1023 lines
34 KiB
Python
1023 lines
34 KiB
Python
"""Interactive onboarding questionnaire for nanobot."""
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import json
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import types
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from dataclasses import dataclass
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from functools import lru_cache
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from typing import Any, NamedTuple, get_args, get_origin
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try:
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import questionary
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except ModuleNotFoundError: # pragma: no cover - exercised in environments without wizard deps
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questionary = None
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from loguru import logger
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from pydantic import BaseModel
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from rich.console import Console
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from rich.panel import Panel
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from rich.table import Table
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from nanobot.cli.model_info import (
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format_token_count,
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get_model_context_limit,
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get_model_suggestions,
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)
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from nanobot.config.loader import get_config_path, load_config
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from nanobot.config.schema import Config
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console = Console()
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@dataclass
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class OnboardResult:
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"""Result of an onboarding session."""
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config: Config
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should_save: bool
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# --- Field Hints for Select Fields ---
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# Maps field names to (choices, hint_text)
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# To add a new select field with hints, add an entry:
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# "field_name": (["choice1", "choice2", ...], "hint text for the field")
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_SELECT_FIELD_HINTS: dict[str, tuple[list[str], str]] = {
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"reasoning_effort": (
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["low", "medium", "high"],
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"low / medium / high - enables LLM thinking mode",
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),
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}
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# --- Key Bindings for Navigation ---
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_BACK_PRESSED = object() # Sentinel value for back navigation
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def _get_questionary():
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"""Return questionary or raise a clear error when wizard deps are unavailable."""
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if questionary is None:
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raise RuntimeError(
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"Interactive onboarding requires the optional 'questionary' dependency. "
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"Install project dependencies and rerun with --wizard."
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)
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return questionary
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def _select_with_back(
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prompt: str, choices: list[str], default: str | None = None
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) -> str | None | object:
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"""Select with Escape/Left arrow support for going back.
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Args:
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prompt: The prompt text to display.
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choices: List of choices to select from. Must not be empty.
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default: The default choice to pre-select. If not in choices, first item is used.
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Returns:
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_BACK_PRESSED sentinel if user pressed Escape or Left arrow
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The selected choice string if user confirmed
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None if user cancelled (Ctrl+C)
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"""
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from prompt_toolkit.application import Application
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from prompt_toolkit.key_binding import KeyBindings
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from prompt_toolkit.keys import Keys
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from prompt_toolkit.layout import Layout
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from prompt_toolkit.layout.containers import HSplit, Window
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from prompt_toolkit.layout.controls import FormattedTextControl
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from prompt_toolkit.styles import Style
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# Validate choices
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if not choices:
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logger.warning("Empty choices list provided to _select_with_back")
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return None
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# Find default index
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selected_index = 0
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if default and default in choices:
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selected_index = choices.index(default)
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# State holder for the result
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state: dict[str, str | None | object] = {"result": None}
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# Build menu items (uses closure over selected_index)
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def get_menu_text():
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items = []
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for i, choice in enumerate(choices):
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if i == selected_index:
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items.append(("class:selected", f"> {choice}\n"))
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else:
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items.append(("", f" {choice}\n"))
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return items
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# Create layout
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menu_control = FormattedTextControl(get_menu_text)
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menu_window = Window(content=menu_control, height=len(choices))
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prompt_control = FormattedTextControl(lambda: [("class:question", f"> {prompt}")])
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prompt_window = Window(content=prompt_control, height=1)
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layout = Layout(HSplit([prompt_window, menu_window]))
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# Key bindings
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bindings = KeyBindings()
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@bindings.add(Keys.Up)
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def _up(event):
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nonlocal selected_index
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selected_index = (selected_index - 1) % len(choices)
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event.app.invalidate()
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@bindings.add(Keys.Down)
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def _down(event):
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nonlocal selected_index
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selected_index = (selected_index + 1) % len(choices)
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event.app.invalidate()
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@bindings.add(Keys.Enter)
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def _enter(event):
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state["result"] = choices[selected_index]
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event.app.exit()
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@bindings.add("escape")
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def _escape(event):
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state["result"] = _BACK_PRESSED
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event.app.exit()
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@bindings.add(Keys.Left)
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def _left(event):
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state["result"] = _BACK_PRESSED
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event.app.exit()
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@bindings.add(Keys.ControlC)
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def _ctrl_c(event):
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state["result"] = None
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event.app.exit()
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# Style
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style = Style.from_dict({
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"selected": "fg:green bold",
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"question": "fg:cyan",
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})
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app = Application(layout=layout, key_bindings=bindings, style=style)
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try:
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app.run()
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except Exception:
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logger.exception("Error in select prompt")
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return None
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return state["result"]
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# --- Type Introspection ---
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class FieldTypeInfo(NamedTuple):
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"""Result of field type introspection."""
