347 lines
13 KiB
Python
347 lines
13 KiB
Python
"""OpenAI Codex Responses Provider."""
|
|
|
|
from __future__ import annotations
|
|
|
|
import asyncio
|
|
import hashlib
|
|
import json
|
|
from collections.abc import Awaitable, Callable
|
|
from typing import Any
|
|
|
|
import httpx
|
|
from loguru import logger
|
|
from oauth_cli_kit import get_token as get_codex_token
|
|
|
|
from nanobot.providers.base import (
|
|
LLMProvider,
|
|
LLMResponse,
|
|
ToolCallRequest,
|
|
resolve_stream_idle_timeout_s,
|
|
)
|
|
from nanobot.providers.openai_responses import (
|
|
consume_sse_with_reasoning,
|
|
convert_messages,
|
|
convert_tools,
|
|
)
|
|
|
|
DEFAULT_CODEX_URL = "https://chatgpt.com/backend-api/codex/responses"
|
|
DEFAULT_ORIGINATOR = "nanobot"
|
|
|
|
|
|
class OpenAICodexProvider(LLMProvider):
|
|
"""Use Codex OAuth to call the Responses API."""
|
|
|
|
supports_progress_deltas = True
|
|
|
|
def __init__(
|
|
self,
|
|
default_model: str = "openai-codex/gpt-5.6-sol",
|
|
proxy: str | None = None,
|
|
extra_body: dict[str, Any] | None = None,
|
|
):
|
|
super().__init__(api_key=None, api_base=None)
|
|
self.default_model = default_model
|
|
self.proxy = proxy or None
|
|
self._extra_body = dict(extra_body or {})
|
|
|
|
async def _call_codex(
|
|
self,
|
|
messages: list[dict[str, Any]],
|
|
tools: list[dict[str, Any]] | None,
|
|
model: str | None,
|
|
reasoning_effort: str | None,
|
|
tool_choice: str | dict[str, Any] | None,
|
|
on_content_delta: Callable[[str], Awaitable[None]] | None = None,
|
|
on_thinking_delta: Callable[[str], Awaitable[None]] | None = None,
|
|
on_tool_call_delta: Callable[[dict[str, Any]], Awaitable[None]] | None = None,
|
|
) -> LLMResponse:
|
|
"""Shared request logic for both chat() and chat_stream()."""
|
|
model = model or self.default_model
|
|
system_prompt, input_items = convert_messages(messages)
|
|
|
|
body: dict[str, Any] = {
|
|
"model": _strip_model_prefix(model),
|
|
"store": False,
|
|
"stream": True,
|
|
"instructions": system_prompt,
|
|
"input": input_items,
|
|
"text": {"verbosity": "medium"},
|
|
"include": ["reasoning.encrypted_content"],
|
|
"prompt_cache_key": _prompt_cache_key(messages[:2]),
|
|
"tool_choice": tool_choice or "auto",
|
|
"parallel_tool_calls": True,
|
|
}
|
|
reasoning_options = _build_reasoning_options(reasoning_effort)
|
|
if reasoning_options:
|
|
body["reasoning"] = reasoning_options
|
|
if tools:
|
|
body["tools"] = convert_tools(tools)
|
|
if self._extra_body:
|
|
# Apply explicit provider overrides last, matching other provider backends.
|
|
body.update(self._extra_body)
|
|
|
|
stage = "oauth_token"
|
|
try:
|
|
token = await asyncio.to_thread(get_codex_token, proxy=self.proxy)
|
|
headers = _build_headers(token.account_id, token.access)
|
|
|
|
stage = "codex_request"
|
|
try:
|
|
content, tool_calls, finish_reason, usage, reasoning_content = await _request_codex(
|
|
DEFAULT_CODEX_URL, headers, body, verify=True,
|
|
proxy=self.proxy,
|
|
on_content_delta=on_content_delta,
|
|
on_thinking_delta=on_thinking_delta,
|
|
on_tool_call_delta=on_tool_call_delta,
|
|
)
|
|
except Exception as e:
|
|
if "CERTIFICATE_VERIFY_FAILED" not in str(e):
|
|
raise
|
|
logger.warning("SSL verification failed for Codex API; retrying with verify=False")
|
|
content, tool_calls, finish_reason, usage, reasoning_content = await _request_codex(
|
|
DEFAULT_CODEX_URL, headers, body, verify=False,
|
|
proxy=self.proxy,
|
|
on_content_delta=on_content_delta,
|
|
on_thinking_delta=on_thinking_delta,
|
|
on_tool_call_delta=on_tool_call_delta,
|
|
)
|
|
return LLMResponse(
|
|
content=content,
|
|
tool_calls=tool_calls,
|
|
finish_reason=finish_reason,
|
|
usage=usage,
|
|
reasoning_content=reasoning_content,
|
|
)
|
|
except Exception as e:
|
|
response = _codex_error_response(e)
|
|
exc_type = "CodexHTTPError" if isinstance(e, _CodexHTTPError) else type(e).__name__
|
|
logger.warning(
|
|
"Codex API request failed: stage={} type={} kind={} retryable={} status={} "
|
|
"error_type={} error_code={} retry_after={} summary={}",
|
|
stage,
|
|
exc_type,
|
|
response.error_kind,
|
|
response.error_should_retry,
|
|
response.error_status_code,
|
|
response.error_type,
|
|
response.error_code,
|
|
response.retry_after,
|
|
_codex_log_summary(exc_type, response),
|
|
)
|
|
return response
|
|
|
|
async def chat(
|
|
self, messages: list[dict[str, Any]], tools: list[dict[str, Any]] | None = None,
|
|
