haiku.rag/src/haiku/rag/cli.py

import asyncio
import warnings
from importlib.metadata import version
from pathlib import Path

import typer

from haiku.rag.config import Config
from haiku.rag.logging import configure_cli_logging
from haiku.rag.utils import is_up_to_date

cli = typer.Typer(
    context_settings={"help_option_names": ["-h", "--help"]}, no_args_is_help=True
)


def complete_document_ids(ctx: typer.Context, incomplete: str):
    """Autocomplete document IDs from the selected DB."""
    db_path = ctx.params.get("db") or (Config.DEFAULT_DATA_DIR / "haiku.rag.lancedb")

    try:
        from haiku.rag.client import HaikuRAG

        async def _list_ids():
            async with HaikuRAG(db_path) as client:
                docs = await client.list_documents()
                return [d.id for d in docs if d.id]

        ids = asyncio.run(_list_ids())
    except Exception:
        return []

    return [i for i in ids if i and i.startswith(incomplete)]


def complete_local_paths(ctx: typer.Context, incomplete: str) -> list[str]:
    """Autocomplete local filesystem paths.

    Provides directory/file suggestions based on the current incomplete input.
    Does not validate or restrict to specific extensions to keep it flexible
    (URLs are still allowed to be typed manually).
    """
    try:
        text = incomplete or ""

        # Expand user home
        from os.path import expanduser

        expanded = expanduser(text)
        p = Path(expanded)

        # Choose directory to list and prefix to filter
        if text == "" or text.endswith(("/", "\\")):
            directory = p
            prefix = ""
        else:
            directory = p.parent
            prefix = p.name

        if not directory.exists():
            return []

        suggestions: list[str] = []
        for entry in directory.iterdir():
            name = entry.name
            if not prefix or name.startswith(prefix):
                suggestion = str(directory / name)
                if entry.is_dir():
                    suggestion += "/"
                suggestions.append(suggestion)
        return suggestions
    except Exception:
        return []


async def check_version():
    """Check if haiku.rag is up to date and show warning if not."""
    up_to_date, current_version, latest_version = await is_up_to_date()
    if not up_to_date:
        typer.echo(
            f"Warning: haiku.rag is outdated. Current: {current_version}, Latest: {latest_version}",
        )
        typer.echo("Please update.")


def version_callback(value: bool):
    if value:
        v = version("haiku.rag")
        typer.echo(f"haiku.rag version {v}")
        raise typer.Exit()


@cli.callback()
def main(
    _version: bool = typer.Option(
        False,
        "-v",
        "--version",
        callback=version_callback,
        help="Show version and exit",
    ),
):
    """haiku.rag CLI - Vector database RAG system"""
    # Configure logging minimally for CLI context
    if Config.ENV == "development":
        # Lazy import logfire only in development
        try:
            import logfire  # type: ignore

            logfire.configure(send_to_logfire="if-token-present")
            logfire.instrument_pydantic_ai()
        except Exception:
            pass
    else:
        configure_cli_logging()
        warnings.filterwarnings("ignore")

    # Run version check before any command
    try:
        asyncio.run(check_version())
    except Exception:
        # Do not block CLI on version check issues
        pass


@cli.command("list", help="List all stored documents")
def list_documents(
    db: Path = typer.Option(
        Config.DEFAULT_DATA_DIR / "haiku.rag.lancedb",
        "--db",
        help="Path to the LanceDB database file",
    ),
):
    from haiku.rag.app import HaikuRAGApp

    app = HaikuRAGApp(db_path=db)
    asyncio.run(app.list_documents())


@cli.command("add", help="Add a document from text input")
def add_document_text(
    text: str = typer.Argument(
        help="The text content of the document to add",
    ),
    db: Path = typer.Option(
        Config.DEFAULT_DATA_DIR / "haiku.rag.lancedb",
        "--db",
        help="Path to the LanceDB database file",
    ),
):
    from haiku.rag.app import HaikuRAGApp

    app = HaikuRAGApp(db_path=db)
    asyncio.run(app.add_document_from_text(text=text))


@cli.command("add-src", help="Add a document from a file path or URL")
def add_document_src(
    source: str = typer.Argument(
        help="The file path or URL of the document to add",
        autocompletion=complete_local_paths,
    ),
    title: str | None = typer.Option(
        None,
        "--title",
        help="Optional human-readable title to store with the document",
    ),
    db: Path = typer.Option(
        Config.DEFAULT_DATA_DIR / "haiku.rag.lancedb",
        "--db",
        help="Path to the LanceDB database file",
    ),
):
    from haiku.rag.app import HaikuRAGApp

    app = HaikuRAGApp(db_path=db)
    asyncio.run(app.add_document_from_source(source=source, title=title))


@cli.command("get", help="Get and display a document by its ID")
def get_document(
    doc_id: str = typer.Argument(
        help="The ID of the document to get",
        autocompletion=complete_document_ids,
    ),
    db: Path = typer.Option(
        Config.DEFAULT_DATA_DIR / "haiku.rag.lancedb",
        "--db",
        help="Path to the LanceDB database file",
    ),
):
    from haiku.rag.app import HaikuRAGApp

    app = HaikuRAGApp(db_path=db)
    asyncio.run(app.get_document(doc_id=doc_id))


@cli.command("delete", help="Delete a document by its ID")
def delete_document(
    doc_id: str = typer.Argument(
        help="The ID of the document to delete",
        autocompletion=complete_document_ids,
    ),
    db: Path = typer.Option(
        Config.DEFAULT_DATA_DIR / "haiku.rag.lancedb",
        "--db",
        help="Path to the LanceDB database file",
    ),
):
    from haiku.rag.app import HaikuRAGApp

    app = HaikuRAGApp(db_path=db)
    asyncio.run(app.delete_document(doc_id=doc_id))


