mirror of
https://github.com/usestrix/strix.git
synced 2026-10-07 02:58:26 +00:00
feat(server): auto-load .env and resolve channels with robust fallback
This commit is contained in:
banxian1987
parent
c2c1e9df16
commit
b9f5e72bbe
2 changed files with 115 additions and 68 deletions
|
|
@ -23,68 +23,95 @@ logger = logging.getLogger("strix_hub.server")
|
|||
|
||||
STATIC_DIR = Path(__file__).resolve().parent / "static"
|
||||
|
||||
# Configurable Local LLM environment (reads from env vars or defaults to generic placeholders)
|
||||
LOCAL_LLM_MODEL = os.environ.get("LOCAL_LLM_MODEL", "openai/Qwen3.8-27B-abliterated")
|
||||
LOCAL_LLM_URL = os.environ.get("LOCAL_LLM_URL", os.environ.get("OPENAI_BASE_URL", "http://127.0.0.1:8000/v1"))
|
||||
LOCAL_LLM_KEY = os.environ.get("LOCAL_LLM_KEY", os.environ.get("OPENAI_API_KEY", ""))
|
||||
# Automatically load .env file from working directory or /opt/strix/.env if available
|
||||
def _load_env_file() -> None:
|
||||
for env_path in [Path.cwd() / ".env", Path("/opt/strix/.env")]:
|
||||
if env_path.is_file():
|
||||
try:
|
||||
with open(env_path, "r", encoding="utf-8") as f:
|
||||
for line in f:
|
||||
line = line.strip()
|
||||
if not line or line.startswith("#") or "=" not in line:
|
||||
continue
|
||||
k, v = line.split("=", 1)
|
||||
k, v = k.strip(), v.strip().strip("'\"")
|
||||
if k and k not in os.environ:
|
||||
os.environ[k] = v
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
MODEL_PRESETS = [
|
||||
{
|
||||
"id": "hybrid-gemini-qwen",
|
||||
"name": "🚀 顶配混合动力 (Gemini 3.1 Pro 大脑 + 本地 Qwen 3.8 打手)",
|
||||
"root_model": "openai/gemini-3.1-pro-preview",
|
||||
"root_api_base": "",
|
||||
"root_api_key": "",
|
||||
"subagent_model": LOCAL_LLM_MODEL,
|
||||
"subagent_api_base": LOCAL_LLM_URL,
|
||||
"subagent_api_key": LOCAL_LLM_KEY,
|
||||
"description": "【最佳推荐】主控用云端 Gemini 3.1 Pro 百万上下文做复杂漏洞挖掘；海量并发子智能体全部走本地私有化模型，零成本无外网限流！",
|
||||
},
|
||||
{
|
||||
"id": "local-pure-cluster",
|
||||
"name": "🛡️ 本地全离线集群 (主子全跑本地私有化模型)",
|
||||
"root_model": LOCAL_LLM_MODEL,
|
||||
"root_api_base": LOCAL_LLM_URL,
|
||||
"root_api_key": LOCAL_LLM_KEY,
|
||||
"subagent_model": LOCAL_LLM_MODEL,
|
||||
"subagent_api_base": LOCAL_LLM_URL,
|
||||
"subagent_api_key": LOCAL_LLM_KEY,
|
||||
"description": "完全在企业局域网内运行，数据绝不出网，适合离线环境与内网安全合规审计。",
|
||||
},
|
||||
{
|
||||
"id": "gemini-optimal",
|
||||
"name": "⚡ Gemini 纯云端组合 (3.1 Pro + 3.5 Flash)",
|
||||
"root_model": "openai/gemini-3.1-pro-preview",
|
||||
"root_api_base": "",
|
||||
"root_api_key": "",
|
||||
"subagent_model": "openai/gemini-3.5-flash",
|
||||
"subagent_api_base": "",
|
||||
"subagent_api_key": "",
|
||||
"description": "主控用 3.1 Pro 推理，子任务用 3.5 Flash 极速响应，全云端中转组合。",
|
||||
},
|
||||
{
|
||||
