452 lines
18 KiB
Python
452 lines
18 KiB
Python
from __future__ import annotations
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import json
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import time
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from typing import Any, Dict, List
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from flask import Blueprint, Response, current_app, jsonify, make_response, request
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from .config import BASE_INSTRUCTIONS, GPT5_CODEX_INSTRUCTIONS
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from .http import build_cors_headers
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from .reasoning import apply_reasoning_to_message, build_reasoning_param, extract_reasoning_from_model_name
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from .upstream import normalize_model_name, start_upstream_request
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from .utils import (
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convert_chat_messages_to_responses_input,
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convert_tools_chat_to_responses,
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sse_translate_chat,
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sse_translate_text,
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)
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openai_bp = Blueprint("openai", __name__)
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def _instructions_for_model(model: str) -> str:
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base = current_app.config.get("BASE_INSTRUCTIONS", BASE_INSTRUCTIONS)
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if model == "gpt-5-codex":
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codex = current_app.config.get("GPT5_CODEX_INSTRUCTIONS") or GPT5_CODEX_INSTRUCTIONS
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if isinstance(codex, str) and codex.strip():
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return codex
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return base
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@openai_bp.route("/v1/chat/completions", methods=["POST"])
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def chat_completions() -> Response:
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verbose = bool(current_app.config.get("VERBOSE"))
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reasoning_effort = current_app.config.get("REASONING_EFFORT", "medium")
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reasoning_summary = current_app.config.get("REASONING_SUMMARY", "auto")
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reasoning_compat = current_app.config.get("REASONING_COMPAT", "think-tags")
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debug_model = current_app.config.get("DEBUG_MODEL")
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if verbose:
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try:
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body_preview = (request.get_data(cache=True, as_text=True) or "")[:2000]
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print("IN POST /v1/chat/completions\n" + body_preview)
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except Exception:
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pass
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raw = request.get_data(cache=True, as_text=True) or ""
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try:
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payload = json.loads(raw) if raw else {}
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except Exception:
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try:
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payload = json.loads(raw.replace("\r", "").replace("\n", ""))
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except Exception:
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return jsonify({"error": {"message": "Invalid JSON body"}}), 400
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requested_model = payload.get("model")
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model = normalize_model_name(requested_model, debug_model)
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messages = payload.get("messages")
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if messages is None and isinstance(payload.get("prompt"), str):
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messages = [{"role": "user", "content": payload.get("prompt") or ""}]
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if messages is None and isinstance(payload.get("input"), str):
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messages = [{"role": "user", "content": payload.get("input") or ""}]
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if messages is None:
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messages = []
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if not isinstance(messages, list):
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return jsonify({"error": {"message": "Request must include messages: []"}}), 400
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if isinstance(messages, list):
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sys_idx = next((i for i, m in enumerate(messages) if isinstance(m, dict) and m.get("role") == "system"), None)
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if isinstance(sys_idx, int):
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sys_msg = messages.pop(sys_idx)
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content = sys_msg.get("content") if isinstance(sys_msg, dict) else ""
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messages.insert(0, {"role": "user", "content": content})
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is_stream = bool(payload.get("stream"))
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stream_options = payload.get("stream_options") if isinstance(payload.get("stream_options"), dict) else {}
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include_usage = bool(stream_options.get("include_usage", False))
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tools_responses = convert_tools_chat_to_responses(payload.get("tools"))
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tool_choice = payload.get("tool_choice", "auto")
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parallel_tool_calls = bool(payload.get("parallel_tool_calls", False))
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responses_tools_payload = payload.get("responses_tools") if isinstance(payload.get("responses_tools"), list) else []
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extra_tools: List[Dict[str, Any]] = []
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had_responses_tools = False
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if isinstance(responses_tools_payload, list):
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for _t in responses_tools_payload:
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if not (isinstance(_t, dict) and isinstance(_t.get("type"), str)):
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continue
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if _t.get("type") not in ("web_search", "web_search_preview"):
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return (
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jsonify(
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{
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"error": {
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"message": "Only web_search/web_search_preview are supported in responses_tools",
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"code": "RESPONSES_TOOL_UNSUPPORTED",
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}
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}
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),
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400,
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)
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extra_tools.append(_t)
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if not extra_tools and bool(current_app.config.get("DEFAULT_WEB_SEARCH")):
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responses_tool_choice = payload.get("responses_tool_choice")
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if not (isinstance(responses_tool_choice, str) and responses_tool_choice == "none"):
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extra_tools = [{"type": "web_search"}]
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if extra_tools:
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import json as _json
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MAX_TOOLS_BYTES = 32768
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try:
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size = len(_json.dumps(extra_tools))
