mirror of
https://github.com/RYDE-WORK/Langchain-Chatchat.git
synced 2026-01-19 13:23:16 +08:00
第一版初步agent实现 (#1503)
* 第一版初步agent实现 * 增加steaming参数 * 修改了weather.py --------- Co-authored-by: zR <zRzRzRzRzRzRzR>
This commit is contained in:
zR
GitHub
parent
13cca9cf81
commit
598eb298df
73
docs/自定义Agent.md
Normal file
73
docs/自定义Agent.md
Normal file
@ -0,0 +1,73 @@
|
||||
## 自定义属于自己的Agent
|
||||
### 1. 创建自己的Agent的py文件
|
||||
开发者在```server/agent```文件中创建一个自己的文件，并将其添加到```tools.py```中。
|
||||
|
||||
例如，您创建了一个```custom_agent.py```文件，其中包含一个```work```函数，那么您需要在```tools.py```中添加如下代码：
|
||||
```python
|
||||
from custom_agent import work
|
||||
Tool.from_function(
|
||||
func=work,
|
||||
name="该函数的名字",
|
||||
description=""
|
||||
)
|
||||
```
|
||||
|
||||
### 2. 修改 custom_template.py文件
|
||||
开发者需要根据自己选择的大模型设定适合该模型的Agent Prompt和自自定义返回格式。
|
||||
在我们的代码中，提供了默认的两种方式，一种是适配于GPT的提示词：
|
||||
```python
|
||||
template = """Answer the following questions as best you can， You have access to the following tools:
|
||||
{tools}
|
||||
Use the following format:
|
||||
|
||||
Question: the input question you must answer
|
||||
Thought: you should always think about what to do
|
||||
Action: the action to take, should be one of [{tool_names}]
|
||||
Action Input: the input to the action
|
||||
Observation: the result of the action
|
||||
... (this Thought/Action/Action Input/Observation can repeat N times)
|
||||
Thought: I now know the final answer
|
||||
Final Answer: the final answer to the original input question
|
||||
|
||||
Begin!
|
||||
|
||||
Previous conversation history:
|
||||
{history}
|
||||
|
||||
New question: {input}
|
||||
{agent_scratchpad}"""
|
||||
```
|
||||
另一种是适配于GLM-130B的提示词：
|
||||
```python
|
||||
template = """
|
||||
尽可能地回答以下问题。你可以使用以下工具:{tools}
|
||||
请按照以下格式进行:
|
||||
Question: 需要你回答的输入问题
|
||||
Thought: 你应该总是思考该做什么
|
||||
Action: 需要使用的工具，应该是[{tool_names}]中的一个
|
||||
Action Input: 传入工具的内容
|
||||
Observation: 行动的结果
|
||||
... (这个Thought/Action/Action Input/Observation可以重复N次)
|
||||
Thought: 我现在知道最后的答案
|
||||
Final Answer: 对原始输入问题的最终答案
|
||||
|
||||
现在开始！
|
||||
|
||||
之前的对话:
|
||||
{history}
|
||||
|
||||
New question: {input}
|
||||
Thought: {agent_scratchpad}"""
|
||||
```
|
||||
|
||||
### 3. 局限性
|
||||
1. 在我们的实验中，小于70B级别的模型，若不经过微调，很难达到较好的效果。因此，我们建议开发者使用大于70B级别的模型进行微调，以达到更好的效果。
|
||||
2. 由于Agent的脆弱性，temperture参数的设置对于模型的效果有很大的影响。我们建议开发者在使用自定义Agent时，对于不同的模型，将其设置成0.1以下，以达到更好的效果。
|
||||
3. 即使使用了大于70B级别的模型，开发者也应该在Prompt上进行深度优化，以让模型能成功的选择工具并完成任务。
|
||||
|
||||
|
||||
### 4. 我们已经支持的Agent
|
||||
我们为开发者编写了三个运用大模型执行的Agent，分别是：
|
||||
1. 翻译工具，实现对输入的任意语言翻译。
|
||||
2. 数学工具，使用LLMMathChain 实现数学计算。
|
||||
3. 天气工具，使用自定义的LLMWetherChain实现天气查询，调用和风天气API。
|
||||
0
server/agent/__init__.py
Normal file
0
server/agent/__init__.py
Normal file
104
server/agent/custom_template.py
Normal file
104
server/agent/custom_template.py
Normal file
@ -0,0 +1,104 @@
|
||||
template = """
|
||||
尽可能地回答以下问题。你可以使用以下工具:{tools}
|
||||
请按照以下格式进行:
|
||||
Question: 需要你回答的输入问题
|
||||
Thought: 你应该总是思考该做什么
|
||||
Action: 需要使用的工具，应该是[{tool_names}]中的一个
|
||||
Action Input: 传入工具的内容
|
||||
Observation: 行动的结果
|
||||
... (这个Thought/Action/Action Input/Observation可以重复N次)
|
||||
Thought: 我现在知道最后的答案
|
||||
Final Answer: 对原始输入问题的最终答案
|
||||
|
||||
现在开始！
|
||||
|
||||
之前的对话:
|
||||
{history}
|
||||
|
||||
New question: {input}
|
||||
Thought: {agent_scratchpad}"""
|
||||
|
||||
|
||||
# ChatGPT 提示词模板
|
||||
# template = """Answer the following questions as best you can， You have access to the following tools:
|
||||
# {tools}
|
||||
# Use the following format:
|
||||
#
|
||||
# Question: the input question you must answer
|
||||
# Thought: you should always think about what to do
|
||||
# Action: the action to take, should be one of [{tool_names}]
|
||||
# Action Input: the input to the action
|
||||
# Observation: the result of the action
|
||||
# ... (this Thought/Action/Action Input/Observation can repeat N times)
|
||||
# Thought: I now know the final answer
|
||||
# Final Answer: the final answer to the original input question
|
||||
#
|
||||
# Begin!
|
||||
#
|
||||
# Previous conversation history:
|
||||
# {history}
|
||||
#
|
||||
# New question: {input}
|
||||
# {agent_scratchpad}"""
|
||||
|
||||
|
||||
from langchain.agents import Tool, AgentExecutor, LLMSingleActionAgent, AgentOutputParser
|
||||
from langchain.prompts import StringPromptTemplate
|
||||
from langchain import OpenAI, SerpAPIWrapper, LLMChain
|
||||
from typing import List, Union
|
||||
from langchain.schema import AgentAction, AgentFinish, OutputParserException
|
||||
from server.agent.tools import tools
|
||||
import re
|
||||
class CustomPromptTemplate(StringPromptTemplate):
|
||||
# The template to use
|
||||
template: str
|
||||
# The list of tools available
|
||||
tools: List[Tool]
|
||||
|
||||
def format(self, **kwargs) -> str:
|
||||
# Get the intermediate steps (AgentAction, Observation tuples)
|
||||
# Format them in a particular way
|
||||
intermediate_steps = kwargs.pop("intermediate_steps")
|
||||
thoughts = ""
|
||||
for action, observation in intermediate_steps:
|
||||
thoughts += action.log
|
||||
thoughts += f"\nObservation: {observation}\nThought: "
|
||||
# Set the agent_scratchpad variable to that value
|
||||
kwargs["agent_scratchpad"] = thoughts
|
||||
# Create a tools variable from the list of tools provided
|
||||
kwargs["tools"] = "\n".join([f"{tool.name}: {tool.description}" for tool in self.tools])
|
||||
# Create a list of tool names for the tools provided
|
||||
kwargs["tool_names"] = ", ".join([tool.name for tool in self.tools])
|
||||
return self.template.format(**kwargs)
|
||||
|
||||
prompt = CustomPromptTemplate(
|
||||
template=template,
|
||||
tools=tools,
|
||||
input_variables=["input", "intermediate_steps", "history"]
|
||||
)
|
||||
class CustomOutputParser(AgentOutputParser):
|
||||
|
||||
def parse(self, llm_output: str) -> Union[AgentAction, AgentFinish]:
|
||||
# Check if agent should finish
|
||||
if "Final Answer:" in llm_output:
|
||||
return AgentFinish(
|
||||
# Return values is generally always a dictionary with a single `output` key
|
||||
# It is not recommended to try anything else at the moment :)
|
||||
return_values={"output": llm_output.split("Final Answer:")[-1].strip()},
|
||||
log=llm_output,
|
||||
)
|
||||
# Parse out the action and action input
|
||||
regex = r"Action\s*\d*\s*:(.*?)\nAction\s*\d*\s*Input\s*\d*\s*:[\s]*(.*)"
|
||||
match = re.search(regex, llm_output, re.DOTALL)
|
||||
if not match:
|
||||
return AgentFinish(
|
||||
return_values={"output": f"调用agent失败: `{llm_output}`"},
|
||||
log=llm_output,
|
||||
)
|
||||
raise OutputParserException(f"调用agent失败: `{llm_output}`")
|
||||
action = match.group(1).strip()
|
||||
action_input = match.group(2)
|
||||
# Return the action and action input
|
||||
return AgentAction(tool=action, tool_input=action_input.strip(" ").strip('"'), log=llm_output)
|
||||
|
||||
|
||||
70
server/agent/math.py
Normal file
70
server/agent/math.py
Normal file
@ -0,0 +1,70 @@
|
||||
from langchain import PromptTemplate
|
||||
from langchain.chains import LLMMathChain
|
||||
from server.chat.utils import wrap_done, get_ChatOpenAI
|
||||
from configs.model_config import LLM_MODEL, TEMPERATURE
|
||||
from langchain.chat_models import ChatOpenAI
|
||||
from langchain.callbacks.manager import CallbackManagerForToolRun
|
||||
|
||||
_PROMPT_TEMPLATE = """将数学问题翻译成可以使用Python的numexpr库执行的表达式。使用运行此代码的输出来回答问题。
|
||||
问题: ${{包含数学问题的问题。}}
|
||||
```text
|
||||
${{解决问题的单行数学表达式}}
|
||||
```
|
||||
...numexpr.evaluate(query)...
|
||||
```output
|
||||
${{运行代码的输出}}
|
||||
```
|
||||
答案: ${{答案}}
|
||||
|
||||
这是两个例子：
|
||||
|
||||
问题: 37593 * 67是多少？
|
||||
```text
|
||||
37593 * 67
|
||||
```
|
||||
...numexpr.evaluate("37593 * 67")...