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type_name: str
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inner_type: Any
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def _get_field_type_info(field_info) -> FieldTypeInfo:
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"""Extract field type info from Pydantic field."""
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annotation = field_info.annotation
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if annotation is None:
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return FieldTypeInfo("str", None)
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origin = get_origin(annotation)
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args = get_args(annotation)
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if origin is types.UnionType:
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non_none_args = [a for a in args if a is not type(None)]
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if len(non_none_args) == 1:
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annotation = non_none_args[0]
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origin = get_origin(annotation)
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args = get_args(annotation)
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_SIMPLE_TYPES: dict[type, str] = {bool: "bool", int: "int", float: "float"}
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if origin is list or (hasattr(origin, "__name__") and origin.__name__ == "List"):
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return FieldTypeInfo("list", args[0] if args else str)
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if origin is dict or (hasattr(origin, "__name__") and origin.__name__ == "Dict"):
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return FieldTypeInfo("dict", None)
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for py_type, name in _SIMPLE_TYPES.items():
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if annotation is py_type:
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return FieldTypeInfo(name, None)
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if isinstance(annotation, type) and issubclass(annotation, BaseModel):
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return FieldTypeInfo("model", annotation)
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return FieldTypeInfo("str", None)
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def _get_field_display_name(field_key: str, field_info) -> str:
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"""Get display name for a field."""
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if field_info and field_info.description:
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return field_info.description
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name = field_key
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suffix_map = {
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"_s": " (seconds)",
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"_ms": " (ms)",
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"_url": " URL",
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"_path": " Path",
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"_id": " ID",
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"_key": " Key",
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"_token": " Token",
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}
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for suffix, replacement in suffix_map.items():
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if name.endswith(suffix):
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name = name[: -len(suffix)] + replacement
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break
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return name.replace("_", " ").title()
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# --- Sensitive Field Masking ---
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_SENSITIVE_KEYWORDS = frozenset({"api_key", "token", "secret", "password", "credentials"})
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def _is_sensitive_field(field_name: str) -> bool:
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"""Check if a field name indicates sensitive content."""
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return any(kw in field_name.lower() for kw in _SENSITIVE_KEYWORDS)
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def _mask_value(value: str) -> str:
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"""Mask a sensitive value, showing only the last 4 characters."""
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if len(value) <= 4:
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return "****"
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return "*" * (len(value) - 4) + value[-4:]
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# --- Value Formatting ---
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def _format_value(value: Any, rich: bool = True, field_name: str = "") -> str:
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"""Single recursive entry point for safe value display. Handles any depth."""
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if value is None or value == "" or value == {} or value == []:
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return "[dim]not set[/dim]" if rich else "[not set]"
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if _is_sensitive_field(field_name) and isinstance(value, str):
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masked = _mask_value(value)
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return f"[dim]{masked}[/dim]" if rich else masked
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if isinstance(value, BaseModel):
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parts = []
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for fname, _finfo in type(value).model_fields.items():
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fval = getattr(value, fname, None)
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formatted = _format_value(fval, rich=False, field_name=fname)
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if formatted != "[not set]":
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parts.append(f"{fname}={formatted}")
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return ", ".join(parts) if parts else ("[dim]not set[/dim]" if rich else "[not set]")
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if isinstance(value, list):
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return ", ".join(str(v) for v in value)
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if isinstance(value, dict):
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return json.dumps(value)
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return str(value)
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def _format_value_for_input(value: Any, field_type: str) -> str:
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"""Format a value for use as input default."""
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if value is None or value == "":
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return ""
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if field_type == "list" and isinstance(value, list):
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return ",".join(str(v) for v in value)
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if field_type == "dict" and isinstance(value, dict):
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return json.dumps(value)
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return str(value)
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# --- Rich UI Components ---
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def _show_config_panel(display_name: str, model: BaseModel, fields: list) -> None:
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"""Display current configuration as a rich table."""
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table = Table(show_header=False, box=None, padding=(0, 2))
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table.add_column("Field", style="cyan")
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table.add_column("Value")
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for fname, field_info in fields:
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value = getattr(model, fname, None)
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display = _get_field_display_name(fname, field_info)
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formatted = _format_value(value, rich=True, field_name=fname)
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table.add_row(display, formatted)
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console.print(Panel(table, title=f"[bold]{display_name}[/bold]", border_style="blue"))
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def _show_main_menu_header() -> None:
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"""Display the main menu header."""
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from nanobot import __logo__, __version__
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console.print()
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# Use Align.CENTER for the single line of text
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from rich.align import Align
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console.print(
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Align.center(f"{__logo__} [bold cyan]nanobot[{__version__}][/bold cyan]")
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)
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console.print()
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def _show_section_header(title: str, subtitle: str = "") -> None:
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"""Display a section header."""
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console.print()
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if subtitle:
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console.print(
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Panel(f"[dim]{subtitle}[/dim]", title=f"[bold]{title}[/bold]", border_style="blue")
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)
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else:
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console.print(Panel("", title=f"[bold]{title}[/bold]", border_style="blue"))
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# --- Input Handlers ---
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def _input_bool(display_name: str, current: bool | None) -> bool | None:
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"""Get boolean input via confirm dialog."""
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return _get_questionary().confirm(
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display_name,
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default=bool(current) if current is not None else False,
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).ask()
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def _input_text(display_name: str, current: Any, field_type: str) -> Any:
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"""Get text input and parse based on field type."""
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default = _format_value_for_input(current, field_type)
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value = _get_questionary().text(f"{display_name}:", default=default).ask()
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if value is None or value == "":
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return None
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if field_type == "int":
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try:
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return int(value)
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except ValueError:
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console.print("[yellow]! Invalid number format, value not saved[/yellow]")
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return None
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elif field_type == "float":
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try:
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return float(value)
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except ValueError:
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console.print("[yellow]! Invalid number format, value not saved[/yellow]")
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return None
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elif field_type == "list":
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return [v.strip() for v in value.split(",") if v.strip()]
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elif field_type == "dict":
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try:
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return json.loads(value)
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except json.JSONDecodeError:
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console.print("[yellow]! Invalid JSON format, value not saved[/yellow]")
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return None
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return value
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def _input_with_existing(
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display_name: str, current: Any, field_type: str
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) -> Any:
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"""Handle input with 'keep existing' option for non-empty values."""
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has_existing = current is not None and current != "" and current != {} and current != []
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if has_existing and not isinstance(current, list):
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choice = _get_questionary().select(
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display_name,
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choices=["Enter new value", "Keep existing value"],
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default="Keep existing value",
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).ask()
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if choice == "Keep existing value" or choice is None:
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return None
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return _input_text(display_name, current, field_type)
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# --- Pydantic Model Configuration ---
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def _get_current_provider(model: BaseModel) -> str:
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"""Get the current provider setting from a model (if available)."""
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if hasattr(model, "provider"):
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return getattr(model, "provider", "auto") or "auto"
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return "auto"
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def _input_model_with_autocomplete(
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display_name: str, current: Any, provider: str
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) -> str | None:
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"""Get model input with autocomplete suggestions.
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"""
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from prompt_toolkit.completion import Completer, Completion
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default = str(current) if current else ""
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class DynamicModelCompleter(Completer):
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"""Completer that dynamically fetches model suggestions."""
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def __init__(self, provider_name: str):
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self.provider = provider_name
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def get_completions(self, document, complete_event):
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text = document.text_before_cursor
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suggestions = get_model_suggestions(text, provider=self.provider, limit=50)
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for model in suggestions:
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# Skip if model doesn't contain the typed text
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if text.lower() not in model.lower():
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continue
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yield Completion(
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model,
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start_position=-len(text),
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display=model,
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)
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value = _get_questionary().autocomplete(
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f"{display_name}:",
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choices=[""], # Placeholder, actual completions from completer
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completer=DynamicModelCompleter(provider),
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default=default,
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qmark=">",
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).ask()
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return value if value else None
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def _input_context_window_with_recommendation(
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display_name: str, current: Any, model_obj: BaseModel
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) -> int | None:
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"""Get context window input with option to fetch recommended value."""
