refactor: simplify LLM integration by replacing volcenginesdkarkruntime with standard OpenAI client and unifying environment configuration.

CLAUDE.md

@@ -51,13 +51,13 @@ uv run uvicorn server:app --host 0.0.0.0 --port 3001 --reload  # 启动开发服
 - **Token 生成**: `security/rtc_token.py`
 
 ### LLM 模式
-通过 `CUSTOM_LLM_MODE` 环境变量切换：
-- `ArkV3`: 直接使用火山方舟 LLM
-- `CustomLLM`: 自定义 LLM 回调（`/api/chat_callback` 端点）
+固定使用 `CustomLLM` 模式：火山 RTC 回调本后端的 `/api/chat_callback`，再由本后端调用方舟 LLM。
 
 ### 关键环境变量（backend/.env）
+
 - `CUSTOM_ACCESS_KEY_ID` / `CUSTOM_SECRET_KEY`: 火山引擎凭证
 - `CUSTOM_RTC_APP_ID` / `CUSTOM_RTC_APP_KEY`: RTC 应用配置
-- `CUSTOM_LLM_MODE` + 对应 LLM 配置
+- `CUSTOM_LLM_URL`: 回调地址（默认本地，生产用 ngrok 公网地址）
+- `LOCAL_LLM_API_KEY` / `LOCAL_LLM_MODEL`: 本地回调调用方舟的凭证
 - `CUSTOM_ASR_APP_ID` / `CUSTOM_TTS_APP_ID`: 语音识别/合成配置
 - `CUSTOM_AVATAR_*`: 数字人配置（可选）

README.md

@@ -43,7 +43,7 @@ cp backend/.env.example backend/.env
     - RoomId、UserId 可自定义也可不填，交由服务端生成。
 - `VoiceChat`: 场景下的 AIGC 配置。
     - `Custom` 场景的 TaskId、Agent 用户信息、欢迎语、System Message 以及 LLM 模式参数均通过 `backend/.env` 注入。
-    - 支持 `ArkV3` 与 `CustomLLM` 两种模式；`CustomLLM` 当前推荐“当前 backend 内置本地回调 + ngrok 暴露公网地址”这条接法。
+    - 固定使用 `CustomLLM` 模式，由本后端提供回调接口，推荐通过 ngrok 暴露公网地址。
     - 可参考 https://www.volcengine.com/docs/6348/1558163 中参数描述，完整填写参数内容。
     - `ASRConfig`、`TTSConfig`、`AvatarConfig` 等复杂结构由 `backend/config/custom_scene.py` 维护默认值，并从 `backend/.env` 读取关键运行参数。
     - 当前首版默认不启用 RAG 主链路，`backend/services/rag_service.py` 仅保留为后续扩展位。
@@ -51,15 +51,13 @@ cp backend/.env.example backend/.env
 
 ### 第三方 CustomLLM 接入
 
-如果你要把本地服务接成 `CustomLLM`，推荐直接让当前 `backend` 自己提供回调接口：
+当前 `backend` 自己提供回调接口，核心配置：
 
 ```dotenv
-CUSTOM_LLM_MODE=CustomLLM
 CUSTOM_LLM_URL=http://127.0.0.1:3001/api/chat_callback
-CUSTOM_LLM_MODEL_NAME=my-model
 CUSTOM_LLM_API_KEY=your-callback-token
-ARK_API_KEY=your-ark-api-key
-ARK_ENDPOINT_ID=your-ark-endpoint-id
+LOCAL_LLM_API_KEY=your-ark-api-key
+LOCAL_LLM_MODEL=your-ark-endpoint-id
 ```
 
 本地起好 `backend` 以后，用 `ngrok` 暴露 `3001` 端口，再把 `CUSTOM_LLM_URL` 改成公网地址：

backend/.env.example

@@ -1,40 +1,34 @@
-# RTC OpenAPI 版本
-RTC_OPENAPI_VERSION=2025-06-01
-
-# Custom 场景基础配置
+# ============ 火山引擎账号凭证 ============
 CUSTOM_ACCESS_KEY_ID=your-access-key-id
 CUSTOM_SECRET_KEY=your-secret-key
+
+# ============ RTC 配置 ============
 CUSTOM_RTC_APP_ID=your-rtc-app-id
 CUSTOM_RTC_APP_KEY=
+CUSTOM_RTC_ROOM_ID=                     # 留空自动生成
+CUSTOM_RTC_USER_ID=                     # 留空自动生成
+CUSTOM_RTC_TOKEN=                       # 留空自动生成
+RTC_OPENAPI_VERSION=2025-06-01
 
-# 留空时由服务端自动生成 RoomId / UserId / Token
-CUSTOM_RTC_ROOM_ID=
-CUSTOM_RTC_USER_ID=
-CUSTOM_RTC_TOKEN=
-
-# Custom 场景业务配置
-CUSTOM_TASK_ID=your-task-id
-CUSTOM_AGENT_USER_ID=your-agent-user-id
-CUSTOM_AGENT_TARGET_USER_ID=
-CUSTOM_AGENT_WELCOME_MESSAGE=你好，我是小乖，有什么需要帮忙的吗？
+# ============ 场景配置 ============
 CUSTOM_SCENE_NAME=自定义助手
 CUSTOM_SCENE_ICON=https://lf3-rtc-demo.volccdn.com/obj/rtc-aigc-assets/DoubaoAvatar.png
+CUSTOM_TASK_ID=your-task-id
+CUSTOM_AGENT_USER_ID=your-agent-user-id
+CUSTOM_AGENT_TARGET_USER_ID=            # 留空默认等于 RTC_USER_ID
+CUSTOM_AGENT_WELCOME_MESSAGE=你好，我是小乖，有什么需要帮忙的吗？
 CUSTOM_INTERRUPT_MODE=0
 
-# LLM 公共配置
-CUSTOM_LLM_MODE=ArkV3
+# ============ LLM 配置 (RTC OpenAPI 侧) ============
+# RTC 会回调 CUSTOM_LLM_URL 指定的地址（通常是本后端的 /api/chat_callback）
 CUSTOM_LLM_SYSTEM_MESSAGE=你是小乖，性格幽默又善解人意。你在表达时需简明扼要，有自己的观点。
-CUSTOM_LLM_VISION_ENABLE=false
 CUSTOM_LLM_THINKING_TYPE=disabled
+CUSTOM_LLM_VISION_ENABLE=false
 
-# ArkV3 模式
-CUSTOM_LLM_ENDPOINT_ID=your-ark-endpoint-id
-
-# CustomLLM 模式
 # 本地调试时，可先保持默认本地回调地址。
 # 等 ngrok 跑起来后，再把 CUSTOM_LLM_URL 改成公网 https 地址，例如：
 # https://your-ngrok-domain.ngrok-free.app/api/chat_callback
-CUSTOM_LLM_URL=http://127.0.0.1:3001/api/chat_callback
+CUSTOM_LLM_URL=https://postvarioloid-leeann-didynamous.ngrok-free.dev
 # 火山调用当前 backend 的 /api/chat_callback 时使用的 Bearer Token，可留空
 CUSTOM_LLM_API_KEY=
 CUSTOM_LLM_MODEL_NAME=
@@ -47,32 +41,16 @@ CUSTOM_LLM_TEMPERATURE=
 CUSTOM_LLM_TOP_P=
 CUSTOM_LLM_MAX_TOKENS=
 
-# 当前 backend 内置的本地 Ark 回调配置
-# /api/chat_callback 会直接使用这组配置调用方舟
-ARK_API_KEY=
-ARK_ENDPOINT_ID=
-ARK_BASE_URL=https://ark.cn-beijing.volces.com/api/v3
-ARK_TIMEOUT_SECONDS=1800
-LOCAL_LLM_SYSTEM_PROMPT= "你是一个测试助手。如果别人问你是谁，你就说你是哈哈哈。"
+# ============ 本地 LLM 回调配置 (/api/chat_callback) ============
+# RTC 回调本后端的 /api/chat_callback 后，
+# 本后端再用以下配置调用方舟（通过 OpenAI SDK）。
+LOCAL_LLM_API_KEY=
+LOCAL_LLM_BASE_URL=https://ark.cn-beijing.volces.com/api/v3
+LOCAL_LLM_MODEL=your-ark-endpoint-id
+LOCAL_LLM_TIMEOUT_SECONDS=1800
 LOCAL_LLM_TEMPERATURE=0.3
 
