import { getExecutorForModel } from './llm.js'
import { type SupportedChatModel, type TaskMode } 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[],
threadId?: string,
taskMode?: TaskMode,
): Promise<{
response: string
costInfo: string
threadId?: string
}> {
const executor = getExecutorForModel(model)
// Get system prompt (with CLI suffix if needed)
const isCliMode =
(model.startsWith('gemini-') && config.geminiMode === 'cli') ||
(model.startsWith('gpt-') && config.openaiMode === 'cli')
const systemPrompt = getSystemPrompt(isCliMode, taskMode)
const {
response,
usage,
threadId: returnedThreadId,
} = await executor.execute(prompt, model, systemPrompt, filePaths, threadId)
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, threadId: returnedThreadId }
}
