tegwick
4631a9f794
Register qwen/qwen3-coder-next under the openrouter provider and extend llm-catalog with a "Known Models" column so all cataloged models are discoverable. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
283 lines
8.9 KiB
Python
283 lines
8.9 KiB
Python
"""
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CLI commands for markitect LLM operations: llm-helper, llm-catalog, llm-check.
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"""
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import json
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import os
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import subprocess
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import sys
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import time
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import click
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from tabulate import tabulate
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from markitect.helper.knowledge import collect_knowledge
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from markitect.llm.config import find_project_root, resolve_api_key
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DEFAULT_PROVIDER = "openrouter"
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DEFAULT_MODEL = "openrouter/aurora-alpha"
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MODEL_ENV_VAR = "MARKITECT_HELPER_MODEL"
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SYSTEM_PROMPT_TEMPLATE = (
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"You are a MarkiTect expert assistant. Answer the user's question "
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"based on the following MarkiTect documentation. Be concise and "
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"accurate. If the documentation does not cover the question, say so.\n\n"
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"{knowledge}"
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)
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_PROVIDER_INFO = {
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"openrouter": {
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"default_model": "anthropic/claude-sonnet-4",
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"env_var": "OPENROUTER_API_KEY",
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"key_file": "apikey-openrouter.txt",
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"models": [
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"anthropic/claude-sonnet-4",
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"openrouter/aurora-alpha",
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"qwen/qwen3-coder-next",
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],
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},
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"claude-code": {
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"default_model": None,
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"env_var": None,
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"key_file": None,
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"models": [],
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},
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"gemini": {
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"default_model": "gemini-2.5-flash",
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"env_var": "GEMINI_API_KEY",
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"key_file": "apikey-geminifree.txt",
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"models": ["gemini-2.5-flash"],
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},
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"openai": {
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"default_model": "gpt-4.1-mini",
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"env_var": "OPENAI_API_KEY",
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"key_file": "apikey-chatgpt.txt",
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"models": ["gpt-4.1-mini"],
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},
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}
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def _probe_key_status(provider: str, info: dict) -> str:
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"""Return a human-readable key status string for a provider."""
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if provider == "claude-code":
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try:
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subprocess.run(
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["claude", "--version"],
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capture_output=True,
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timeout=5,
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)
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return "ok (claude --version)"
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except (FileNotFoundError, subprocess.TimeoutExpired):
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return "not found (claude CLI)"
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env_var = info["env_var"]
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key_file = info["key_file"]
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root = find_project_root()
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# Check env var first.
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if env_var and os.environ.get(env_var, "").strip():
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return "found (env)"
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# Check key file.
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if key_file and root:
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key_path = root / key_file
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try:
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if key_path.read_text().strip():
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return "found (file)"
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except OSError:
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pass
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return "not found"
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# ---------------------------------------------------------------------------
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# llm-helper (renamed from helper)
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# ---------------------------------------------------------------------------
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@click.command("llm-helper")
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@click.argument("question", nargs=-1, required=True)
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@click.option(
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"--provider", "-p",
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default=DEFAULT_PROVIDER,
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type=click.Choice(["openrouter", "claude-code", "gemini", "openai"]),
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show_default=True,
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help="LLM provider to use.",
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)
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@click.option(
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"--model", "-m",
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default=None,
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help=(
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f"Model name. Overrides {MODEL_ENV_VAR} env var and the default "
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f"({DEFAULT_MODEL})."
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),
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)
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def helper_command(question, provider, model):
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"""Ask a question about MarkiTect and get an answer from the docs.
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\b
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Examples:
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markitect llm-helper "What is markitect?"
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markitect llm-helper How do schemas work
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markitect llm-helper -m anthropic/claude-sonnet-4 "Explain templates"
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"""
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from markitect.llm import create_adapter
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from markitect.llm.exceptions import LLMConfigurationError, LLMError
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from markitect.prompts.execution.models import RunConfig
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# Join multi-word question into a single string.
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question_text = " ".join(question)
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if not question_text.strip():
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click.echo("Error: empty question.", err=True)
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sys.exit(1)
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# Resolve model: --model flag > env var > default.
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resolved_model = model or os.environ.get(MODEL_ENV_VAR) or DEFAULT_MODEL
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# Build knowledge context.
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click.echo("Loading markitect knowledge base...", err=True)
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knowledge = collect_knowledge()
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if not knowledge:
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click.echo("Warning: no documentation files found.", err=True)
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system_prompt = SYSTEM_PROMPT_TEMPLATE.format(knowledge=knowledge)
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# Create adapter.
