mirror of
https://github.com/HKUDS/nanobot.git
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581 lines
22 KiB
Python
581 lines
22 KiB
Python
"""OpenAI Codex Responses Provider."""
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# pyright: reportMissingTypeStubs=false, reportPrivateUsage=false
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from __future__ import annotations
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import asyncio
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import hashlib
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import json
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from collections.abc import Awaitable, Callable
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from typing import Any, cast
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import httpx
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from loguru import logger
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from oauth_cli_kit import get_token as get_codex_token
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from nanobot.providers.base import (
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LLMProvider,
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LLMResponse,
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ProviderCallContext,
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ProviderConversationState,
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resolve_stream_idle_timeout_s,
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)
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from nanobot.providers.openai_responses import (
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ResponsesStreamCapture,
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build_responses_state,
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consume_sse_with_reasoning,
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convert_tools,
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is_compaction_compatibility_error,
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is_replayable_finish_reason,
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prepare_responses_input,
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resolve_compact_threshold,
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responses_state_context_tokens,
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responses_state_items,
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responses_state_matches,
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)
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DEFAULT_CODEX_URL = "https://chatgpt.com/backend-api/codex/responses"
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DEFAULT_ORIGINATOR = "nanobot"
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_COMPACTION_RETAINED_CHAR_BUDGET = 256_000
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class OpenAICodexProvider(LLMProvider):
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"""Use Codex OAuth to call the Responses API."""
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supports_progress_deltas = True
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def __init__(
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self,
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default_model: str = "openai-codex/gpt-5.6-sol",
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proxy: str | None = None,
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extra_body: dict[str, Any] | None = None,
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):
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super().__init__(api_key=None, api_base=None)
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self.default_model = default_model
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self.proxy = proxy or None
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self._extra_body = dict(extra_body or {})
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self._native_compaction_available = True
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async def _call_codex(
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self,
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messages: list[dict[str, Any]],
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tools: list[dict[str, Any]] | None,
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model: str | None,
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max_tokens: int,
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reasoning_effort: str | None,
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tool_choice: str | dict[str, Any] | None,
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on_content_delta: Callable[[str], Awaitable[None]] | None = None,
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on_thinking_delta: Callable[[str], Awaitable[None]] | None = None,
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on_tool_call_delta: Callable[[dict[str, Any]], Awaitable[None]] | None = None,
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provider_context: ProviderCallContext | None = None,
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) -> LLMResponse:
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"""Shared request logic for both chat() and chat_stream()."""
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model = model or self.default_model
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sanitized_messages = self._sanitize_empty_content(messages)
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sanitized_state = (
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provider_context.conversation_state
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if provider_context is not None
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else None
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)
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if sanitized_state is not None:
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sanitized_state = sanitized_state.with_pending_messages(
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self._sanitize_empty_content(sanitized_state.pending_messages)
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)
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system_prompt, input_items, replayed = prepare_responses_input(
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sanitized_messages,
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state=sanitized_state,
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provider=self._responses_state_provider(),
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model=_strip_model_prefix(model),
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)
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body: dict[str, Any] = {
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"model": _strip_model_prefix(model),
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"store": False,
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"stream": True,
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"instructions": system_prompt,
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"input": input_items,
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"text": {"verbosity": "medium"},
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"prompt_cache_key": _prompt_cache_key(messages[:2]),
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"tool_choice": tool_choice or "auto",
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"parallel_tool_calls": True,
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}
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body["include"] = ["reasoning.encrypted_content"]
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reasoning_options = _build_reasoning_options(reasoning_effort)
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if replayed and "gpt-5.6" in _strip_model_prefix(model).lower():
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reasoning_options = dict(reasoning_options or {})
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reasoning_options["context"] = "all_turns"
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if reasoning_options:
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body["reasoning"] = reasoning_options
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if tools:
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body["tools"] = convert_tools(tools)
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if self._extra_body:
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# Apply explicit provider overrides last, matching other provider backends.