|
||||
```output
|
||||
2518731
|
||||
|
||||
答案: 2518731
|
||||
|
||||
问题: 37593的五次方根是多少？
|
||||
```text
|
||||
37593**(1/5)
|
||||
```
|
||||
...numexpr.evaluate("37593**(1/5)")...
|
||||
```output
|
||||
8.222831614237718
|
||||
|
||||
答案: 8.222831614237718
|
||||
|
||||
|
||||
问题: 2的平方是多少？
|
||||
```text
|
||||
2 ** 2
|
||||
```
|
||||
...numexpr.evaluate("2 ** 2")...
|
||||
```output
|
||||
4
|
||||
|
||||
答案: 4
|
||||
|
||||
|
||||
现在，这是我的问题：
|
||||
问题: {question}
|
||||
"""
|
||||
PROMPT = PromptTemplate(
|
||||
input_variables=["question"],
|
||||
template=_PROMPT_TEMPLATE,
|
||||
)
|
||||
|
||||
|
||||
def calculate(query: str):
|
||||
model = get_ChatOpenAI(
|
||||
streaming=False,
|
||||
model_name=LLM_MODEL,
|
||||
temperature=TEMPERATURE,
|
||||
)
|
||||
llm_math = LLMMathChain.from_llm(model, verbose=True, prompt=PROMPT)
|
||||
ans = llm_math.run(query)
|
||||
return ans
|
||||
28
server/agent/tools.py
Normal file
28
server/agent/tools.py
Normal file
@ -0,0 +1,28 @@
|
||||
|
||||
import sys
|
||||
import os
|
||||
sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
|
||||
|
||||
from server.agent.math import calculate
|
||||
from server.agent.translator import translate
|
||||
from server.agent.weather import weathercheck
|
||||
from langchain.agents import Tool
|
||||
|
||||
tools = [
|
||||
Tool.from_function(
|
||||
func=calculate,
|
||||
name="计算器工具",
|
||||
description=""
|
||||
),
|
||||
Tool.from_function(
|
||||
func=translate,
|
||||
name="翻译工具",
|
||||
description=""
|
||||
),
|
||||
Tool.from_function(
|
||||
func=weathercheck,
|
||||
name="天气查询工具",
|
||||
description="",
|
||||
)
|
||||
]
|
||||
tool_names = [tool.name for tool in tools]
|
||||
41
server/agent/translator.py
Normal file
41
server/agent/translator.py
Normal file
@ -0,0 +1,41 @@
|
||||
from langchain import PromptTemplate, LLMChain
|
||||
import sys
|
||||
import os
|
||||
|
||||
from server.chat.utils import get_ChatOpenAI
|
||||
|
||||
sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
|
||||
from langchain.chains.llm_math.prompt import PROMPT
|
||||
from configs.model_config import LLM_MODEL,TEMPERATURE
|
||||
|
||||
_PROMPT_TEMPLATE = '''
|
||||
# 指令
|
||||
接下来，作为一个专业的翻译专家，当我给出英文句子或段落时，你将提供通顺且具有可读性的对应语言的翻译。注意：
|
||||
1. 确保翻译结果流畅且易于理解
|
||||
2. 无论提供的是陈述句或疑问句，只进行翻译
|
||||
3. 不添加与原文无关的内容
|
||||
|
||||
原文: ${{用户需要翻译的原文和目标语言}}
|
||||
{question}
|
||||
```output
|
||||
${{翻译结果}}
|
||||
```
|
||||
答案: ${{答案}}
|
||||
'''
|
||||
|
||||
PROMPT = PromptTemplate(
|
||||
input_variables=["question"],
|
||||
template=_PROMPT_TEMPLATE,
|
||||
)
|
||||
|
||||
|
||||
def translate(query: str):
|
||||
model = get_ChatOpenAI(
|
||||
streaming=False,
|
||||
model_name=LLM_MODEL,
|
||||
temperature=TEMPERATURE,
|
||||
)
|
||||
llm_translate = LLMChain(llm=model, prompt=PROMPT)
|
||||
ans = llm_translate.run(query)
|
||||
|
||||
return ans
|
||||
355
server/agent/weather.py
Normal file
355
server/agent/weather.py
Normal file
@ -0,0 +1,355 @@
|
||||
## 使用和风天气API查询天气
|
||||
|
||||
from __future__ import annotations
|
||||
import sys
|
||||
import os
|
||||
|
||||
from server.chat.utils import get_ChatOpenAI
|
||||
|
||||
sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
|
||||
|
||||
import re
|
||||
import warnings
|
||||
from typing import Dict
|
||||
|
||||
from langchain.callbacks.manager import (
|
||||
AsyncCallbackManagerForChainRun,
|
||||
CallbackManagerForChainRun,
|
||||
)
|
||||
from langchain.chains.base import Chain
|
||||
from langchain.chains.llm import LLMChain
|
||||
from langchain.pydantic_v1 import Extra, root_validator
|
||||
from langchain.schema import BasePromptTemplate
|
||||
from langchain.schema.language_model import BaseLanguageModel
|
||||
import requests
|
||||
from typing import List, Any, Optional
|
||||
from configs.model_config import LLM_MODEL, TEMPERATURE
|
||||
|
||||
|
||||
def get_city_info(location, adm, key):
|
||||
base_url = 'https://geoapi.qweather.com/v2/city/lookup?'
