fix(embedding): 修复嵌入模型配置中的兼容性问题

- 修复 get_embedding_config 中对 model/name 字段的兼容性处理
- 改进错误处理和日志记录
- 支持多种配置格式以保持向后兼容性
- 修复 lightrag.py 中 embedding 函数的模型名称获取逻辑

Author: xerrors

src/knowledge/implementations/lightrag.py

@@ -200,6 +200,7 @@ async def llm_model_func(prompt, system_prompt=None, history_messages=[], **kwar
     def _get_embedding_func(self, embed_info: dict):
         """获取 embedding 函数"""
         config_dict = get_embedding_config(embed_info)
+        logger.debug(f"Embedding config dict: {config_dict}")
 
         if config_dict.get("model_id") and config_dict["model_id"].startswith("ollama"):
             from lightrag.llm.ollama import ollama_embed
@@ -219,15 +220,22 @@ def _get_embedding_func(self, embed_info: dict):
                 ),
             )
 
+        # 尝试获取模型名称，支持多种键名以保持兼容性
+        if "name" in config_dict and config_dict["name"]:
+            model_name = config_dict["name"]
+        elif "model" in config_dict and config_dict["model"]:
+            model_name = config_dict["model"]
+        else:
+            raise ValueError(f"Neither 'name' nor 'model' found in config_dict or both are empty: {config_dict}")
         return EmbeddingFunc(
             embedding_dim=config_dict["dimension"],
             max_token_size=8192,
             func=lambda texts: openai_embed(
                 texts=texts,
-                model=config_dict["model"],
+                model=model_name,
                 api_key=config_dict["api_key"],
                 base_url=config_dict["base_url"].replace("/embeddings", ""),
-            ),
+            )
         )
 
     async def add_content(self, db_id: str, items: list[str], params: dict | None = None) -> list[dict]:

src/knowledge/utils/kb_utils.py

@@ -289,30 +289,52 @@ def get_embedding_config(embed_info: dict) -> dict:
     Returns:
         dict: 标准化的嵌入配置
     """
-    config_dict = {}
-
     try:
-        if embed_info:
-            # 优先检查是否有 model_id 字段
-            if "model_id" in embed_info:
-                return config.embed_model_names[embed_info["model_id"]].model_dump()
-            elif hasattr(embed_info, "name") and isinstance(embed_info, EmbedModelInfo):
-                return embed_info.model_dump()
-            else:
-                # 字典形式（保持向后兼容）
-                config_dict["model"] = embed_info["name"]
-                config_dict["api_key"] = os.getenv(embed_info["api_key"]) or embed_info["api_key"]
-                config_dict["base_url"] = embed_info["base_url"]
-                config_dict["dimension"] = embed_info.get("dimension", 1024)
-        else:
-            return config.embed_model_names[config.embed_model].model_dump()
+        # 检查 embed_info 是否有效
+        if not embed_info or ("model" not in embed_info and "name" not in embed_info):
+            logger.error(f"Invalid embed_info: {embed_info}, using default embedding model config")
+            raise ValueError("Invalid embed_info: must be a non-empty dictionary")
+
+        # 优先检查是否有 model_id 字段
+        if "model_id" in embed_info and embed_info["model_id"]:
+            logger.warning(f"Using model_id: {embed_info['model_id']}")
+            config_dict = config.embed_model_names[embed_info["model_id"]].model_dump()
+            config_dict["api_key"] = os.getenv(config_dict["api_key"]) or config_dict["api_key"]
+            return config_dict
+
+        # 检查是否是 EmbedModelInfo 对象（在某些情况下可能直接传入对象）
+        if hasattr(embed_info, "name") and isinstance(embed_info, EmbedModelInfo):
+            logger.debug(f"Using EmbedModelInfo object: {embed_info.name}")
+            config_dict = embed_info.model_dump()
+            config_dict["api_key"] = os.getenv(config_dict["api_key"]) or config_dict["api_key"]
+            return config_dict
+
+        # 字典形式（保持向后兼容）
+        # 检查必需字段是否存在
+        if not embed_info.get("name") or not embed_info.get("base_url"):
+            logger.warning(f"embed_info missing required 'name' or 'base_url' field: {embed_info}, using default")
+            raise ValueError("embed_info missing required 'name' or 'base_url' field")
+
+        config_dict = {
+            "model": embed_info["name"],
+            "api_key": os.getenv(embed_info["api_key"]) or embed_info["api_key"],
+            "base_url": embed_info["base_url"],
+            "dimension": embed_info.get("dimension", 1024)
+        }
+        logger.debug(f"Embedding config from dict: {config_dict}")
+        return config_dict
 
     except Exception as e:
-        logger.error(f"Error in get_embedding_config: {e}, {embed_info}")
-        raise ValueError(f"Error in get_embedding_config: {e}")
-
-    logger.debug(f"Embedding config: {config_dict}")
-    return config_dict
+        logger.error(f"Error in get_embedding_config: {e}, embed_info={embed_info}")
+        # 返回默认配置作为fallback
+        logger.warning("Falling back to default embedding model config")
+        try:
+            config_dict = config.embed_model_names[config.embed_model].model_dump()
+            config_dict["api_key"] = os.getenv(config_dict["api_key"]) or config_dict["api_key"]
+            return config_dict
+        except Exception as fallback_error:
+            logger.error(f"Failed to get default embedding config: {fallback_error}")
+            raise ValueError(f"Failed to get embedding config and fallback failed: {e}")
 
 
 def is_minio_url(file_path: str) -> bool: