在AutoGen中使用嵌套聊天的OptiGuide
这是OptiGuide的嵌套聊天重新实现，它是一个基于LLM的供应链优化框架。
除了AutoGen之外，本笔记本还需要eventlet和Gurobipy。eventlet包在本笔记本中用于限制代码执行的超时，而gurobipy包是一个用于解决混合整数线性和二次优化问题的数学优化软件库。
Requirements
import re
from typing import Union
# import auxiliary packages
import requests # for loading the example source code
from eventlet.timeout import Timeout
# test Gurobi installation
from gurobipy import GRB
from termcolor import colored
import autogen
from autogen.code_utils import extract_code
config_list_gpt4 = autogen.config_list_from_json(
"OAI_CONFIG_LIST",
filter_dict={
"model": ["gpt-4", "gpt4", "gpt-3.5-turbo" "gpt-4-32k", "gpt-4-32k-0314", "gpt-4-32k-v0314"],
},
)
llm_config = {"config_list": config_list_gpt4}
tip
了解更多关于为agent配置LLM的信息在这里.
OptiGuide中的预期代理编排。
步骤0：准备辅助函数
下面的代码单元格包含几个由代理使用的辅助函数。这些辅助函数直接来自OptiGuide。
实用功能
定义了一些实用函数（replace, insert_code, run_with_exec）来动态地操作和执行源代码：
-
replace(src_code, old_code, new_code) -> str:
将源代码中的指定代码块替换为新代码。这对于基于聊天机器人和用户交互动态更新代码至关重要。 -
insert_code(src_code, new_lines) -> str:
确定在源代码中基于特定标记插入新代码行的位置。它用于无缝集成生成的代码片段。 -
run_with_exec(src_code) -> Union[str, Exception]:
在受控环境中执行修改后的源代码，捕获并返回输出或发生的任何错误。此函数对于测试生成代码解决方案的可行性和正确性至关重要。
外部代码检索功能
该单元还包括使用requests库从远程仓库检索示例源代码的逻辑。这些示例代码作为聊天机器人工作的基础，使用户能够看到讨论概念的实际应用。
检索到的代码随后显示，展示了源代码的开头和结尾，以便一窥其结构和内容。
def replace(src_code: str, old_code: str, new_code: str) -> str:
"""
Inserts new code into the source code by replacing a specified old
code block.
Args:
src_code (str): The source code to modify.
old_code (str): The code block to be replaced.
new_code (str): The new code block to insert.
Returns:
str: The modified source code with the new code inserted.
Raises:
None
Example:
src_code = 'def hello_world():\n print("Hello, world!")\n\n# Some
other code here'
old_code = 'print("Hello, world!")'
new_code = 'print("Bonjour, monde!")\nprint("Hola, mundo!")'
modified_code = _replace(src_code, old_code, new_code)
print(modified_code)
# Output:
# def hello_world():
# print("Bonjour, monde!")
# print("Hola, mundo!")
# Some other code here
"""
pattern = r"( *){old_code}".format(old_code=old_code)
head_spaces = re.search(pattern, src_code, flags=re.DOTALL).group(1)
new_code = "\n".join([head_spaces + line for line in new_code.split("\n")])
rst = re.sub(pattern, new_code, src_code)
return rst
def insert_code(src_code: str, new_lines: str) -> str:
"""insert a code patch into the source code.
Args:
src_code (str): the full source code
new_lines (str): The new code.
Returns:
str: the full source code after insertion (replacement).
"""
if new_lines.find("addConstr") >= 0:
return replace(src_code, CONSTRAINT_CODE_STR, new_lines)
else:
return replace(src_code, DATA_CODE_STR, new_lines)
def run_with_exec(src_code: str) -> Union[str, Exception]:
"""Run the code snippet with exec.
Args:
src_code (str): The source code to run.
Returns:
object: The result of the code snippet.
If the code succeed, returns the objective value (float or string).