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except Exception:
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size = 0
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if size > MAX_TOOLS_BYTES:
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return jsonify({"error": {"message": "responses_tools too large", "code": "RESPONSES_TOOLS_TOO_LARGE"}}), 400
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had_responses_tools = True
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tools_responses = (tools_responses or []) + extra_tools
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responses_tool_choice = payload.get("responses_tool_choice")
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if isinstance(responses_tool_choice, str) and responses_tool_choice in ("auto", "none"):
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tool_choice = responses_tool_choice
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input_items = convert_chat_messages_to_responses_input(messages)
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if not input_items and isinstance(payload.get("prompt"), str) and payload.get("prompt").strip():
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input_items = [
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{"type": "message", "role": "user", "content": [{"type": "input_text", "text": payload.get("prompt")}]}
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]
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model_reasoning = extract_reasoning_from_model_name(requested_model)
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reasoning_overrides = payload.get("reasoning") if isinstance(payload.get("reasoning"), dict) else model_reasoning
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reasoning_param = build_reasoning_param(reasoning_effort, reasoning_summary, reasoning_overrides)
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upstream, error_resp = start_upstream_request(
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model,
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input_items,
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instructions=_instructions_for_model(model),
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tools=tools_responses,
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tool_choice=tool_choice,
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parallel_tool_calls=parallel_tool_calls,
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reasoning_param=reasoning_param,
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)
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if error_resp is not None:
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return error_resp
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created = int(time.time())
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if upstream.status_code >= 400:
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try:
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raw = upstream.content
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err_body = json.loads(raw.decode("utf-8", errors="ignore")) if raw else {"raw": upstream.text}
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except Exception:
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err_body = {"raw": upstream.text}
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if had_responses_tools:
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if verbose:
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print("[Passthrough] Upstream rejected tools; retrying without extra tools (args redacted)")
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base_tools_only = convert_tools_chat_to_responses(payload.get("tools"))
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safe_choice = payload.get("tool_choice", "auto")
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upstream2, err2 = start_upstream_request(
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model,
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input_items,
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instructions=BASE_INSTRUCTIONS,
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tools=base_tools_only,
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tool_choice=safe_choice,
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parallel_tool_calls=parallel_tool_calls,
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reasoning_param=reasoning_param,
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)
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if err2 is None and upstream2 is not None and upstream2.status_code < 400:
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upstream = upstream2
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else:
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return (
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jsonify(
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{
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"error": {
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"message": (err_body.get("error", {}) or {}).get("message", "Upstream error"),
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"code": "RESPONSES_TOOLS_REJECTED",
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}
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}
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),
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(upstream2.status_code if upstream2 is not None else upstream.status_code),
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)
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else:
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if verbose:
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print("Upstream error status=", upstream.status_code)
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return (
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jsonify({"error": {"message": (err_body.get("error", {}) or {}).get("message", "Upstream error")}}),
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upstream.status_code,
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)
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if is_stream:
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resp = Response(
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sse_translate_chat(
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upstream,
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requested_model or model,
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created,
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verbose=verbose,
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vlog=print if verbose else None,
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reasoning_compat=reasoning_compat,
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include_usage=include_usage,
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),
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status=upstream.status_code,
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mimetype="text/event-stream",
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headers={"Cache-Control": "no-cache", "Connection": "keep-alive"},
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)
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for k, v in build_cors_headers().items():
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resp.headers.setdefault(k, v)
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return resp
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full_text = ""
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reasoning_summary_text = ""
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reasoning_full_text = ""
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response_id = "chatcmpl"
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tool_calls: List[Dict[str, Any]] = []
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error_message: str | None = None
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usage_obj: Dict[str, int] | None = None
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def _extract_usage(evt: Dict[str, Any]) -> Dict[str, int] | None:
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try:
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usage = (evt.get("response") or {}).get("usage")
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if not isinstance(usage, dict):
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return None
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pt = int(usage.get("input_tokens") or 0)
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ct = int(usage.get("output_tokens") or 0)
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tt = int(usage.get("total_tokens") or (pt + ct))
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return {"prompt_tokens": pt, "completion_tokens": ct, "total_tokens": tt}
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except Exception:
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return None
