refactor: remove developer chat model config

agent_fastapi.py

@@ -68,18 +68,6 @@ USE_SESSION_SUBDIR = True
 
 CUSTOM_MODEL_KEY = "__custom__"
 
-# Load keys
-DEFAULT_LLM_API_KEY = os.getenv("DEEPSEEK_API_KEY")
-DEFAULT_LLM_API_URL = os.getenv("DEEPSEEK_API_URL")
-DEFAULT_LLM_API_NAME = os.getenv("DEEPSEEK_API_NAME", "deepseek-chat")
-DEFAULT_VLM_API_KEY = os.getenv("GLM_V4_6_API_KEY")
-DEFAULT_VLM_API_URL = os.getenv("GLM_V4_6_API_URL")
-DEFAULT_VLM_API_NAME = os.getenv("GLM_V4_6_API_NAME", "qwen3-vl-8b-instruct")
-print("DEEPSEEK_API_KEY exists:", bool(os.getenv("DEEPSEEK_API_KEY")))
-print("QWEN3_VL_8B_API_KEY exists:", bool(os.getenv("QWEN3_VL_8B_API_KEY")))
-print("DEEPSEEK_API_URL:", repr(os.getenv("DEEPSEEK_API_URL")))
-print("QWEN3_VL_8B_API_URL:", repr(os.getenv("QWEN3_VL_8B_API_URL")))
-
 def debug_traceback_print(cfg: Settings):
     if cfg.developer.developer_mode:
         traceback.print_exc()
@@ -91,65 +79,82 @@ def _norm_url(u: Any) -> str:
     u = _s(u)
     return u.rstrip("/") if u else ""
 
-def _env_fallback_for_model(model_name: str) -> Tuple[str, str]:
+MODEL_ENV_KEYS = {
+    "llm": {
+        "model": "OPENSTORYLINE_LLM_MODEL",
+        "base_url": "OPENSTORYLINE_LLM_BASE_URL",
+        "api_key": "OPENSTORYLINE_LLM_API_KEY",
+    },
+    "vlm": {
+        "model": "OPENSTORYLINE_VLM_MODEL",
+        "base_url": "OPENSTORYLINE_VLM_BASE_URL",
+        "api_key": "OPENSTORYLINE_VLM_API_KEY",
+    },
+}
+
+def _read_model_env(kind: str) -> Dict[str, str]:
+    keys = MODEL_ENV_KEYS[kind]
+    return {
+        "model": os.getenv(keys.get("model", "")) or "",
+        "base_url": _norm_url(os.getenv(keys.get("base_url", ""))),
+        "api_key": os.getenv(keys.get("api_key", "")) or "",
+    }
+
+def _resolve_builtin_model_override(kind: str, cfg_block: Any) -> Tuple[Optional[Dict[str, Any]], Optional[str]]:
     """
-    - deepseek* -> DEEPSEEK_API_URL / DEEPSEEK_API_KEY
-    - qwen3*  -> QWEN3_VL_8B_API_URL / QWEN3_VL_8B_API_KEY
+    Default model parsing rules:
+    1. Prioritize using `[llm]/[vlm]` from `config.toml`
+    2. If any of `model`, `base_url`, or `api_key` is missing, the entire set will fall back to environment variables.
+    3. Runtime parameters such as `timeout`, `temperature`, and `max_retries` are still read from the config block.
     """
-    m = _s(model_name).lower()
-    if "deepseek" in m:
-        return (_s(os.getenv("DEEPSEEK_API_URL")), _s(os.getenv("DEEPSEEK_API_KEY")))
-    if m.startswith("qwen3-vl-8b-instruct") or "qwen3-vl-8b-instruct" in m:
-        return (_s(os.getenv("QWEN3_VL_8B_API_URL")), _s(os.getenv("QWEN3_VL_8B_API_KEY")))
-    return ("", "")
+    kind = kind.strip().lower()
+    if kind not in ("llm", "vlm"):
+        return None, f"unknown model kind: {kind}"
 
