Files
Ali Khokhar 6bee3104fe Refactor CLI surfaces around launchers and managed Claude (#861)
## Problem

The CLI package preserved a generic adapter layer and managed Codex
parser path that did not match the supported customer workflows.
Messaging runs Claude Code sessions, while Codex is supported through
`fcc-codex` and extensions.

## Changes

| Before | After |
| --- | --- |
| `fcc-claude` and `fcc-codex` shared generic adapter plumbing. |
`fcc-claude` and `fcc-codex` use explicit launcher modules. |
| Messaging depended on a generic CLI session abstraction. | Messaging
depends on managed Claude Code sessions. |
| Codex catalog generation lived as a top-level CLI helper. | Codex
catalog generation lives under the Codex launcher owner. |
| Tests asserted deleted internal adapter shapes. | Tests assert
launcher, managed-Claude, and customer-surface behavior. |
2026-06-18 18:36:37 -07:00

187 lines
5.8 KiB
Python

"""Build Codex model catalogs from the FCC model-list route."""
from __future__ import annotations
import json
import uuid
from collections.abc import Mapping
from dataclasses import dataclass
from pathlib import Path
from typing import Any
from api.gateway_model_ids import (
GATEWAY_MODEL_ID_PREFIX,
NO_THINKING_GATEWAY_MODEL_ID_PREFIX,
)
from config.provider_ids import SUPPORTED_PROVIDER_IDS
SUPPORTED_REASONING_LEVELS = [
{"effort": "low", "description": "Fast responses with lighter reasoning"},
{
"effort": "medium",
"description": "Balances speed and reasoning depth for everyday tasks",
},
{"effort": "high", "description": "Greater reasoning depth for complex problems"},
{
"effort": "xhigh",
"description": "Extra high reasoning depth for complex problems",
},
]
CODEX_BASE_INSTRUCTIONS = (
"You are Codex, a coding agent. Help the user understand, modify, test, "
"and review code in their workspace. Follow the user's instructions, use "
"tools when needed, and communicate concise progress and verification."
)
@dataclass(frozen=True, slots=True)
class _CatalogCandidate:
slug: str
provider_model_ref: str
display_name: str
force_no_thinking: bool
def build_codex_model_catalog(models_response: Mapping[str, Any]) -> dict[str, Any]:
"""Convert FCC `/v1/models` data into Codex `model_catalog_json` payload."""
candidates = list(_catalog_candidates(models_response))
normal_provider_refs = {
candidate.provider_model_ref
for candidate in candidates
if not candidate.force_no_thinking
}
models: list[dict[str, Any]] = []
seen_slugs: set[str] = set()
for candidate in candidates:
if (
candidate.force_no_thinking
and candidate.provider_model_ref in normal_provider_refs
):
continue
if candidate.slug in seen_slugs:
continue
seen_slugs.add(candidate.slug)
models.append(_codex_catalog_entry(candidate, priority=len(models)))
return {"models": models}
def write_codex_model_catalog(catalog_path: Path, catalog: Mapping[str, Any]) -> None:
"""Atomically write a Codex model catalog JSON file."""
catalog_path.parent.mkdir(parents=True, exist_ok=True)
temp_path = catalog_path.with_name(f".{catalog_path.name}.{uuid.uuid4().hex}.tmp")
temp_path.write_text(
json.dumps(catalog, ensure_ascii=True, indent=2) + "\n",
encoding="utf-8",
)
temp_path.replace(catalog_path)
def _catalog_candidates(
models_response: Mapping[str, Any],
) -> list[_CatalogCandidate]:
data = models_response.get("data")
if not isinstance(data, list):
return []
candidates: list[_CatalogCandidate] = []
for item in data:
if not isinstance(item, Mapping):
continue
model_id = _string_value(item.get("id"))
if model_id is None:
continue
candidate = _candidate_from_model_id(
model_id,
display_name=_string_value(item.get("display_name")) or model_id,
)
if candidate is not None:
candidates.append(candidate)
return candidates
def _candidate_from_model_id(
model_id: str, *, display_name: str
) -> _CatalogCandidate | None:
prefix, separator, remainder = model_id.partition("/")
if not separator:
return None
if prefix == GATEWAY_MODEL_ID_PREFIX:
if not _is_provider_model_ref(remainder):
return None
return _CatalogCandidate(
slug=remainder,
provider_model_ref=remainder,
display_name=display_name,
force_no_thinking=False,
)
if prefix == NO_THINKING_GATEWAY_MODEL_ID_PREFIX:
if not _is_provider_model_ref(remainder):
return None
return _CatalogCandidate(
slug=model_id,
provider_model_ref=remainder,
display_name=display_name,
force_no_thinking=True,
)
if prefix in SUPPORTED_PROVIDER_IDS and remainder:
return _CatalogCandidate(
slug=model_id,
provider_model_ref=model_id,
display_name=display_name,
force_no_thinking=False,
)
return None
def _codex_catalog_entry(
candidate: _CatalogCandidate, *, priority: int
) -> dict[str, Any]:
return {
"slug": candidate.slug,
"display_name": candidate.display_name,
"description": "Free Claude Code provider model",
"default_reasoning_level": "medium",
"supported_reasoning_levels": SUPPORTED_REASONING_LEVELS,
"shell_type": "shell_command",
"visibility": "list",
"supported_in_api": True,
"priority": priority,
"additional_speed_tiers": [],
"service_tiers": [],
"base_instructions": CODEX_BASE_INSTRUCTIONS,
"supports_reasoning_summaries": True,
"default_reasoning_summary": "none",
"support_verbosity": True,
"default_verbosity": "low",
"apply_patch_tool_type": "freeform",
"web_search_tool_type": "text_and_image",
"truncation_policy": {"mode": "tokens", "limit": 10000},
"supports_parallel_tool_calls": True,
"supports_image_detail_original": True,
"context_window": 200000,
"max_context_window": 200000,
"effective_context_window_percent": 95,
"experimental_supported_tools": [],
"input_modalities": ["text"],
"supports_search_tool": True,
"use_responses_lite": False,
}
def _is_provider_model_ref(value: str) -> bool:
provider_id, separator, provider_model = value.partition("/")
return bool(separator and provider_model and provider_id in SUPPORTED_PROVIDER_IDS)
def _string_value(value: Any) -> str | None:
return value if isinstance(value, str) else None