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try:
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adapter = create_adapter(
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provider=provider,
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model=resolved_model,
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system_prompt=system_prompt,
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)
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except LLMConfigurationError as exc:
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click.echo(f"Configuration error: {exc}", err=True)
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if "api" in str(exc).lower() or "key" in str(exc).lower():
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click.echo(
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"Hint: set OPENROUTER_API_KEY (or the relevant provider key) "
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"in your environment.",
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err=True,
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)
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sys.exit(1)
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# Execute the question.
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click.echo(f"Asking {provider} ({resolved_model})...", err=True)
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try:
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config = RunConfig(
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model_name=resolved_model,
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max_tokens=4000,
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temperature=0.3,
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)
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response = adapter.execute_prompt(question_text, config)
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except LLMError as exc:
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click.echo(f"LLM error: {exc}", err=True)
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sys.exit(1)
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click.echo(response.content)
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# ---------------------------------------------------------------------------
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# llm-catalog
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# ---------------------------------------------------------------------------
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@click.command("llm-catalog")
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@click.option(
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"--format", "output_format",
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type=click.Choice(["table", "json"]),
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default="table",
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show_default=True,
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help="Output format.",
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)
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def llm_catalog(output_format):
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"""Show all known LLM providers with their default model and key status."""
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rows = []
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for provider, info in _PROVIDER_INFO.items():
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key_status = _probe_key_status(provider, info)
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models = info.get("models", [])
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rows.append({
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"provider": provider,
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"default_model": info["default_model"] or "(none, uses CLI)",
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"models": ", ".join(models) if models else "\u2014",
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"env_var": info["env_var"] or "\u2014",
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"key_file": info["key_file"] or "\u2014",
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"key_status": key_status,
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})
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if output_format == "json":
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click.echo(json.dumps(rows, indent=2))
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else:
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headers = {
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"provider": "Provider",
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"default_model": "Default Model",
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"models": "Known Models",
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"env_var": "API Key Env Var",
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"key_file": "Key File",
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"key_status": "Key Status",
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}
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click.echo(tabulate(rows, headers=headers, tablefmt="simple"))
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# ---------------------------------------------------------------------------
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# llm-check
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# ---------------------------------------------------------------------------
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@click.command("llm-check")
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@click.option(
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"--provider", "-p",
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default=DEFAULT_PROVIDER,
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type=click.Choice(["openrouter", "claude-code", "gemini", "openai"]),
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show_default=True,
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help="LLM provider to check.",
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)
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@click.option(
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"--model", "-m",
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default=None,
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help=(
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f"Model name. Overrides {MODEL_ENV_VAR} env var and the default "
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f"({DEFAULT_MODEL})."
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),
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)
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def llm_check(provider, model):
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"""Send a minimal prompt to verify a provider is reachable and responding."""
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from markitect.llm import create_adapter
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from markitect.llm.exceptions import LLMConfigurationError, LLMError
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from markitect.prompts.execution.models import RunConfig
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resolved_model = model or os.environ.get(MODEL_ENV_VAR) or DEFAULT_MODEL
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click.echo(f"Checking {provider} ({resolved_model})...")
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try:
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adapter = create_adapter(
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provider=provider,
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model=resolved_model,
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)
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except LLMConfigurationError as exc:
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click.echo(f"ERROR \u2014 Configuration: {exc}", err=True)
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sys.exit(1)
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config = RunConfig(
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model_name=resolved_model,
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max_tokens=16,
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temperature=0.0,
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)
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start = time.monotonic()
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try:
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response = adapter.execute_prompt("Reply with only the word OK.", config)
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except LLMError as exc:
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elapsed = time.monotonic() - start
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click.echo(f"ERROR \u2014 LLM error after {elapsed:.1f}s: {exc}", err=True)
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sys.exit(1)
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except Exception as exc:
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elapsed = time.monotonic() - start
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click.echo(f"ERROR \u2014 Unexpected error after {elapsed:.1f}s: {exc}", err=True)
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sys.exit(1)
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elapsed = time.monotonic() - start
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resp_model = response.metadata.get("model", resolved_model)
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total_tokens = sum(response.usage.values()) if response.usage else "?"
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click.echo(
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f"OK \u2014 response in {elapsed:.1f}s, model: {resp_model}, "
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f"tokens: {total_tokens}"
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)
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