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current_val = current if current else ""
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choices = ["Enter new value"]
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if current_val:
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choices.append("Keep existing value")
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choices.append("[?] Get recommended value")
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choice = _get_questionary().select(
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display_name,
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choices=choices,
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default="Enter new value",
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).ask()
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if choice is None:
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return None
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if choice == "Keep existing value":
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return None
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if choice == "[?] Get recommended value":
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# Get the model name from the model object
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model_name = getattr(model_obj, "model", None)
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if not model_name:
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console.print("[yellow]! Please configure the model field first[/yellow]")
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return None
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provider = _get_current_provider(model_obj)
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context_limit = get_model_context_limit(model_name, provider)
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if context_limit:
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console.print(f"[green]+ Recommended context window: {format_token_count(context_limit)} tokens[/green]")
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return context_limit
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else:
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console.print("[yellow]! Could not fetch model info, please enter manually[/yellow]")
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# Fall through to manual input
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# Manual input
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value = _get_questionary().text(
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f"{display_name}:",
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default=str(current_val) if current_val else "",
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).ask()
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|
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if value is None or value == "":
|
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return None
|
|
|
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try:
|
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return int(value)
|
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except ValueError:
|
|
console.print("[yellow]! Invalid number format, value not saved[/yellow]")
|
|
return None
|
|
|
|
|
|
def _handle_model_field(
|
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working_model: BaseModel, field_name: str, field_display: str, current_value: Any
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) -> None:
|
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"""Handle the 'model' field with autocomplete and context-window auto-fill."""
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provider = _get_current_provider(working_model)
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new_value = _input_model_with_autocomplete(field_display, current_value, provider)
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if new_value is not None and new_value != current_value:
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setattr(working_model, field_name, new_value)
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_try_auto_fill_context_window(working_model, new_value)
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def _handle_context_window_field(
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working_model: BaseModel, field_name: str, field_display: str, current_value: Any
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) -> None:
|
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"""Handle context_window_tokens with recommendation lookup."""
|
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new_value = _input_context_window_with_recommendation(
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field_display, current_value, working_model
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)
|
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if new_value is not None:
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setattr(working_model, field_name, new_value)
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_FIELD_HANDLERS: dict[str, Any] = {
|
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"model": _handle_model_field,
|
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"context_window_tokens": _handle_context_window_field,
|
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}
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|
|
|
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def _configure_pydantic_model(
|
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model: BaseModel,
|
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display_name: str,
|
|
*,
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|
skip_fields: set[str] | None = None,
|
|
) -> BaseModel | None:
|
|
"""Configure a Pydantic model interactively.
|
|
|
|
Returns the updated model only when the user explicitly selects "Done".
