部分配置key修正

docs/powermem-integration.md

@@ -59,7 +59,9 @@ Memory:
     llm_model: qwen-plus
     # 嵌入模型配置
     embedding_api_key: 你的嵌入模型API密钥
-    embedding_model: text-embedding-v3
+    embedding_model: text-embedding-v4
+    # 可选：自定义嵌入服务地址（OpenAI兼容模式）
+    # embedding_openai_base_url: https://dashscope.aliyuncs.com/compatible-mode/v1
 ```
 
 ### 配置参数详解
@@ -72,10 +74,21 @@ Memory:
 | `embedding_provider` | 嵌入模型提供商 | `qwen` | `qwen`, `zhipu`, `openai`, 等 |
 | `llm_api_key` | LLM API 密钥 | - | - |
 | `llm_model` | LLM 模型名称 | - | 根据提供商选择 |
-| `llm_base_url` | LLM API 地址（可选） | - | - |
+| `llm_base_url` | LLM API 地址（可选） | - | 根据provider自动选择参数名 |
 | `embedding_api_key` | 嵌入模型 API 密钥 | - | - |
 | `embedding_model` | 嵌入模型名称 | - | 根据提供商选择 |
-| `embedding_base_url` | 嵌入模型 API 地址（可选） | - | - |
+| `embedding_base_url` | 嵌入模型 API 地址（可选） | - | 通用配置，根据provider自动选择 |
+| `embedding_openai_base_url` | OpenAI兼容的嵌入服务地址（可选） | - | 优先级高于 embedding_base_url |
+| `embedding_dashscope_base_url` | 阿里云灵积嵌入服务地址（可选） | - | 优先级高于 embedding_base_url |
+
+#### Base URL 配置优先级
+
+| 配置项 | 优先级 | 适用 Provider |
+|--------|--------|---------------|
+| `embedding_openai_base_url` | 最高 | openai |
+| `embedding_dashscope_base_url` | 最高 | qwen |
+| `embedding_base_url` | 中 | 通用，根据 provider 自动选择 |
+| 默认值 | 最低 | 使用 provider 的默认服务地址 |
 
 ### 记忆模式说明
 
@@ -102,7 +115,9 @@ Memory:
     llm_api_key: sk-xxxxxxxxxxxxxxxx
     llm_model: qwen-plus
     embedding_api_key: sk-xxxxxxxxxxxxxxxx
-    embedding_model: text-embedding-v3
+    embedding_model: text-embedding-v4
+    # 可选：使用 OpenAI 兼容模式的服务地址
+    embedding_openai_base_url: https://dashscope.aliyuncs.com/compatible-mode/v1
 ```
 
 ### 使用智谱免费 LLM（完全免费方案）
@@ -118,14 +133,14 @@ Memory:
   powermem:
     type: powermem
     database_provider: sqlite
-    llm_provider: zhipu
+    llm_provider: openai  # 使用 openai 兼容模式
-    embedding_provider: zhipu
+    embedding_provider: openai  # 使用 openai 兼容模式
     llm_api_key: xxxxxxxxxxxxxxxx.xxxxxxxxxxxxxxxx
     llm_model: glm-4-flash
     llm_base_url: https://open.bigmodel.cn/api/paas/v4/
     embedding_api_key: xxxxxxxxxxxxxxxx.xxxxxxxxxxxxxxxx
     embedding_model: embedding-3
-    embedding_base_url: https://open.bigmodel.cn/api/paas/v4/
+    embedding_openai_base_url: https://open.bigmodel.cn/api/paas/v4/
 ```
 
 ### 使用 OpenAI
@@ -142,7 +157,7 @@ Memory:
     llm_base_url: https://api.openai.com/v1
     embedding_api_key: sk-xxxxxxxxxxxxxxxx
     embedding_model: text-embedding-3-small
-    embedding_base_url: https://api.openai.com/v1
+    embedding_openai_base_url: https://api.openai.com/v1
 ```
 
 ### 使用 OceanBase（最佳性能方案）
@@ -164,7 +179,8 @@ Memory:
     llm_api_key: sk-xxxxxxxxxxxxxxxx
     llm_model: qwen-plus
     embedding_api_key: sk-xxxxxxxxxxxxxxxx
-    embedding_model: text-embedding-v3
+    embedding_model: text-embedding-v4
+    embedding_openai_base_url: https://dashscope.aliyuncs.com/compatible-mode/v1
     # OceanBase 数据库连接配置
     vector_store:
       provider: oceanbase
@@ -198,10 +214,11 @@ Memory:
         model: qwen-plus
     # 嵌入模型配置
     embedder:
-      provider: qwen
+      provider: openai  # 使用 OpenAI 兼容模式
       config:
         api_key: sk-xxxxxxxxxxxxxxxx
-        model: text-embedding-v3
+        model: text-embedding-v4
+        openai_base_url: https://dashscope.aliyuncs.com/compatible-mode/v1
 ```
 
 ## 设备记忆隔离
@@ -231,7 +248,8 @@ Memory:
     llm_api_key: sk-xxxxxxxxxxxxxxxx
     llm_model: qwen-plus
     embedding_api_key: sk-xxxxxxxxxxxxxxxx
-    embedding_model: text-embedding-v3
+    embedding_model: text-embedding-v4
+    embedding_openai_base_url: https://dashscope.aliyuncs.com/compatible-mode/v1
     # OceanBase 数据库连接配置
     vector_store:
       provider: oceanbase

main/xiaozhi-server/config.yaml

@@ -301,9 +301,15 @@ Memory:
     # 智谱AI(免费): https://bigmodel.cn/usercenter/proj-mgmt/apikeys
     llm_api_key: 你的LLM API密钥
     llm_model: qwen-plus
+    # llm_base_url: https://dashscope.aliyuncs.com/api/v1  # 可选，自定义LLM服务地址
     # 嵌入模型配置 (使用通义千问为例)
     embedding_api_key: 你的嵌入模型API密钥
-    embedding_model: text-embedding-v3
+    embedding_model: text-embedding-v4
+    embedding_openai_base_url: https://dashscope.aliyuncs.com/compatible-mode/v1 # OpenAI兼容的嵌入服务地址
+    # embedding_base_url: https://dashscope.aliyuncs.com/api/v1  # 可选，自定义嵌入模型服务地址
+    # 或使用独立的base_url配置（优先级更高）:
+    # embedding_openai_base_url: https://api.openai.com/v1  # OpenAI兼容的嵌入服务地址
+    # embedding_dashscope_base_url: https://dashscope.aliyuncs.com/api/v1  # 阿里云灵积嵌入服务地址
   nomem:
     # 不想使用记忆功能，可以使用nomem
     type: nomem

main/xiaozhi-server/core/providers/memory/powermem/powermem.py

@@ -21,13 +21,13 @@ TAG = __name__
 class MemoryProvider(MemoryProviderBase):
     """
     PowerMem memory provider implementation.
-    
+
     PowerMem is an open-source agent memory component that provides
     efficient memory management for AI agents.
-    
+
     Supports multiple storage backends (sqlite, oceanbase, postgres),
     LLM providers (qwen, openai, etc.) and embedding providers.
-    
+
     Config options:
         - enable_user_profile: bool - Enable UserMemory for user profiling (requires OceanBase)
         - database_provider: str - Storage backend (sqlite, oceanbase, postgres)
@@ -40,16 +40,16 @@ class MemoryProvider(MemoryProviderBase):
         self.use_powermem = False
         self.memory_client = None
         self.enable_user_profile = False
-        
+
         try:
             # Check if user profile mode is enabled
             self.enable_user_profile = config.get("enable_user_profile", False)
-            
+
             # Get configuration parameters
             database_provider = config.get("database_provider", "sqlite")
             llm_provider = config.get("llm_provider", "qwen")
             embedding_provider = config.get("embedding_provider", "qwen")
-            
+
             # UserMemory requires OceanBase
             if self.enable_user_profile and database_provider not in ["oceanbase"]:
                 logger.bind(tag=TAG).warning(
@@ -57,13 +57,13 @@ class MemoryProvider(MemoryProviderBase):
                     "Falling back to AsyncMemory mode."