-# RAG 配置
-# 方式一：火山引擎知识库（语义检索）
-# 设置 VOLC_KB_ENABLED=true 后，每次对话前自动检索知识库并注入上下文
-VOLC_KB_ENABLED=false
-VOLC_KB_NAME=your_collection_name       # 知识库名称（与 VOLC_KB_RESOURCE_ID 二选一）
-VOLC_KB_RESOURCE_ID=                    # 知识库唯一 ID（优先级高于 NAME）
-VOLC_KB_PROJECT=default                 # 知识库所属项目
-VOLC_KB_ENDPOINT=https://api-knowledgebase.mlp.cn-beijing.volces.com
-VOLC_KB_TOP_K=5                         # 检索返回条数
-VOLC_KB_RERANK=false                    # 是否开启 rerank 重排
-VOLC_KB_ATTACHMENT_LINK=false           # 是否返回图片临时链接（图文混合场景开启，链接有效期 10 分钟）
-
-# 方式二：静态上下文占位（VOLC_KB_ENABLED=false 时生效）
-RAG_STATIC_CONTEXT=
-RAG_CONTEXT_FILE=
-
-# ASR / TTS
+# ============ ASR / TTS ============
 CUSTOM_ASR_APP_ID=your-asr-app-id
 CUSTOM_TTS_APP_ID=your-tts-app-id
 CUSTOM_ASR_PROVIDER=volcano
@@ -85,7 +63,23 @@ CUSTOM_TTS_SPEED_RATIO=1
 CUSTOM_TTS_PITCH_RATIO=1
 CUSTOM_TTS_VOLUME_RATIO=1
 
-# 数字人配置
+# ============ RAG 配置 ============
+# 火山引擎知识库（语义检索）
+# 设置 VOLC_KB_ENABLED=true 后，每次对话前自动检索知识库并注入上下文
+VOLC_KB_ENABLED=false
+VOLC_KB_NAME=your_collection_name       # 知识库名称（与 VOLC_KB_RESOURCE_ID 二选一）
+VOLC_KB_RESOURCE_ID=                    # 知识库唯一 ID（优先级高于 NAME）
+VOLC_KB_PROJECT=default                 # 知识库所属项目
+VOLC_KB_ENDPOINT=https://api-knowledgebase.mlp.cn-beijing.volces.com
+VOLC_KB_TOP_K=5                         # 检索返回条数
+VOLC_KB_RERANK=false                    # 是否开启 rerank 重排
+VOLC_KB_ATTACHMENT_LINK=false           # 是否返回图片临时链接（图文混合场景开启，链接有效期 10 分钟）
+
+# 静态上下文占位（VOLC_KB_ENABLED=false 时的降级方案）
+RAG_STATIC_CONTEXT=
+RAG_CONTEXT_FILE=
+
+# ============ 数字人 (可选) ============
 CUSTOM_AVATAR_ENABLED=false
 CUSTOM_AVATAR_TYPE=3min
 CUSTOM_AVATAR_ROLE=250623-zhibo-linyunzhi

backend/README.md

@@ -63,22 +63,13 @@ cp .env.example .env
 | `CUSTOM_RTC_TOKEN` | RTC Token；留空时服务端会自动生成 |
 | `RTC_OPENAPI_VERSION` | `StartVoiceChat/StopVoiceChat` 使用的 OpenAPI 版本，默认 `2025-06-01` |
 