|
|
Back and cancel actions discard the section draft.
|
|
"""
|
|
skip_fields = skip_fields or set()
|
|
working_model = model.model_copy(deep=True)
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|
|
fields = [
|
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(name, info)
|
|
for name, info in type(working_model).model_fields.items()
|
|
if name not in skip_fields
|
|
]
|
|
if not fields:
|
|
console.print(f"[dim]{display_name}: No configurable fields[/dim]")
|
|
return working_model
|
|
|
|
def get_choices() -> list[str]:
|
|
items = []
|
|
for fname, finfo in fields:
|
|
value = getattr(working_model, fname, None)
|
|
display = _get_field_display_name(fname, finfo)
|
|
formatted = _format_value(value, rich=False, field_name=fname)
|
|
items.append(f"{display}: {formatted}")
|
|
return items + ["[Done]"]
|
|
|
|
while True:
|
|
console.clear()
|
|
_show_config_panel(display_name, working_model, fields)
|
|
choices = get_choices()
|
|
answer = _select_with_back("Select field to configure:", choices)
|
|
|
|
if answer is _BACK_PRESSED or answer is None:
|
|
return None
|
|
if answer == "[Done]":
|
|
return working_model
|
|
|
|
field_idx = next((i for i, c in enumerate(choices) if c == answer), -1)
|
|
if field_idx < 0 or field_idx >= len(fields):
|
|
return None
|
|
|
|
field_name, field_info = fields[field_idx]
|
|
current_value = getattr(working_model, field_name, None)
|
|
ftype = _get_field_type_info(field_info)
|
|
field_display = _get_field_display_name(field_name, field_info)
|
|
|
|
# Nested Pydantic model - recurse
|
|
if ftype.type_name == "model":
|
|
nested = current_value
|
|
created = nested is None
|
|
if nested is None and ftype.inner_type:
|
|
nested = ftype.inner_type()
|
|
if nested and isinstance(nested, BaseModel):
|
|
updated = _configure_pydantic_model(nested, field_display)
|
|
if updated is not None:
|
|
setattr(working_model, field_name, updated)
|
|
elif created:
|
|
setattr(working_model, field_name, None)
|
|
continue
|
|
|
|
# Registered special-field handlers
|
|
handler = _FIELD_HANDLERS.get(field_name)
|
|
if handler:
|
|
handler(working_model, field_name, field_display, current_value)
|
|
continue
|
|
|
|
# Select fields with hints (e.g. reasoning_effort)
|
|
if field_name in _SELECT_FIELD_HINTS:
|
|
choices_list, hint = _SELECT_FIELD_HINTS[field_name]
|
|
select_choices = choices_list + ["(clear/unset)"]
|
|
console.print(f"[dim] Hint: {hint}[/dim]")
|
|
new_value = _select_with_back(
|
|
field_display, select_choices, default=current_value or select_choices[0]
|
|
)
|
|
if new_value is _BACK_PRESSED:
|
|
continue
|
|
if new_value == "(clear/unset)":
|
|
setattr(working_model, field_name, None)
|
|
elif new_value is not None:
|
|
setattr(working_model, field_name, new_value)
|
|
continue
|
|
|
|
# Generic field input
|
|
if ftype.type_name == "bool":
|
|
new_value = _input_bool(field_display, current_value)
|
|
else:
|
|
new_value = _input_with_existing(field_display, current_value, ftype.type_name)
|
|
if new_value is not None:
|
|
setattr(working_model, field_name, new_value)
|
|
|
|
|
|
def _try_auto_fill_context_window(model: BaseModel, new_model_name: str) -> None:
|
|
"""Try to auto-fill context_window_tokens if it's at default value.
|
|
|
|
Note:
|
|
This function imports AgentDefaults from nanobot.config.schema to get
|
|
the default context_window_tokens value. If the schema changes, this
|
|
coupling needs to be updated accordingly.
|
|
"""
|
|
# Check if context_window_tokens field exists
|
|
if not hasattr(model, "context_window_tokens"):
|
|
return
|
|
|
|
current_context = getattr(model, "context_window_tokens", None)
|
|
|
|
# Check if current value is the default (65536)
|
|
# We only auto-fill if the user hasn't changed it from default
|
|
from nanobot.config.schema import AgentDefaults
|
|
|
|
default_context = AgentDefaults.model_fields["context_window_tokens"].default
|
|
|
|
if current_context != default_context:
|
|
return # User has customized it, don't override
|
|
|
|
provider = _get_current_provider(model)
|
|
context_limit = get_model_context_limit(new_model_name, provider)
|
|
|
|
if context_limit:
|
|
setattr(model, "context_window_tokens", context_limit)
|
|
console.print(f"[green]+ Auto-filled context window: {format_token_count(context_limit)} tokens[/green]")
|
|
else:
|
|
console.print("[dim](i) Could not auto-fill context window (model not in database)[/dim]")
|
|
|
|
|
|
# --- Provider Configuration ---
|
|
|
|
|
|
@lru_cache(maxsize=1)
|
|
def _get_provider_info() -> dict[str, tuple[str, bool, bool, str]]:
|
|
"""Get provider info from registry (cached)."""
|
|
from nanobot.providers.registry import PROVIDERS
|
|
|
|
return {
|
|
spec.name: (
|
|
spec.display_name or spec.name,
|
|
spec.is_gateway,
|
|
spec.is_local,
|
|
spec.default_api_base,
|
|
)
|
|
for spec in PROVIDERS
|
|
}
|
|
|
|
|
|
def _get_provider_names() -> dict[str, str]:
|
|
"""Get provider display names."""
|
|
info = _get_provider_info()
|
|
return {name: data[0] for name, data in info.items() if name}
|
|
|
|
|
|
def _configure_provider(config: Config, provider_name: str) -> None:
|
|
"""Configure a single LLM provider."""
|
|
provider_config = getattr(config.providers, provider_name, None)
|
|
if provider_config is None:
|
|
console.print(f"[red]Unknown provider: {provider_name}[/red]")
|
|
return
|
|
|
|
display_name = _get_provider_names().get(provider_name, provider_name)
|
|
info = _get_provider_info()
|
|
default_api_base = info.get(provider_name, (None, None, None, None))[3]
|
|
|
|
if default_api_base and not provider_config.api_base:
|
|
provider_config.api_base = default_api_base
|
|
|
|
updated_provider = _configure_pydantic_model(
|
|
provider_config,
|
|
display_name,
|
|
)
|
|
if updated_provider is not None:
|
|
setattr(config.providers, provider_name, updated_provider)
|
|
|
|
|
|
def _configure_providers(config: Config) -> None:
|
|
"""Configure LLM providers."""
