#!/usr/bin/env bash MODEL="qwen3-coder-next-80b-instruct" SYSTEM_PROMPT="You are a shell command expert. Given a natural language query, generate a single shell command that accomplishes the task." # Colors CYAN='\033[0;36m' YELLOW='\033[1;33m' GREEN='\033[0;32m' RESET='\033[0m' hey-intern() { local query="$*" # Help if [ -z "$query" ]; then echo "Usage: hey-intern \"your query here\"" >&2 return 1 fi # Execute LLM Request response=$(curl -s -X POST "https://llm-api.va.reichard.io/v1/chat/completions" \ -H "Content-Type: application/json" \ -d "$(jq -n \ --arg model "$MODEL" \ --arg system "$SYSTEM_PROMPT" \ --arg user "$query" \ '{ model: $model, temperature: 0.2, messages: [ {role: "system", content: $system}, {role: "user", content: $user} ], tools: [{ type: "function", function: { name: "generate_shell_command", description: "Generate a shell command to answer a query", parameters: { type: "object", properties: { command: {type: "string", description: "The shell command to execute"} }, required: ["command"] } } }] }')" | jq -r '.choices[0].message.tool_calls[0].function.arguments // empty') # Extract Command local command=$(echo "$response" | jq -r '.command // empty') if [ -n "$command" ]; then echo -e "\n ${CYAN}${command}${RESET}\n" read -p "$(echo -e "${YELLOW}Would you like to run this command? [y/N]${RESET} ")" -n 1 -r echo "" if [[ $REPLY =~ ^[Yy]$ ]]; then echo -e "${GREEN}Running...${RESET}\n" history -s "$command" eval "$command" fi else echo "Failed to generate a valid command from the response." >&2 echo "Raw response: $response" >&2 return 1 fi } # Export Script if [[ "${BASH_SOURCE[0]}" == "${0}" ]]; then hey-intern "$@" fi