(isCliMode) const { response, usage } = await executor.execute( prompt, model, systemPrompt, filePaths, ) if (!response) { throw new Error('No response from the model') } let costInfo: string if (usage) { // Calculate costs only if usage data is available (from API) const { inputCost, outputCost, totalCost } = calculateCost(usage, model) costInfo = `Tokens: ${usage.prompt_tokens} input, ${usage.completion_tokens} output | Cost: $${totalCost.toFixed(6)} (input: $${inputCost.toFixed(6)}, output: $${outputCost.toFixed(6)})` } else { // Handle case where usage is not available (from CLI) costInfo = 'Cost data not available (using CLI mode)' } return { response, costInfo } }