|
|
|
|
def get_provider_choices() -> list[str]:
|
|
"""Build provider choices with config status indicators."""
|
|
choices = []
|
|
for name, display in _get_provider_names().items():
|
|
provider = getattr(config.providers, name, None)
|
|
if provider and provider.api_key:
|
|
choices.append(f"{display} *")
|
|
else:
|
|
choices.append(display)
|
|
return choices + ["<- Back"]
|
|
|
|
while True:
|
|
try:
|
|
console.clear()
|
|
_show_section_header("LLM Providers", "Select a provider to configure API key and endpoint")
|
|
choices = get_provider_choices()
|
|
answer = _select_with_back("Select provider:", choices)
|
|
|
|
if answer is _BACK_PRESSED or answer is None or answer == "<- Back":
|
|
break
|
|
|
|
# Type guard: answer is now guaranteed to be a string
|
|
assert isinstance(answer, str)
|
|
# Extract provider name from choice (remove " *" suffix if present)
|
|
provider_name = answer.replace(" *", "")
|
|
# Find the actual provider key from display names
|
|
for name, display in _get_provider_names().items():
|
|
if display == provider_name:
|
|
_configure_provider(config, name)
|
|
break
|
|
|
|
except KeyboardInterrupt:
|
|
console.print("\n[dim]Returning to main menu...[/dim]")
|
|
break
|
|
|
|
|
|
# --- Channel Configuration ---
|
|
|
|
|
|
@lru_cache(maxsize=1)
|
|
def _get_channel_info() -> dict[str, tuple[str, type[BaseModel]]]:
|
|
"""Get channel info (display name + config class) from channel modules."""
|
|
import importlib
|
|
|
|
from nanobot.channels.registry import discover_all
|
|
|
|
result: dict[str, tuple[str, type[BaseModel]]] = {}
|
|
for name, channel_cls in discover_all().items():
|
|
try:
|
|
mod = importlib.import_module(f"nanobot.channels.{name}")
|
|
config_name = channel_cls.__name__.replace("Channel", "Config")
|
|
config_cls = getattr(mod, config_name, None)
|
|
if config_cls and isinstance(config_cls, type) and issubclass(config_cls, BaseModel):
|
|
display_name = getattr(channel_cls, "display_name", name.capitalize())
|
|
result[name] = (display_name, config_cls)
|
|
except Exception:
|
|
logger.warning(f"Failed to load channel module: {name}")
|
|
return result
|
|
|
|
|
|
def _get_channel_names() -> dict[str, str]:
|
|
"""Get channel display names."""
|
|
return {name: info[0] for name, info in _get_channel_info().items()}
|
|
|
|
|
|
def _get_channel_config_class(channel: str) -> type[BaseModel] | None:
|
|
"""Get channel config class."""
|
|
entry = _get_channel_info().get(channel)
|
|
return entry[1] if entry else None
|
|
|
|
|
|
def _configure_channel(config: Config, channel_name: str) -> None:
|
|
"""Configure a single channel."""
|
|
channel_dict = getattr(config.channels, channel_name, None)
|
|
if channel_dict is None:
|
|
channel_dict = {}
|
|
setattr(config.channels, channel_name, channel_dict)
|
|
|
|
display_name = _get_channel_names().get(channel_name, channel_name)
|
|
config_cls = _get_channel_config_class(channel_name)
|
|
|
|
if config_cls is None:
|
|
console.print(f"[red]No configuration class found for {display_name}[/red]")
|
|
return
|
|
|
|
model = config_cls.model_validate(channel_dict) if channel_dict else config_cls()
|
|
|
|
updated_channel = _configure_pydantic_model(
|
|
model,
|
|
display_name,
|
|
)
|
|
if updated_channel is not None:
|
|
new_dict = updated_channel.model_dump(by_alias=True, exclude_none=True)
|
|
setattr(config.channels, channel_name, new_dict)
|
|
|
|
|
|
def _configure_channels(config: Config) -> None:
|
|
"""Configure chat channels."""