else, return the error (exception)
"""
locals_dict = {}
locals_dict.update(globals())
locals_dict.update(locals())
timeout = Timeout(
60,
TimeoutError("This is a timeout exception, in case " "GPT's code falls into infinite loop."),
)
try:
exec(src_code, locals_dict, locals_dict)
except Exception as e:
return e
finally:
timeout.cancel()
try:
status = locals_dict["m"].Status
if status != GRB.OPTIMAL:
if status == GRB.UNBOUNDED:
ans = "unbounded"
elif status == GRB.INF_OR_UNBD:
ans = "inf_or_unbound"
elif status == GRB.INFEASIBLE:
ans = "infeasible"
m = locals_dict["m"]
m.computeIIS()
constrs = [c.ConstrName for c in m.getConstrs() if c.IISConstr]
ans += "\nConflicting Constraints:\n" + str(constrs)
else:
ans = "Model Status:" + str(status)
else:
ans = "Optimization problem solved. The objective value is: " + str(locals_dict["m"].objVal)
except Exception as e:
return e
return ans
步骤1. 构建Agent
此单元格介绍了Writer和OptiGuide代理类及其实例，用于管理用户、聊天机器人和优化求解器之间的交互。这简化了生成、评估和集成代码解决方案以解决供应链优化问题的流程。
定义的类
-
OptiGuide: 继承自autogen.AssistantAgent，作为处理供应链优化逻辑的主类。它维护了诸如源代码、剩余的调试尝试、成功状态和用户聊天记录等状态信息。关键方法包括set_success和update_debug_times，用于根据交互结果更新代理的状态。 -
Writer: 也继承自autogen.AssistantAgent，这个类专门用于管理和生成Python代码解决方案的解释。它会跟踪源代码和示例问答，以帮助生成对用户查询的响应。
代理实例
-
writer、safeguard和optiguide_commander： 上述类的实例，每个实例在代码生成和评估过程中都有特定的角色配置。这些代理共同工作，确保用户的问题能够通过安全、优化且易于理解的Python代码解决方案得到回答。 -
user: 一个autogen.UserProxyAgent的实例，代表与笔记本交互的最终用户。它被配置为模拟用户输入和响应，从而促进互动且引人入胜的学习体验。
代理系统消息
每个代理都与相应的系统消息相关联。这些系统消息直接来自 OptiGuide。
# %% System Messages
WRITER_SYSTEM_MSG = """You are a chatbot to:
(1) write Python code to answer users questions for supply chain-related coding
project;
(2) explain solutions from a Gurobi/Python solver.
--- SOURCE CODE ---
{source_code}
--- DOC STR ---
{doc_str}
---
Here are some example questions and their answers and codes:
--- EXAMPLES ---
{example_qa}
---
The execution result of the original source code is below.
--- Original Result ---
{execution_result}
Note that your written code will be added to the lines with substring:
"# OPTIGUIDE *** CODE GOES HERE"
So, you don't need to write other code, such as m.optimize() or m.update().
You just need to write code snippet in ```python ...``` block.
"""
SAFEGUARD_SYSTEM_MSG = """
Given the source code:
{source_code}
Is the source code safe (not malicious code to break security
and privacy) to run?
Answer only one word.
If not safe, answer `DANGER`; else, answer `SAFE`.
"""
# %% Constant strings to match code lines in the source code.
DATA_CODE_STR = "# OPTIGUIDE DATA CODE GOES HERE"
CONSTRAINT_CODE_STR = "# OPTIGUIDE CONSTRAINT CODE GOES HERE"
# In-context learning examples.
example_qa = """
----------
Question: Why is it not recommended to use just one supplier for roastery 2?
Answer Code:
```python
z = m.addVars(suppliers, vtype=GRB.BINARY, name="z")
m.addConstr(sum(z[s] for s in suppliers) <= 1, "_")
for s in suppliers:
m.addConstr(x[s,'roastery2'] <= capacity_in_supplier[s] * z[s], "_")
```
----------
Question: What if there's a 13% jump in the demand for light coffee at cafe1?
Answer Code:
```python
light_coffee_needed_for_cafe["cafe1"] = light_coffee_needed_for_cafe["cafe1"] * (1 + 13/100)
```
"""
CODE_PROMPT = """
Answer Code:
"""
DEBUG_PROMPT = """
While running the code you suggested, I encountered the {error_type}:
--- ERROR MESSAGE ---
{error_message}
Please try to resolve this bug, and rewrite the code snippet.
--- NEW CODE ---
"""
SAFEGUARD_PROMPT = """
--- Code ---
{code}
--- One-Word Answer: SAFE or DANGER ---
"""
INTERPRETER_PROMPT = """Here are the execution results: {execution_rst}
Can you organize these information to a human readable answer?
Remember to compare the new results to the original results you obtained in the
beginning.
--- HUMAN READABLE ANSWER ---
"""
# Get the source code of the coffee example from OptiGuide's official repo
code_url = "https://raw.githubusercontent.com/microsoft/OptiGuide/main/benchmark/application/coffee.py"
response = requests.get(code_url)
# Check if the request was successful
if response.status_code == 200:
# Get the text content from the response
code = response.text
else:
raise RuntimeError("Failed to retrieve the file.")