|
||||
params = {'location': location, 'adm': adm, 'key': key}
|
||||
response = requests.get(base_url, params=params)
|
||||
data = response.json()
|
||||
return data
|
||||
|
||||
|
||||
from datetime import datetime
|
||||
|
||||
|
||||
def format_weather_data(data):
|
||||
hourly_forecast = data['hourly']
|
||||
formatted_data = ''
|
||||
for forecast in hourly_forecast:
|
||||
# 将预报时间转换为datetime对象
|
||||
forecast_time = datetime.strptime(forecast['fxTime'], '%Y-%m-%dT%H:%M%z')
|
||||
# 获取预报时间的时区
|
||||
forecast_tz = forecast_time.tzinfo
|
||||
# 获取当前时间（使用预报时间的时区）
|
||||
now = datetime.now(forecast_tz)
|
||||
# 计算预报日期与当前日期的差值
|
||||
days_diff = (forecast_time.date() - now.date()).days
|
||||
if days_diff == 0:
|
||||
forecast_date_str = '今天'
|
||||
elif days_diff == 1:
|
||||
forecast_date_str = '明天'
|
||||
elif days_diff == 2:
|
||||
forecast_date_str = '后天'
|
||||
else:
|
||||
forecast_date_str = str(days_diff) + '天后'
|
||||
forecast_time_str = forecast_date_str + ' ' + forecast_time.strftime('%H:%M')
|
||||
# 计算预报时间与当前时间的差值
|
||||
time_diff = forecast_time - now
|
||||
# 将差值转换为小时
|
||||
hours_diff = time_diff.total_seconds() // 3600
|
||||
if hours_diff < 1:
|
||||
hours_diff_str = '1小时后'
|
||||
elif hours_diff >= 24:
|
||||
# 如果超过24小时，转换为天数
|
||||
days_diff = hours_diff // 24
|
||||
hours_diff_str = str(int(days_diff)) + '天后'
|
||||
else:
|
||||
hours_diff_str = str(int(hours_diff)) + '小时后'
|
||||
# 将预报时间和当前时间的差值添加到输出中
|
||||
formatted_data += '预报时间: ' + hours_diff_str + '\n'
|
||||
formatted_data += '具体时间: ' + forecast_time_str + '\n'
|
||||
formatted_data += '温度: ' + forecast['temp'] + '°C\n'
|
||||
formatted_data += '天气: ' + forecast['text'] + '\n'
|
||||
formatted_data += '风向: ' + forecast['windDir'] + '\n'
|
||||
formatted_data += '风速: ' + forecast['windSpeed'] + '级\n'
|
||||
formatted_data += '湿度: ' + forecast['humidity'] + '%\n'
|
||||
formatted_data += '降水概率: ' + forecast['pop'] + '%\n'
|
||||
# formatted_data += '降水量: ' + forecast['precip'] + 'mm\n'
|
||||
formatted_data += '\n\n'
|
||||
return formatted_data
|
||||
|
||||
|
||||
def get_weather(key, location_id, time: str = "24"):
|
||||
if time:
|
||||
url = "https://devapi.qweather.com/v7/weather/" + time + "h?"
|
||||
else:
|
||||
time = "3" # 免费订阅只能查看3天的天气
|
||||
url = "https://devapi.qweather.com/v7/weather/" + time + "d?"
|
||||
params = {
|
||||
'location': location_id,
|
||||
'key': key,
|
||||
}
|
||||
response = requests.get(url, params=params)
|
||||
data = response.json()
|
||||
return format_weather_data(data)
|
||||
|
||||
|
||||
def split_query(query):
|
||||
parts = query.split()
|
||||
location = parts[0] if parts[0] != 'None' else parts[1]
|
||||
adm = parts[1]
|
||||
time = parts[2]
|
||||
return location, adm, time
|
||||
|
||||
|
||||
def weather(query):
|
||||
location, adm, time = split_query(query)
|
||||
if time != "None" and int(time) > 24:
|
||||
return "只能查看24小时内的天气，无法回答"
|
||||
if time == "None":
|
||||
time = "24" # 免费的版本只能24小时内的天气
|
||||
key = "" # 和风天气API Key
|
||||
if key == "":
|
||||
return "请先在代码中填入和风天气API Key"
|
||||
city_info = get_city_info(location=location, adm=adm, key=key)
|
||||
location_id = city_info['location'][0]['id']
|
||||
weather_data = get_weather(key=key, location_id=location_id, time=time)
|
||||
return weather_data
|
||||
|
||||
|
||||
class LLMWeatherChain(Chain):
|
||||
llm_chain: LLMChain
|
||||
llm: Optional[BaseLanguageModel] = None
|
||||
"""[Deprecated] LLM wrapper to use."""
|
||||
prompt: BasePromptTemplate
|
||||
"""[Deprecated] Prompt to use to translate to python if necessary."""
|
||||
input_key: str = "question" #: :meta private:
|
||||
output_key: str = "answer" #: :meta private:
|
||||
|
||||
class Config:
|
||||
"""Configuration for this pydantic object."""