|
|
channel_names = list(_get_channel_names().keys())
|
|
choices = channel_names + ["<- Back"]
|
|
|
|
while True:
|
|
try:
|
|
console.clear()
|
|
_show_section_header("Chat Channels", "Select a channel to configure connection settings")
|
|
answer = _select_with_back("Select channel:", choices)
|
|
|
|
if answer is _BACK_PRESSED or answer is None or answer == "<- Back":
|
|
break
|
|
|
|
# Type guard: answer is now guaranteed to be a string
|
|
assert isinstance(answer, str)
|
|
_configure_channel(config, answer)
|
|
except KeyboardInterrupt:
|
|
console.print("\n[dim]Returning to main menu...[/dim]")
|
|
break
|
|
|
|
|
|
# --- General Settings ---
|
|
|
|
_SETTINGS_SECTIONS: dict[str, tuple[str, str, set[str] | None]] = {
|
|
"Agent Settings": ("Agent Defaults", "Configure default model, temperature, and behavior", None),
|
|
"Gateway": ("Gateway Settings", "Configure server host, port, and heartbeat", None),
|
|
"Tools": ("Tools Settings", "Configure web search, shell exec, and other tools", {"mcp_servers"}),
|
|
}
|
|
|
|
_SETTINGS_GETTER = {
|
|
"Agent Settings": lambda c: c.agents.defaults,
|
|
"Gateway": lambda c: c.gateway,
|
|
"Tools": lambda c: c.tools,
|
|
}
|
|
|
|
_SETTINGS_SETTER = {
|
|
"Agent Settings": lambda c, v: setattr(c.agents, "defaults", v),
|
|
"Gateway": lambda c, v: setattr(c, "gateway", v),
|
|
"Tools": lambda c, v: setattr(c, "tools", v),
|
|
}
|
|
|
|
|
|
def _configure_general_settings(config: Config, section: str) -> None:
|
|
"""Configure a general settings section (header + model edit + writeback)."""
|
|
meta = _SETTINGS_SECTIONS.get(section)
|
|
if not meta:
|
|
return
|
|
display_name, subtitle, skip = meta
|
|
model = _SETTINGS_GETTER[section](config)
|
|
updated = _configure_pydantic_model(model, display_name, skip_fields=skip)
|
|
if updated is not None:
|
|
_SETTINGS_SETTER[section](config, updated)
|
|
|
|
|
|
# --- Summary ---
|
|
|
|
|
|
def _summarize_model(obj: BaseModel) -> list[tuple[str, str]]:
|
|
"""Recursively summarize a Pydantic model. Returns list of (field, value) tuples."""
|
|
items: list[tuple[str, str]] = []
|
|
for field_name, field_info in type(obj).model_fields.items():
|
|
value = getattr(obj, field_name, None)
|
|
if value is None or value == "" or value == {} or value == []:
|
|
continue
|
|
display = _get_field_display_name(field_name, field_info)
|
|
ftype = _get_field_type_info(field_info)
|
|
if ftype.type_name == "model" and isinstance(value, BaseModel):
|
|
for nested_field, nested_value in _summarize_model(value):
|
|
items.append((f"{display}.{nested_field}", nested_value))
|
|
continue
|
|
formatted = _format_value(value, rich=False, field_name=field_name)
|
|
if formatted != "[not set]":
|
|
items.append((display, formatted))
|
|
return items
|
|
|
|
|
|
def _print_summary_panel(rows: list[tuple[str, str]], title: str) -> None:
|
|
"""Build a two-column summary panel and print it."""
|
|
if not rows:
|
|
return
|
|
table = Table(show_header=False, box=None, padding=(0, 2))
|
|
table.add_column("Setting", style="cyan")
|
|
table.add_column("Value")
|
|
for field, value in rows:
|
|
table.add_row(field, value)
|
|
console.print(Panel(table, title=f"[bold]{title}[/bold]", border_style="blue"))
|
|
|
|
|
|
def _show_summary(config: Config) -> None:
|
|
"""Display configuration summary using rich."""