# code = open(code_url, "r").read() # for local files
# show the first head and tail of the source code
print("\n".join(code.split("\n")[:10]))
print(".\n" * 3)
print("\n".join(code.split("\n")[-10:]))
writer_system_msg = WRITER_SYSTEM_MSG.format(
source_code=code,
doc_str="",
example_qa=example_qa,
execution_result="",
)
safeguard_system_msg = SAFEGUARD_SYSTEM_MSG.format(source_code=code)
class OptiGuide(autogen.AssistantAgent):
source_code: str = code
debug_times: int = 3
debug_times_left: int = 3
example_qa: str = ""
success: bool = False
user_chat_history: str = ""
class Writer(autogen.AssistantAgent):
source_code: str = code
example_qa: str = ""
user_chat_history: str = ""
writer = Writer("writer", llm_config=llm_config)
safeguard = autogen.AssistantAgent("safeguard", llm_config=llm_config)
optiguide_commander = OptiGuide("commander", llm_config=llm_config)
user = autogen.UserProxyAgent(
"user", max_consecutive_auto_reply=0, human_input_mode="NEVER", code_execution_config=False
)
import time
from gurobipy import GRB, Model
# Example data
capacity_in_supplier = {'supplier1': 150, 'supplier2': 50, 'supplier3': 100}
shipping_cost_from_supplier_to_roastery = {
('supplier1', 'roastery1'): 5,
.
.
.
# Solve
m.update()
model.optimize()
print(time.ctime())
if m.status == GRB.OPTIMAL:
print(f'Optimal cost: {m.objVal}')
else:
print("Not solved to optimality. Optimization status:", m.status)
步骤 2. 编排嵌套聊天
这三个代理实例按照以下方式编排，将writer和safeguard嵌套在commander代理中作为内部独白。
下一个单元格定义了管理OptiGuide系统、用户和处理及响应查询的内部逻辑之间交互的关键功能。每个功能在指导对话、处理代码生成请求、确保代码安全性和总结结果中扮演着特定角色。这确保代理通过代码探索供应链优化问题时，能够接收到明确的指令、及时的反馈和一个安全的环境。
关于聊天序列的信息可以在register_nested_chats函数的chat_queue参数中指定。以下字段特别有用：- recipient（必填）指定嵌套代理；- message指定发送给嵌套接收者代理的消息。在嵌套聊天序列中，如果未指定message字段，我们将使用注册代理接收到的最后一条消息作为第一个聊天的初始消息，并且将跳过队列中任何没有message字段的后续聊天。您可以提供一个字符串或定义一个返回字符串的可调用对象。- summary_method决定从嵌套聊天中获取什么。您可以从现有选项（包括“last_msg”和“reflection_with_llm”）中选择，或者通过一个Callable定义自己的方式以从嵌套聊天中获取内容。- max_turns确定相关代理对之间进行多少轮对话。
def writer_init_messsage(recipient, messages, sender, config):
if recipient.success:
return None
msg_content = messages[-1].get("content", "")
# board = config
# get execution result of the original source code
sender_history = recipient.chat_messages[sender]
user_chat_history = "\nHere are the history of discussions:\n" f"{sender_history}"
if sender.name == "user":
execution_result = msg_content # TODO: get the execution result of the original source code
else:
execution_result = ""
writer_sys_msg = (
WRITER_SYSTEM_MSG.format(
source_code=recipient.source_code,
doc_str="",
example_qa=example_qa,
execution_result=execution_result,
)
+ user_chat_history
)
# safeguard.reset() #TODO: reset safeguard
recipient.debug_times_left = recipient.debug_times
recipient.success = False
return writer_sys_msg + "\n" + CODE_PROMPT
def writer_success_summary(recipient, sender):
if sender.success:
return sender.last_message(recipient)["content"].replace("TERMINATE", "")
else:
return "Sorry. I cannot answer your question."
def safeguard_init_message(recipient, messages, sender, config):
if recipient.success:
return None
last_msg_content = messages[-1].get("content", "")
_, code = extract_code(last_msg_content)[0]
if _ != "unknown":
return SAFEGUARD_SYSTEM_MSG.format(source_code=code) + sender.user_chat_history
else:
return
# return SAFEGUARD_SYSTEM_MSG.format(source_code=recipient.source_code)
def safeguard_summary(recipient, sender):
safe_msg = sender.last_message(recipient)["content"].replace("TERMINATE", "")
if safe_msg.find("DANGER") < 0:
# Step 4 and 5: Run the code and obtain the results
src_code = insert_code(sender.source_code, code)
execution_rst = run_with_exec(src_code)
print(colored(str(execution_rst), "yellow"))
if type(execution_rst) in [str, int, float]:
# we successfully run the code and get the result
sender.success = True
# Step 6: request to interpret results
return INTERPRETER_PROMPT.format(execution_rst=execution_rst)
else:
# DANGER: If not safe, try to debug. Redo coding
execution_rst = """
Sorry, this new code is not safe to run. I would not allow you to execute it.
