258 lines
14 KiB
Python
258 lines
14 KiB
Python
from langchain.memory import ConversationBufferWindowMemory
|
||
|
||
from server.agent.custom_agent.ChatGLM3Agent import initialize_glm3_agent
|
||
from server.agent.tools_select import tools, tool_names, search_tool_names
|
||
from server.agent.callbacks import CustomAsyncIteratorCallbackHandler, Status
|
||
from langchain.agents import LLMSingleActionAgent, AgentExecutor
|
||
from server.agent.custom_template import CustomOutputParser, CustomPromptTemplate
|
||
from fastapi import Body
|
||
from sse_starlette.sse import EventSourceResponse
|
||
from configs import LLM_MODELS, TEMPERATURE, HISTORY_LEN, Agent_MODEL
|
||
from server.utils import wrap_done, get_ChatOpenAI, get_prompt_template
|
||
from langchain.chains import LLMChain
|
||
from typing import AsyncIterable, Optional
|
||
import asyncio
|
||
from typing import List
|
||
from server.chat.utils import History
|
||
import json
|
||
from server.agent import model_container
|
||
from server.knowledge_base.kb_service.base import get_kb_details
|
||
import ast
|
||
import re
|
||
from configs.basic_config import *
|
||
async def agent_chat(query: str = Body(..., description="用户输入", examples=["恼羞成怒"]),
|
||
history: List[History] = Body([],
|
||
description="历史对话",
|
||
examples=[[
|
||
{"role": "user",
|
||
"content": "请使用天气查询工具查询今天北京天气"},
|
||
{"role": "assistant",
|
||
"content": "今天是2024年3月22日，受冷空气影响，白天有3、4级偏北风，阵风6、7"
|
||
"级，西部山区阵风相对明显，局地伴有扬沙。白天晴，局地有扬沙,偏北风,1级转3、4级，阵风6、7级,"
|
||
"最高气温22℃。夜间晴间多云,偏北风,1、2级,最低气温6℃。"}]]
|
||
),
|
||
stream: bool = Body(False, description="流式输出"),
|
||
model_name: str = Body(LLM_MODELS[0], description="LLM 模型名称。"),
|
||
temperature: float = Body(TEMPERATURE, description="LLM 采样温度", ge=0.0, le=1.0),
|
||
max_tokens: Optional[int] = Body(None, description="限制LLM生成Token数量，默认None代表模型最大值"),
|
||
prompt_name: str = Body("default",
|
||
description="使用的prompt模板名称(在configs/prompt_config.py中配置)"),
|
||
):
|
||
history = [History.from_data(h) for h in history]
|
||
query = "帮我搜索一下：" + query
|
||
logger.info(f"agent query: {query}")
|
||
async def agent_chat_iterator(
|
||
query: str,
|
||
history: Optional[List[History]],
|
||
model_name: str = LLM_MODELS[0],
|
||
prompt_name: str = prompt_name,
|
||
) -> AsyncIterable[str]:
|
||
nonlocal max_tokens
|
||
callback = CustomAsyncIteratorCallbackHandler()
|
||
if isinstance(max_tokens, int) and max_tokens <= 0:
|
||
max_tokens = None
|
||
|
||
model = get_ChatOpenAI(
|
||
model_name=model_name,
|
||
temperature=temperature,
|
||
max_tokens=max_tokens,
|
||
callbacks=[callback],
|
||
)
|
||
|
||
## 传入全局变量来实现agent调用
|
||
kb_list = {x["kb_name"]: x for x in get_kb_details()}
|
||
model_container.DATABASE = {name: details['kb_info'] for name, details in kb_list.items()}
|
||
|
||
if Agent_MODEL:
|
||
## 如果有指定使用Agent模型来完成任务
|
||
model_agent = get_ChatOpenAI(
|
||
model_name=Agent_MODEL,
|
||
temperature=temperature,
|
||
max_tokens=max_tokens,
|
||
callbacks=[callback],
|
||
)
|
||
model_container.MODEL = model_agent
|
||
else:
|
||
model_container.MODEL = model
|
||
|
||
prompt_template = get_prompt_template("agent_chat", prompt_name)
|
||
type(prompt_template)
|
||
prompt_template_agent = CustomPromptTemplate(
|
||
template=prompt_template,
|
||
tools=tools,
|
||
input_variables=["input", "intermediate_steps", "history"]
|
||
)
|
||
output_parser = CustomOutputParser()
|
||
llm_chain = LLMChain(llm=model, prompt=prompt_template_agent)
|
||
# 把history转成agent的memory
|
||
memory = ConversationBufferWindowMemory(k=HISTORY_LEN * 2)
|
||
for message in history:
|
||
# 检查消息的角色
|
||
if message.role == 'user':
|
||
# 添加用户消息
|
||
memory.chat_memory.add_user_message(message.content)
|
||
else:
|
||
# 添加AI消息
|
||
memory.chat_memory.add_ai_message(message.content)
|
||
|
||
if "chatglm3" in model_container.MODEL.model_name:
|
||
agent_executor = initialize_glm3_agent(
|
||
llm=model,
|
||
tools=tools,
|
||
callback_manager=None,
|
||
# Langchain Prompt is not constructed directly here, it is constructed inside the GLM3 agent.
|
||
prompt=prompt_template,
|
||
input_variables=["input", "intermediate_steps", "history"],
|
||
memory=memory,
|
||
verbose=True,
|
||
)
|
||
else:
|
||
agent = LLMSingleActionAgent(
|
||
llm_chain=llm_chain,
|
||
output_parser=output_parser,
|
||
stop=["\nObservation:", "Observation"],
|
||
allowed_tools=tool_names,
|
||
)
|
||
agent_executor = AgentExecutor.from_agent_and_tools(agent=agent,
|
||
tools=tools,
|
||
verbose=True,
|
||
memory=memory,
|
||
)
|
||
while True:
|
||
try:
|
||
task = asyncio.create_task(wrap_done(
|
||
agent_executor.acall(query, callbacks=[callback], include_run_info=True),
|
||
callback.done))
|
||
break
|
||
except:
|
||
pass
|
||
|
||
if stream:
|
||
search_answer = ""
|
||
policy_answer = ""
|
||
async for chunk in callback.aiter():
|
||
tools_use = []
|
||
# Use server-sent-events to stream the response
|
||
data = json.loads(chunk)
|
||
if data["status"] == Status.start or data["status"] == Status.complete:
|
||
continue
|
||
elif data["status"] == Status.error:
|
||
tools_use.append("\n```\n")
|
||
tools_use.append("工具名称: " + data["tool_name"])
|
||
tools_use.append("工具状态: " + "调用失败")
|
||
tools_use.append("错误信息: " + data["error"])
|
||
tools_use.append("重新开始尝试")
|
||
tools_use.append("\n```\n")
|
||
yield json.dumps({"tools": tools_use}, ensure_ascii=False)
|
||
elif data["status"] == Status.tool_finish:
|
||
tools_use.append("\n```\n")
|
||
tools_use.append("工具名称: " + data["tool_name"])
|
||
tools_use.append("工具状态: " + "调用成功")
|
||
tools_use.append("工具输入: " + data["input_str"])
|
||
if data["tool_name"] == "联网思索":
|
||
if "政策类资料" in data["output_str"]:
|
||
try:
|
||
# 尝试使用ast.literal_eval来安全地解析字符串为列表
|
||
output_arr = ast.literal_eval(data["output_str"])
|
||
except ValueError:
|
||
# 如果解析失败，处理错误
|
||
print("无法解析字符串为列表")
|
||
output_arr = []
|
||
policy_content = ''.join(output_arr[:5])
|
||
policy_answer = ''.join(output_arr[5:10])
|
||
# search_content = output_arr[-2]
|
||
search_answer_str = str(output_arr[-1])
|
||
if "暂未找到相关资料" in search_answer_str:
|
||
search_answer = "\n知识中心资料: 暂无"
|
||
else:
|
||
search_answer_arr = search_answer_str[2: len(search_answer_str) - 2].replace("\\n", "")
|
||
search_answer = '\n'.join(search_answer_arr.split("\', \'"))
|
||
data["output_str"] = ''.join(policy_content + policy_answer)
|
||
print("<<<工具输出>>>\n", data["output_str"])
|
||
elif "暂未找到相关资料" in data["output_str"]:
|
||
print("output_str", data["output_str"])
|
||
try:
|
||
output_arr = ast.literal_eval(data["output_str"])
|
||
except ValueError:
|
||
# 如果解析失败，处理错误
|
||
print("无法解析字符串为列表")
|
||
output_arr = []
|
||
# 计算列表中的列表和字符串数量
|
||
search_content = output_arr[:0]
|
||
search_answer = str(output_arr[-1])[2: len(output_arr[-1]) - 3]
|
||
data["output_str"] = str(search_content)
|
||
print("<<<工具输出>>>\n", data["output_str"])
|
||
else:
|
||
search_output_str = data["output_str"][2: len(data["output_str"]) - 3].replace("\\n", "")
|
||
search_output_arr = search_output_str.split("\', [\'")
|
||
search_content = str(search_output_arr[0])
|
||
search_output_str1 = str(search_output_arr[1])
|