-### LLM 模式一：ArkV3
+### CustomLLM 回调配置
+
+当前 `backend` 自己对外提供回调接口，火山 RTC 会回调 `CUSTOM_LLM_URL` 指定的地址：
 
 ```dotenv
-CUSTOM_LLM_MODE=ArkV3
-CUSTOM_LLM_ENDPOINT_ID=your-ark-endpoint-id
-```
-
-### LLM 模式二：CustomLLM 本地回调
-
-这个模式不是再起一个额外代理服务，而是直接由当前 `backend` 自己对外提供回调接口：
-
-```dotenv
-CUSTOM_LLM_MODE=CustomLLM
 CUSTOM_LLM_URL=http://127.0.0.1:3001/api/chat_callback
 CUSTOM_LLM_API_KEY=your-callback-token
-CUSTOM_LLM_MODEL_NAME=my-model
 ```
 
 推荐调试流程：
@@ -93,20 +84,19 @@ CUSTOM_LLM_URL=https://your-ngrok-domain.ngrok-free.app/api/chat_callback
 
 `CUSTOM_LLM_API_KEY` 是火山调用你这个本地回调接口时带上的 Bearer Token；如果你不需要这层鉴权，可以留空。
 
-### 当前 backend 内置的 Ark 配置
+### 当前 backend 内置的本地 LLM 回调配置
 
-`/api/chat_callback` 内部会直接调用方舟 SDK，因此还需要：
+`/api/chat_callback` 内部会通过 OpenAI SDK 调用方舟，因此还需要：
 
 ```dotenv
-ARK_API_KEY=your-ark-api-key
-ARK_ENDPOINT_ID=your-ark-endpoint-id
-ARK_BASE_URL=https://ark.cn-beijing.volces.com/api/v3
-ARK_TIMEOUT_SECONDS=1800
-LOCAL_LLM_SYSTEM_PROMPT=
+LOCAL_LLM_API_KEY=your-ark-api-key
+LOCAL_LLM_MODEL=your-ark-endpoint-id
+LOCAL_LLM_BASE_URL=https://ark.cn-beijing.volces.com/api/v3
+LOCAL_LLM_TIMEOUT_SECONDS=1800
 LOCAL_LLM_TEMPERATURE=0.3
 ```
 
-如果 `LOCAL_LLM_SYSTEM_PROMPT` 留空，会回退使用 `CUSTOM_LLM_SYSTEM_MESSAGE`。
+System Prompt 统一使用 `CUSTOM_LLM_SYSTEM_MESSAGE`，Thinking 模式统一使用 `CUSTOM_LLM_THINKING_TYPE`。
 
 ### 可选 RAG 占位配置
 
@@ -187,4 +177,4 @@ RAG_CONTEXT_FILE=
 ## 说明
 
 - 当前前端不会直接决定模型供应商，模型切换统一由后端环境变量控制。
-- 如果缺少关键 `CUSTOM_*` 或 `ARK_*` 配置，服务会在启动阶段直接报错，而不是进入半可用状态。
+- 如果缺少关键 `CUSTOM_*` 或 `LOCAL_LLM_*` 配置，服务会在启动阶段直接报错，而不是进入半可用状态。

backend/config/custom_scene.py

@@ -25,11 +25,8 @@ DEFAULT_SCENE_NAME = "自定义助手"
 DEFAULT_SCENE_ICON = (
     "https://lf3-rtc-demo.volccdn.com/obj/rtc-aigc-assets/DoubaoAvatar.png"
 )
-DEFAULT_LLM_MODE = "ArkV3"
 DEFAULT_LLM_THINKING_TYPE = "disabled"
 DEFAULT_RTC_OPENAPI_VERSION = "2025-06-01"
-DEFAULT_CUSTOM_LLM_CALLBACK_URL = "http://127.0.0.1:3001/api/chat_callback"
-SUPPORTED_LLM_MODES = {"ArkV3", "CustomLLM"}
 DEFAULT_ASR_PROVIDER = "volcano"
 DEFAULT_ASR_MODE = "smallmodel"
 DEFAULT_ASR_CLUSTER = "volcengine_streaming_common"
@@ -46,18 +43,13 @@ def get_rtc_openapi_version() -> str:
 
