使用AutoGen进行嵌套聊天的OptiGuide

这是一个嵌套聊天的重新实现，基于OptiGuide，OptiGuide是一个基于LLM的供应链优化框架。

除了AutoGen之外，这个笔记本还需要安装eventlet和Gurobipy。在这个笔记本中，eventlet包用于限制代码执行的超时时间，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。

需求

这个笔记本需要一些额外的依赖项，可以通过pip安装：

pip install pyautogen eventlet gurobipy

更多信息，请参考安装指南。

import re
from typing import Union

# import auxiliary packages
import requests  # 用于加载示例源代码
from eventlet.timeout import Timeout

# 测试Gurobi安装
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

了解有关为代理配置LLM的更多信息，请点击这里。

OptiGuide中的代理编排。

步骤0. 准备辅助函数

下面的代码单元格包含了几个辅助函数，供代理使用。这些辅助函数直接采用自OptiGuide。

实用函数

定义了几个实用函数（replace、insert_code、run_with_exec）来动态操作和执行源代码：

• 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：
  根据源代码中的特定标记确定插入新代码的位置。 代码。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。这个函数对于测试生成的代码解决方案的可行性和正确性至关重要。

外部代码检索函数

该单元还包括从远程存储库中使用 requests 库检索示例源代码的逻辑。这个示例代码作为聊天机器人的基础，让用户可以看到所讨论概念的实际应用。

然后显示检索到的代码，同时显示源代码的开头和结尾，以便一瞥其结构和内容。

def replace(src_code: str, old_code: str, new_code: str) -> str:
    """
    通过替换指定的旧代码块，将新代码插入到源代码中。

    参数:
        src_code (str): 要修改的源代码。
        old_code (str): 要替换的代码块。
        new_code (str): 要插入的新代码块。

    返回:
        str: 插入新代码后的修改后的源代码。

    异常:
        None

    示例:
        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)
        # 输出:
        # 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:
    """将代码补丁插入源代码中。

    参数:
        src_code (str): 完整的源代码。
        new_lines (str): 新代码。

    返回:
        str: 插入（替换）新代码后的完整源代码。
    """
    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]:
    """使用 exec 运行代码片段。

    参数:
        src_code (str): 要运行的源代码。

    返回:
        object: 代码片段的结果。
            如果代码成功运行，返回目标值（浮点数或字符串）。
            否则，返回错误（异常）。
    """
    locals_dict = {}
    locals_dict.update(globals())
    locals_dict.update(locals())

    timeout = Timeout(
        60,
        TimeoutError("这是一个超时异常，以防万一 GPT 的代码陷入无限循环。"),
    )
    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 = "无界"
            elif status == GRB.INF_OR_UNBD:
                ans = "无界或无解"
            elif status == GRB.INFEASIBLE:
                ans = "不可行"
                m = locals_dict["m"]
                m.computeIIS()
                constrs = [c.ConstrName for c in m.getConstrs() if c.IISConstr]
                ans += "\n冲突约束:\n" + str(constrs)
            else:
                ans = "模型状态：" + str(status)
        else:
            ans = "优化问题已解决。目标值为：" + str(locals_dict["m"].objVal)
    except Exception as e:
        return e

    return ans

第一步：Agent构建

这个部分介绍了Writer和OptiGuide agent类以及它们的实例，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、评估和集成供应链优化问题的代码解决方案的过程。

定义的类

• OptiGuide：继承自autogen.AssistantAgent，作为处理供应链优化逻辑的主要类。它维护状态信息，如源代码、剩余的调试尝试次数、成功状态和用户聊天历史记录。关键方法包括set_success和update_debug_times，用于根据交互结果更新agent的状态。

• Writer：也继承自autogen.AssistantAgent，这个类专门用于管理生成和解释Python代码解决方案。它跟踪源代码和示例问答，以帮助生成对用户查询的响应。

Agent实例

• writer、safeguard和optiguide_commander：上述定义的类的实例，每个实例在代码生成和评估过程中扮演特定的角色。这些agent共同工作，确保用安全、优化和易懂的Python代码解决方案回答用户的问题。

