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部分配置key修正

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wayyoungboy
2026-01-08 23:52:19 +08:00
parent de7aedab96
commit 2bf1123647
3 changed files with 97 additions and 54 deletions
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docs/powermem-integration.md
+30 -12
View File
@@ -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
embedding_provider: zhipu
llm_provider: openai # 使用 openai 兼容模式
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
+7 -1
View File
@@ -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
+60 -41
View File
@@ -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({})