mirror of
https://github.com/RYDE-WORK/Langchain-Chatchat.git
synced 2026-01-23 23:40:03 +08:00
b4c68ddd05
* 优化在线 API ，支持 completion 和 embedding，简化在线 API 开发方式 新功能 - 智谱AI、Minimax、千帆、千问 4 个在线模型支持 embeddings（不通过Fastchat，后续会单独提供相关api接口） - 在线模型自动检测传入参数，在传入非 messages 格式的 prompt 时，自动转换为 completion 形式，以支持 completion 接口 开发者： - 重构ApiModelWorker: - 所有在线 API 请求封装到 do_chat 方法：自动传入参数 ApiChatParams，简化参数与配置项的获取；自动处理与fastchat的接口 - 加强 API 请求错误处理，返回更有意义的信息 - 改用 qianfan sdk 重写 qianfan-api - 将所有在线模型的测试用例统一在一起，简化测试用例编写 * Delete requirements_langflow.txt
167 lines
8.3 KiB
Python
167 lines
8.3 KiB
Python
from langchain.memory import ConversationBufferWindowMemory
|
||
from server.agent.tools_select import tools, tool_names
|
||
from server.agent.callbacks import CustomAsyncIteratorCallbackHandler, Status
|
||
from langchain.agents import AgentExecutor, LLMSingleActionAgent
|
||
from server.agent.custom_template import CustomOutputParser, CustomPromptTemplate
|
||
from fastapi import Body
|
||
from fastapi.responses import StreamingResponse
|
||
from configs import LLM_MODEL, 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, Dict
|
||
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
|
||
|
||
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_MODEL, 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)"),
|
||
# top_p: float = Body(TOP_P, description="LLM 核采样。勿与temperature同时设置", gt=0.0, lt=1.0),
|
||
):
|
||
history = [History.from_data(h) for h in history]
|
||
|
||
async def agent_chat_iterator(
|
||
query: str,
|
||
history: Optional[List[History]],
|
||
model_name: str = LLM_MODEL,
|
||
prompt_name: str = prompt_name,
|
||
) -> AsyncIterable[str]:
|
||
callback = CustomAsyncIteratorCallbackHandler()
|
||
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)
|
||
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)
|
||
agent = LLMSingleActionAgent(
|
||
llm_chain=llm_chain,
|
||
output_parser=output_parser,
|
||
stop=["\nObservation:", "Observation:", "<|im_end|>"], # Qwen模型中使用这个
|
||
allowed_tools=tool_names,
|
||
)
|
||
# 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)
|
||
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():
|
||
# 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 StreamingResponse(agent_chat_iterator(query=query,
|
||
history=history,
|
||
model_name=model_name,
|
||
prompt_name=prompt_name),
|
||
media_type="text/event-stream")
|