 
 def build_llm_settings_from_env(missing: list[str]) -> dict[str, Any]:
-    llm_mode = env_str("CUSTOM_LLM_MODE", DEFAULT_LLM_MODE)
-    if llm_mode not in SUPPORTED_LLM_MODES:
-        modes = ", ".join(sorted(SUPPORTED_LLM_MODES))
-        raise ValueError(f"CUSTOM_LLM_MODE 仅支持以下取值: {modes}")
-
     settings = {
-        "mode": llm_mode,
         "system_message": require_env("CUSTOM_LLM_SYSTEM_MESSAGE", missing),
         "vision_enable": env_bool("CUSTOM_LLM_VISION_ENABLE", False),
         "thinking_type": env_str(
             "CUSTOM_LLM_THINKING_TYPE", DEFAULT_LLM_THINKING_TYPE
         ),
+        "url": require_env("CUSTOM_LLM_URL", missing),
         "api_key": env_str("CUSTOM_LLM_API_KEY"),
         "model_name": env_str("CUSTOM_LLM_MODEL_NAME"),
         "history_length": env_optional_int("CUSTOM_LLM_HISTORY_LENGTH"),
@@ -72,34 +64,22 @@ def build_llm_settings_from_env(missing: list[str]) -> dict[str, Any]:
         "max_tokens": env_optional_int("CUSTOM_LLM_MAX_TOKENS"),
     }
 
-    if llm_mode == "ArkV3":
-        settings["endpoint_id"] = require_env("CUSTOM_LLM_ENDPOINT_ID", missing)
-        return settings
-
-    settings["url"] = env_str("CUSTOM_LLM_URL", DEFAULT_CUSTOM_LLM_CALLBACK_URL)
-    settings["endpoint_id"] = env_str("CUSTOM_LLM_ENDPOINT_ID")
-    require_env("ARK_API_KEY", missing)
-    require_env("ARK_ENDPOINT_ID", missing)
+    require_env("LOCAL_LLM_API_KEY", missing)
+    require_env("LOCAL_LLM_MODEL", missing)
 
     return settings
 
 
 def build_llm_config(llm_settings: dict[str, Any]) -> dict[str, Any]:
     llm_config = {
-        "Mode": llm_settings["mode"],
+        "Mode": "CustomLLM",
         "SystemMessages": [llm_settings["system_message"]],
         "VisionConfig": {
             "Enable": llm_settings["vision_enable"],
         },
+        "Url": llm_settings["url"],
     }
 
-    if llm_settings["mode"] == "ArkV3":
-        llm_config["EndPointId"] = llm_settings["endpoint_id"]
-        llm_config["ThinkingType"] = llm_settings["thinking_type"]
-        return llm_config
-
-    llm_config["Url"] = llm_settings["url"]
-
     if llm_settings["api_key"]:
         llm_config["APIKey"] = llm_settings["api_key"]
 

backend/pyproject.toml

@@ -9,7 +9,7 @@ dependencies = [
     "httpx>=0.27.0",
     "python-dotenv>=1.2.2",
     "python-multipart>=0.0.9",
-    "volcengine-python-sdk[ark]>=4.0.6",
+    "openai>=1.0.0",
 ]
 
 [tool.uv]

backend/security/signer.py

@@ -1,11 +1,3 @@
-"""
-Copyright 2025 Beijing Volcano Engine Technology Co., Ltd. All Rights Reserved.
-SPDX-license-identifier: BSD-3-Clause
-
-Migrated from @volcengine/openapi Signer (AWS SigV4 compatible)
-Reference: https://www.volcengine.com/docs/6348/69828
-"""
-
 import hashlib
 import hmac
 import json

backend/server.py

@@ -1,3 +1,4 @@
+import logging
 from pathlib import Path
 
 from dotenv import load_dotenv
@@ -5,6 +6,17 @@ from dotenv import load_dotenv
 BASE_DIR = Path(__file__).parent
 load_dotenv(BASE_DIR / ".env", override=False)
 
