15ea23d5d6
* feat: introduce hindsight-api-slim and hindsight-all-slim packages Closes #552 - Move all source code from hindsight-api/ to new hindsight-api-slim/ - hindsight-api-slim has heavy ML deps (torch, sentence-transformers, transformers, einops, flashrank, mlx, mlx-lm, safetensors) and pg0-embedded as optional extras: [local-ml], [embedded-db], [all] - hindsight-api becomes a zero-code meta-package depending on hindsight-api-slim[all] for full backward compatibility - Add hindsight-all-slim meta-package: hindsight-api-slim + client + embed - hindsight-all updated to depend on hindsight-api-slim[all] - pg0.py: lazy-import pg0 with clear ImportError pointing to [embedded-db] - Dockerfile: replace sed hack with proper uv sync --extra flags - Update release.yml, test.yml, lint.sh, release.sh, CLAUDE.md and all path references throughout the repo * refactor: rename hindsight/ directory to hindsight-all/ * docs: document hindsight-api-slim and hindsight-all-slim package variants Add package variants table and extras explanation to installation.md * docs: remove emojis from installation.md, use professional tone * docs: link Docker slim variant to pip package variants section * docs: consolidate Docker image variants into single table * ci: fix working-directory paths after package restructure - Replace all hindsight-api → hindsight-api-slim in test.yml - Replace hindsight → hindsight-all in test.yml - Add --extra embedded-db to test-embed API install step * ci: add local-ml and embedded-db extras to API sync steps These extras were previously implicit in the old hindsight-api package (which bundled everything). Now that hindsight-api-slim uses optional extras, we must explicitly request local-ml and embedded-db in CI. * ci: add API install step with embedded-db to test-embed smoke test The smoke test starts hindsight-api as a daemon, which requires pg0-embedded. Add a dedicated install step for hindsight-api-slim with embedded-db extra so the daemon can start successfully. * ci: remove --no-install-project when using optional extras When --no-install-project is combined with --extra, the optional deps are not installed because extras require the project to be active. Remove --no-install-project from steps that need local-ml or embedded-db. * ci: fix ordering of uv sync steps to preserve optional extras When uv sync runs for a different workspace member, it removes optional extras installed for other members. Fix by always running extra-requiring API sync last, after other workspace member syncs. Also remove --no-install-project from embedded-db sync in test-embed, as --no-install-project prevents optional extras from being active. * ci: add local-ml extra to test-embed API install for smoke test The smoke test starts the full API server which needs sentence-transformers for local embeddings (default provider). Add local-ml extra to the install. * ci: simplify extras with --all-extras and add slim pip smoke test - Replace explicit --extra local-ml --extra embedded-db with --all-extras for cleaner, more maintainable sync steps - Add test-pip-slim job: tests hindsight-api-slim[embedded-db] without local ML models, using Cohere for embeddings/reranking (mirrors Docker slim smoke test approach) * ci: simplify slim smoke test to health check only (mirrors Docker test)
621 lines
23 KiB
Python
621 lines
23 KiB
Python
"""
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OpenAI Codex LLM provider using ChatGPT Plus/Pro OAuth authentication.
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This provider enables using ChatGPT Plus/Pro subscriptions for API calls
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without separate OpenAI Platform API credits. It uses OAuth tokens from
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~/.codex/auth.json and communicates with the ChatGPT backend API.
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"""
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import asyncio
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import json
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import logging
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import os
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import time
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import uuid
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from pathlib import Path
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from typing import Any
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import httpx
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from hindsight_api.engine.llm_interface import LLMInterface, OutputTooLongError
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from hindsight_api.engine.response_models import LLMToolCall, LLMToolCallResult, TokenUsage
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from hindsight_api.metrics import get_metrics_collector
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logger = logging.getLogger(__name__)
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class CodexLLM(LLMInterface):
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"""
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LLM provider using OpenAI Codex OAuth authentication.
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Authenticates using ChatGPT Plus/Pro credentials stored in ~/.codex/auth.json
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and makes API calls to chatgpt.com/backend-api/codex/responses.