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body.update(self._extra_body)
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stage = "oauth_token"
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try:
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token = await asyncio.to_thread(get_codex_token, proxy=self.proxy)
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headers = _build_headers(cast(str, token.account_id), token.access)
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async def _send(
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request_body: dict[str, Any],
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*,
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emit_deltas: bool,
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) -> LLMResponse:
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wire_body = _without_response_item_ids(request_body)
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try:
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return await _request_codex(
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DEFAULT_CODEX_URL,
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headers,
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wire_body,
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verify=True,
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proxy=self.proxy,
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on_content_delta=on_content_delta if emit_deltas else None,
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on_thinking_delta=on_thinking_delta if emit_deltas else None,
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on_tool_call_delta=on_tool_call_delta if emit_deltas else None,
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)
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except Exception as exc:
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if "CERTIFICATE_VERIFY_FAILED" not in str(exc):
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raise
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logger.warning(
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"SSL verification failed for Codex API; retrying with verify=False"
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)
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return await _request_codex(
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DEFAULT_CODEX_URL,
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headers,
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wire_body,
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verify=False,
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proxy=self.proxy,
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on_content_delta=on_content_delta if emit_deltas else None,
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on_thinking_delta=on_thinking_delta if emit_deltas else None,
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on_tool_call_delta=on_tool_call_delta if emit_deltas else None,
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)
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compact_threshold = resolve_compact_threshold(
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(
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provider_context.context_window_tokens
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if provider_context is not None
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else None
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),
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max_tokens,
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)
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if (
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self.supports_native_compaction(model)
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and replayed
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and sanitized_state is not None
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and compact_threshold is not None
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and responses_state_context_tokens(sanitized_state) >= compact_threshold
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):
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stage = "codex_compaction"
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compact_body = {
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**body,
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"input": [*input_items, {"type": "compaction_trigger"}],
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}
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try:
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compact_result = await _send(compact_body, emit_deltas=False)
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compact_items = (
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responses_state_items(compact_result.provider_state)
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if compact_result.provider_state is not None
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else None
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)
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if not compact_items or compact_items[-1].get("type") not in {
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"compaction",
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"compaction_summary",
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"context_compaction",
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}:
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raise RuntimeError("Codex compaction returned no compaction item")
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body["input"] = [
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*_retained_compaction_messages(input_items),
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*compact_items,
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]
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except Exception as compact_error:
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if is_compaction_compatibility_error(compact_error):
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self._native_compaction_available = False
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logger.warning(
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"Codex native compaction unavailable; continuing without it "
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"(type={} status={} disabled={})",
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type(compact_error).__name__,
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getattr(compact_error, "status_code", None),
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not self._native_compaction_available,
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)
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stage = "codex_request"
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return await _send(body, emit_deltas=True)
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except Exception as e:
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response = _codex_error_response(e)
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exc_type = "CodexHTTPError" if isinstance(e, _CodexHTTPError) else type(e).__name__
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logger.warning(
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"Codex API request failed: stage={} type={} kind={} retryable={} status={} "
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"error_type={} error_code={} retry_after={} summary={}",
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stage,
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exc_type,
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response.error_kind,
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response.error_should_retry,
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response.error_status_code,
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response.error_type,
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response.error_code,
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response.retry_after,
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_codex_log_summary(exc_type, response),
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)
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return response
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async def chat(
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self, messages: list[dict[str, Any]], tools: list[dict[str, Any]] | None = None,
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model: str | None = None, max_tokens: int = 4096, temperature: float = 0.7,
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reasoning_effort: str | None = None,
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tool_choice: str | dict[str, Any] | None = None,
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provider_context: ProviderCallContext | None = None,
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) -> LLMResponse:
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return await self._call_codex(
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messages,
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tools,
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model,
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max_tokens,
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reasoning_effort,
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tool_choice,
