from __future__ import annotations import tomllib import httpx import openai import pytest from app import secrets_store from app.api import settings as settings_api from app.config import settings from app.services import ai_provider from app.services.ai_provider import ( _format_openai_error, _is_temperature_rejected, normalize_openai_base_url, ) def test_normalize_openai_base_url_adds_v1_for_root_gateway(): assert normalize_openai_base_url("http://ai.zedbox.cn:8080") == "http://ai.zedbox.cn:8080/v1" def test_normalize_openai_base_url_preserves_v1_base(): assert normalize_openai_base_url("http://ai.zedbox.cn:8080/v1") == "http://ai.zedbox.cn:8080/v1" def test_normalize_openai_base_url_strips_chat_completions_path(): assert normalize_openai_base_url("http://ai.zedbox.cn:8080/v1/chat/completions") == "http://ai.zedbox.cn:8080/v1" def test_normalize_openai_base_url_preserves_glm_v4(): """智谱 GLM 用 /api/paas/v4, 不能强制补成 /v4/v1 (会 404)。""" assert normalize_openai_base_url("https://open.bigmodel.cn/api/paas/v4") == "https://open.bigmodel.cn/api/paas/v4" def test_normalize_openai_base_url_strips_chat_completions_from_glm_v4(): """用户填完整 /v4/chat/completions 时, 去掉后缀归一化为 /v4。""" assert normalize_openai_base_url("https://open.bigmodel.cn/api/paas/v4/chat/completions") == "https://open.bigmodel.cn/api/paas/v4" def test_normalize_openai_base_url_preserves_other_version_segments(): """其它非 v1 版本号 (/v2 等) 也应保持原样。""" assert normalize_openai_base_url("https://example.com/api/v2") == "https://example.com/api/v2" def test_normalize_openai_base_url_strips_trailing_slash(): assert normalize_openai_base_url("https://open.bigmodel.cn/api/paas/v4/") == "https://open.bigmodel.cn/api/paas/v4" def test_format_openai_error_hides_html_gateway_body(): response = httpx.Response( 504, headers={"content-type": "text/html; charset=utf-8"}, text="

Gateway Timeout

", request=httpx.Request("POST", "https://example.com/v1/chat/completions"), ) exc = openai.InternalServerError("gateway timeout", response=response, body=response.text) message = _format_openai_error(exc) assert message == "AI 服务请求失败(504): AI 上游服务超时, 请稍后重试或检查 AI Base URL / 网络" assert "html" not in message.lower() assert "Gateway Timeout" not in message def test_format_openai_error_prefers_upstream_detail_when_available(): """有可读的上游 detail 时优先透出, 而不是用 400 通用文案吞掉。""" response = httpx.Response( 400, json={"error": {"message": "model context length exceeded"}}, request=httpx.Request("POST", "https://example.com/v1/chat/completions"), ) exc = openai.BadRequestError( "bad request", response=response, body={"error": {"message": "model context length exceeded"}}, ) message = _format_openai_error(exc) assert message == "AI 服务请求失败(400): model context length exceeded" def test_format_openai_error_falls_back_to_status_message_without_detail(): """上游无可读 detail (如 HTML 网关页) 时, 才回落到 400 通用文案。""" response = httpx.Response( 400, headers={"content-type": "text/html; charset=utf-8"}, text="", request=httpx.Request("POST", "https://example.com/v1/chat/completions"), ) exc = openai.BadRequestError("bad request", response=response, body=None) message = _format_openai_error(exc) assert message == "AI 服务请求失败(400): 请求参数无效, 请检查模型名称和上下文长度" def test_is_temperature_rejected_matches_moonshot_message(): """Moonshot 对 reasoning 模型报 'only 1 is allowed for this model'。""" response = httpx.Response( 400, json={"error": {"message": "invalid temperature: only 1 is allowed for this model"}}, request=httpx.Request("POST", "https://api.moonshot.cn/v1/chat/completions"), ) exc = openai.BadRequestError( "bad request", response=response, body={"error": {"message": "invalid temperature: only 1 is allowed for this model"}}, ) assert _is_temperature_rejected(exc) is True def test_optional_openai_params_use_targeted_400_fallbacks(): response = httpx.Response( 