-def _resolve_default_model_override(cfg: Settings, model_name: str) -> Tuple[Optional[Dict[str, Any]], Optional[str]]:
-    """
-    1. get config from [developer.chat_models_config."<model_name>"]
-    2. rollback to env
-    """
-    model_name = _s(model_name)
-    if not model_name:
-        return None, "default model name is empty"
+    model = _s(getattr(cfg_block, "model", None))
+    base_url = _norm_url(getattr(cfg_block, "base_url", None))
+    api_key = _s(getattr(cfg_block, "api_key", None))
 
-    model_cfg: Dict[str, Any] = {}
-    try:
-        model_cfg = (cfg.developer.chat_models_config.get(model_name) or {}) if getattr(cfg, "developer", None) else {}
-    except Exception:
-        model_cfg = {}
+    config_complete = bool(model and base_url and api_key)
 
-    if not isinstance(model_cfg, dict):
-        model_cfg = {}
+    if not config_complete:
+        env_cfg = _read_model_env(kind)
+        model = env_cfg["model"]
+        base_url = env_cfg["base_url"]
+        api_key = env_cfg["api_key"]
 
-    base_url = _norm_url(model_cfg.get("base_url"))
-    api_key = _s(model_cfg.get("api_key"))
-
-    if not base_url or not api_key:
-        env_url, env_key = _env_fallback_for_model(model_name)
-        if not base_url:
-            base_url = _norm_url(env_url)
-        if not api_key:
-            api_key = _s(env_key)
-
-    override: Dict[str, Any] = {"model": model_name}
-    if base_url:
-        override["base_url"] = base_url
-    if api_key:
-        override["api_key"] = api_key
-
-    for k in ("timeout", "temperature", "max_retries", "top_p", "max_tokens"):
-        if k in model_cfg and model_cfg.get(k) not in (None, ""):
-            override[k] = model_cfg.get(k)
-
-    if not override.get("base_url") or not override.get("api_key"):
+    if not (model and base_url and api_key):
+        keys = MODEL_ENV_KEYS[kind]
         return None, (
-            f"cannot find base_url/api_key of default model: {model_name}. "
-            f"please fill in base_url/api_key of [developer.chat_models_config.\"{model_name}\" in config.toml]"
-            f"or set environment variables（DEEPSEEK_API_URL/DEEPSEEK_API_KEY / QWEN3_VL_8B_API_URL/QWEN3_VL_8B_API_KEY）。"
+            f"{kind} config incomplete. "
+            f"Please either fill [{kind}].model/base_url/api_key in config.toml, "
+            f"or set env vars: "
+            f"{keys['model']}, {keys['base_url']}, {keys['api_key']}"
+            f"\nand then rerun."
         )
 
+    override: Dict[str, Any] = {
+        "model": model,
+        "base_url": base_url,
+        "api_key": api_key,
+    }
+
+    for k in ("timeout", "temperature", "max_retries", "top_p", "max_tokens"):
+        v = getattr(cfg_block, k, None)
+        if v not in (None, ""):
+            override[k] = v
+
     return override, None
 
+def _peek_builtin_model_name(kind: str, cfg: Settings) -> str:
+    # Get the model name displayed on the front end
+    cfg_block = cfg.llm if kind == "llm" else cfg.vlm
+    resolved, _ = _resolve_builtin_model_override(kind, cfg_block)
+    if resolved and resolved.get("model"):
+        return _s(resolved["model"])
+
+    return "unknown model"
+
 def _stable_dict_key(d: Optional[Dict[str, Any]]) -> str:
     try:
         return json.dumps(d or {}, sort_keys=True, ensure_ascii=False)
@@ -1024,8 +1029,8 @@ class ChatSession:
         self.cfg = cfg
         self.lang = "zh"
 
-        default_llm = _s(getattr(getattr(cfg, "developer", None), "default_llm", "")) or "deepseek-chat"
-        default_vlm = _s(getattr(getattr(cfg, "developer", None), "default_vlm", "")) or "qwen3-vl-8b-instruct"
+        default_llm = _peek_builtin_model_name("llm", self.cfg)
+        default_vlm = _peek_builtin_model_name("vlm", self.cfg)
 
         self.chat_models = [default_llm, CUSTOM_MODEL_KEY]
         self.chat_model_key = default_llm
@@ -1187,7 +1192,7 @@ class ChatSession:
                 raise RuntimeError("please fill in model/base_url/api_key of custom LLM")
             llm_override = self.custom_llm_config
         else:
-            llm_override, err = _resolve_default_model_override(self.cfg, self.chat_model_key)
+            llm_override, err = _resolve_builtin_model_override("llm", self.cfg.llm)
             if err:
                 raise RuntimeError(err)
 