• user：autogen.UserProxyAgent的一个实例，代表与笔记本交互的最终用户。它被配置为模拟用户的输入和响应，促进交互和引人入胜的学习体验。

Agent系统消息

每个agent都与相应的系统消息相关联。这些系统消息直接采用自OptiGuide。

# %% 系统消息
WRITER_SYSTEM_MSG = """你是一个聊天机器人，可以：
(1) 编写供应链相关的Python代码来回答用户的问题；
(2) 解释Gurobi/Python求解器的解决方案。

--- 源代码 ---
{source_code}

--- 文档字符串 ---

{doc_str}
---

以下是一些示例问题及其答案和代码：
--- 示例 ---
{example_qa}
---

原始源代码的执行结果如下。
--- 原始结果 ---
{execution_result}

请注意，你编写的代码将添加到包含子字符串的行中：
"# OPTIGUIDE *** CODE GOES HERE"
因此，你不需要编写其他代码，如m.optimize()或m.update()。
你只需要在 ```python ...``` 块中编写代码片段。
"""

SAFEGUARD_SYSTEM_MSG = """
给定源代码：
{source_code}

源代码是否安全（不会破坏安全性和隐私）？
只需回答一个词。
如果不安全，请回答“DANGER”；否则，请回答“SAFE”。
"""

# %% 用于匹配源代码中的代码行的常量字符串。
DATA_CODE_STR = "# OPTIGUIDE DATA CODE GOES HERE"
CONSTRAINT_CODE_STR = "# OPTIGUIDE CONSTRAINT CODE GOES HERE"

# 在上下文学习示例中。
example_qa = """
----------
问题：为什么不建议仅使用一个供应商来供应烘焙厂2？
答案代码：

import time

from gurobipy import GRB, Model

# 示例数据

capacity_in_supplier = {'supplier1': 150, 'supplier2': 50, 'supplier3': 100}

shipping_cost_from_supplier_to_roastery = {
    ('supplier1', 'roastery1'): 5,
.
.
.

# 求解
m.update()
model.optimize()

print(time.ctime())
if m.status == GRB.OPTIMAL:
    print(f'最优成本：{m.objVal}')
else:
    print("未达到最优解。优化状态：", m.status)

第二步：编排嵌套对话

这三个代理实例的编排方式如下，将writer和safeguard嵌套到commander代理中作为内部独白。

下一个单元格定义了关键函数，用于管理OptiGuide系统、用户和内部逻辑之间的交互，以处理和响应查询。每个函数在引导对话、处理代码生成请求、确保代码安全性和总结结果方面发挥特定的作用。这确保了代理接收到清晰的指令、即时的反馈，并通过代码在安全环境中探索供应链优化问题。

可以在register_nested_chats函数的chat_queue参数中指定对话的顺序。以下字段特别有用：- recipient（必填）指定嵌套代理；- message指定要发送给嵌套接收者代理的消息。在一系列的嵌套对话中，如果未指定message字段，我们将使用注册代理收到的最后一条消息作为第一个对话中的初始消息，并跳过队列中没有message字段的任何后续对话。您可以提供一个字符串，也可以定义一个返回字符串的可调用对象。- summary_method决定从嵌套对话中获取什么。您可以从现有选项中选择，包括“last_msg”和“reflection_with_llm”，或者使用可调用对象定义自己从嵌套对话中获取什么。- max_turns确定相关代理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
    # 获取原始源代码的执行结果
    sender_history = recipient.chat_messages[sender]
    user_chat_history = "\n以下是讨论的历史记录：\n" f"{sender_history}"

    if sender.name == "user":
        execution_result = msg_content  # TODO: 获取原始源代码的执行结果
    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: 重置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 "抱歉，我无法回答您的问题。"