                 )
                 self.enable_user_profile = False
-            
+
             # Build powermem configuration dict
             # PowerMem supports two config styles:
             # 1. powermem style: database, llm, embedding
             # 2. mem0 style: vector_store, llm, embedder
             powermem_config = {}
-            
+
             # Configure vector store / database
             if "vector_store" in config:
                 powermem_config["vector_store"] = config["vector_store"]
@@ -74,7 +74,7 @@ class MemoryProvider(MemoryProviderBase):
                     "provider": database_provider,
                     "config": {}
                 }
-            
+
             # Configure LLM
             if "llm" in config:
                 powermem_config["llm"] = config["llm"]
@@ -84,13 +84,23 @@ class MemoryProvider(MemoryProviderBase):
                     llm_config["api_key"] = config["llm_api_key"]
                 if "llm_model" in config:
                     llm_config["model"] = config["llm_model"]
+                # Handle base_url based on provider type
+                # - qwen provider uses dashscope_base_url
+                # - openai provider uses openai_base_url
                 if "llm_base_url" in config:
-                    llm_config["base_url"] = config["llm_base_url"]
+                    if llm_provider == "qwen":
+                        llm_config["dashscope_base_url"] = config["llm_base_url"]
+                    else:
+                        llm_config["openai_base_url"] = config["llm_base_url"]
+                if "openai_base_url" in config:
+                    llm_config["openai_base_url"] = config["openai_base_url"]
+                if "dashscope_base_url" in config:
+                    llm_config["dashscope_base_url"] = config["dashscope_base_url"]
                 powermem_config["llm"] = {
                     "provider": llm_provider,
                     "config": llm_config
                 }
-            
+
             # Configure embedder
             if "embedder" in config:
                 powermem_config["embedder"] = config["embedder"]
@@ -100,13 +110,25 @@ class MemoryProvider(MemoryProviderBase):
                     embedder_config["api_key"] = config["embedding_api_key"]
                 if "embedding_model" in config:
                     embedder_config["model"] = config["embedding_model"]
+                # Handle base_url based on provider type
+                # - qwen provider uses dashscope_base_url
+                # - openai provider uses openai_base_url
+                # Priority: embedding_xxx_base_url > embedding_base_url > xxx_base_url
                 if "embedding_base_url" in config:
-                    embedder_config["base_url"] = config["embedding_base_url"]
+                    if embedding_provider == "qwen":
+                        embedder_config["dashscope_base_url"] = config["embedding_base_url"]
+                    else:
+                        embedder_config["openai_base_url"] = config["embedding_base_url"]
+                # Embedding-specific base_url (higher priority)
+                if "embedding_openai_base_url" in config:
+                    embedder_config["openai_base_url"] = config["embedding_openai_base_url"]
+                if "embedding_dashscope_base_url" in config:
+                    embedder_config["dashscope_base_url"] = config["embedding_dashscope_base_url"]
                 powermem_config["embedder"] = {
                     "provider": embedding_provider,
                     "config": embedder_config
                 }
-            
+
             # Initialize memory client based on mode
             if self.enable_user_profile:
                 from powermem import UserMemory
@@ -116,14 +138,14 @@ class MemoryProvider(MemoryProviderBase):
                 from powermem import AsyncMemory
                 self.memory_client = AsyncMemory(config=powermem_config)
                 memory_mode = "AsyncMemory (普通记忆模式)"
-            
+
             self.use_powermem = True
-            
+
             logger.bind(tag=TAG).info(
                 f"PowerMem initialized successfully: mode={memory_mode}, "
                 f"database={database_provider}, llm={llm_provider}, embedding={embedding_provider}"
             )
-            
+
         except ImportError as e:
             logger.bind(tag=TAG).error(
                 f"PowerMem not installed. Please install with: pip install powermem. Error: {e}"
@@ -137,17 +159,17 @@ class MemoryProvider(MemoryProviderBase):
     async def save_memory(self, msgs):
         """
         Save conversation messages to PowerMem.