model: str | None = None, max_tokens: int = 4096, temperature: float = 0.7,
|
|
reasoning_effort: str | None = None,
|
|
tool_choice: str | dict[str, Any] | None = None,
|
|
) -> LLMResponse:
|
|
return await self._call_codex(messages, tools, model, reasoning_effort, tool_choice)
|
|
|
|
async def chat_stream(
|
|
self, messages: list[dict[str, Any]], tools: list[dict[str, Any]] | None = None,
|
|
model: str | None = None, max_tokens: int = 4096, temperature: float = 0.7,
|
|
reasoning_effort: str | None = None,
|
|
tool_choice: str | dict[str, Any] | None = None,
|
|
on_content_delta: Callable[[str], Awaitable[None]] | None = None,
|
|
on_thinking_delta: Callable[[str], Awaitable[None]] | None = None,
|
|
on_tool_call_delta: Callable[[dict[str, Any]], Awaitable[None]] | None = None,
|
|
) -> LLMResponse:
|
|
return await self._call_codex(
|
|
messages,
|
|
tools,
|
|
model,
|
|
reasoning_effort,
|
|
tool_choice,
|
|
on_content_delta,
|
|
on_thinking_delta,
|
|
on_tool_call_delta,
|
|
)
|
|
|
|
def get_default_model(self) -> str:
|
|
return self.default_model
|
|
|
|
|
|
def _strip_model_prefix(model: str) -> str:
|
|
if model.startswith("openai-codex/") or model.startswith("openai_codex/"):
|
|
return model.split("/", 1)[1]
|
|
return model
|
|
|
|
|
|
def _build_reasoning_options(reasoning_effort: str | None) -> dict[str, str] | None:
|
|
"""Opt in to visible summaries without changing provider-default effort."""
|
|
if reasoning_effort and reasoning_effort.lower() == "none":
|
|
return {"effort": "none"}
|
|
options = {"summary": "auto"}
|
|
if reasoning_effort:
|
|
options["effort"] = reasoning_effort
|
|
return options
|
|
|
|
|
|
def _build_headers(account_id: str, token: str) -> dict[str, str]:
|
|
return {
|
|
"Authorization": f"Bearer {token}",
|
|
"chatgpt-account-id": account_id,
|
|
"OpenAI-Beta": "responses=experimental",
|
|
"originator": DEFAULT_ORIGINATOR,
|
|
"User-Agent": "nanobot (python)",
|
|
"accept": "text/event-stream",
|
|
"content-type": "application/json",
|
|
}
|
|
|
|
|
|
class _CodexHTTPError(RuntimeError):
|
|
def __init__(
|
|
self,
|
|
message: str,
|
|
*,
|
|
status_code: int | None = None,
|
|
retry_after: float | None = None,
|
|
error_type: str | None = None,
|
|
error_code: str | None = None,
|
|
should_retry: bool | None = None,
|
|
):
|
|
super().__init__(message)
|
|
self.status_code = status_code
|
|
self.retry_after = retry_after
|
|
self.error_type = error_type
|
|
self.error_code = error_code
|
|
self.should_retry = should_retry
|
|
|
|
|
|
async def _request_codex(
|
|
url: str,
|
|
headers: dict[str, str],
|
|
body: dict[str, Any],
|
|
verify: bool,
|
|
proxy: str | None = None,
|
|
on_content_delta: Callable[[str], Awaitable[None]] | None = None,
|
|
on_thinking_delta: Callable[[str], Awaitable[None]] | None = None,
|
|
on_tool_call_delta: Callable[[dict[str, Any]], Awaitable[None]] | None = None,
|
|
) -> tuple[str, list[ToolCallRequest], str, dict[str, int], str | None]:
|
|
idle_timeout_s = resolve_stream_idle_timeout_s()
|
|
client_kwargs: dict[str, Any] = {"timeout": idle_timeout_s, "verify": verify}
|
|
if proxy:
|
|
client_kwargs["proxy"] = proxy
|
|
client_kwargs["trust_env"] = False
|
|
async with httpx.AsyncClient(**client_kwargs) as client:
|
|
async with client.stream("POST", url, headers=headers, json=body) as response:
|
|
if response.status_code != 200:
|
|
text = await response.aread()
|
|
raw = text.decode("utf-8", "ignore")
|
|
retry_after = LLMProvider._extract_retry_after_from_headers(response.headers)
|
|
error_type, error_code = LLMProvider._extract_error_type_code(raw)
|
|
raise _CodexHTTPError(
|
|
_friendly_error(response.status_code, raw),
|
|
status_code=response.status_code,
|
|
retry_after=retry_after,
|
|
error_type=error_type,
|
|
error_code=error_code,
|
|
should_retry=_should_retry_status(response.status_code, error_type, error_code, raw),
|
|
)
|
|
return await consume_sse_with_reasoning(
|
|
response,
|
|
on_content_delta=on_content_delta,
|
|
on_tool_call_delta=on_tool_call_delta,
|
|
on_reasoning_delta=on_thinking_delta,
|
|
)
|
|
|
|
|
|
def _prompt_cache_key(messages: list[dict[str, Any]]) -> str:
|
|
raw = json.dumps(messages, ensure_ascii=True, sort_keys=True)
|
|
return hashlib.sha256(raw.encode("utf-8")).hexdigest()
|
|
|
|
|
|
def _friendly_error(status_code: int, raw: str) -> str:
|
|
_ = raw
|
|
if status_code == 429:
|
|
return "ChatGPT usage quota exceeded or rate limit triggered. Please try again later."