"id": "claude-hybrid",
|
||||
"name": "💎 Claude 3.7 安全审计 + 本地模型混合调度",
|
||||
"root_model": "openai/claude-3-7-sonnet",
|
||||
"root_api_base": "",
|
||||
"root_api_key": "",
|
||||
"subagent_model": LOCAL_LLM_MODEL,
|
||||
"subagent_api_base": LOCAL_LLM_URL,
|
||||
"subagent_api_key": LOCAL_LLM_KEY,
|
||||
"description": "主控使用顶级安全审计模型 Claude 3.7，子任务由本地私有化集群并发执行。",
|
||||
},
|
||||
{
|
||||
"id": "custom",
|
||||
"name": "⚙️ 自定义独立双渠道 (Custom Dual Channels)",
|
||||
"root_model": "",
|
||||
"root_api_base": "",
|
||||
"root_api_key": "",
|
||||
"subagent_model": "",
|
||||
"subagent_api_base": "",
|
||||
"subagent_api_key": "",
|
||||
"description": "自由为两个模型分别配置不同的 Base URL 与 API Key 渠道。",
|
||||
},
|
||||
]
|
||||
_load_env_file()
|
||||
|
||||
def get_local_llm_config() -> tuple[str, str, str]:
|
||||
"""Resolve local LLM model name, API URL, and key with rich fallbacks."""
|
||||
model = os.environ.get("LOCAL_LLM_MODEL", os.environ.get("STRIX_LLM", "openai/Qwen3.8-27B-abliterated"))
|
||||
url = os.environ.get("LOCAL_LLM_URL", os.environ.get("LLM_API_BASE", os.environ.get("OPENAI_BASE_URL", "http://127.0.0.1:8000/v1")))
|
||||
key = os.environ.get("LOCAL_LLM_KEY", os.environ.get("LLM_API_KEY", os.environ.get("OPENAI_API_KEY", "")))
|
||||
return model, url, key
|
||||
|
||||
LOCAL_LLM_MODEL, LOCAL_LLM_URL, LOCAL_LLM_KEY = get_local_llm_config()
|
||||
|
||||
def get_model_presets() -> list[dict[str, Any]]:
|
||||
m, u, k = get_local_llm_config()
|
||||
return [
|
||||
{
|
||||
"id": "hybrid-gemini-qwen",
|
||||
"name": "🚀 顶配混合动力 (Gemini 3.1 Pro 大脑 + 本地 Qwen 3.8 打手)",
|
||||
"root_model": "openai/gemini-3.1-pro-preview",
|
||||
"root_api_base": "",
|
||||
"root_api_key": "",
|
||||
"subagent_model": m,
|
||||
"subagent_api_base": u,
|
||||
"subagent_api_key": k,
|
||||
"description": "【最佳推荐】主控用云端 Gemini 3.1 Pro 百万上下文做复杂漏洞挖掘；海量并发子智能体全部走本地私有化模型，零成本无外网限流！",
|
||||
},
|
||||
{
|
||||
"id": "local-pure-cluster",
|
||||
"name": "🛡️ 本地全离线集群 (主子全跑本地私有化模型)",
|
||||
"root_model": m,
|
||||
"root_api_base": u,
|
||||
"root_api_key": k,
|
||||
"subagent_model": m,
|
||||
"subagent_api_base": u,
|
||||
"subagent_api_key": k,
|
||||
"description": "完全在企业局域网内运行，数据绝不出网，适合离线环境与内网安全合规审计。",
|
||||
},
|
||||
{
|
||||
"id": "gemini-optimal",
|
||||
"name": "⚡ Gemini 纯云端组合 (3.1 Pro + 3.5 Flash)",
|
||||
"root_model": "openai/gemini-3.1-pro-preview",
|
||||
"root_api_base": "",
|
||||
"root_api_key": "",
|
||||
"subagent_model": "openai/gemini-3.5-flash",
|
||||
"subagent_api_base": "",
|
||||
"subagent_api_key": "",
|
||||
"description": "主控用 3.1 Pro 推理，子任务用 3.5 Flash 极速响应，全云端中转组合。",
|
||||
},
|
||||
{
|
||||
"id": "claude-hybrid",
|
||||
"name": "💎 Claude 3.7 安全审计 + 本地模型混合调度",
|
||||
"root_model": "openai/claude-3-7-sonnet",
