126 lines
4.1 KiB
Python
126 lines
4.1 KiB
Python
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"""Command line entry point: uv run miniflow <command> ..."""
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import argparse
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import json
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import sys
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from datetime import datetime
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from pathlib import Path
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from miniflow import memory, tools
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from miniflow.engine import WorkflowError, load_workflow, run_workflow
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def parse_inputs(pairs: list[str]) -> dict[str, str]:
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inputs = {}
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for pair in pairs:
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if "=" not in pair:
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raise WorkflowError(f"Input must look like name=value, got '{pair}'")
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name, value = pair.split("=", 1)
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inputs[name.strip()] = value
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return inputs
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def cmd_run(args: argparse.Namespace) -> int:
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workflow = load_workflow(args.flow)
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print(f"Workflow: {workflow.name}")
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if workflow.description:
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print(f" {workflow.description}")
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print("-" * 72)
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report = run_workflow(workflow, parse_inputs(args.input))
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print("-" * 72)
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print(f"Status: {report['status']}")
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print("Result:")
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result = report["result"]
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print(result if isinstance(result, str) else json.dumps(result, ensure_ascii=False, indent=2))
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runs = Path("runs")
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runs.mkdir(exist_ok=True)
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log_file = runs / f"{datetime.now():%Y%m%d-%H%M%S}-{Path(args.flow).stem}.json"
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log_file.write_text(json.dumps(report, ensure_ascii=False, indent=2), encoding="utf-8")
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print(f"Trace saved: {log_file}")
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return 0 if report["status"] == "completed" else 1
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def cmd_validate(args: argparse.Namespace) -> int:
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workflow = load_workflow(args.flow)
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print(f"OK: {workflow.name} ({len(workflow.nodes)} nodes, {len(workflow.edges)} edges)")
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print("Execution order: " + " -> ".join(workflow.order()))
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return 0
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def cmd_show(args: argparse.Namespace) -> int:
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print(load_workflow(args.flow).to_mermaid())
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return 0
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def cmd_tools(_: argparse.Namespace) -> int:
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for name, doc in tools.describe_tools():
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print(f"{name:<14} {doc}")
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return 0
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def cmd_check(_: argparse.Namespace) -> int:
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import os
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import time
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from miniflow import llm
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provider = os.getenv("MINIFLOW_LLM", "mock").lower()
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print(f"MINIFLOW_LLM : {provider}")
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if provider == "ollama":
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print(f"OLLAMA_HOST : {os.getenv('OLLAMA_HOST', 'http://localhost:11434')}")
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print(f"OLLAMA_MODEL : {os.getenv('OLLAMA_MODEL', 'gemma3:4b')}")
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started = time.perf_counter()
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try:
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reply, used = llm.complete({"temperature": 0}, "Reply with exactly one word: ready", "", {})
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except llm.LLMError as error:
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print(f"FAILED: {error}")
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return 1
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print(f"Reply ({used}, {time.perf_counter() - started:.1f}s): {reply[:80]}")
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return 0
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def cmd_memory(args: argparse.Namespace) -> int:
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if args.clear:
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memory.clear()
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print("Memory cleared")
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return 0
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path = memory.memory_path()
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print(path.read_text(encoding="utf-8") if path.exists() else "{}")
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return 0
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def main(argv: list[str] | None = None) -> int:
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parser = argparse.ArgumentParser(prog="miniflow", description="Run JSON-defined AI workflows")
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sub = parser.add_subparsers(dest="command", required=True)
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run = sub.add_parser("run", help="run a workflow file")
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run.add_argument("flow")
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run.add_argument("--input", "-i", action="append", default=[], metavar="NAME=VALUE")
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run.set_defaults(func=cmd_run)
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validate = sub.add_parser("validate", help="check a workflow file without running it")
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validate.add_argument("flow")
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validate.set_defaults(func=cmd_validate)
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show = sub.add_parser("show", help="print the workflow as a Mermaid diagram")
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show.add_argument("flow")
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show.set_defaults(func=cmd_show)
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sub.add_parser("tools", help="list available tools").set_defaults(func=cmd_tools)
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sub.add_parser("check", help="check the configured LLM provider").set_defaults(func=cmd_check)
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mem = sub.add_parser("memory", help="show or clear the memory store")
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mem.add_argument("--clear", action="store_true")
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mem.set_defaults(func=cmd_memory)
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args = parser.parse_args(argv)
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try:
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return args.func(args)
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except (WorkflowError, FileNotFoundError, json.JSONDecodeError) as error:
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print(f"Error: {error}", file=sys.stderr)
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return 2
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if __name__ == "__main__":
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raise SystemExit(main())
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