+logging.basicConfig(
+    level=logging.DEBUG,
+    format="%(asctime)s [%(levelname)s] %(name)s: %(message)s",
+    datefmt="%H:%M:%S",
+)
+# 压制第三方库的 DEBUG 噪音
+logging.getLogger("httpx").setLevel(logging.WARNING)
+logging.getLogger("httpcore").setLevel(logging.WARNING)
+logging.getLogger("openai").setLevel(logging.WARNING)
+logging.getLogger("uvicorn").setLevel(logging.INFO)
+
 # 路由必须在 load_dotenv 之后导入，因为模块级代码会读取环境变量
 from routes.chat_callback import router as chat_callback_router  # noqa: E402
 from routes.debug import router as debug_router  # noqa: E402

backend/services/local_llm_service.py

@@ -1,20 +1,15 @@
-"""
-本地 CustomLLM 服务。
-
-当前实现直接在同一个 FastAPI 进程内调用方舟 SDK，
-并由 /api/chat_callback 对外提供火山要求的 SSE 回调接口。
-"""
-
 from __future__ import annotations
 
+import logging
 from dataclasses import dataclass
 from typing import Any, Iterator
-
+from openai import OpenAI
 from utils.env import env_float, env_int, env_str
 
-DEFAULT_ARK_BASE_URL = "https://ark.cn-beijing.volces.com/api/v3"
-DEFAULT_ARK_TIMEOUT_SECONDS = 1800
-DEFAULT_ARK_TEMPERATURE = 0
+logger = logging.getLogger(__name__)
+
+DEFAULT_LLM_TIMEOUT_SECONDS = 1800
+DEFAULT_LLM_TEMPERATURE = 0
 
 
 def _coalesce(*values):
@@ -27,34 +22,38 @@ def _coalesce(*values):
 @dataclass(frozen=True)
 class LocalLLMSettings:
     api_key: str
-    endpoint_id: str
+    model: str
     base_url: str
     timeout_seconds: int
     system_prompt: str
     default_temperature: float
+    thinking_type: str
 
 
 def _load_settings() -> LocalLLMSettings:
-    api_key = env_str("ARK_API_KEY")
-    endpoint_id = env_str("ARK_ENDPOINT_ID")
+    api_key = env_str("LOCAL_LLM_API_KEY")
+    model = env_str("LOCAL_LLM_MODEL")
+    base_url = env_str("LOCAL_LLM_BASE_URL")
 
     if not api_key:
-        raise ValueError("ARK_API_KEY 不能为空")
-    if not endpoint_id:
-        raise ValueError("ARK_ENDPOINT_ID 不能为空")
+        raise ValueError("LOCAL_LLM_API_KEY 不能为空")
+    if not model:
+        raise ValueError("LOCAL_LLM_MODEL 不能为空")
+    if not base_url:
+        raise ValueError("LOCAL_LLM_BASE_URL 不能为空")
 
     return LocalLLMSettings(
         api_key=api_key,
-        endpoint_id=endpoint_id,
-        base_url=env_str("ARK_BASE_URL", DEFAULT_ARK_BASE_URL),
-        timeout_seconds=env_int("ARK_TIMEOUT_SECONDS", DEFAULT_ARK_TIMEOUT_SECONDS),
-        system_prompt=env_str(
-            "LOCAL_LLM_SYSTEM_PROMPT",
-            env_str("CUSTOM_LLM_SYSTEM_MESSAGE"),
+        model=model,
+        base_url=base_url,
+        timeout_seconds=env_int(
+            "LOCAL_LLM_TIMEOUT_SECONDS", DEFAULT_LLM_TIMEOUT_SECONDS
         ),
+        system_prompt=env_str("CUSTOM_LLM_SYSTEM_MESSAGE"),
         default_temperature=env_float(
-            "LOCAL_LLM_TEMPERATURE", DEFAULT_ARK_TEMPERATURE
+            "LOCAL_LLM_TEMPERATURE", DEFAULT_LLM_TEMPERATURE
         ),
+        thinking_type=env_str("CUSTOM_LLM_THINKING_TYPE", "disabled"),
     )
 
 
@@ -73,17 +72,11 @@ class LocalLLMService:
         if self._client is not None:
             return self._client
 