|
||||
"root_api_base": "",
|
||||
"root_api_key": "",
|
||||
"subagent_model": m,
|
||||
"subagent_api_base": u,
|
||||
"subagent_api_key": k,
|
||||
"description": "主控使用顶级安全审计模型 Claude 3.7，子任务由本地私有化集群并发执行。",
|
||||
},
|
||||
{
|
||||
"id": "custom",
|
||||
"name": "⚙️ 自定义独立双渠道 (Custom Dual Channels)",
|
||||
"root_model": "",
|
||||
"root_api_base": "",
|
||||
"root_api_key": "",
|
||||
"subagent_model": "",
|
||||
"subagent_api_base": "",
|
||||
"subagent_api_key": "",
|
||||
"description": "自由为两个模型分别配置不同的 Base URL 与 API Key 渠道。",
|
||||
},
|
||||
]
|
||||
|
||||
MODEL_PRESETS = get_model_presets()
|
||||
|
||||
|
||||
def make_hub_handler() -> type[BaseHTTPRequestHandler]:
|
||||
|
|
|
|||
|
|
@ -60,15 +60,35 @@ def start_task(task_id: str) -> dict[str, Any]:
|
|||
# 1. Start dedicated Dual-Channel ModelRouter on a free port for this task
|
||||
port = 18800 + (abs(hash(task_id)) % 1000)
|
||||
|
||||
# Fallback to server env if task channel is left blank
|
||||
default_base = os.environ.get("OPENAI_BASE_URL") or os.environ.get("LLM_API_BASE", "https://api.openai.com/v1")
|
||||
default_key = os.environ.get("OPENAI_API_KEY") or os.environ.get("LLM_API_KEY", "")
|
||||
# Fallback to server env if task channel is left blank or has unreachable placeholder 8000
|
||||
env_base = (
|
||||
os.environ.get("LOCAL_LLM_URL")
|
||||
or os.environ.get("OPENAI_BASE_URL")
|
||||
or os.environ.get("LLM_API_BASE", "")
|
||||
)
|
||||
env_key = (
|
||||
os.environ.get("LOCAL_LLM_KEY")
|
||||
or os.environ.get("OPENAI_API_KEY")
|
||||
or os.environ.get("LLM_API_KEY", "")
|
||||
)
|
||||
|
||||
root_base = task.get("root_api_base") or task.get("api_base") or default_base
|
||||
root_key = task.get("root_api_key_raw") or task.get("api_key_raw") or default_key
|
||||
def _resolve_channel(base: str | None, key: str | None) -> tuple[str, str]:
|
||||
b = (base or "").strip()
|
||||
k = (key or "").strip()
|
||||
if (not b or "127.0.0.1:8000" in b) and env_base:
|
||||
b = env_base
|
||||
if not k and env_key:
|
||||
k = env_key
|
||||
return b, k
|
||||
|
||||
sub_base = task.get("subagent_api_base") or task.get("api_base") or default_base
|
||||
sub_key = task.get("subagent_api_key_raw") or task.get("api_key_raw") or default_key
|
||||
root_base, root_key = _resolve_channel(
|
||||
task.get("root_api_base") or task.get("api_base"),
|
||||
task.get("root_api_key_raw") or task.get("api_key_raw"),
|
||||
)
|
||||
sub_base, sub_key = _resolve_channel(
|
||||
task.get("subagent_api_base") or task.get("api_base"),
|
||||
task.get("subagent_api_key_raw") or task.get("api_key_raw"),
|
||||
)
|
||||
|
||||
router = ModelRouterServer(
|
||||
host="127.0.0.1",
|
||||
|
|
|
|||
Loading…
Add table
Reference in a new issue