Please try to find a new way (coding) to answer the question."""
if sender.debug_times_left > 0:
# Try to debug and write code again (back to step 2)
sender.debug_times_left -= 1
return DEBUG_PROMPT.format(error_type=type(execution_rst), error_message=str(execution_rst))
writer_chat_queue = [{"recipient": writer, "message": writer_init_messsage, "summary_method": writer_success_summary}]
safeguard_chat_queue = [
{"recipient": safeguard, "message": safeguard_init_message, "max_turns": 1, "summary_method": safeguard_summary}
]
# safeguard is triggered only when receiving a message from the writer
optiguide_commander.register_nested_chats(safeguard_chat_queue, trigger="writer")
# writer is triggered only when receiving a message from the user
optiguide_commander.register_nested_chats(writer_chat_queue, trigger="user")
让代理们进行对话
chat_res = user.initiate_chat(
optiguide_commander, message="What if we prohibit shipping from supplier 1 to roastery 2?"
)
user (to commander):
What if we prohibit shipping from supplier 1 to roastery 2?
--------------------------------------------------------------------------------
********************************************************************************
Start a new chat with the following message:
You are a chatbot to:
(1) write Python code to answer users questions for supply chain-related coding
project;
(2) explain solutions from a Gurobi/Python solver.
--- SOURCE CODE ---
import time
from gurobipy import GRB, Model
# Example data
capacity_in_supplier = {'supplier1': 150, 'supplier2': 50, 'supplier3': 100}
shipping_cost_from_supplier_to_roastery = {
('supplier1', 'roastery1'): 5,
('supplier1', 'roastery2'): 4,
('supplier2', 'roastery1'): 6,
('supplier2', 'roastery2'): 3,
('supplier3', 'roastery1'): 2,
('supplier3', 'roastery2'): 7
}
roasting_cost_light = {'roastery1': 3, 'roastery2': 5}
roasting_cost_dark = {'roastery1': 5, 'roastery2': 6}
shipping_cost_from_roastery_to_cafe = {
('roastery1', 'cafe1'): 5,
('roastery1', 'cafe2'): 3,
('roastery1', 'cafe3'): 6,
('roastery2', 'cafe1'): 4,
('roastery2', 'cafe2'): 5,
('roastery2', 'cafe3'): 2
}
light_coffee_needed_for_cafe = {'cafe1': 20, 'cafe2': 30, 'cafe3': 40}
dark_coffee_needed_for_cafe = {'cafe1': 20, 'cafe2': 20, 'cafe3': 100}
cafes = list(set(i[1] for i in shipping_cost_from_roastery_to_cafe.keys()))
roasteries = list(
set(i[1] for i in shipping_cost_from_supplier_to_roastery.keys()))
suppliers = list(
set(i[0] for i in shipping_cost_from_supplier_to_roastery.keys()))
# Create a new model
model = Model("coffee_distribution")
# OPTIGUIDE DATA CODE GOES HERE
# Create variables
x = model.addVars(shipping_cost_from_supplier_to_roastery.keys(),
vtype=GRB.INTEGER,
name="x")
y_light = model.addVars(shipping_cost_from_roastery_to_cafe.keys(),
vtype=GRB.INTEGER,
name="y_light")
y_dark = model.addVars(shipping_cost_from_roastery_to_cafe.keys(),
vtype=GRB.INTEGER,
name="y_dark")
# Set objective
model.setObjective(
sum(x[i] * shipping_cost_from_supplier_to_roastery[i]
for i in shipping_cost_from_supplier_to_roastery.keys()) +
sum(roasting_cost_light[r] * y_light[r, c] +
roasting_cost_dark[r] * y_dark[r, c]
for r, c in shipping_cost_from_roastery_to_cafe.keys()) + sum(
(y_light[j] + y_dark[j]) * shipping_cost_from_roastery_to_cafe[j]
for j in shipping_cost_from_roastery_to_cafe.keys()), GRB.MINIMIZE)
# Conservation of flow constraint
for r in set(i[1] for i in shipping_cost_from_supplier_to_roastery.keys()):
model.addConstr(
sum(x[i] for i in shipping_cost_from_supplier_to_roastery.keys()
if i[1] == r) == sum(
y_light[j] + y_dark[j]
for j in shipping_cost_from_roastery_to_cafe.keys()
if j[0] == r), f"flow_{r}")
# Add supply constraints
for s in set(i[0] for i in shipping_cost_from_supplier_to_roastery.keys()):
model.addConstr(
sum(x[i] for i in shipping_cost_from_supplier_to_roastery.keys()
if i[0] == s) <= capacity_in_supplier[s], f"supply_{s}")
# Add demand constraints
for c in set(i[1] for i in shipping_cost_from_roastery_to_cafe.keys()):
model.addConstr(
sum(y_light[j] for j in shipping_cost_from_roastery_to_cafe.keys()
if j[1] == c) >= light_coffee_needed_for_cafe[c],
f"light_demand_{c}")
model.addConstr(
sum(y_dark[j] for j in shipping_cost_from_roastery_to_cafe.keys()
if j[1] == c) >= dark_coffee_needed_for_cafe[c],
f"dark_demand_{c}")
# Optimize model
model.optimize()
m = model
# OPTIGUIDE CONSTRAINT CODE GOES HERE
# Solve
m.update()
model.optimize()
print(time.ctime())
if m.status == GRB.OPTIMAL:
print(f'Optimal cost: {m.objVal}')
else:
print("Not solved to optimality. Optimization status:", m.status)
--- DOC STR ---
---
Here are some example questions and their answers and codes:
--- EXAMPLES ---
----------
Question: Why is it not recommended to use just one supplier for roastery 2?
Answer Code:
```python
z = m.addVars(suppliers, vtype=GRB.BINARY, name="z")
m.addConstr(sum(z[s] for s in suppliers) <= 1, "_")
for s in suppliers:
m.addConstr(x[s,'roastery2'] <= capacity_in_supplier[s] * z[s], "_")
```
----------
Question: What if there's a 13% jump in the demand for light coffee at cafe1?
Answer Code:
```python
light_coffee_needed_for_cafe["cafe1"] = light_coffee_needed_for_cafe["cafe1"] * (1 + 13/100)
```
---
The execution result of the original source code is below.
--- Original Result ---
What if we prohibit shipping from supplier 1 to roastery 2?
Note that your written code will be added to the lines with substring:
"# OPTIGUIDE *** CODE GOES HERE"
So, you don't need to write other code, such as m.optimize() or m.update().
You just need to write code snippet in ```python ...``` block.
Here are the history of discussions:
[{'content': 'What if we prohibit shipping from supplier 1 to roastery 2?', 'role': 'user'}]
Answer Code:
With the following carryover:
********************************************************************************
commander (to writer):
You are a chatbot to:
(1) write Python code to answer users questions for supply chain-related coding
project;
(2) explain solutions from a Gurobi/Python solver.
--- SOURCE CODE ---
import time
from gurobipy import GRB, Model
# Example data
capacity_in_supplier = {'supplier1': 150, 'supplier2': 50, 'supplier3': 100}
shipping_cost_from_supplier_to_roastery = {
('supplier1', 'roastery1'): 5,
('supplier1', 'roastery2'): 4,
('supplier2', 'roastery1'): 6,
('supplier2', 'roastery2'): 3,
('supplier3', 'roastery1'): 2,
('supplier3', 'roastery2'): 7
}
roasting_cost_light = {'roastery1': 3, 'roastery2': 5}
roasting_cost_dark = {'roastery1': 5, 'roastery2': 6}
shipping_cost_from_roastery_to_cafe = {
('roastery1', 'cafe1'): 5,
('roastery1', 'cafe2'): 3,
('roastery1', 'cafe3'): 6,
('roastery2', 'cafe1'): 4,
('roastery2', 'cafe2'): 5,
('roastery2', 'cafe3'): 2
}
light_coffee_needed_for_cafe = {'cafe1': 20, 'cafe2': 30, 'cafe3': 40}
dark_coffee_needed_for_cafe = {'cafe1': 20, 'cafe2': 20, 'cafe3': 100}
cafes = list(set(i[1] for i in shipping_cost_from_roastery_to_cafe.keys()))
roasteries = list(
set(i[1] for i in shipping_cost_from_supplier_to_roastery.keys()))
suppliers = list(
set(i[0] for i in shipping_cost_from_supplier_to_roastery.keys()))
# Create a new model
model = Model("coffee_distribution")
# OPTIGUIDE DATA CODE GOES HERE
# Create variables
x = model.addVars(shipping_cost_from_supplier_to_roastery.keys(),
vtype=GRB.INTEGER,
name="x")
y_light = model.addVars(shipping_cost_from_roastery_to_cafe.keys(),
vtype=GRB.INTEGER,
name="y_light")
y_dark = model.addVars(shipping_cost_from_roastery_to_cafe.keys(),
vtype=GRB.INTEGER,
name="y_dark")
# Set objective
model.setObjective(
sum(x[i] * shipping_cost_from_supplier_to_roastery[i]
for i in shipping_cost_from_supplier_to_roastery.keys()) +
sum(roasting_cost_light[r] * y_light[r, c] +
roasting_cost_dark[r] * y_dark[r, c]
for r, c in shipping_cost_from_roastery_to_cafe.keys()) + sum(
(y_light[j] + y_dark[j]) * shipping_cost_from_roastery_to_cafe[j]
for j in shipping_cost_from_roastery_to_cafe.keys()), GRB.MINIMIZE)
# Conservation of flow constraint
for r in set(i[1] for i in shipping_cost_from_supplier_to_roastery.keys()):
model.addConstr(
sum(x[i] for i in shipping_cost_from_supplier_to_roastery.keys()
if i[1] == r) == sum(
y_light[j] + y_dark[j]
for j in shipping_cost_from_roastery_to_cafe.keys()
if j[0] == r), f"flow_{r}")
# Add supply constraints
for s in set(i[0] for i in shipping_cost_from_supplier_to_roastery.keys()):
model.addConstr(
sum(x[i] for i in shipping_cost_from_supplier_to_roastery.keys()
if i[0] == s) <= capacity_in_supplier[s], f"supply_{s}")
# Add demand constraints
for c in set(i[1] for i in shipping_cost_from_roastery_to_cafe.keys()):
model.addConstr(
sum(y_light[j] for j in shipping_cost_from_roastery_to_cafe.keys()
if j[1] == c) >= light_coffee_needed_for_cafe[c],
f"light_demand_{c}")
model.addConstr(
sum(y_dark[j] for j in shipping_cost_from_roastery_to_cafe.keys()
if j[1] == c) >= dark_coffee_needed_for_cafe[c],
f"dark_demand_{c}")
# Optimize model
model.optimize()
m = model
# OPTIGUIDE CONSTRAINT CODE GOES HERE
# Solve
m.update()
model.optimize()
print(time.ctime())
if m.status == GRB.OPTIMAL:
print(f'Optimal cost: {m.objVal}')
else:
print("Not solved to optimality. Optimization status:", m.status)
--- DOC STR ---
---
Here are some example questions and their answers and codes:
--- EXAMPLES ---
----------
Question: Why is it not recommended to use just one supplier for roastery 2?
Answer Code:
```python
z = m.addVars(suppliers, vtype=GRB.BINARY, name="z")
m.addConstr(sum(z[s] for s in suppliers) <= 1, "_")
for s in suppliers:
m.addConstr(x[s,'roastery2'] <= capacity_in_supplier[s] * z[s], "_")
```
----------
Question: What if there's a 13% jump in the demand for light coffee at cafe1?
Answer Code:
```python
light_coffee_needed_for_cafe["cafe1"] = light_coffee_needed_for_cafe["cafe1"] * (1 + 13/100)
```
---
The execution result of the original source code is below.
--- Original Result ---
What if we prohibit shipping from supplier 1 to roastery 2?
Note that your written code will be added to the lines with substring:
"# OPTIGUIDE *** CODE GOES HERE"
So, you don't need to write other code, such as m.optimize() or m.update().
You just need to write code snippet in ```python ...``` block.
Here are the history of discussions:
[{'content': 'What if we prohibit shipping from supplier 1 to roastery 2?', 'role': 'user'}]
Answer Code:
--------------------------------------------------------------------------------
writer (to commander):
To answer the user's question regarding the prohibition of shipping from supplier 1 to roastery 2, we need to add a constraint to the optimization model that sets the shipping quantity `x` for the (`supplier1`, `roastery2`) route to zero. This will effectively disallow any shipping from supplier 1 to roastery 2. The code to do this should be placed in the section of the source code that is indicated by the comment "# OPTIGUIDE DATA CODE GOES HERE".
Here's the Python code snippet to add this constraint:
```python
# filename: supply_chain_code.py
model.addConstr(x[('supplier1', 'roastery2')] == 0, "prohibit_supplier1_roastery2")
```
Adding this constraint will prevent the optimizer from selecting any transport from `supplier1` to `roastery2`, which is what the prohibition is intended to achieve.
Instruct the user to add this code snippet in the appropriate place in the source code that they have provided. After adding this code, the `m.optimize()` function will solve the modified version of the problem that includes the new constraint.
--------------------------------------------------------------------------------
********************************************************************************
Start a new chat with the following message:
Given the source code:
# filename: supply_chain_code.py
model.addConstr(x[('supplier1', 'roastery2')] == 0, "prohibit_supplier1_roastery2")
Is the source code safe (not malicious code to break security
and privacy) to run?
Answer only one word.
If not safe, answer `DANGER`; else, answer `SAFE`.