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provider_context=provider_context,
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)
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async def chat_with_context(
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self,
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*,
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provider_context: ProviderCallContext,
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**kwargs: Any,
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) -> LLMResponse:
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return await self.chat(
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**kwargs,
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provider_context=provider_context,
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)
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async def chat_stream(
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self, messages: list[dict[str, Any]], tools: list[dict[str, Any]] | None = None,
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model: str | None = None, max_tokens: int = 4096, temperature: float = 0.7,
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reasoning_effort: str | None = None,
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tool_choice: str | dict[str, Any] | None = None,
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on_content_delta: Callable[[str], Awaitable[None]] | None = None,
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on_thinking_delta: Callable[[str], Awaitable[None]] | None = None,
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on_tool_call_delta: Callable[[dict[str, Any]], Awaitable[None]] | None = None,
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provider_context: ProviderCallContext | None = None,
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) -> LLMResponse:
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return await self._call_codex(
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messages=messages,
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tools=tools,
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model=model,
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max_tokens=max_tokens,
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reasoning_effort=reasoning_effort,
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tool_choice=tool_choice,
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on_content_delta=on_content_delta,
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on_thinking_delta=on_thinking_delta,
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on_tool_call_delta=on_tool_call_delta,
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provider_context=provider_context,
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)
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async def chat_stream_with_context(
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self,
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*,
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provider_context: ProviderCallContext,
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**kwargs: Any,
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) -> LLMResponse:
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return await self.chat_stream(
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**kwargs,
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provider_context=provider_context,
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)
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def get_default_model(self) -> str:
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return self.default_model
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@staticmethod
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def _responses_state_provider() -> str:
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return f"openai_codex:{DEFAULT_CODEX_URL.rstrip('/')}"
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def can_resume_conversation_state(
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self,
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state: ProviderConversationState,
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model: str | None = None,
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) -> bool:
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return responses_state_matches(
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state,
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provider=self._responses_state_provider(),
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model=_strip_model_prefix(model or self.default_model),
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)
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def supports_native_compaction(self, model: str | None = None) -> bool:
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"""Use the Codex backend's inline compaction trigger when needed."""
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_ = model
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return self._native_compaction_available
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def _strip_model_prefix(model: str) -> str:
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if model.startswith("openai-codex/") or model.startswith("openai_codex/"):
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return model.split("/", 1)[1]
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return model
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def _without_response_item_ids(
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request_body: dict[str, Any],
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) -> dict[str, Any]:
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"""Match Codex's default ``store=false`` request-item contract."""
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if request_body.get("store") is True:
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return request_body
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raw_input = request_body.get("input")
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if not isinstance(raw_input, list):
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return request_body
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input_items: list[object] = cast(list[object], raw_input)
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sanitized_input: list[object] = []
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for raw_item in input_items:
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if not isinstance(raw_item, dict):
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sanitized_input.append(raw_item)
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continue
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item = cast(dict[str, Any], raw_item)
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sanitized_input.append({
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key: value
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for key, value in item.items()
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if key != "id"
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})
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body = dict(request_body)
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body["input"] = sanitized_input
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return body
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def _retained_compaction_messages(
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input_items: list[dict[str, Any]],
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) -> list[dict[str, Any]]:
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"""Mirror Codex's bounded retention of user/developer/system messages."""
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retained_reversed: list[dict[str, Any]] = []
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remaining = _COMPACTION_RETAINED_CHAR_BUDGET
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for item in reversed(input_items):
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if item.get("type") not in {None, "message"} or item.get("role") not in {
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"user",
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"developer",
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"system",
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}:
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continue
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size = len(json.dumps(item, ensure_ascii=False))
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if size > remaining and retained_reversed:
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continue
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retained_reversed.append(item)
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remaining = max(0, remaining - size)
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if remaining == 0:
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break
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retained_reversed.reverse()
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return retained_reversed
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def _build_reasoning_options(reasoning_effort: str | None) -> dict[str, str] | None:
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"""Opt in to visible summaries without changing provider-default effort."""
