import json
import asyncio
from fastapi import Body
from sse_starlette.sse import EventSourceResponse
from configs import LLM_MODELS, TEMPERATURE, HISTORY_LEN, Agent_MODEL
from langchain.chains import LLMChain
from langchain.memory import ConversationBufferWindowMemory
from langchain.agents import LLMSingleActionAgent, AgentExecutor
from typing import AsyncIterable, Optional, List
from server.utils import wrap_done, get_ChatOpenAI, get_prompt_template
from server.knowledge_base.kb_service.base import get_kb_details
from server.agent.custom_agent.ChatGLM3Agent import initialize_glm3_agent
from server.agent.tools_select import tools, tool_names
from server.agent.callbacks import CustomAsyncIteratorCallbackHandler, Status
from server.chat.utils import History
from server.agent import model_container
from server.agent.custom_template import CustomOutputParser, CustomPromptTemplate
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": "请使用知识库工具查询今天北京天气"},
{"role": "assistant",
"content": "使用天气查询工具查询到今天北京多云，10-14摄氏度，东北风2级，易感冒"}]]
),
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]
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],
)
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:
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)
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)
memory = ConversationBufferWindowMemory(k=HISTORY_LEN * 2)
for message in history:
if message.role == 'user':
memory.chat_memory.add_user_message(message.content)
else:
memory.chat_memory.add_ai_message(message.content)
if "chatglm3" in model_container.MODEL.model_name or "zhipu-api" in model_container.MODEL.model_name:
agent_executor = initialize_glm3_agent(
llm=model,
tools=tools,
callback_manager=None,
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:
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"])
tools_use.append("工具输出: " + data["output_str"])
tools_use.append("\n```\n")
yield json.dumps({"tools": tools_use}, ensure_ascii=False)
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():
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),
)