With the following carryover:
********************************************************************************
commander (to safeguard):
Given the source code:
# filename: supply_chain_code.py
model.addConstr(x[('supplier1', 'roastery2')] == 0, "prohibit_supplier1_roastery2")
Is the source code safe (not malicious code to break security
and privacy) to run?
Answer only one word.
If not safe, answer `DANGER`; else, answer `SAFE`.
--------------------------------------------------------------------------------
safeguard (to commander):
SAFE
--------------------------------------------------------------------------------
Gurobi Optimizer version 11.0.0 build v11.0.0rc2 (mac64[arm] - Darwin 23.2.0 23C71)
CPU model: Apple M3 Max
Thread count: 14 physical cores, 14 logical processors, using up to 14 threads
Optimize a model with 11 rows, 18 columns and 36 nonzeros
Model fingerprint: 0x8aa2c280
Variable types: 0 continuous, 18 integer (0 binary)
Coefficient statistics:
Matrix range [1e+00, 1e+00]
Objective range [2e+00, 1e+01]
Bounds range [0e+00, 0e+00]
RHS range [2e+01, 2e+02]
Found heuristic solution: objective 2900.0000000
Presolve time: 0.00s
Presolved: 11 rows, 18 columns, 36 nonzeros
Variable types: 0 continuous, 18 integer (0 binary)
Found heuristic solution: objective 2896.0000000
Root relaxation: objective 2.470000e+03, 11 iterations, 0.00 seconds (0.00 work units)
Nodes | Current Node | Objective Bounds | Work
Expl Unexpl | Obj Depth IntInf | Incumbent BestBd Gap | It/Node Time
* 0 0 0 2470.0000000 2470.00000 0.00% - 0s
Explored 1 nodes (11 simplex iterations) in 0.00 seconds (0.00 work units)
Thread count was 14 (of 14 available processors)
Solution count 3: 2470 2896 2900
Optimal solution found (tolerance 1.00e-04)
Best objective 2.470000000000e+03, best bound 2.470000000000e+03, gap 0.0000%
Gurobi Optimizer version 11.0.0 build v11.0.0rc2 (mac64[arm] - Darwin 23.2.0 23C71)
CPU model: Apple M3 Max
Thread count: 14 physical cores, 14 logical processors, using up to 14 threads
Optimize a model with 11 rows, 18 columns and 36 nonzeros
Model fingerprint: 0x8aa2c280
Variable types: 0 continuous, 18 integer (0 binary)
Coefficient statistics:
Matrix range [1e+00, 1e+00]
Objective range [2e+00, 1e+01]
Bounds range [0e+00, 0e+00]
RHS range [2e+01, 2e+02]
Found heuristic solution: objective 2900.0000000
Presolve time: 0.00s
Presolved: 11 rows, 18 columns, 36 nonzeros
Variable types: 0 continuous, 18 integer (0 binary)
Found heuristic solution: objective 2896.0000000
Root relaxation: objective 2.470000e+03, 11 iterations, 0.00 seconds (0.00 work units)
Nodes | Current Node | Objective Bounds | Work
Expl Unexpl | Obj Depth IntInf | Incumbent BestBd Gap | It/Node Time
* 0 0 0 2470.0000000 2470.00000 0.00% - 0s
Explored 1 nodes (11 simplex iterations) in 0.00 seconds (0.00 work units)
Thread count was 14 (of 14 available processors)
Solution count 3: 2470 2896 2900
Optimal solution found (tolerance 1.00e-04)
Best objective 2.470000000000e+03, best bound 2.470000000000e+03, gap 0.0000%
Gurobi Optimizer version 11.0.0 build v11.0.0rc2 (mac64[arm] - Darwin 23.2.0 23C71)
CPU model: Apple M3 Max
Thread count: 14 physical cores, 14 logical processors, using up to 14 threads
Optimize a model with 11 rows, 18 columns and 36 nonzeros
Model fingerprint: 0x8aa2c280
Variable types: 0 continuous, 18 integer (0 binary)
Coefficient statistics:
Matrix range [1e+00, 1e+00]
Objective range [2e+00, 1e+01]
Bounds range [0e+00, 0e+00]
RHS range [2e+01, 2e+02]
Presolved: 11 rows, 18 columns, 36 nonzeros
Continuing optimization...