|
||||
|
||||
extra = Extra.forbid
|
||||
arbitrary_types_allowed = True
|
||||
|
||||
@root_validator(pre=True)
|
||||
def raise_deprecation(cls, values: Dict) -> Dict:
|
||||
if "llm" in values:
|
||||
warnings.warn(
|
||||
"Directly instantiating an LLMWeatherChain with an llm is deprecated. "
|
||||
"Please instantiate with llm_chain argument or using the from_llm "
|
||||
"class method."
|
||||
)
|
||||
if "llm_chain" not in values and values["llm"] is not None:
|
||||
prompt = values.get("prompt", PROMPT)
|
||||
values["llm_chain"] = LLMChain(llm=values["llm"], prompt=prompt)
|
||||
return values
|
||||
|
||||
@property
|
||||
def input_keys(self) -> List[str]:
|
||||
"""Expect input key.
|
||||
|
||||
:meta private:
|
||||
"""
|
||||
return [self.input_key]
|
||||
|
||||
@property
|
||||
def output_keys(self) -> List[str]:
|
||||
"""Expect output key.
|
||||
|
||||
:meta private:
|
||||
"""
|
||||
return [self.output_key]
|
||||
|
||||
def _evaluate_expression(self, expression: str) -> str:
|
||||
try:
|
||||
output = weather(expression)
|
||||
except Exception as e:
|
||||
output = "输入的信息有误，请再次尝试"
|
||||
# raise ValueError(f"错误: {expression}，输入的信息不对")
|
||||
|
||||
return output
|
||||
|
||||
def _process_llm_result(
|
||||
self, llm_output: str, run_manager: CallbackManagerForChainRun
|
||||
) -> Dict[str, str]:
|
||||
|
||||
run_manager.on_text(llm_output, color="green", verbose=self.verbose)
|
||||
|
||||
llm_output = llm_output.strip()
|
||||
text_match = re.search(r"^```text(.*?)```", llm_output, re.DOTALL)
|
||||
if text_match:
|
||||
expression = text_match.group(1)
|
||||
output = self._evaluate_expression(expression)
|
||||
run_manager.on_text("\nAnswer: ", verbose=self.verbose)
|
||||
run_manager.on_text(output, color="yellow", verbose=self.verbose)
|
||||
answer = "Answer: " + output
|
||||
elif llm_output.startswith("Answer:"):
|
||||
answer = llm_output
|
||||
elif "Answer:" in llm_output:
|
||||
answer = "Answer: " + llm_output.split("Answer:")[-1]
|
||||
else:
|
||||
raise ValueError(f"unknown format from LLM: {llm_output}")
|
||||
return {self.output_key: answer}
|
||||
|
||||
async def _aprocess_llm_result(
|
||||
self,
|
||||
llm_output: str,
|
||||
run_manager: AsyncCallbackManagerForChainRun,
|
||||
) -> Dict[str, str]:
|
||||
await run_manager.on_text(llm_output, color="green", verbose=self.verbose)
|
||||
llm_output = llm_output.strip()
|
||||
text_match = re.search(r"^```text(.*?)```", llm_output, re.DOTALL)
|
||||
if text_match:
|
||||
expression = text_match.group(1)
|
||||
output = self._evaluate_expression(expression)
|
||||
await run_manager.on_text("\nAnswer: ", verbose=self.verbose)
|
||||
await run_manager.on_text(output, color="yellow", verbose=self.verbose)
|
||||
answer = "Answer: " + output
|
||||
elif llm_output.startswith("Answer:"):
|
||||
answer = llm_output
|
||||
elif "Answer:" in llm_output:
|
||||
answer = "Answer: " + llm_output.split("Answer:")[-1]
|
||||
else:
|
||||
raise ValueError(f"unknown format from LLM: {llm_output}")
|
||||
return {self.output_key: answer}
|
||||
|
||||
def _call(
|
||||
self,
|
||||
inputs: Dict[str, str],
|
||||
run_manager: Optional[CallbackManagerForChainRun] = None,
|
||||
) -> Dict[str, str]:
|
||||
_run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
|
||||
_run_manager.on_text(inputs[self.input_key])
|
||||
llm_output = self.llm_chain.predict(
|
||||
question=inputs[self.input_key],
|
||||
stop=["```output"],
|
||||
callbacks=_run_manager.get_child(),
|
||||
)
|
||||
return self._process_llm_result(llm_output, _run_manager)
|
||||
|
||||
async def _acall(
|
||||
self,
|
||||
inputs: Dict[str, str],
|
||||
run_manager: Optional[AsyncCallbackManagerForChainRun] = None,
|
||||
) -> Dict[str, str]:
|
||||
_run_manager = run_manager or AsyncCallbackManagerForChainRun.get_noop_manager()
|
||||
await _run_manager.on_text(inputs[self.input_key])
|
||||
llm_output = await self.llm_chain.apredict(
|
||||
question=inputs[self.input_key],
|
||||
stop=["```output"],
|
||||
callbacks=_run_manager.get_child(),
|
||||
)
|
||||
return await self._aprocess_llm_result(llm_output, _run_manager)
|
||||
|
||||
@property
|
||||
def _chain_type(self) -> str:
|
||||
return "llm_weather_chain"
|
||||
|
||||
@classmethod
|
||||
def from_llm(
|
||||
cls,
|
||||
llm: BaseLanguageModel,
|
||||
prompt: BasePromptTemplate,
|
||||
**kwargs: Any,
|
||||
) -> LLMWeatherChain:
|
||||
llm_chain = LLMChain(llm=llm, prompt=prompt)
|
||||
return cls(llm_chain=llm_chain, **kwargs)
|
||||
|
||||
|
||||
from langchain import PromptTemplate
|
||||
|
||||
_PROMPT_TEMPLATE = """用户将会向您咨询天气问题，您不需要自己回答天气问题，而是将用户提问的信息提取出来区，市和时间三个元素后使用我为你编写好的工具进行查询并返回结果，格式为 区+市+时间 每个元素用空格隔开。如果缺少信息，则用 None 代替。
|
||||
问题: ${{用户的问题}}
|
||||
|
||||
```text
|
||||
|
||||
${{拆分的区，市和时间}}
|
||||
```
|
||||
|
||||
... weather(query)...