||
search_answer = '\n'.join(search_output_str1.split("\', \'"))
|
||
data["output_str"] = search_content
|
||
print("<<<工具输出>>>\n", data["output_str"])
|
||
if data["tool_name"] == "policy_knowledgebase":
|
||
# policy_output_str = data["output_str"][2: len(data["output_str"]) - 2].replace("\n", "")
|
||
policy_output_str = ast.literal_eval((data["output_str"].replace("\n", "\\n")))
|
||
id_str = policy_output_str[0]
|
||
processed_lines = [line.strip() + '\n' for line in policy_output_str[1]]
|
||
policy_answer = id_str + '\n\n' + ''.join(processed_lines)
|
||
# print("policy_output_str: ", policy_output_str)
|
||
# policy_answer = '\n'.join(policy_output_str.split("\', \'"))
|
||
# policy_answer += policy_output
|
||
print("policy_answer：", policy_answer)
|
||
tools_use.append("工具输出: " + data["output_str"])
|
||
tools_use.append("\n```\n")
|
||
# 格式化工具的输出
|
||
yield json.dumps({"tools": tools_use}, ensure_ascii=False)
|
||
elif data["status"] == Status.agent_finish and search_answer:
|
||
# yield json.dumps({"final_answer": data["final_answer"] + "\n\n参考资料：\n\n" + search_answer.replace("\\n", '\n').replace("\', \'", '').replace("\'], [\'",'')}, ensure_ascii=False)
|
||
if policy_answer:
|
||
yield json.dumps({"final_answer": data["final_answer"] + "\n\n参考资料：\n\n" + str(policy_answer) + str(search_answer)}, ensure_ascii=False)
|
||
else:
|
||
yield json.dumps({"final_answer": data["final_answer"] + "\n\n参考资料：\n\n" + str(search_answer)}, ensure_ascii=False)
|
||
print("search_answer_output：", search_answer)
|
||
elif data["status"] == Status.agent_finish and policy_answer:
|
||
# yield json.dumps({"final_answer": policy_answer.replace("\\n", '\n').replace("\', \'", '\n').replace("\', [\'",'\n\n')}, ensure_ascii=False)
|
||
yield json.dumps({"final_answer": policy_answer}, ensure_ascii=False)
|
||
print("policy_answer_output：", policy_answer)
|
||
elif data["status"] == Status.agent_finish:
|
||
yield json.dumps({"final_answer": data["final_answer"]}, ensure_ascii=False)
|
||
else:
|
||
yield json.dumps({"answer": data["llm_token"]}, ensure_ascii=False)
|
||
|
||
|
||
else:
|
||
answer = ""
|
||
final_answer = ""
|
||
async for chunk in callback.aiter():
|
||
# Use server-sent-events to stream the response
|
||
data = json.loads(chunk)
|
||
if data["status"] == Status.start or data["status"] == Status.complete:
|
||
continue
|
||
if data["status"] == Status.error:
|
||
answer += "\n```\n"
|
||
answer += "工具名称: " + data["tool_name"] + "\n"
|
||
answer += "工具状态: " + "调用失败" + "\n"
|
||
answer += "错误信息: " + data["error"] + "\n"
|
||
answer += "\n```\n"
|
||
if data["status"] == Status.tool_finish:
|
||
answer += "\n```\n"
|
||
answer += "工具名称: " + data["tool_name"] + "\n"
|
||
answer += "工具状态: " + "调用成功" + "\n"
|
||
answer += "工具输入: " + data["input_str"] + "\n"
|
||
answer += "工具输出: " + data["output_str"] + "\n"
|
||
answer += "\n```\n"
|
||
if data["status"] == Status.agent_finish:
|
||
final_answer = data["final_answer"]
|
||
else:
|
||
answer += data["llm_token"]
|
||
|
||
yield json.dumps({"answer": answer, "final_answer": final_answer}, ensure_ascii=False)
|
||
await task
|
||
|
||
return EventSourceResponse(agent_chat_iterator(query=query,
|
||
history=history,
|
||
model_name=model_name,
|
||
prompt_name=prompt_name),
|
||
)
|