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"""
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def __init__(
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self,
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provider: str,
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api_key: str, # Will be ignored, reads from ~/.codex/auth.json
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base_url: str,
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model: str,
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reasoning_effort: str = "low",
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**kwargs: Any,
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):
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"""Initialize Codex LLM provider."""
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super().__init__(provider, api_key, base_url, model, reasoning_effort, **kwargs)
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# Load Codex OAuth credentials
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try:
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self.access_token, self.account_id = self._load_codex_auth()
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logger.info(f"Loaded Codex OAuth credentials for account: {self.account_id}")
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except Exception as e:
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raise RuntimeError(
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f"Failed to load Codex OAuth credentials from ~/.codex/auth.json: {e}\n\n"
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"To set up Codex authentication:\n"
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"1. Install Codex CLI: npm install -g @openai/codex\n"
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"2. Login: codex auth login\n"
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"3. Verify: ls ~/.codex/auth.json\n\n"
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"Or use a different provider (openai, anthropic, gemini) with API keys."
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) from e
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# Use ChatGPT backend API endpoint
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if not self.base_url:
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self.base_url = "https://chatgpt.com/backend-api"
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# Normalize model name (strip openai/ prefix if present)
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if self.model.startswith("openai/"):
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self.model = self.model[len("openai/") :]
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# Map reasoning effort to Codex reasoning summary format
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# Codex supports: "auto", "concise", "detailed"
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self.reasoning_summary = self._map_reasoning_effort(reasoning_effort)
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# HTTP client for SSE streaming
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self._client = httpx.AsyncClient(timeout=120.0)
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def _load_codex_auth(self) -> tuple[str, str]:
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"""
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Load OAuth credentials from ~/.codex/auth.json.
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Returns:
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Tuple of (access_token, account_id).
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Raises:
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FileNotFoundError: If auth file doesn't exist.
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ValueError: If auth file is invalid.
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"""
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auth_file = Path.home() / ".codex" / "auth.json"
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if not auth_file.exists():
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raise FileNotFoundError(
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f"Codex auth file not found: {auth_file}\nRun 'codex auth login' to authenticate with ChatGPT Plus/Pro."
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)
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with open(auth_file) as f:
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data = json.load(f)
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# Validate auth structure
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auth_mode = data.get("auth_mode")
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if auth_mode != "chatgpt":
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raise ValueError(f"Expected auth_mode='chatgpt', got: {auth_mode}")
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tokens = data.get("tokens", {})
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access_token = tokens.get("access_token")
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account_id = tokens.get("account_id")
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if not access_token:
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raise ValueError("No access_token found in Codex auth file. Run 'codex auth login' again.")
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return access_token, account_id
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def _map_reasoning_effort(self, effort: str) -> str:
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"""
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Map standard reasoning effort to Codex reasoning summary format.
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Args:
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effort: Standard effort level ("low", "medium", "high", "xhigh").
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Returns:
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Codex reasoning summary: "concise", "detailed", or "auto".
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"""
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mapping = {
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"low": "concise",
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"medium": "auto",
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"high": "detailed",
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"xhigh": "detailed",
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}
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return mapping.get(effort.lower(), "auto")
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async def verify_connection(self) -> None:
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"""Verify Codex connection by making a simple test call."""
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try:
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logger.info(f"Verifying Codex LLM: model={self.model}, account={self.account_id}...")
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await self.call(
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messages=[{"role": "user", "content": "Say 'ok'"}],
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max_completion_tokens=10,
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max_retries=2,
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initial_backoff=0.5,
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max_backoff=2.0,
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scope="verification",
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)
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logger.info(f"Codex LLM verified: {self.model}")
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except Exception as e:
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raise RuntimeError(f"Codex LLM connection verification failed for {self.model}: {e}") from e
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async def call(
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self,
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messages: list[dict[str, str]],
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response_format: Any | None = None,
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max_completion_tokens: int | None = None,
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temperature: float | None = None,
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scope: str = "memory",
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max_retries: int = 10,
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initial_backoff: float = 1.0,
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max_backoff: float = 60.0,
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skip_validation: bool = False,
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strict_schema: bool = False,
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return_usage: bool = False,
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) -> Any:
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"""Make API call to Codex backend with SSE streaming."""
