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增强Qwen类模型应用Agent解析时正则表达式的匹配+修复创建模型时的bug #5257

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增强Qwen类模型应用Agent解析时正则表达式的匹配+修复创建模型时的bug #5257

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9 changes: 5 additions & 4 deletions libs/chatchat-server/chatchat/server/agent/agent_factory/qwen_agent.py
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Original file line number Diff line number Diff line change
Expand Up @@ -104,10 +104,11 @@ class QwenChatAgentOutputParserCustom(StructuredChatOutputParser):

def parse(self, text: str) -> Union[AgentAction, AgentFinish]:
if s := re.findall(
r"\nAction:\s*(.+)\nAction\sInput:\s*(.+)", text, flags=re.DOTALL
r"\*{0,2}Action:\*{0,2}\s*(.+?)\s*\*{0,2}Action Input:\*{0,2}\s*(?:```(?:json\n)?)?({.*?})(?:```)?", text, flags=re.DOTALL
):
s = s[-1]
json_string: str = s[1]
tool_name: str = s[0].replace("[", "").replace("]", "").replace("*", "").strip()
json_string: str = s[1].replace("\n", "").replace(" ", "").replace("*", "").strip()
json_input = None
try:
json_input = json.loads(json_string)
Expand Down Expand Up @@ -135,7 +136,7 @@ def parse(self, text: str) -> Union[AgentAction, AgentFinish]:
if "command" in json_input:
json_input["query"] = json_input.pop("command")

return AgentAction(tool=s[0].strip(), tool_input=json_input, log=text)
return AgentAction(tool=tool_name.strip(), tool_input=json_input, log=text)
elif s := re.findall(r"\nFinal\sAnswer:\s*(.+)", text, flags=re.DOTALL):
s = s[-1]
return AgentFinish({"output": s}, log=text)
Expand Down Expand Up @@ -192,7 +193,7 @@ def create_structured_qwen_chat_agent(
RunnablePassthrough.assign(agent_scratchpad=itemgetter("intermediate_steps"))
| prompt
| llm.bind(
stop=["<|endoftext|>", "<|im_start|>", "<|im_end|>", "\nObservation:"]
stop=["<|endoftext|>", "<|im_start|>", "<|im_end|>", "\nObservation:", "\n**Observation:**"]
)
| output_parser
)
Expand Down
11 changes: 6 additions & 5 deletions libs/chatchat-server/chatchat/server/chat/chat.py
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Original file line number Diff line number Diff line change
Expand Up @@ -39,20 +39,21 @@ def create_models_from_config(configs, callbacks, stream, max_tokens):
models = {}
prompts = {}
for model_type, params in configs.items():
model_name = params.get("model", "").strip() or get_default_llm()
callbacks = callbacks if params.get("callbacks", False) else None
model_name = list(params.keys())[0].strip() or get_default_llm()
params_model = params[model_name]
callbacks = callbacks if params_model.get("callbacks", False) else None
# 判断是否传入 max_tokens 的值, 如果传入就按传入的赋值(api 调用且赋值), 如果没有传入则按照初始化配置赋值(ui 调用或 api 调用未赋值)
max_tokens_value = max_tokens if max_tokens is not None else params.get("max_tokens", 1000)
max_tokens_value = max_tokens if max_tokens is not None else params_model.get("max_tokens", 1000)
model_instance = get_ChatOpenAI(
model_name=model_name,
temperature=params.get("temperature", 0.5),
temperature=params_model.get("temperature", 0.5),
max_tokens=max_tokens_value,
callbacks=callbacks,
streaming=stream,
local_wrap=True,
)
models[model_type] = model_instance
prompt_name = params.get("prompt_name", "default")
prompt_name = params_model.get("prompt_name", "default")
prompt_template = get_prompt_template(type=model_type, name=prompt_name)
prompts[model_type] = prompt_template
return models, prompts
Expand Down