|
|
console.print()
|
|
|
|
# Providers
|
|
provider_rows = []
|
|
for name, display in _get_provider_names().items():
|
|
provider = getattr(config.providers, name, None)
|
|
status = "[green]configured[/green]" if (provider and provider.api_key) else "[dim]not configured[/dim]"
|
|
provider_rows.append((display, status))
|
|
_print_summary_panel(provider_rows, "LLM Providers")
|
|
|
|
# Channels
|
|
channel_rows = []
|
|
for name, display in _get_channel_names().items():
|
|
channel = getattr(config.channels, name, None)
|
|
if channel:
|
|
enabled = (
|
|
channel.get("enabled", False)
|
|
if isinstance(channel, dict)
|
|
else getattr(channel, "enabled", False)
|
|
)
|
|
status = "[green]enabled[/green]" if enabled else "[dim]disabled[/dim]"
|
|
else:
|
|
status = "[dim]not configured[/dim]"
|
|
channel_rows.append((display, status))
|
|
_print_summary_panel(channel_rows, "Chat Channels")
|
|
|
|
# Settings sections
|
|
for title, model in [
|
|
("Agent Settings", config.agents.defaults),
|
|
("Gateway", config.gateway),
|
|
("Tools", config.tools),
|
|
("Channel Common", config.channels),
|
|
]:
|
|
_print_summary_panel(_summarize_model(model), title)
|
|
|
|
|
|
# --- Main Entry Point ---
|
|
|
|
|
|
def _has_unsaved_changes(original: Config, current: Config) -> bool:
|
|
"""Return True when the onboarding session has committed changes."""
|
|
return original.model_dump(by_alias=True) != current.model_dump(by_alias=True)
|
|
|
|
|
|
def _prompt_main_menu_exit(has_unsaved_changes: bool) -> str:
|
|
"""Resolve how to leave the main menu."""
|
|
if not has_unsaved_changes:
|
|
return "discard"
|
|
|
|
answer = _get_questionary().select(
|
|
"You have unsaved changes. What would you like to do?",
|
|
choices=[
|
|
"[S] Save and Exit",
|
|
"[X] Exit Without Saving",
|
|
"[R] Resume Editing",
|
|
],
|
|
default="[R] Resume Editing",
|
|
qmark=">",
|
|
).ask()
|
|
|
|
if answer == "[S] Save and Exit":
|
|
return "save"
|
|
if answer == "[X] Exit Without Saving":
|
|
return "discard"
|
|
return "resume"
|
|
|
|
|
|
def run_onboard(initial_config: Config | None = None) -> OnboardResult:
|
|
"""Run the interactive onboarding questionnaire.
|
|
|
|
Args:
|
|
initial_config: Optional pre-loaded config to use as starting point.
|
|
If None, loads from config file or creates new default.
|
|
"""
|
|
_get_questionary()
|
|
|
|
if initial_config is not None:
|
|
base_config = initial_config.model_copy(deep=True)
|
|
else:
|
|
config_path = get_config_path()
|
|
if config_path.exists():
|
|
base_config = load_config()
|
|
else:
|
|
base_config = Config()
|
|
|
|
original_config = base_config.model_copy(deep=True)
|
|
config = base_config.model_copy(deep=True)
|
|
|
|
while True:
|
|
console.clear()
|
|
_show_main_menu_header()
|
|
|
|
try:
|
|
answer = _get_questionary().select(
|
|
"What would you like to configure?",
|
|
choices=[
|
|
"[P] LLM Provider",
|
|
"[C] Chat Channel",
|
|
"[A] Agent Settings",
|
|
"[G] Gateway",
|
|
"[T] Tools",
|
|
"[V] View Configuration Summary",
|
|
"[S] Save and Exit",
|
|
"[X] Exit Without Saving",
|
|
],
|
|
qmark=">",
|
|
).ask()
|
|
except KeyboardInterrupt:
|
|
answer = None
|
|
|
|
if answer is None:
|
|
action = _prompt_main_menu_exit(_has_unsaved_changes(original_config, config))
|
|
if action == "save":
|
|
return OnboardResult(config=config, should_save=True)
|
|
if action == "discard":
|
|
return OnboardResult(config=original_config, should_save=False)
|
|
continue
|
|
|
|
_MENU_DISPATCH = {
|
|
"[P] LLM Provider": lambda: _configure_providers(config),
|
|
"[C] Chat Channel": lambda: _configure_channels(config),
|
|
"[A] Agent Settings": lambda: _configure_general_settings(config, "Agent Settings"),
|
|
"[G] Gateway": lambda: _configure_general_settings(config, "Gateway"),
|
|
"[T] Tools": lambda: _configure_general_settings(config, "Tools"),
|
|
"[V] View Configuration Summary": lambda: _show_summary(config),
|
|
}
|
|
|
|
if answer == "[S] Save and Exit":
|
|
return OnboardResult(config=config, should_save=True)
|
|
if answer == "[X] Exit Without Saving":
|
|
return OnboardResult(config=original_config, should_save=False)
|
|
|
|
action_fn = _MENU_DISPATCH.get(answer)
|
|
if action_fn:
|
|
action_fn()
|