@@ -1197,7 +1202,7 @@ class ChatSession:
                 raise RuntimeError("please fill in model/base_url/api_key of custom VLM")
             vlm_override = self.custom_vlm_config
         else:
-            vlm_override, err = _resolve_default_model_override(self.cfg, self.vlm_model_key)
+            vlm_override, err = _resolve_builtin_model_override("vlm", self.cfg.vlm)
             if err:
                 raise RuntimeError(err)
 

config.toml

@@ -1,23 +1,8 @@
 # ============= 开发者选项 / Developer Options ===============
 [developer]
-developer_mode = false
-default_llm = "deepseek-chat"
-default_vlm = "qwen3-vl-8b-instruct"
+developer_mode = true
 print_context = false # 在拦截器打印模型拿到的全部上下文，会很长 / Print full context in interceptor (output will be very long)
 
-# ============= 模型配置 for 体验网页 ===============
-[developer.chat_models_config."deepseek-chat"]
-base_url = ""
-api_key = ""
-temperature = 0.1
-
-[developer.chat_models_config."qwen3-vl-8b-instruct"]
-base_url = ""
-api_key = "" 
-timeout = 20.0
-temperature = 0.1
-max_retries = 2
-
 # ============= 项目路径 / Project Paths ======================
 [project]
 media_dir = "./outputs/media"
@@ -26,7 +11,7 @@ outputs_dir = "./outputs"
 
 # ============= 模型配置 for user / Model Config for User =============
 [llm]
-model = "deepseek-chat"
+model = ""
 base_url = ""
 api_key = "" 
 timeout = 30.0 # 单位：秒
@@ -34,14 +19,13 @@ temperature = 0.1
 max_retries = 2
 
 [vlm]
-model = "qwen3-vl-8b-instruct"
+model = ""
 base_url = ""
 api_key = "" 
 timeout = 20.0 # 单位：秒
 temperature = 0.1
 max_retries = 2
 
-
 # ============= MCP Server 相关 / MCP Server Related =============
 [local_mcp_server]
 server_name = "storyline"

docs/source/en/api-key.md

@@ -21,8 +21,12 @@ Note: For users outside China, we recommend using large language models such as
    - **API Key**: Fill in the Key obtained in the previous step
 
 3. **API Configuration**
-   - **Web Usage**: Select "Use Custom Model" in the LLM model form, and fill in the model according to the configuration parameters
-   - **Local Deployment**: In config.toml, locate `[developer.chat_models_config."deepseek-chat"]` and fill in the configuration parameters to make the default configuration accessible from the Web page. Locate `[llm]` and configure model, base_url, and api_key
+   - **Web Usage**:
+      - In the LLM model dropdown, select **Custom Model**, then fill in the model settings according to your configuration parameters.
+      - Or, open `config.toml`, locate `[llm]`, and configure `model`, `base_url`, and `api_key`. The model you entered will then appear in the dropdown on the Web page.
+   - **CLI**:
+      - If you prefer the CLI entry point, you need to open `config.toml`, locate `[llm]`, and configure `model`, `base_url`, and `api_key`.
+
 
 ## 2. Multimodal Large Language Model (VLM)
 
@@ -42,15 +46,12 @@ Note: For users outside China, we recommend using large language models such as
   - **Model Name**: `qwen3-vl-8b-instruct`
   - **Base URL**: `https://dashscope.aliyuncs.com/compatible-mode/v1`
 