-        
+
         Args:
             msgs: List of message objects with 'role' and 'content' attributes
-            
+
         Returns:
             Result from PowerMem API or None if failed
         """
         if not self.use_powermem or self.memory_client is None:
             logger.bind(tag=TAG).warning("PowerMem is not available, skipping save_memory")
             return None
-            
+
         if len(msgs) < 2:
             logger.bind(tag=TAG).debug("Not enough messages to save (need at least 2)")
             return None
@@ -159,16 +181,16 @@ class MemoryProvider(MemoryProviderBase):
                 for message in msgs
                 if message.role != "system"
             ]
-            
+
             # Add memory using PowerMem SDK
             result = await self.memory_client.add(
                 messages=messages,
                 user_id=self.role_id
             )
-            
+
             logger.bind(tag=TAG).debug(f"Save memory result: {result}")
             return result
-                        
+
         except Exception as e:
             logger.bind(tag=TAG).error(f"Error saving memory: {str(e)}")
             logger.bind(tag=TAG).debug(f"Detailed error: {traceback.format_exc()}")
@@ -177,24 +199,24 @@ class MemoryProvider(MemoryProviderBase):
     async def query_memory(self, query: str) -> str:
         """
         Query memories from PowerMem based on similarity search.
-        
+
         Args:
             query: The search query string
-            
+
         Returns:
             Formatted string of relevant memories or empty string if none found
         """
         if not self.use_powermem or self.memory_client is None:
             logger.bind(tag=TAG).warning("PowerMem is not available, skipping query_memory")
             return ""
-            
+
         try:
             if not getattr(self, "role_id", None):
                 logger.bind(tag=TAG).debug("No role_id set, returning empty memory")
                 return ""
 
             result_parts = []
-            
+
             # If user profile mode is enabled, include user profile in results
             if self.enable_user_profile:
                 profile = await self.get_user_profile()
@@ -207,7 +229,7 @@ class MemoryProvider(MemoryProviderBase):
                 user_id=self.role_id,
                 limit=30
             )
-                    
+
             if results and "results" in results:
                 # Format each memory entry with its update time
                 memories = []
@@ -218,7 +240,7 @@ class MemoryProvider(MemoryProviderBase):
                         timestamp = str(entry["updated_at"])
                     elif "created_at" in entry and entry["created_at"]:
                         timestamp = str(entry["created_at"])
-                        
+
                     if timestamp:
                         try:
                             # Parse and reformat the timestamp (remove milliseconds if present)
@@ -231,7 +253,7 @@ class MemoryProvider(MemoryProviderBase):
                             formatted_time = timestamp
                     else:
                         formatted_time = ""
-                        
+
                     memory = entry.get("memory", "") or entry.get("content", "")
                     if memory:
                         if formatted_time:
@@ -251,7 +273,7 @@ class MemoryProvider(MemoryProviderBase):
             final_result = "\n\n".join(result_parts)
             logger.bind(tag=TAG).debug(f"Query results: {final_result}")
             return final_result
-            
+
         except Exception as e:
             logger.bind(tag=TAG).error(f"Error querying memory: {str(e)}")
             logger.bind(tag=TAG).debug(f"Detailed error: {traceback.format_exc()}")
@@ -260,40 +282,37 @@ class MemoryProvider(MemoryProviderBase):
     async def get_user_profile(self) -> str:
         """
         Get user profile from PowerMem (only available in UserMemory mode).
-        
+
         Returns:
             Formatted user profile string or empty string if not available
         """
         if not self.use_powermem or self.memory_client is None:
             return ""
-            
+
         if not self.enable_user_profile:
             logger.bind(tag=TAG).debug("User profile mode is not enabled")
             return ""
-            
+
         try:
             if not getattr(self, "role_id", None):
                 return ""
-                
+
             # Get user profile using UserMemory SDK
             profile = await self.memory_client.get_profile(user_id=self.role_id)
-            
+
             if not profile:
                 return ""
-            
+
             # Format profile as readable string
             profile_parts = []
             for key, value in profile.items():
                 if value:
                     profile_parts.append(f"- {key}: {value}")
-            
+
             return "\n".join(profile_parts)
-            
+
         except Exception as e:
             logger.bind(tag=TAG).error(f"Error getting user profile: {str(e)}")
             logger.bind(tag=TAG).debug(f"Detailed error: {traceback.format_exc()}")
             return ""
 
-
-# Register the memory provider instance
-powermem = MemoryProvider({})