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start_time = time.time()
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# Prepare system instructions
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system_instruction = ""
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user_messages = []
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for msg in messages:
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role = msg.get("role", "user")
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content = msg.get("content", "")
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if role == "system":
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system_instruction += ("\n\n" + content) if system_instruction else content
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else:
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user_messages.append(msg)
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# Add JSON schema instruction if response_format is provided
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if response_format is not None and hasattr(response_format, "model_json_schema"):
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schema = response_format.model_json_schema()
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schema_msg = f"\n\nYou must respond with valid JSON matching this schema:\n{json.dumps(schema, indent=2)}"
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system_instruction += schema_msg
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# gpt-5.2-codex only supports "detailed" reasoning summary
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reasoning_summary = "detailed" if "5.2" in self.model else self.reasoning_summary
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# Build Codex request payload
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payload = {
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"model": self.model,
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"instructions": system_instruction,
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"input": [
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{
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"type": "message",
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"role": msg.get("role", "user"),
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"content": msg.get("content", ""),
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}
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for msg in user_messages
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],
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"tools": [],
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"tool_choice": "auto",
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"parallel_tool_calls": True,
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"reasoning": {"summary": reasoning_summary},
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"store": False, # Codex uses stateless mode
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"stream": True, # SSE streaming
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"include": ["reasoning.encrypted_content"],
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"prompt_cache_key": str(uuid.uuid4()),
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}
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headers = {
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"Authorization": f"Bearer {self.access_token}",
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"Content-Type": "application/json",
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"OpenAI-Account-ID": self.account_id,
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"User-Agent": "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7)",
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"Origin": "https://chatgpt.com",
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}
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url = f"{self.base_url}/codex/responses"
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last_exception = None
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for attempt in range(max_retries + 1):
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try:
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response = await self._client.post(url, json=payload, headers=headers, timeout=120.0)
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response.raise_for_status()
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# Parse SSE stream
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content = await self._parse_sse_stream(response)
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# Handle structured output
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if response_format is not None:
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# Models may wrap JSON in markdown
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clean_content = content
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if "```json" in content:
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clean_content = content.split("```json")[1].split("```")[0].strip()
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elif "```" in content:
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clean_content = content.split("```")[1].split("```")[0].strip()
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try:
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json_data = json.loads(clean_content)
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except json.JSONDecodeError as e:
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logger.warning(f"Codex JSON parse error (attempt {attempt + 1}/{max_retries + 1}): {e}")
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if attempt < max_retries:
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backoff = min(initial_backoff * (2**attempt), max_backoff)
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await asyncio.sleep(backoff)
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last_exception = e
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continue
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raise
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if skip_validation:
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result = json_data
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else:
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result = response_format.model_validate(json_data)
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else:
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result = content
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# Record metrics
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duration = time.time() - start_time
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metrics = get_metrics_collector()
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metrics.record_llm_call(
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provider=self.provider,
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model=self.model,
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scope=scope,
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duration=duration,
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input_tokens=0, # Codex doesn't report token counts in SSE
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output_tokens=0,
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success=True,
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)
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# Record trace span
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from hindsight_api.tracing import get_span_recorder
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# Estimate tokens for tracing
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estimated_input = sum(len(m.get("content", "")) for m in messages) // 4
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estimated_output = len(content) // 4
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span_recorder = get_span_recorder()
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span_recorder.record_llm_call(
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provider=self.provider,
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model=self.model,
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scope=scope,
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messages=messages,
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response_content=result if isinstance(result, str) else json.dumps(result),
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input_tokens=estimated_input,
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output_tokens=estimated_output,
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duration=duration,
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finish_reason=None,
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error=None,
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)
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if return_usage:
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# Codex doesn't provide token counts, estimate based on content
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estimated_input = sum(len(m.get("content", "")) for m in messages) // 4
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estimated_output = len(content) // 4
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token_usage = TokenUsage(
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input_tokens=estimated_input,
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output_tokens=estimated_output,
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total_tokens=estimated_input + estimated_output,
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)
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return result, token_usage
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return result
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except httpx.HTTPStatusError as e:
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last_exception = e
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status_code = e.response.status_code
|
|
|
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# Fast fail on auth errors
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if status_code in (401, 403):
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logger.error(f"Codex auth error (HTTP {status_code}): {e.response.text[:200]}")
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raise RuntimeError(
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"Codex authentication failed. Your OAuth token may have expired.\n"
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"Run 'codex auth login' to re-authenticate."