Explored 1 nodes (11 simplex iterations) in 0.00 seconds (0.00 work units)
Thread count was 14 (of 14 available processors)
Solution count 3: 2470 2896 2900
Optimal solution found (tolerance 1.00e-04)
Best objective 2.470000000000e+03, best bound 2.470000000000e+03, gap 0.0000%
Sun Feb 25 18:56:45 2024
Optimal cost: 2470.0
Gurobi Optimizer version 11.0.0 build v11.0.0rc2 (mac64[arm] - Darwin 23.2.0 23C71)
CPU model: Apple M3 Max
Thread count: 14 physical cores, 14 logical processors, using up to 14 threads
Optimize a model with 11 rows, 18 columns and 36 nonzeros
Model fingerprint: 0x8aa2c280
Variable types: 0 continuous, 18 integer (0 binary)
Coefficient statistics:
Matrix range [1e+00, 1e+00]
Objective range [2e+00, 1e+01]
Bounds range [0e+00, 0e+00]
RHS range [2e+01, 2e+02]
Presolved: 11 rows, 18 columns, 36 nonzeros
Continuing optimization...
Explored 1 nodes (11 simplex iterations) in 0.00 seconds (0.00 work units)
Thread count was 14 (of 14 available processors)
Solution count 3: 2470 2896 2900
Optimal solution found (tolerance 1.00e-04)
Best objective 2.470000000000e+03, best bound 2.470000000000e+03, gap 0.0000%
Sun Feb 25 18:56:45 2024
Optimal cost: 2470.0
Optimization problem solved. The objective value is: 2470.0
commander (to writer):
Here are the execution results: Optimization problem solved. The objective value is: 2470.0
Can you organize these information to a human readable answer?
Remember to compare the new results to the original results you obtained in the
beginning.
--- HUMAN READABLE ANSWER ---
--------------------------------------------------------------------------------
writer (to commander):
Certainly! Here's a human-readable answer:
After integrating the constraint to prohibit shipping from supplier 1 to roastery 2, the optimization problem was solved with a total cost of $2470.0.
To provide context to this result, we need to compare it with the original total cost prior to the implementation of the new constraint. However, the original total cost was not provided in the information given to me. If the original total cost was known, we would compare the two to understand the financial impact of this constraint on the supply chain.
If prohibiting shipping from supplier 1 to roastery 2 results in a higher cost than the original scenario, it suggests that the previous ability to ship from supplier 1 to roastery 2 had been cost-effective and beneficial for minimizing overall expenses. On the other hand, if the cost is the same or lower, then shipping from supplier 1 to roastery 2 may not have been crucial for the cost optimization of the supply chain, or other routes may have been able to compensate effectively for this restriction.
Without the original cost for comparison, we can't conclusively say how the prohibition impacted the cost of operation. But the new total cost of $2470.0 will be the baseline for future changes or optimizations done following this new restriction.
--------------------------------------------------------------------------------
commander (to user):
Certainly! Here's a human-readable answer:
After integrating the constraint to prohibit shipping from supplier 1 to roastery 2, the optimization problem was solved with a total cost of $2470.0.
To provide context to this result, we need to compare it with the original total cost prior to the implementation of the new constraint. However, the original total cost was not provided in the information given to me. If the original total cost was known, we would compare the two to understand the financial impact of this constraint on the supply chain.
If prohibiting shipping from supplier 1 to roastery 2 results in a higher cost than the original scenario, it suggests that the previous ability to ship from supplier 1 to roastery 2 had been cost-effective and beneficial for minimizing overall expenses. On the other hand, if the cost is the same or lower, then shipping from supplier 1 to roastery 2 may not have been crucial for the cost optimization of the supply chain, or other routes may have been able to compensate effectively for this restriction.
Without the original cost for comparison, we can't conclusively say how the prohibition impacted the cost of operation. But the new total cost of $2470.0 will be the baseline for future changes or optimizations done following this new restriction.
--------------------------------------------------------------------------------
从返回的ChatResult对象获取最终结果
print(chat_res.summary)
Certainly! Here's a human-readable answer:
After integrating the constraint to prohibit shipping from supplier 1 to roastery 2, the optimization problem was solved with a total cost of $2470.0.
To provide context to this result, we need to compare it with the original total cost prior to the implementation of the new constraint. However, the original total cost was not provided in the information given to me. If the original total cost was known, we would compare the two to understand the financial impact of this constraint on the supply chain.
If prohibiting shipping from supplier 1 to roastery 2 results in a higher cost than the original scenario, it suggests that the previous ability to ship from supplier 1 to roastery 2 had been cost-effective and beneficial for minimizing overall expenses. On the other hand, if the cost is the same or lower, then shipping from supplier 1 to roastery 2 may not have been crucial for the cost optimization of the supply chain, or other routes may have been able to compensate effectively for this restriction.
Without the original cost for comparison, we can't conclusively say how the prohibition impacted the cost of operation. But the new total cost of $2470.0 will be the baseline for future changes or optimizations done following this new restriction.