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:
        # 步骤4和5：运行代码并获取结果
        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]:
            # 成功运行代码并获得结果
            sender.success = True
            # 步骤6：请求解释结果
            return INTERPRETER_PROMPT.format(execution_rst=execution_rst)
    else:
        # DANGER：如果不安全，尝试调试。重新编码
        execution_rst = """
        抱歉，这段新代码不安全，我不允许您执行它。
        请尝试找到新的方法（编码）来回答问题。"""
        if sender.debug_times_left > 0:
            # 尝试调试并重新编写代码（回到步骤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}
]
# 只有在从writer接收到消息时才触发safeguard
optiguide_commander.register_nested_chats(safeguard_chat_queue, trigger="writer")
# 只有在从user接收到消息时才触发writer
optiguide_commander.register_nested_chats(writer_chat_queue, trigger="user")

让代理人对话

chat_res = user.initiate_chat(
    optiguide_commander, message="如果我们禁止从供应商1运送到烘焙厂2会怎样？"
)

用户（对指挥官）：

如果我们禁止从供应商1向烘焙厂2运输会怎么样？

--------------------------------------------------------------------------------

********************************************************************************
发送以下消息开始一个新的聊天：
你是一个聊天机器人，用来：
（1）编写供应链相关编码项目的Python代码；
（2）解释Gurobi/Python求解器的解决方案。

--- 源代码 ---
import time

from gurobipy import GRB, Model

# 示例数据

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()))

# 创建一个新模型
model = Model("coffee_distribution")

# OPTIGUIDE 数据代码放在这里

# 创建变量
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")

# 设置目标
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)

# 流量守恒约束
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}")

# 添加供应约束
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}")

# 添加需求约束
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}")

# 优化模型
model.optimize()
m = model

# OPTIGUIDE 约束代码放在这里

# 求解
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 ---

---

以下是一些示例问题及其答案和代码：
--- EXAMPLES ---

----------
问题：为什么不建议仅使用一个供应商为烘焙厂2提供供应？
答案代码：
```python
```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], "_")
```

----------
问题：如果cafe1对浅焙咖啡的需求增加了13%，会怎样？
答案代码：
```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
```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], "_")
```

----------
问题：如果cafe1对浅焙咖啡的需求增加了13%，会怎么样？
答案代码：
```python
light_coffee_needed_for_cafe["cafe1"] = light_coffee_needed_for_cafe["cafe1"] * (1 + 13/100)
```


---

原始源代码的执行结果如下。
--- 原始结果 ---
如果我们禁止从供应商1运送到烘焙厂2，会怎么样？

请注意，您编写的代码将添加到包含子字符串的行中：
"# OPTIGUIDE *** CODE GOES HERE"
因此，您不需要编写其他代码，例如m.optimize()或m.update()。
您只需要在```python ...```块中编写代码片段。

以下是讨论的历史记录：
[{'content': '如果我们禁止从供应商1运送到烘焙厂2，会怎么样？', 'role': 'user'}]

答案代码：


--------------------------------------------------------------------------------
写手（给指挥官）：

为了回答用户关于禁止从供应商1运送到烘焙厂2的问题，我们需要向优化模型添加一个约束条件，将(`supplier1`, `roastery2`)路线的运送数量`x`设置为零。这将有效地禁止从供应商1运送到烘焙厂2。将此代码放置在源代码中由注释“# OPTIGUIDE DATA CODE GOES HERE”指示的部分。

以下是添加此约束的Python代码片段：

```python
# 文件名：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 对象中获取最终结果

print(chat_res.summary)

当然！这是一个可读性强的答案：

在将禁止从供应商 1 运送到烘焙坊 2 的约束集成后，优化问题得到解决，总成本为 $2470.0。

为了给这个结果提供背景，我们需要将其与在实施新约束之前的原始总成本进行比较。然而，在给我的信息中没有提供原始总成本。如果知道原始总成本，我们可以比较这两个值，以了解这个约束对供应链的财务影响。

如果禁止从供应商 1 运送到烘焙坊 2 的成本高于原始情况，这表明以前从供应商 1 运送到烘焙坊 2 是具有成本效益且有利于最小化总费用的。另一方面，如果成本相同或更低，则说明从供应商 1 运送到烘焙坊 2 对供应链的成本优化可能并不关键，或者其他路径可能能够有效地弥补这个限制。

没有原始成本进行比较，我们无法确定禁止运输对运营成本的影响。但是，新的总成本 $2470.0 将成为在这个新限制之后进行未来更改或优化的基准。