|
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) from e
|
|
|
|
# Log the actual error message from the API
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error_detail = e.response.text[:500] if hasattr(e.response, "text") else str(e)
|
|
|
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if attempt < max_retries:
|
|
backoff = min(initial_backoff * (2**attempt), max_backoff)
|
|
logger.warning(
|
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f"Codex HTTP error {status_code} (attempt {attempt + 1}/{max_retries + 1}): {error_detail}"
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)
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|
await asyncio.sleep(backoff)
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|
continue
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|
else:
|
|
logger.error(
|
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f"Codex HTTP error after {max_retries + 1} attempts: Status {status_code}, Detail: {error_detail}"
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)
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raise
|
|
|
|
except httpx.RequestError as e:
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|
last_exception = e
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|
if attempt < max_retries:
|
|
backoff = min(initial_backoff * (2**attempt), max_backoff)
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logger.warning(f"Codex connection error (attempt {attempt + 1}/{max_retries + 1}): {e}")
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await asyncio.sleep(backoff)
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continue
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else:
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logger.error(f"Codex connection error after {max_retries + 1} attempts: {e}")
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raise
|
|
|
|
except Exception as e:
|
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logger.error(f"Unexpected Codex error: {type(e).__name__}: {e}")
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raise
|
|
|
|
if last_exception:
|
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raise last_exception
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raise RuntimeError("Codex call failed after all retries")
|
|
|
|
async def _parse_sse_stream(self, response: httpx.Response) -> str:
|
|
"""
|
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Parse Server-Sent Events (SSE) stream from Codex API.
|
|
|
|
Args:
|
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response: HTTP response with SSE stream.
|
|
|
|
Returns:
|
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Extracted text content from stream.
|
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"""
|
|
full_text = ""
|
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event_type = None
|
|
|
|
async for line in response.aiter_lines():
|
|
if not line:
|
|
continue
|
|
|
|
# Track event type
|
|
if line.startswith("event: "):
|
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event_type = line[7:]
|
|
|
|
# Parse data
|
|
elif line.startswith("data: "):
|
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data_str = line[6:]
|
|
if data_str == "[DONE]":
|
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break
|
|
|
|
try:
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data = json.loads(data_str)
|
|
|
|
# Extract content based on event type
|
|
if event_type == "response.text.delta" and "delta" in data:
|
|
full_text += data["delta"]
|
|
elif event_type == "response.content_part.delta" and "delta" in data:
|
|
full_text += data["delta"]
|
|
# Check for item content
|
|
elif "item" in data:
|
|
item = data["item"]
|
|
if "content" in item:
|
|
content = item["content"]
|
|
if isinstance(content, list):
|
|
for part in content:
|
|
if isinstance(part, dict) and "text" in part:
|
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full_text += part["text"]
|
|
elif isinstance(content, str):
|
|
full_text += content
|
|
|
|
except json.JSONDecodeError:
|
|
# Skip malformed JSON events
|
|
pass
|
|
|
|
return full_text
|
|
|
|
async def call_with_tools(
|
|
self,
|
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messages: list[dict[str, Any]],
|
|
tools: list[dict[str, Any]],
|
|
max_completion_tokens: int | None = None,
|
|
temperature: float | None = None,
|
|
scope: str = "tools",
|
|
max_retries: int = 5,
|
|
initial_backoff: float = 1.0,
|
|
max_backoff: float = 30.0,
|
|
tool_choice: str | dict[str, Any] = "auto",
|
|
) -> LLMToolCallResult:
|
|
"""
|
|
Make API call with tool calling support.