-  - Parameter Configuration: Select "Use Custom Model" in the VLM Model form and fill in the parameters. For local deployment, locate `[vlm]` and configure model, base_url, and api_key. Add the following fields in config.toml as the default Web API configuration:
-   ```
-   [developer.chat_models_config."qwen3-vl-8b-instruct"]
-   base_url = "https://dashscope.aliyuncs.com/compatible-mode/v1"
-   api_key = "YOUR_API_KEY" 
-   timeout = 20.0
-   temperature = 0.1
-   max_retries = 2
-   ```
+  - Parameter Configuration: 
+    - **Web Usage**:
+      - In the VLM model dropdown, select **Custom Model**, then fill in the model settings according to your configuration parameters.
+      - Or, open `config.toml`, locate `[vlm]`, and configure `model`, `base_url`, and `api_key`. The model you entered will then appear in the dropdown on the Web page.
+    - **CLI**: 
+      - If you prefer the CLI entry point, you need to open `config.toml`, locate `[vlm]`, and configure `model`, `base_url`, and `api_key`.
 
 ### 2.3 Using Qwen3-Omni
 

docs/source/zh/api-key.md

@@ -21,8 +21,11 @@
    - **API Key**：填写上一步获取的 Key
 
 3. **API填写**
-   - **Web使用**: 在LLM模型表单中选择使用自定义模型，模型按照配置参数进行填写
-   - **本地部署**: 在config.toml中 找到`[developer.chat_models_config."deepseek-chat"]` 将配置参数填写上去，使得Web页面可以访问到该默认配置。 找到`[llm]`并配置model、base_url、api_key
+   - **Web使用**: 
+      - 在LLM模型下拉框中选择使用自定义模型，模型按照配置参数进行填写
+      - 或是在`config.toml`中 找到`[llm]`并配置model、base_url、api_key。Web页面下拉框会出现你填写的模型。
+   - **CLI**：
+      - 如果你偏好 CLI 入口，需要在`config.toml`中找到`[llm]`并配置model、base_url、api_key。
 
 ## 二、多模态大模型 (VLM)
 
@@ -42,15 +45,12 @@
  - **模型名称**：`qwen3-vl-8b-instruct`
  - **Base URL**：`https://dashscope.aliyuncs.com/compatible-mode/v1`
 
- - **参数填写**：在VLM Model表单中选择"使用自定义模型"进行参数填写。本地部署时，找到`[vlm]`并配置model、base_url、api_key，在config.toml中新增以下字段作为Web的API默认配置：
-    ```
-   [developer.chat_models_config."qwen3-vl-8b-instruct"]
-   base_url = "https://dashscope.aliyuncs.com/compatible-mode/v1"
-   api_key = "YOUR_API_KEY" 
-   timeout = 20.0
-   temperature = 0.1
-   max_retries = 2
-   ```
+ - **参数填写**：
+    - **Web使用**: 
+      - 在VLM模型下拉框中选择使用自定义模型，模型按照配置参数进行填写。
+      - 或是在`config.toml`中 找到`[vlm]`并配置model、base_url、api_key。Web页面下拉框会出现你填写的模型。
+   - **CLI**：
+      - 如果你偏好 CLI 入口，需要在`config.toml`中找到`[vlm]`并配置model、base_url、api_key。
 
 
 ### 2.3 使用Qwen3-Omni

src/open_storyline/agent.py

@@ -57,6 +57,7 @@ async def validate_api_key(base_url: str, api_key: str, model: str, provider: st
                     raise ValueError(f"{provider} returned non-JSON response. Check base_url/gateway.")
                 choices = data.get("choices")
                 if isinstance(choices, list) and len(choices) > 0:
+                    print(f"{model} validation successful")
                     return True
                 raise ValueError(
                     f"{provider} returned a non-OpenAI-compatible response for chat.completions. "

src/open_storyline/config.py

@@ -1,4 +1,4 @@
-# open_storyline/configuration_utils.py
+# /src/open_storyline/config.py
 from __future__ import annotations
 import os
 from pathlib import Path
@@ -76,9 +76,6 @@ class ConfigBaseModel(BaseModel):
 
 class DeveloperConfig(ConfigBaseModel):
     developer_mode: bool = False
-    default_llm: str = "deepseek-chat"
-    default_vlm: str = "qwen3-vl-8b-instruct"
-    chat_models_config: dict[str, dict[str, Any]] = Field(default_factory=dict)
     print_context: bool = False
 
 class ProjectConfig(ConfigBaseModel):