400, json={"error": {"message": "unsupported parameter: temperature"}}, request=httpx.Request("POST", "https://example.com/v1/chat/completions"), ) exc = openai.BadRequestError( "bad request", response=response, body={"error": {"message": "unsupported parameter: temperature"}}, ) assert _is_temperature_rejected(exc) is True kwargs = {"max_tokens": 1000, "temperature": 0.3, "reasoning_effort": "high"} assert ai_provider._openai_retry_kwargs(exc, kwargs) == { "max_tokens": 1000, "reasoning_effort": "high", } response = httpx.Response( 400, json={"error": {"message": "unrecognized request argument", "param": "reasoning_effort"}}, request=httpx.Request("POST", "https://example.com/v1/chat/completions"), ) exc = openai.BadRequestError( "bad request", response=response, body={"error": {"message": "unrecognized request argument", "param": "reasoning_effort"}}, ) assert _is_temperature_rejected(exc) is False assert ai_provider._is_reasoning_effort_rejected(exc) is True assert ai_provider._openai_retry_kwargs(exc, kwargs) == { "max_tokens": 1000, "temperature": 0.3, } assert kwargs == {"max_tokens": 1000, "temperature": 0.3, "reasoning_effort": "high"} def test_is_temperature_rejected_false_for_other_400(): """非 temperature 相关的 400 (如 model not found) 不应触发去 temperature 重试。""" response = httpx.Response( 400, json={"error": {"message": "model not found"}}, request=httpx.Request("POST", "https://example.com/v1/chat/completions"), ) exc = openai.BadRequestError( "bad request", response=response, body={"error": {"message": "model not found"}}, ) assert _is_temperature_rejected(exc) is False def test_is_temperature_rejected_false_for_non_400(): response = httpx.Response( 401, json={"error": {"message": "invalid api key"}}, request=httpx.Request("POST", "https://example.com/v1/chat/completions"), ) exc = openai.AuthenticationError("unauthorized", response=response, body=None) assert _is_temperature_rejected(exc) is False def test_openai_kwargs_include_configured_reasoning_effort(monkeypatch): stored = {"ai_provider": "openai_compat"} monkeypatch.setattr(secrets_store, "load", lambda: stored) assert "reasoning_effort" not in ai_provider._openai_kwargs(temperature=None, max_tokens=1000) stored["ai_provider"] = "openai" assert ai_provider._openai_kwargs(temperature=None, max_tokens=1000)["reasoning_effort"] == "high" stored["ai_reasoning_effort"] = "custom-high" kwargs = ai_provider._openai_kwargs(temperature=0.3, max_tokens=1000) assert kwargs == { "max_tokens": 1000, "temperature": 0.3, "reasoning_effort": "custom-high", } stored["ai_reasoning_effort"] = "" assert "reasoning_effort" not in ai_provider._openai_kwargs(temperature=None, max_tokens=1000) stored["ai_reasoning_effort"] = "custom-high" stored["ai_provider"] = "openai_compat" assert "reasoning_effort" not in ai_provider._openai_kwargs(temperature=None, max_tokens=1000) def test_openai_kwargs_none_max_tokens_omits_limit(): """max_tokens=None → 不传上限(推理模型思考 token 计入预算, 分析类调用放开)。""" kwargs = ai_provider._openai_kwargs(temperature=0.5, max_tokens=None) assert "max_tokens" not in kwargs assert kwargs.get("temperature") == 0.5 # 显式数值仍正常下发(策略标题生成等小任务依赖) assert ai_provider._openai_kwargs(temperature=None, max_tokens=8) == {"max_tokens": 8} def test_codex_prompt_none_max_tokens_skips_length_hint(): prompt = ai_provider._codex_prompt([{"role": "user", "content": "hi"}], max_tokens=None) assert "Keep the final answer" not in prompt bounded = ai_provider._codex_prompt([{"role": "user", "content": "hi"}], max_tokens=300) assert "Keep the final answer" in bounded def test_ai_settings_keep_provider_models_separate(monkeypatch): stored = { "ai_provider": "openai_compat", "ai_model": "custom-api-model", } def save(updates: dict) -> dict: stored.update(updates) return stored def clear(*keys: str) -> dict: for key in keys: stored.pop(key, None) return stored monkeypatch.setattr(secrets_store, "load", lambda: stored) monkeypatch.setattr(secrets_store, "save", save) monkeypatch.setattr(secrets_store, "clear", clear) monkeypatch.setattr(ai_provider, "ai_configured", lambda provider=None: True) monkeypatch.setattr(settings, "ai_provider", "openai_compat") monkeypatch.setattr(settings, "ai_base_url", "") monkeypatch.setattr(settings, "ai_model", "") monkeypatch.setattr(settings, "ai_codex_command", "codex") monkeypatch.setattr(settings, "ai_codex_reasoning_effort", "") monkeypatch.setattr(settings, "ai_user_agent", "") settings_api.save_ai_settings( settings_api.AiSettingsIn( provider="codex_cli", model="gpt-5.6-sol", codex_command="codex", codex_reasoning_effort="high", ) ) assert stored["ai_model"] == "custom-api-model" assert stored["ai_codex_model"] == "gpt-5.6-sol" settings_api.save_ai_settings( settings_api.AiSettingsIn( provider="openai", base_url="https://api.openai.com/v1", model="openai-model", reasoning_effort="vendor-high", ) ) assert stored["ai_model"] == "openai-model" assert stored["ai_reasoning_effort"] == "vendor-high" assert stored["ai_codex_model"] == "gpt-5.6-sol" settings_api.save_ai_settings( settings_api.AiSettingsIn( provider="openai_compat", base_url="https://example.com/v1", model="new-custom-model", ) ) assert stored["ai_model"] == "new-custom-model" assert stored["ai_reasoning_effort"] == "vendor-high" assert stored["ai_codex_model"] == "gpt-5.6-sol" # ── 输出上限 / 上下文窗口配置 ───────────────────────────────── def test_resolve_max_tokens_none_stays_unlimited(monkeypatch): """None = 不传上限(推理模型放开) — 不能被映射成配置上限, 见 main 语义。""" monkeypatch.setattr(ai_provider, "current_ai_max_output_tokens", lambda: 8192) assert ai_provider._resolve_max_tokens(None) is None def test_resolve_max_tokens_clamps_above_cap(monkeypatch): monkeypatch.setattr(ai_provider, "current_ai_max_output_tokens", lambda: 3000) assert ai_provider._resolve_max_tokens(9000) == 3000 def test_resolve_max_tokens_keeps_below_cap(monkeypatch): monkeypatch.setattr(ai_provider, "current_ai_max_output_tokens", lambda: 8192) assert ai_provider._resolve_max_tokens(2000) == 2000 def test_estimate_input_tokens_counts_cjk_and_ascii(): # 中文按 1 字 1 token cjk = [{"role": "user", "content": "中文" * 100}] # 200 字 assert ai_provider._estimate_input_tokens(cjk) >= 200 # 英文按 ~4 字符 1 token ascii_msg = [{"role": "user", "content": "a" * 400}] assert ai_provider._estimate_input_tokens(ascii_msg) <= 200 def test_check_input_budget_raises_when_over_window(monkeypatch): monkeypatch.setattr(ai_provider, "current_ai_context_window", lambda: 100) big = [{"role": "user", "content": "中" * 200}] # 估算输入 ~200 tokens with pytest.raises(ValueError, match="上下文窗口"): ai_provider._check_input_budget(big, max_tokens=3000) def test_check_input_budget_passes_within_window(monkeypatch): monkeypatch.setattr(ai_provider, "current_ai_context_window", lambda: 64000) small = [{"role": "user", "content": "中" * 100}] # 不抛异常 ai_provider._check_input_budget(small, max_tokens=2000) @pytest.mark.asyncio async def test_generate_ai_text_clamps_max_tokens_to_config_cap(monkeypatch): captured: dict = {} monkeypatch.setattr(ai_provider, "is_codex_cli_provider", lambda: False) monkeypatch.setattr(ai_provider, "current_ai_max_output_tokens", lambda: 3000) monkeypatch.setattr(ai_provider, "current_ai_context_window", lambda: 64000) async def fake_run(messages, *, temperature, max_tokens, timeout): captured["max_tokens"] = max_tokens return "ok" monkeypatch.setattr(ai_provider, "_run_openai_once", fake_run) text = await ai_provider.generate_ai_text( [{"role": "user", "content": "hi"}], max_tokens=9000 ) assert text == "ok" assert captured["max_tokens"] == 3000 @pytest.mark.asyncio async def