|
||||
```output
|
||||
|
||||
${{提取后的答案}}
|
||||
```
|
||||
答案: ${{答案}}
|
||||
|
||||
这是两个例子：
|
||||
问题: 上海浦东未来1小时天气情况？
|
||||
|
||||
```text
|
||||
|
||||
浦东 上海 1
|
||||
```
|
||||
...weather(浦东 上海 1)...
|
||||
|
||||
```output
|
||||
|
||||
预报时间: 1小时后
|
||||
具体时间: 今天 18:00
|
||||
温度: 24°C
|
||||
天气: 多云
|
||||
风向: 西南风
|
||||
风速: 7级
|
||||
湿度: 88%
|
||||
降水概率: 16%
|
||||
|
||||
Answer:
|
||||
预报时间: 1小时后
|
||||
具体时间: 今天 18:00
|
||||
温度: 24°C
|
||||
天气: 多云
|
||||
风向: 西南风
|
||||
风速: 7级
|
||||
湿度: 88%
|
||||
降水概率: 16%
|
||||
|
||||
问题: 北京市朝阳区未来24小时天气如何？
|
||||
```text
|
||||
|
||||
朝阳 北京 24
|
||||
```
|
||||
...weather(朝阳 北京 24)...
|
||||
```output
|
||||
预报时间: 23小时后
|
||||
具体时间: 明天 17:00
|
||||
温度: 26°C
|
||||
天气: 霾
|
||||
风向: 西南风
|
||||
风速: 11级
|
||||
湿度: 65%
|
||||
降水概率: 20%
|
||||
Answer:
|
||||
预报时间: 23小时后
|
||||
具体时间: 明天 17:00
|
||||
温度: 26°C
|
||||
天气: 霾
|
||||
风向: 西南风
|
||||
风速: 11级
|
||||
湿度: 65%
|
||||
降水概率: 20%
|
||||
|
||||
现在，这是我的问题：
|
||||
问题: {question}
|
||||
"""
|
||||
PROMPT = PromptTemplate(
|
||||
input_variables=["question"],
|
||||
template=_PROMPT_TEMPLATE,
|
||||
)
|
||||
|
||||
|
||||
def weathercheck(query: str):
|
||||
model = get_ChatOpenAI(
|
||||
streaming=False,
|
||||
model_name=LLM_MODEL,
|
||||
temperature=TEMPERATURE,
|
||||
)
|
||||
llm_weather = LLMWeatherChain.from_llm(model, verbose=True, prompt=PROMPT)
|
||||
ans = llm_weather.run(query)
|
||||
return ans
|
||||
|
||||
@ -12,12 +12,12 @@ import uvicorn
|
||||
from fastapi.middleware.cors import CORSMiddleware
|
||||
from starlette.responses import RedirectResponse
|
||||
from server.chat import (chat, knowledge_base_chat, openai_chat,
|
||||
search_engine_chat)
|
||||
search_engine_chat, agent_chat)
|
||||
from server.knowledge_base.kb_api import list_kbs, create_kb, delete_kb
|
||||
from server.knowledge_base.kb_doc_api import (list_files, upload_docs, delete_docs,
|
||||
update_docs, download_doc, recreate_vector_store,
|
||||
search_docs, DocumentWithScore)
|
||||
from server.llm_api import list_running_models,list_config_models, change_llm_model, stop_llm_model
|
||||
from server.llm_api import list_running_models, list_config_models, change_llm_model, stop_llm_model
|
||||
from server.utils import BaseResponse, ListResponse, FastAPI, MakeFastAPIOffline
|
||||
from typing import List
|
||||
|
||||
@ -67,6 +67,10 @@ def create_app():
|
||||
tags=["Chat"],
|
||||
summary="与搜索引擎对话")(search_engine_chat)
|
||||
|
||||
app.post("/chat/agent_chat",
|
||||
tags=["Chat"],
|
||||
summary="与agent对话")(agent_chat)
|
||||
|
||||
# Tag: Knowledge Base Management
|
||||
app.get("/knowledge_base/list_knowledge_bases",
|
||||
tags=["Knowledge Base Management"],
|
||||
@ -126,24 +130,24 @@ def create_app():
|
||||
|
||||
# LLM模型相关接口
|
||||
app.post("/llm_model/list_running_models",
|
||||
tags=["LLM Model Management"],
|
||||
summary="列出当前已加载的模型",
|
||||
)(list_running_models)
|
||||
tags=["LLM Model Management"],
|
||||
summary="列出当前已加载的模型",
|
||||
)(list_running_models)
|
||||
|
||||
app.post("/llm_model/list_config_models",
|
||||
tags=["LLM Model Management"],
|
||||
summary="列出configs已配置的模型",
|
||||
)(list_config_models)
|
||||
tags=["LLM Model Management"],
|
||||
summary="列出configs已配置的模型",
|
||||
)(list_config_models)
|
||||
|
||||
app.post("/llm_model/stop",
|
||||
tags=["LLM Model Management"],
|
||||
summary="停止指定的LLM模型（Model Worker)",
|
||||
)(stop_llm_model)
|
||||
tags=["LLM Model Management"],
|
||||
summary="停止指定的LLM模型（Model Worker)",
|
||||
)(stop_llm_model)
|
||||
|
||||
app.post("/llm_model/change",
|
||||
tags=["LLM Model Management"],
|
||||
summary="切换指定的LLM模型（Model Worker)",
|
||||
)(change_llm_model)
|
||||
tags=["LLM Model Management"],
|
||||
summary="切换指定的LLM模型（Model Worker)",
|
||||
)(change_llm_model)
|
||||
|
||||
return app
|
||||
|
||||
|
||||
@ -2,3 +2,4 @@ from .chat import chat
|
||||
from .knowledge_base_chat import knowledge_base_chat
|
||||
from .openai_chat import openai_chat
|
||||
from .search_engine_chat import search_engine_chat
|
||||
from .agent_chat import agent_chat
|
||||
73
server/chat/agent_chat.py
Normal file
73
server/chat/agent_chat.py
Normal file
@ -0,0 +1,73 @@
|
||||
from langchain.memory import ConversationBufferWindowMemory
|
||||
from server.agent.tools import tools, tool_names
|
||||
from langchain.agents import AgentExecutor, LLMSingleActionAgent
|
||||
from server.agent.custom_template import CustomOutputParser, prompt
|
||||
from fastapi import Body
|
||||
from fastapi.responses import StreamingResponse
|
||||
from configs.model_config import LLM_MODEL, TEMPERATURE, HISTORY_LEN
|
||||
from server.chat.utils import wrap_done, get_ChatOpenAI
|
||||
from langchain import LLMChain
|
||||
from langchain.callbacks import AsyncIteratorCallbackHandler
|
||||
from langchain.callbacks.streaming_aiter_final_only import AsyncFinalIteratorCallbackHandler
|
||||
from typing import AsyncIterable
|
||||
import asyncio
|
||||
from langchain.prompts.chat import ChatPromptTemplate
|
||||
from typing import List
|
||||
from server.chat.utils import History
|
||||
|
||||
memory = ConversationBufferWindowMemory(k=HISTORY_LEN)
|
||||
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": "我们来玩成语接龙，我先来，生龙活虎"},
|
||||
{"role": "assistant", "content": "虎头虎脑"}]]
|
||||
),
|
||||
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", gt=0.0, le=1.0),
|
||||
# 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 chat_iterator(query: str,
|
||||
history: List[History] = [],
|
||||
model_name: str = LLM_MODEL,
|
||||
) -> AsyncIterable[str]:
|
||||
callback = AsyncFinalIteratorCallbackHandler()
|
||||
model = get_ChatOpenAI(
|
||||
model_name=model_name,
|
||||
temperature=temperature,
|
||||
callbacks=[callback],
|
||||
)
|
||||
output_parser = CustomOutputParser()
|
||||
llm_chain = LLMChain(llm=model, prompt=prompt)
|
||||
agent = LLMSingleActionAgent(
|
||||
llm_chain=llm_chain,
|
||||
output_parser=output_parser,
|
||||
stop=["\nObservation:"],
|
||||
allowed_tools=tool_names
|
||||
)
|
||||
agent_executor = AgentExecutor.from_agent_and_tools(agent=agent, tools=tools, verbose=True, memory=memory,
|
||||
callbacks=[callback])
|
||||
input_msg = History(role="user", content="{{ input }}").to_msg_template(False)
|
||||
chat_prompt = ChatPromptTemplate.from_messages(
|
||||
[i.to_msg_template() for i in history] + [input_msg])
|
||||
task = asyncio.create_task(wrap_done(
|
||||
agent_executor.acall(query),
|
||||
callback.done),
|
||||
)
|
||||
if stream:
|
||||
async for token in callback.aiter():
|
||||
# Use server-sent-events to stream the response
|
||||
yield token
|
||||
else:
|
||||
answer = ""
|
||||
async for token in callback.aiter():
|
||||
answer += token
|
||||
yield answer
|
||||
|
||||
await task
|
||||
|
||||
return StreamingResponse(chat_iterator(query, history, model_name),
|
||||
media_type="text/event-stream")
|
||||
@ -10,11 +10,12 @@ from typing import Awaitable, List, Tuple, Dict, Union, Callable
|
||||
def get_ChatOpenAI(
|
||||
model_name: str,
|
||||
temperature: float,
|
||||
streaming: bool = True,
|
||||
callbacks: List[Callable] = [],
|
||||
) -> ChatOpenAI:
|
||||
config = get_model_worker_config(model_name)
|
||||
model = ChatOpenAI(
|
||||
streaming=True,
|
||||
streaming=streaming,
|
||||
verbose=True,
|
||||
callbacks=callbacks,
|
||||
openai_api_key=config.get("api_key", "EMPTY"),
|
||||
|
||||
40
tests/agent/test_agent_function.py
Normal file
40
tests/agent/test_agent_function.py
Normal file
@ -0,0 +1,40 @@
|
||||
import sys