|
|
|
|
Parses Codex SSE stream to extract tool calls from response.output_item.done events.
|
|
Tools are converted from OpenAI format to Codex format (flat structure at top level).
|
|
|
|
Args:
|
|
messages: List of message dicts. Can include tool results with role='tool'.
|
|
tools: List of tool definitions in OpenAI format.
|
|
max_completion_tokens: Maximum tokens in response.
|
|
temperature: Sampling temperature.
|
|
scope: Scope identifier for tracking.
|
|
max_retries: Maximum retry attempts.
|
|
initial_backoff: Initial backoff time in seconds.
|
|
max_backoff: Maximum backoff time in seconds.
|
|
tool_choice: How to choose tools - "auto", "none", "required", or specific function.
|
|
|
|
Returns:
|
|
LLMToolCallResult with content and/or tool_calls.
|
|
"""
|
|
start_time = time.time()
|
|
|
|
# Prepare system instructions
|
|
system_instruction = ""
|
|
user_messages = []
|
|
|
|
for msg in messages:
|
|
role = msg.get("role", "user")
|
|
content = msg.get("content", "")
|
|
|
|
if role == "system":
|
|
system_instruction += ("\n\n" + content) if system_instruction else content
|
|
elif role == "tool":
|
|
# Handle tool results
|
|
user_messages.append(
|
|
{
|
|
"type": "message",
|
|
"role": "user",
|
|
"content": f"Tool result: {content}",
|
|
}
|
|
)
|
|
else:
|
|
user_messages.append(
|
|
{
|
|
"type": "message",
|
|
"role": role,
|
|
"content": content,
|
|
}
|
|
)
|
|
|
|
# Convert tools to Codex format
|
|
# Codex expects tools with type and name/description/parameters at top level
|
|
codex_tools = []
|
|
for tool in tools:
|
|
func = tool.get("function", {})
|
|
codex_tools.append(
|
|
{
|
|
"type": "function",
|
|
"name": func.get("name", ""),
|
|
"description": func.get("description", ""),
|
|
"parameters": func.get("parameters", {}),
|
|
}
|
|
)
|
|
|
|
# gpt-5.2-codex only supports "detailed" reasoning summary
|
|
reasoning_summary = "detailed" if "5.2" in self.model else self.reasoning_summary
|
|
|
|
payload = {
|
|
"model": self.model,
|
|
"instructions": system_instruction,
|
|
"input": user_messages,
|
|
"tools": codex_tools,
|
|
"tool_choice": tool_choice,
|
|
"parallel_tool_calls": True,
|
|
"reasoning": {"summary": reasoning_summary},
|
|
"store": False,
|
|
"stream": True,
|
|
"include": ["reasoning.encrypted_content"],
|
|
"prompt_cache_key": str(uuid.uuid4()),
|
|
}
|
|
|
|
headers = {
|
|
"Authorization": f"Bearer {self.access_token}",
|
|
"Content-Type": "application/json",
|
|
"OpenAI-Account-ID": self.account_id,
|
|
"User-Agent": "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7)",
|
|
"Origin": "https://chatgpt.com",
|
|
}
|
|
|
|
url = f"{self.base_url}/codex/responses"
|
|
|
|
# Debug logging for troubleshooting
|
|
logger.debug(f"Codex tool call request: url={url}, model={payload['model']}, tools={len(codex_tools)}")
|
|
|
|
try:
|
|
response = await self._client.post(url, json=payload, headers=headers, timeout=120.0)
|
|
|
|
# Log response details on error
|
|
if response.status_code != 200:
|
|
logger.error(f"Codex API error {response.status_code}: {response.text[:500]}")