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if reasoning_effort and reasoning_effort.lower() == "none":
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return {"effort": "none"}
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options = {"summary": "auto"}
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if reasoning_effort:
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options["effort"] = reasoning_effort
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return options
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def _build_headers(account_id: str, token: str) -> dict[str, str]:
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return {
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"Authorization": f"Bearer {token}",
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"chatgpt-account-id": account_id,
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"OpenAI-Beta": "responses=experimental",
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"originator": DEFAULT_ORIGINATOR,
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"User-Agent": "nanobot (python)",
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"accept": "text/event-stream",
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"content-type": "application/json",
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}
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class _CodexHTTPError(RuntimeError):
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def __init__(
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self,
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message: str,
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*,
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status_code: int | None = None,
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retry_after: float | None = None,
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error_type: str | None = None,
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error_code: str | None = None,
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should_retry: bool | None = None,
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compaction_unsupported: bool = False,
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):
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super().__init__(message)
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self.status_code = status_code
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self.retry_after = retry_after
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self.error_type = error_type
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self.error_code = error_code
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self.should_retry = should_retry
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self.compaction_unsupported = compaction_unsupported
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async def _request_codex(
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url: str,
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headers: dict[str, str],
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body: dict[str, Any],
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verify: bool,
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proxy: str | None = None,
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on_content_delta: Callable[[str], Awaitable[None]] | None = None,
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on_thinking_delta: Callable[[str], Awaitable[None]] | None = None,
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on_tool_call_delta: Callable[[dict[str, Any]], Awaitable[None]] | None = None,
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) -> LLMResponse:
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idle_timeout_s = resolve_stream_idle_timeout_s()
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client_kwargs: dict[str, Any] = {"timeout": idle_timeout_s, "verify": verify}
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if proxy:
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client_kwargs["proxy"] = proxy
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client_kwargs["trust_env"] = False
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async with httpx.AsyncClient(**client_kwargs) as client:
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async with client.stream("POST", url, headers=headers, json=body) as response:
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if response.status_code != 200:
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text = await response.aread()
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raw = text.decode("utf-8", "ignore")
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retry_after = LLMProvider._extract_retry_after_from_headers(response.headers)
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error_type, error_code = LLMProvider._extract_error_type_code(raw)
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compaction_unsupported = (
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response.status_code in {400, 404, 422}
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and any(
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marker in raw.lower()
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for marker in (
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"context_management",
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"compact_threshold",
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"compaction_trigger",
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)
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)
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)
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raise _CodexHTTPError(
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_friendly_error(response.status_code, raw),
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status_code=response.status_code,
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retry_after=retry_after,
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error_type=error_type,
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error_code=error_code,
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should_retry=_should_retry_status(response.status_code, error_type, error_code, raw),
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compaction_unsupported=compaction_unsupported,
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)
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capture = ResponsesStreamCapture()
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(
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content,
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tool_calls,
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finish_reason,
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usage,
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reasoning_content,
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) = await consume_sse_with_reasoning(
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response,
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on_content_delta=on_content_delta,
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on_tool_call_delta=on_tool_call_delta,
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on_reasoning_delta=on_thinking_delta,
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capture=capture,
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)
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result = LLMResponse(
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content=content,
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tool_calls=tool_calls,
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finish_reason=finish_reason,
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usage=usage,
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reasoning_content=reasoning_content,
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)
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if capture.completed and is_replayable_finish_reason(finish_reason):
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result.provider_state = build_responses_state(
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provider=f"openai_codex:{url.rstrip('/')}",
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model=str(body.get("model") or ""),
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input_items=cast(list[dict[str, Any]], body.get("input") or []),
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output_items=capture.output_items,
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usage=usage,
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)
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return result
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def _prompt_cache_key(messages: list[dict[str, Any]]) -> str:
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raw = json.dumps(messages, ensure_ascii=True, sort_keys=True)
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return hashlib.sha256(raw.encode("utf-8")).hexdigest()
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|
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def _friendly_error(status_code: int, raw: str) -> str:
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_ = raw
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if status_code == 429:
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return "ChatGPT usage quota exceeded or rate limit triggered. Please try again later."
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return f"HTTP {status_code}: Codex API request failed"
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def _codex_error_response(exc: Exception) -> LLMResponse:
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"""Convert Codex transport/API failures into actionable, retryable metadata."""
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|
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
|