-        try:
-            from volcenginesdkarkruntime import Ark
-        except ImportError as exc:
-            raise RuntimeError(
-                "未安装 volcenginesdkarkruntime，请先执行 uv sync 安装依赖"
-            ) from exc
-
         s = self.settings
-        self._client = Ark(
-            base_url=s.base_url,
+        # 显式传入以确保使用 settings 中加载的值（支持多环境覆盖）
+        self._client = OpenAI(
             api_key=s.api_key,
+            base_url=s.base_url,
             timeout=s.timeout_seconds,
         )
         return self._client
@@ -111,7 +104,7 @@ class LocalLLMService:
         messages.extend(history_messages)
 
         payload: dict[str, Any] = {
-            "model": settings.endpoint_id,
+            "model": settings.model,
             "messages": messages,
             "temperature": _coalesce(
                 request_options.get("temperature"),
@@ -126,13 +119,20 @@ class LocalLLMService:
         if request_options.get("top_p") is not None:
             payload["top_p"] = request_options["top_p"]
 
-        print(f"🚀 发起流式调用 (Endpoint: {settings.endpoint_id})")
+        # thinking 仅在启用时通过 extra_body 传递（避免不支持的模型报错）
+        if settings.thinking_type != "disabled":
+            payload["extra_body"] = {"thinking": {"type": settings.thinking_type}}
+            payload["reasoning_effort"] = "low"
+
+        import json as _json
+        logger.info("发起流式调用 (Model: %s, Thinking: %s)", settings.model, settings.thinking_type)
+        logger.debug("请求 payload:\n%s", _json.dumps(payload, ensure_ascii=False, indent=2))
         try:
             stream = client.chat.completions.create(**payload)
             for chunk in stream:
                 yield chunk
         except Exception as exc:
-            print(f"❌ LLM 调用失败: {exc}")
+            logger.error("LLM 调用失败: %s", exc)
             raise
 
 

backend/services/rag_service.py

@@ -1,13 +1,3 @@
-"""
-RAG 服务。
-
-支持两种模式：
-- VOLC_KB_ENABLED=false（默认）：走旧的静态上下文逻辑（RAG_STATIC_CONTEXT / RAG_CONTEXT_FILE）
-- VOLC_KB_ENABLED=true：调用火山引擎知识库 API 进行语义检索
-
-出错时自动降级为空字符串，不阻断主链路 LLM 调用。
-"""
-
 from __future__ import annotations
 
 import json

backend/services/scene_service.py

@@ -75,9 +75,6 @@ def get_custom_llm_callback_settings() -> dict[str, Any]:
         missing_str = ", ".join(dict.fromkeys(missing))
         raise ValueError(f"Custom 场景缺少以下环境变量: {missing_str}")
 
-    if settings["mode"] != "CustomLLM":
-        raise ValueError("当前 CUSTOM_LLM_MODE 不是 CustomLLM，无法使用本地回调接口")
-
     return settings
 
 

backend/uv.lock

@@ -9,20 +9,20 @@ source = { virtual = "." }
 dependencies = [
     { name = "fastapi" },
     { name = "httpx" },
+    { name = "openai" },
     { name = "python-dotenv" },
     { name = "python-multipart" },
     { name = "uvicorn", extra = ["standard"] },
-    { name = "volcengine-python-sdk", extra = ["ark"] },
 ]
 
 [package.metadata]
 requires-dist = [
     { name = "fastapi", specifier = ">=0.110.0" },
     { name = "httpx", specifier = ">=0.27.0" },
+    { name = "openai", specifier = ">=1.0.0" },
     { name = "python-dotenv", specifier = ">=1.2.2" },
     { name = "python-multipart", specifier = ">=0.0.9" },
     { name = "uvicorn", extras = ["standard"], specifier = ">=0.29.0" },
-    { name = "volcengine-python-sdk", extras = ["ark"], specifier = ">=4.0.6" },
 ]
 
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-version = "2.0.0"
-source = { registry = "https://pypi.org/simple" }
-dependencies = [
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