test_generate_ai_text_default_cap_and_none_passthrough(monkeypatch): """默认 3000 且被钳制; 显式 None 贯穿为不限制。""" captured: dict = {} monkeypatch.setattr(ai_provider, "is_codex_cli_provider", lambda: False) monkeypatch.setattr(ai_provider, "current_ai_max_output_tokens", lambda: 4000) monkeypatch.setattr(ai_provider, "current_ai_context_window", lambda: 64000) async def fake_run(messages, *, temperature, max_tokens, timeout): captured["max_tokens"] = max_tokens return "ok" monkeypatch.setattr(ai_provider, "_run_openai_once", fake_run) await ai_provider.generate_ai_text([{"role": "user", "content": "hi"}]) assert captured["max_tokens"] == 3000 # 默认值, 未超 cap 原样下发 await ai_provider.generate_ai_text( [{"role": "user", "content": "hi"}], max_tokens=None, ) assert captured["max_tokens"] is None # None = 推理模型放开, 不钳制 def test_save_ai_settings_persists_token_sizes(monkeypatch): from app.api import settings as settings_api from app.config import settings as app_settings saved: dict = {} monkeypatch.setattr(settings_api.secrets_store, "save", lambda updates: saved.update(updates)) monkeypatch.setattr(settings_api.secrets_store, "load", lambda: saved) original_output = app_settings.ai_max_output_tokens original_window = app_settings.ai_context_window try: req = settings_api.AiSettingsIn( provider="openai_compat", base_url="https://example.com/v1", api_key="sk-test", model="gpt-x", max_output_tokens=5000, context_window=128000, ) result = settings_api.save_ai_settings(req) assert saved["ai_max_output_tokens"] == 5000 assert saved["ai_context_window"] == 128000 assert result["ai_max_output_tokens"] == 5000 assert result["ai_context_window"] == 128000 finally: app_settings.ai_max_output_tokens = original_output app_settings.ai_context_window = original_window def test_save_ai_settings_rejects_non_positive(monkeypatch): from app.api import settings as settings_api from fastapi import HTTPException req = settings_api.AiSettingsIn(provider="openai_compat", max_output_tokens=-1) with pytest.raises(HTTPException): settings_api.save_ai_settings(req) req2 = settings_api.AiSettingsIn(provider="openai_compat", context_window=0) with pytest.raises(HTTPException): settings_api.save_ai_settings(req2) def test_codex_process_env_excludes_application_secrets(monkeypatch, tmp_path): monkeypatch.setenv("PATH", "test-path") monkeypatch.setenv("HTTPS_PROXY", "http://proxy.example") monkeypatch.setenv("TICKFLOW_API_KEY", "tickflow-secret") monkeypatch.setenv("AI_API_KEY", "ai-secret") monkeypatch.setenv("OPENAI_API_KEY", "openai-secret") monkeypatch.setenv("AUTH_PASSWORD", "password-secret") env = ai_provider._codex_process_env(tmp_path / "codex-home") assert env["PATH"] == "test-path" assert env["HTTPS_PROXY"] == "http://proxy.example" assert env["NO_COLOR"] == "1" assert env["CODEX_HOME"] == str(tmp_path / "codex-home") assert "TICKFLOW_API_KEY" not in env assert "AI_API_KEY" not in env assert "OPENAI_API_KEY" not in env assert "AUTH_PASSWORD" not in env def test_codex_config_adapts_local_access_provider_for_docker(monkeypatch, tmp_path): monkeypatch.setenv("CODEX_DOCKER_HOST", "host.docker.internal") monkeypatch.setattr(ai_provider, "current_ai_model", lambda: "") monkeypatch.setattr(ai_provider, "current_codex_reasoning_effort", lambda: "") monkeypatch.setattr( ai_provider, "_read_codex_config", lambda: { "model_provider": "codex_local_access", "model": "gpt-5.6-sol", "model_providers": { "codex_local_access": { "name": "Codex API Service", "base_url": "http://localhost:62678/v1", "wire_api": "responses", "requires_openai_auth": True, "supports_websockets": False, "experimental_bearer_token": "local-secret", } }, }, ) path = tmp_path / "config.toml" ai_provider._write_compatible_codex_config(path) with path.open("rb") as f: config = tomllib.load(f) assert config["model_provider"] == "codex_local_access" provider = config["model_providers"]["codex_local_access"] assert provider["base_url"] == "http://host.docker.internal:62678/v1" assert provider["experimental_bearer_token"] == "local-secret" assert provider["requires_openai_auth"] is True assert provider["supports_websockets"] is False def test_codex_config_preserves_remote_provider_without_docker_rewrite(monkeypatch, tmp_path): monkeypatch.delenv("CODEX_DOCKER_HOST", raising=False) monkeypatch.setattr(ai_provider, "current_ai_model", lambda: "") monkeypatch.setattr(ai_provider, "current_codex_reasoning_effort", lambda: "") monkeypatch.setattr( ai_provider, "_read_codex_config", lambda: { "model_provider": "remote-api", "openai_base_url": "https://builtin.example/v1", "model_providers": { "remote-api": { "base_url": "https://custom.example/v1", "wire_api": "responses", "requires_openai_auth": True, } }, }, ) path = tmp_path / "config.toml" ai_provider._write_compatible_codex_config(path) with path.open("rb") as f: config = tomllib.load(f) assert config["model_provider"] == "remote-api" assert config["openai_base_url"] == "https://builtin.example/v1" provider = config["model_providers"]["remote-api"] assert provider["base_url"] == "https://custom.example/v1" assert provider["wire_api"] == "responses" assert provider["requires_openai_auth"] is True # ---- Codex CLI 可用性检测 (实跑 --version, 不再仅 which) ---- class _FakeCompleted: def __init__(self, returncode: int): self.returncode = returncode self.stdout = b"" self.stderr = b"" def test_codex_cli_available_runs_version_check(monkeypatch): """实跑 --version: 能发现 npm 壳存在但原生二进制跑不起来的情况。""" calls: list[list[str]] = [] def fake_run(args, **kwargs): calls.append(list(args)) return _FakeCompleted(0) monkeypatch.setattr(ai_provider, "_codex_base_command", lambda: ["codex"]) monkeypatch.setattr(ai_provider.subprocess, "run", fake_run) assert ai_provider.codex_cli_available() is True assert calls == [["codex", "--version"]] def test_codex_cli_available_false_when_version_fails(monkeypatch): import subprocess monkeypatch.setattr(ai_provider, "_codex_base_command", lambda: ["codex"]) monkeypatch.setattr( ai_provider.subprocess, "run", lambda a, **k: _FakeCompleted(1) ) assert ai_provider.codex_cli_available() is False # 壳报 "Codex CLI not available" 这类非零退出同样判定不可用 monkeypatch.setattr( ai_provider.subprocess, "run", lambda a, **k: (_ for _ in ()).throw(subprocess.TimeoutExpired("codex", 1)), ) assert ai_provider.codex_cli_available() is False def test_codex_cli_available_false_when_command_missing(monkeypatch): def raise_not_found(): raise RuntimeError("未找到 Codex CLI 命令: codex") monkeypatch.setattr(ai_provider, "_codex_base_command", raise_not_found) assert ai_provider.codex_cli_available() is False @pytest.mark.asyncio async def test_codex_exec_args_exclude_ephemeral(monkeypatch): """exec 参数不含 --ephemeral: 老版本 codex(如 0.58)无此参数, 传了直接报错。""" captured: dict = {} def fake_run_process(args, prompt, env, timeout): captured["args"] = list(args) return 0, b"ok", b"" monkeypatch.setattr(ai_provider, "_codex_base_command", lambda: ["codex"]) monkeypatch.setattr(ai_provider, "_prepare_codex_home", lambda p: None) monkeypatch.setattr(ai_provider, "_codex_process_env", lambda p: {}) monkeypatch.setattr(ai_provider, "_run_codex_process", fake_run_process) monkeypatch.setattr(ai_provider, "_read_output_file", lambda p: "ok") monkeypatch.setattr(ai_provider, "_remove_tree_best_effort", lambda p: None) monkeypatch.setattr(ai_provider, "current_ai_model", lambda: "gpt-5.6-sol") out = await ai_provider._run_codex_cli( [{"role": "user", "content": "hi"}], max_tokens=None, timeout=1.0, ) assert out == "ok" args = captured["args"] assert "--ephemeral" not in args assert "exec" in args and "--skip-git-repo-check" in args assert args[args.index("--model") + 1] == "gpt-5.6-sol"