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update: 记忆模块使用独立LLM openai增加超参

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Sakura-RanChen
2025-05-22 17:14:58 +08:00
parent d934e0a69d
commit 02cb9c35b3
6 changed files with 75 additions and 23 deletions
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main/xiaozhi-server/config.yaml
+4
View File
@@ -220,6 +220,10 @@ Memory:
mem_local_short:
# 本地记忆功能，通过selected_module的llm总结，数据保存在本地，不会上传到服务器
type: mem_local_short
# 配备记忆存储独立的思考模型
# 如果这里不填，则会默认使用selected_module.LLM的模型作为意图识别的思考模型
# 如果你的不想使用selected_module.LLM记忆存储，这里最好使用独立的LLM作为意图识别，例如使用免费的ChatGLMLLM
llm: ChatGLMLLM
ASR:
FunASR:
main/xiaozhi-server/core/connection.py
+28
View File
@@ -452,6 +452,34 @@ class ConnectionHandler:
save_to_file=not self.read_config_from_api,
)
# 获取记忆总结配置
memory_config = self.config["Memory"]
memory_type = self.config["Memory"][self.config["selected_module"]["Memory"]][
"type"
]
# 如果使用 nomen，直接返回
if memory_type == "nomem":
return
# 使用 mem_local_short 模式
elif memory_type == "mem_local_short":
memory_llm_name = memory_config[self.config["selected_module"]["Memory"]]["llm"]
if memory_llm_name and memory_llm_name in self.config["LLM"]:
# 如果配置了专用LLM，则创建独立的LLM实例
from core.utils import llm as llm_utils
memory_llm_config = self.config["LLM"][memory_llm_name]
memory_llm_type = memory_llm_config.get("type", memory_llm_name)
memory_llm = llm_utils.create_instance(
memory_llm_type, memory_llm_config
)
self.logger.bind(tag=TAG).info(
f"为记忆总结创建了专用LLM: {memory_llm_name}, 类型: {memory_llm_type}"
)
self.memory.set_llm(memory_llm)
else:
# 否则使用主LLM
self.memory.set_llm(self.llm)
self.logger.bind(tag=TAG).info("使用主LLM作为意图识别模型")
def _initialize_intent(self):
self.intent_type = self.config["Intent"][
self.config["selected_module"]["Intent"]
main/xiaozhi-server/core/providers/llm/base.py
+3 -3
View File
@@ -10,7 +10,7 @@ class LLMProviderBase(ABC):
"""LLM response generator"""
pass
def response_no_stream(self, system_prompt, user_prompt):
def response_no_stream(self, system_prompt, user_prompt, **kwargs):
try:
# 构造对话格式
dialogue = [
@@ -18,7 +18,7 @@ class LLMProviderBase(ABC):
{"role": "user", "content": user_prompt}
]
result = ""
for part in self.response("", dialogue):
for part in self.response("", dialogue, **kwargs):
result += part
return result
@@ -30,7 +30,7 @@ class LLMProviderBase(ABC):
"""
Default implementation for function calling (streaming)
This should be overridden by providers that support function calls
Returns: generator that yields either text tokens or a special function call token
"""
# For providers that don't support functions, just return regular response
main/xiaozhi-server/core/providers/llm/openai/openai.py
+21 -10
View File
@@ -16,26 +16,37 @@ class LLMProvider(LLMProviderBase):
self.base_url = config.get("base_url")
else:
self.base_url = config.get("url")
max_tokens = config.get("max_tokens")
if max_tokens is None or max_tokens == "":
max_tokens = 500
try:
max_tokens = int(max_tokens)
except (ValueError, TypeError):
max_tokens = 500
self.max_tokens = max_tokens
param_defaults = {
"max_tokens": (500, int),
"temperature": (0.7, lambda x: round(float(x), 1)),
"top_p": (1.0, lambda x: round(float(x), 1)),
"frequency_penalty": (0, lambda x: round(float(x), 1))
}
for param, (default, converter) in param_defaults.items():
value = config.get(param)
try:
setattr(self, param, converter(value) if value not in (None, "") else default)
except (ValueError, TypeError):
setattr(self, param, default)
logger.debug(
f"意图识别参数初始化: {self.temperature}, {self.max_tokens}, {self.top_p}, {self.frequency_penalty}")