|
|
return f"HTTP {status_code}: Codex API request failed"
|
|
|
|
|
|
def _codex_error_response(exc: Exception) -> LLMResponse:
|
|
"""Convert Codex transport/API failures into actionable, retryable metadata."""
|
|
exc_type = "CodexHTTPError" if isinstance(exc, _CodexHTTPError) else type(exc).__name__
|
|
detail = str(exc).strip()
|
|
|
|
status_code = getattr(exc, "status_code", None)
|
|
error_kind: str | None = None
|
|
default_detail: str | None = None
|
|
should_retry: bool | None = getattr(exc, "should_retry", None)
|
|
|
|
if isinstance(exc, (httpx.TimeoutException, asyncio.TimeoutError)):
|
|
error_kind = "timeout"
|
|
default_detail = "timed out waiting for response"
|
|
should_retry = True if should_retry is None else should_retry
|
|
elif isinstance(exc, httpx.RemoteProtocolError):
|
|
error_kind = "connection"
|
|
default_detail = "network protocol error while reading response"
|
|
should_retry = True if should_retry is None else should_retry
|
|
elif isinstance(exc, (httpx.NetworkError, httpx.TransportError)):
|
|
error_kind = "connection"
|
|
default_detail = "network connection failed"
|
|
should_retry = True if should_retry is None else should_retry
|
|
elif isinstance(exc, _CodexHTTPError):
|
|
error_kind = "http"
|
|
default_detail = "HTTP request failed"
|
|
|
|
if status_code is not None and should_retry is None:
|
|
retry_content = None if int(status_code) == 429 and isinstance(exc, _CodexHTTPError) else detail
|
|
should_retry = _should_retry_status(
|
|
int(status_code),
|
|
getattr(exc, "error_type", None),
|
|
getattr(exc, "error_code", None),
|
|
retry_content,
|
|
)
|
|
|
|
detail = detail or default_detail or "unexpected error"
|
|
message = f"Error calling Codex ({exc_type}): {detail}"
|
|
retry_after = getattr(exc, "retry_after", None) or LLMProvider._extract_retry_after(message)
|
|
return LLMResponse(
|
|
content=message,
|
|
finish_reason="error",
|
|
retry_after=retry_after,
|
|
error_status_code=int(status_code) if status_code is not None else None,
|
|
error_kind=error_kind,
|
|
error_type=getattr(exc, "error_type", None),
|
|
error_code=getattr(exc, "error_code", None),
|
|
error_retry_after_s=retry_after,
|
|
error_should_retry=should_retry,
|
|
)
|
|
|
|
|
|
def _codex_log_summary(exc_type: str, response: LLMResponse) -> str:
|
|
"""Return a bounded diagnostic summary without request body or raw upstream payload."""
|
|
if response.error_status_code is not None:
|
|
parts = [f"HTTP {response.error_status_code}"]
|
|
if response.error_type:
|
|
parts.append(f"type={response.error_type}")
|
|
if response.error_code:
|
|
parts.append(f"code={response.error_code}")
|
|
return " ".join(parts)
|
|
|
|
kind = (response.error_kind or "").strip()
|
|
if kind:
|
|
return f"{exc_type} {kind}"
|
|
|
|
return exc_type
|
|
|
|
|
|
def _should_retry_status(
|
|
status_code: int,
|
|
error_type: str | None,
|
|
error_code: str | None,
|
|
content: str | None,
|
|
) -> bool:
|
|
if status_code == 429:
|
|
return LLMProvider._is_retryable_429_response(
|
|
LLMResponse(
|
|
content=content or "",
|
|
finish_reason="error",
|
|
error_status_code=status_code,
|
|
error_type=error_type,
|
|
error_code=error_code,
|
|
)
|
|
)
|
|
return status_code in LLMProvider._RETRYABLE_STATUS_CODES or status_code >= 500
|