|
||||
import os
|
||||
sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
|
||||
from configs import LLM_MODEL, TEMPERATURE
|
||||
from server.chat.utils import get_ChatOpenAI
|
||||
from langchain import LLMChain
|
||||
from langchain.agents import LLMSingleActionAgent, AgentExecutor
|
||||
from server.agent.tools import tools, tool_names
|
||||
from langchain.memory import ConversationBufferWindowMemory
|
||||
|
||||
memory = ConversationBufferWindowMemory(k=5)
|
||||
model = get_ChatOpenAI(
|
||||
model_name=LLM_MODEL,
|
||||
temperature=TEMPERATURE,
|
||||
)
|
||||
from server.agent.custom_template import CustomOutputParser, prompt
|
||||
|
||||
output_parser = CustomOutputParser()
|
||||
llm_chain = LLMChain(llm=model, prompt=prompt)
|
||||
agent = LLMSingleActionAgent(
|
||||
llm_chain=llm_chain,
|
||||
output_parser=output_parser,
|
||||
stop=["\nObservation:"],
|
||||
allowed_tools=tool_names
|
||||
)
|
||||
|
||||
agent_executor = AgentExecutor.from_agent_and_tools(agent=agent, tools=tools, memory=memory, verbose=True)
|
||||
|
||||
import pytest
|
||||
@pytest.mark.parametrize("text_prompt",
|
||||
["北京市朝阳区未来24小时天气如何？", # 天气功能函数
|
||||
"计算 (2 + 2312312)/4 是多少？", # 计算功能函数
|
||||
"翻译这句话成中文：Life is the art of drawing sufficient conclusions form insufficient premises."] # 翻译功能函数
|
||||
)
|
||||
def test_different_agent_function(text_prompt):
|
||||
try:
|
||||
text_answer = agent_executor.run(text_prompt)
|
||||
assert text_answer is not None
|
||||
except Exception as e:
|
||||
pytest.fail(f"agent_function failed with {text_prompt}, error: {str(e)}")
|
||||
@ -58,6 +58,7 @@ def dialogue_page(api: ApiRequest):
|
||||
["LLM 对话",
|
||||
"知识库问答",
|
||||
"搜索引擎问答",
|
||||
"自定义Agent问答",
|
||||
],
|
||||
index=1,
|
||||
on_change=on_mode_change,
|
||||
@ -152,6 +153,19 @@ def dialogue_page(api: ApiRequest):
|
||||
text += t
|
||||
chat_box.update_msg(text)
|
||||
chat_box.update_msg(text, streaming=False) # 更新最终的字符串，去除光标
|
||||
|
||||
elif dialogue_mode == "自定义Agent问答":
|
||||
chat_box.ai_say("正在调用工具回答...")
|
||||
text = ""
|
||||
r = api.agent_chat(prompt, history=history, model=llm_model, temperature=temperature)
|
||||
for t in r:
|
||||
if error_msg := check_error_msg(t): # check whether error occured
|
||||
st.error(error_msg)
|
||||
break
|
||||
text += t
|
||||
chat_box.update_msg(text)
|
||||
chat_box.update_msg(text, streaming=False) # 更新最终的字符串，去除光标
|
||||
|
||||
elif dialogue_mode == "知识库问答":
|
||||
history = get_messages_history(history_len)
|
||||
chat_box.ai_say([
|
||||
|
||||
@ -342,6 +342,39 @@ class ApiRequest:
|
||||
response = self.post("/chat/chat", json=data, stream=True)
|
||||
return self._httpx_stream2generator(response)
|
||||
|
||||
def agent_chat(
|
||||
self,
|
||||
query: str,
|
||||
history: List[Dict] = [],
|
||||
stream: bool = True,
|
||||
model: str = LLM_MODEL,
|
||||
temperature: float = TEMPERATURE,
|
||||
no_remote_api: bool = None,
|
||||
):
|
||||
'''
|
||||
对应api.py/chat/agent_chat 接口
|
||||
'''
|
||||
if no_remote_api is None:
|
||||
no_remote_api = self.no_remote_api
|
||||
|
||||
data = {
|
||||
"query": query,
|
||||
"history": history,
|
||||
"stream": stream,
|
||||
"model_name": model,
|
||||
"temperature": temperature,
|
||||
}
|
||||
|
||||
print(f"received input message:")
|
||||
pprint(data)
|
||||
|
||||
if no_remote_api:
|
||||
from server.chat.agent_chat import agent_chat
|
||||
response = run_async(agent_chat(**data))
|
||||
return self._fastapi_stream2generator(response)
|
||||
else:
|
||||
response = self.post("/chat/agent_chat", json=data, stream=True)
|
||||
return self._httpx_stream2generator(response)
|
||||
def knowledge_base_chat(
|
||||
self,
|
||||
query: str,
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user