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try:
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for raw in upstream.iter_lines(decode_unicode=False):
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if not raw:
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continue
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line = raw.decode("utf-8", errors="ignore") if isinstance(raw, (bytes, bytearray)) else raw
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if not line.startswith("data: "):
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continue
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data = line[len("data: "):].strip()
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if not data:
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continue
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if data == "[DONE]":
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break
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try:
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evt = json.loads(data)
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except Exception:
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continue
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kind = evt.get("type")
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mu = _extract_usage(evt)
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if mu:
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usage_obj = mu
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if isinstance(evt.get("response"), dict) and isinstance(evt["response"].get("id"), str):
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response_id = evt["response"].get("id") or response_id
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if kind == "response.output_text.delta":
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full_text += evt.get("delta") or ""
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elif kind == "response.reasoning_summary_text.delta":
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reasoning_summary_text += evt.get("delta") or ""
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elif kind == "response.reasoning_text.delta":
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reasoning_full_text += evt.get("delta") or ""
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elif kind == "response.output_item.done":
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item = evt.get("item") or {}
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if isinstance(item, dict) and item.get("type") == "function_call":
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call_id = item.get("call_id") or item.get("id") or ""
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name = item.get("name") or ""
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args = item.get("arguments") or ""
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if isinstance(call_id, str) and isinstance(name, str) and isinstance(args, str):
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tool_calls.append(
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{
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"id": call_id,
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"type": "function",
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"function": {"name": name, "arguments": args},
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}
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)
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elif kind == "response.failed":
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error_message = evt.get("response", {}).get("error", {}).get("message", "response.failed")
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elif kind == "response.completed":
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break
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finally:
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upstream.close()
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if error_message:
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resp = make_response(jsonify({"error": {"message": error_message}}), 502)
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for k, v in build_cors_headers().items():
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resp.headers.setdefault(k, v)
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return resp
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message: Dict[str, Any] = {"role": "assistant", "content": full_text if full_text else None}
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if tool_calls:
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message["tool_calls"] = tool_calls
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message = apply_reasoning_to_message(message, reasoning_summary_text, reasoning_full_text, reasoning_compat)
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completion = {
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"id": response_id or "chatcmpl",
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"object": "chat.completion",
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"created": created,
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"model": requested_model or model,
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"choices": [
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{
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"index": 0,
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"message": message,
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"finish_reason": "stop",
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}
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],
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**({"usage": usage_obj} if usage_obj else {}),
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}
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resp = make_response(jsonify(completion), upstream.status_code)
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for k, v in build_cors_headers().items():
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resp.headers.setdefault(k, v)
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return resp
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@openai_bp.route("/v1/completions", methods=["POST"])
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def completions() -> Response:
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verbose = bool(current_app.config.get("VERBOSE"))
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debug_model = current_app.config.get("DEBUG_MODEL")
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reasoning_effort = current_app.config.get("REASONING_EFFORT", "medium")
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reasoning_summary = current_app.config.get("REASONING_SUMMARY", "auto")
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raw = request.get_data(cache=True, as_text=True) or ""
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try:
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payload = json.loads(raw) if raw else {}
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except Exception:
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return jsonify({"error": {"message": "Invalid JSON body"}}), 400
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requested_model = payload.get("model")
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model = normalize_model_name(requested_model, debug_model)
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prompt = payload.get("prompt")
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if isinstance(prompt, list):
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prompt = "".join([p if isinstance(p, str) else "" for p in prompt])
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if not isinstance(prompt, str):
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prompt = payload.get("suffix") or ""
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stream_req = bool(payload.get("stream", False))
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stream_options = payload.get("stream_options") if isinstance(payload.get("stream_options"), dict) else {}
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include_usage = bool(stream_options.get("include_usage", False))
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messages = [{"role": "user", "content": prompt or ""}]
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input_items = convert_chat_messages_to_responses_input(messages)
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model_reasoning = extract_reasoning_from_model_name(requested_model)
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reasoning_overrides = payload.get("reasoning") if isinstance(payload.get("reasoning"), dict) else model_reasoning
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reasoning_param = build_reasoning_param(reasoning_effort, reasoning_summary, reasoning_overrides)