check_model_key("LLM", self.api_key)
self.client = openai.OpenAI(api_key=self.api_key, base_url=self.base_url)
def response(self, session_id, dialogue):
def response(self, session_id, dialogue, **kwargs):
try:
responses = self.client.chat.completions.create(
model=self.model_name,
messages=dialogue,
stream=True,
max_tokens=self.max_tokens,
max_tokens=kwargs.get("max_tokens", self.max_tokens),
temperature=kwargs.get("temperature", self.temperature),
top_p=kwargs.get("top_p", self.top_p),
frequency_penalty=kwargs.get("frequency_penalty", self.frequency_penalty),
)
is_active = True
main/xiaozhi-server/core/providers/memory/base.py
+7 -1
View File
@@ -9,7 +9,13 @@ class MemoryProviderBase(ABC):
def __init__(self, config):
self.config = config
self.role_id = None
self.llm = None
def set_llm(self, llm):
self.llm = llm
# 获取模型名称和类型信息
model_name = getattr(llm, "model_name", str(llm.__class__.__name__))
# 记录更详细的日志
logger.bind(tag=TAG).info(f"记忆总结设置LLM: {model_name}")
@abstractmethod
async def save_memory(self, msgs):
main/xiaozhi-server/core/providers/memory/mem_local_short/mem_local_short.py
+12 -9
View File
@@ -107,7 +107,7 @@ TAG = __name__
class MemoryProvider(MemoryProviderBase):
def __init__(self, config, summary_memory):
super().__init__(config)
self.short_momery = ""
self.short_memory = ""
self.save_to_file = True
self.memory_path = get_project_dir() + "data/.memory.yaml"
self.load_memory(summary_memory)
@@ -122,7 +122,7 @@ class MemoryProvider(MemoryProviderBase):
def load_memory(self, summary_memory):
# api获取到总结记忆后直接返回
if summary_memory or not self.save_to_file:
self.short_momery = summary_memory
self.short_memory = summary_memory
return
all_memory = {}
@@ -130,18 +130,21 @@ class MemoryProvider(MemoryProviderBase):
with open(self.memory_path, "r", encoding="utf-8") as f:
all_memory = yaml.safe_load(f) or {}
if self.role_id in all_memory:
self.short_momery = all_memory[self.role_id]
self.short_memory = all_memory[self.role_id]
def save_memory_to_file(self):
all_memory = {}
if os.path.exists(self.memory_path):
with open(self.memory_path, "r", encoding="utf-8") as f:
all_memory = yaml.safe_load(f) or {}
all_memory[self.role_id] = self.short_momery
all_memory[self.role_id] = self.short_memory
with open(self.memory_path, "w", encoding="utf-8") as f:
yaml.dump(all_memory, f, allow_unicode=True)
async def save_memory(self, msgs):
# 打印使用的模型信息
model_info = getattr(self.llm, "model_name", str(self.llm.__class__.__name__))
logger.bind(tag=TAG).debug(f"使用记忆保存模型: {model_info}")
if self.llm is None:
logger.bind(tag=TAG).error("LLM is not set for memory provider")
return None
@@ -155,9 +158,9 @@ class MemoryProvider(MemoryProviderBase):
msgStr += f"User: {msg.content}\n"
elif msg.role == "assistant":
msgStr += f"Assistant: {msg.content}\n"
if self.short_momery and len(self.short_momery) > 0:
if self.short_memory and len(self.short_memory) > 0:
msgStr += "历史记忆：\n"
msgStr += self.short_momery
msgStr += self.short_memory
# 当前时间
time_str = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())
@@ -168,7 +171,7 @@ class MemoryProvider(MemoryProviderBase):
json_str = extract_json_data(result)
try:
json.loads(json_str) # 检查json格式是否正确
self.short_momery = json_str
self.short_memory = json_str
self.save_memory_to_file()
except Exception as e:
print("Error:", e)
@@ -179,7 +182,7 @@ class MemoryProvider(MemoryProviderBase):
save_mem_local_short(self.role_id, result)
logger.bind(tag=TAG).info(f"Save memory successful - Role: {self.role_id}")
return self.short_momery
return self.short_memory
async def query_memory(self, query: str) -> str:
return self.short_momery
return self.short_memory