|
|
|
|
response.raise_for_status()
|
|
|
|
# Parse SSE for tool calls and content
|
|
content, tool_calls = await self._parse_sse_tool_stream(response)
|
|
|
|
duration = time.time() - start_time
|
|
metrics = get_metrics_collector()
|
|
metrics.record_llm_call(
|
|
provider=self.provider,
|
|
model=self.model,
|
|
scope=scope,
|
|
duration=duration,
|
|
input_tokens=0,
|
|
output_tokens=0,
|
|
success=True,
|
|
)
|
|
|
|
# Record OpenTelemetry span
|
|
from hindsight_api.tracing import get_span_recorder
|
|
|
|
span_recorder = get_span_recorder()
|
|
# Convert LLMToolCall objects to dicts for span recording
|
|
tool_calls_dict = (
|
|
[{"id": tc.id, "name": tc.name, "arguments": tc.arguments} for tc in tool_calls] if tool_calls else None
|
|
)
|
|
span_recorder.record_llm_call(
|
|
provider=self.provider,
|
|
model=self.model,
|
|
scope=scope,
|
|
messages=messages,
|
|
response_content=content,
|
|
input_tokens=0, # Codex doesn't provide token counts
|
|
output_tokens=0,
|
|
duration=duration,
|
|
finish_reason="tool_calls" if tool_calls else "stop",
|
|
error=None,
|
|
tool_calls=tool_calls_dict,
|
|
)
|
|
|
|
return LLMToolCallResult(
|
|
content=content,
|
|
tool_calls=tool_calls,
|
|
finish_reason="tool_calls" if tool_calls else "stop",
|
|
input_tokens=0,
|
|
output_tokens=0,
|
|
)
|
|
|
|
except Exception as e:
|
|
logger.error(f"Codex tool call error: {e}")
|
|
raise
|
|
|
|
async def _parse_sse_tool_stream(self, response: httpx.Response) -> tuple[str | None, list[LLMToolCall]]:
|
|
"""
|
|
Parse SSE stream for tool calls and content.
|
|
|
|
Returns:
|
|
Tuple of (content, tool_calls).
|
|
"""
|
|
content = ""
|
|
tool_calls: list[LLMToolCall] = []
|
|
event_type = None
|
|
|
|
async for line in response.aiter_lines():
|
|
if not line:
|
|
continue
|
|
|
|
if line.startswith("event: "):
|
|
event_type = line[7:]
|
|
|
|
elif line.startswith("data: "):
|
|
data_str = line[6:]
|
|
if data_str == "[DONE]":
|
|
break
|
|
|
|
try:
|
|
data = json.loads(data_str)
|
|
|
|
# Extract text content
|
|
if event_type == "response.text.delta" and "delta" in data:
|
|
content += data["delta"]
|
|
|
|
# Extract completed tool calls from response.output_item.done
|
|
elif event_type == "response.output_item.done":
|
|
item = data.get("item", {})
|
|
if item.get("type") == "function_call" and item.get("status") == "completed":
|
|
tool_name = item.get("name", "")
|
|
arguments_str = item.get("arguments", "{}")
|
|
call_id = item.get("call_id", "")
|
|
|
|
try:
|
|
arguments = json.loads(arguments_str)
|
|
except json.JSONDecodeError:
|
|
logger.warning(f"Failed to parse tool arguments: {arguments_str}")
|
|
arguments = {}
|
|
|
|
tool_calls.append(
|
|
LLMToolCall(
|
|
id=call_id,
|
|
name=tool_name,
|
|
arguments=arguments,
|
|
)
|
|
)
|
|
|
|
except json.JSONDecodeError as e:
|
|
logger.warning(f"Failed to parse SSE data: {e}, data_str: {data_str[:200]}")
|
|
|
|
return content if content else None, tool_calls
|
|
|
|
async def cleanup(self) -> None:
|
|
"""Clean up HTTP client."""
|
|
await self._client.aclose()
|