# Add alias `rm` for delete
cli.command("rm", help="Alias for delete: remove a document by its ID")(delete_document)


@cli.command("search", help="Search for documents by a query")
def search(
    query: str = typer.Argument(
        help="The search query to use",
    ),
    limit: int = typer.Option(
        5,
        "--limit",
        "-l",
        help="Maximum number of results to return",
    ),
    db: Path = typer.Option(
        Config.DEFAULT_DATA_DIR / "haiku.rag.lancedb",
        "--db",
        help="Path to the LanceDB database file",
    ),
):
    from haiku.rag.app import HaikuRAGApp

    app = HaikuRAGApp(db_path=db)
    asyncio.run(app.search(query=query, limit=limit))


@cli.command("ask", help="Ask a question using the QA agent")
def ask(
    question: str = typer.Argument(
        help="The question to ask",
    ),
    db: Path = typer.Option(
        Config.DEFAULT_DATA_DIR / "haiku.rag.lancedb",
        "--db",
        help="Path to the LanceDB database file",
    ),
    cite: bool = typer.Option(
        False,
        "--cite",
        help="Include citations in the response",
    ),
):
    from haiku.rag.app import HaikuRAGApp

    app = HaikuRAGApp(db_path=db)
    asyncio.run(app.ask(question=question, cite=cite))


@cli.command("research", help="Run multi-agent research and output a concise report")
def research(
    question: str = typer.Argument(
        help="The research question to investigate",
    ),
    max_iterations: int = typer.Option(
        3,
        "--max-iterations",
        "-n",
        help="Maximum search/analyze iterations",
    ),
    confidence_threshold: float = typer.Option(
        0.8,
        "--confidence-threshold",
        help="Minimum confidence (0-1) to stop",
    ),
    max_concurrency: int = typer.Option(
        1,
        "--max-concurrency",
        help="Max concurrent searches per iteration (planned)",
    ),
    db: Path = typer.Option(
        Config.DEFAULT_DATA_DIR / "haiku.rag.lancedb",
        "--db",
        help="Path to the LanceDB database file",
    ),
    verbose: bool = typer.Option(
        False,
        "--verbose",
        help="Show verbose progress output",
    ),
):
    from haiku.rag.app import HaikuRAGApp

    app = HaikuRAGApp(db_path=db)
    asyncio.run(
        app.research(
            question=question,
            max_iterations=max_iterations,
            confidence_threshold=confidence_threshold,
            max_concurrency=max_concurrency,
            verbose=verbose,
        )
    )


@cli.command("settings", help="Display current configuration settings")
def settings():
    from haiku.rag.app import HaikuRAGApp

    app = HaikuRAGApp(db_path=Path())  # Don't need actual DB for settings
    app.show_settings()


@cli.command(
    "rebuild",
    help="Rebuild the database by deleting all chunks and re-indexing all documents",
)
def rebuild(
    db: Path = typer.Option(
        Config.DEFAULT_DATA_DIR / "haiku.rag.lancedb",
        "--db",
        help="Path to the LanceDB database file",
    ),
):
    from haiku.rag.app import HaikuRAGApp

    app = HaikuRAGApp(db_path=db)
    asyncio.run(app.rebuild())


@cli.command("vacuum", help="Optimize and clean up all tables to reduce disk usage")
def vacuum(
    db: Path = typer.Option(
        Config.DEFAULT_DATA_DIR / "haiku.rag.lancedb",
        "--db",
        help="Path to the LanceDB database file",
    ),
):
    from haiku.rag.app import HaikuRAGApp

    app = HaikuRAGApp(db_path=db)
    asyncio.run(app.vacuum())


@cli.command("info", help="Show read-only database info (no upgrades or writes)")
def info(
    db: Path = typer.Option(
        Config.DEFAULT_DATA_DIR / "haiku.rag.lancedb",
        "--db",
        help="Path to the LanceDB database file",
    ),
):
    from haiku.rag.app import HaikuRAGApp

    app = HaikuRAGApp(db_path=db)
    asyncio.run(app.info())


@cli.command("download-models", help="Download Docling and Ollama models per config")
def download_models_cmd():
    from haiku.rag.utils import prefetch_models

    try:
        prefetch_models()
        typer.echo("Models downloaded successfully.")
    except Exception as e:
        typer.echo(f"Error downloading models: {e}")
        raise typer.Exit(1)


@cli.command(
    "serve", help="Start the haiku.rag MCP server (by default in streamable HTTP mode)"
)
def serve(
    db: Path = typer.Option(
        Config.DEFAULT_DATA_DIR / "haiku.rag.lancedb",
        "--db",
        help="Path to the LanceDB database file",
    ),
    stdio: bool = typer.Option(
        False,
        "--stdio",
        help="Run MCP server on stdio Transport",
    ),
) -> None:
    """Start the MCP server."""
    from haiku.rag.app import HaikuRAGApp

    app = HaikuRAGApp(db_path=db)

    transport = None
    if stdio:
        transport = "stdio"

    asyncio.run(app.serve(transport=transport))


@cli.command("migrate", help="Migrate an SQLite database to LanceDB")
def migrate(
    sqlite_path: Path = typer.Argument(
        help="Path to the SQLite database file to migrate",
    ),
):
    # Generate LanceDB path in same parent directory
    lancedb_path = sqlite_path.parent / (sqlite_path.stem + ".lancedb")

    # Lazy import to avoid heavy deps on simple invocations
    from haiku.rag.migration import migrate_sqlite_to_lancedb

    success = asyncio.run(migrate_sqlite_to_lancedb(sqlite_path, lancedb_path))

    if not success:
        raise typer.Exit(1)


if __name__ == "__main__":
    cli()