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upstream, error_resp = start_upstream_request(
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model,
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input_items,
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instructions=_instructions_for_model(model),
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reasoning_param=reasoning_param,
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)
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if error_resp is not None:
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return error_resp
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created = int(time.time())
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if upstream.status_code >= 400:
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try:
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err_body = json.loads(upstream.content.decode("utf-8", errors="ignore")) if upstream.content else {"raw": upstream.text}
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except Exception:
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err_body = {"raw": upstream.text}
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return (
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jsonify({"error": {"message": (err_body.get("error", {}) or {}).get("message", "Upstream error")}}),
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upstream.status_code,
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)
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if stream_req:
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resp = Response(
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sse_translate_text(
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upstream,
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requested_model or model,
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created,
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verbose=verbose,
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vlog=(print if verbose else None),
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include_usage=include_usage,
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),
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status=upstream.status_code,
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mimetype="text/event-stream",
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headers={"Cache-Control": "no-cache", "Connection": "keep-alive"},
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)
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for k, v in build_cors_headers().items():
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resp.headers.setdefault(k, v)
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return resp
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full_text = ""
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response_id = "cmpl"
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usage_obj: Dict[str, int] | None = None
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def _extract_usage(evt: Dict[str, Any]) -> Dict[str, int] | None:
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try:
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usage = (evt.get("response") or {}).get("usage")
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if not isinstance(usage, dict):
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return None
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pt = int(usage.get("input_tokens") or 0)
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ct = int(usage.get("output_tokens") or 0)
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tt = int(usage.get("total_tokens") or (pt + ct))
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return {"prompt_tokens": pt, "completion_tokens": ct, "total_tokens": tt}
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except Exception:
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return None
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try:
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for raw_line in upstream.iter_lines(decode_unicode=False):
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if not raw_line:
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continue
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line = raw_line.decode("utf-8", errors="ignore") if isinstance(raw_line, (bytes, bytearray)) else raw_line
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if not line.startswith("data: "):
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continue
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data = line[len("data: "):].strip()
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if not data or data == "[DONE]":
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if data == "[DONE]":
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break
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continue
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try:
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evt = json.loads(data)
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except Exception:
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continue
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if isinstance(evt.get("response"), dict) and isinstance(evt["response"].get("id"), str):
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response_id = evt["response"].get("id") or response_id
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mu = _extract_usage(evt)
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if mu:
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usage_obj = mu
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kind = evt.get("type")
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if kind == "response.output_text.delta":
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full_text += evt.get("delta") or ""
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elif kind == "response.completed":
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break
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finally:
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upstream.close()
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completion = {
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"id": response_id or "cmpl",
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"object": "text_completion",
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"created": created,
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"model": requested_model or model,
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"choices": [
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{"index": 0, "text": full_text, "finish_reason": "stop", "logprobs": None}
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],
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**({"usage": usage_obj} if usage_obj else {}),
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}
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resp = make_response(jsonify(completion), upstream.status_code)
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for k, v in build_cors_headers().items():
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resp.headers.setdefault(k, v)
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return resp
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@openai_bp.route("/v1/models", methods=["GET"])
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def list_models() -> Response:
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expose_variants = bool(current_app.config.get("EXPOSE_REASONING_MODELS"))
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model_groups = [
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("gpt-5", ["high", "medium", "low", "minimal"]),
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("gpt-5-codex", ["high", "medium", "low"]),
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]
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model_ids: List[str] = []
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for base, efforts in model_groups:
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model_ids.append(base)
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if expose_variants:
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model_ids.extend([f"{base}-{effort}" for effort in efforts])
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data = [{"id": mid, "object": "model", "owned_by": "owner"} for mid in model_ids]
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models = {"object": "list", "data": data}
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resp = make_response(jsonify(models), 200)
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for k, v in build_cors_headers().items():
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resp.headers.setdefault(k, v)
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return resp
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