当使用语言模型时，通常情况下，获得出色性能所需的只是一个好的提示和合适的工具。然而，当处理许多独特的流程时，事情可能会变得棘手。这本食谱将引导您了解解决此问题的一种方法。
我们将介绍例程和交接的概念，然后逐步介绍实现过程，并展示如何以简单、强大且可控的方式使用它们来编排多个代理。
最后，我们提供了一个示例仓库 Swarm，它实现了这些想法以及示例。
当使用语言模型时，通常情况下，获得出色性能所需的只是一个好的提示和合适的工具。然而，当处理许多独特的流程时，事情可能会变得棘手。这本食谱将引导您了解解决此问题的一种方法。
我们将介绍例程和交接的概念，然后逐步介绍实现过程，并展示如何以简单、强大且可控的方式使用它们来编排多个代理。
最后，我们提供了一个示例仓库 Swarm，它实现了这些想法以及示例。
让我们从设置导入开始。
from openai import OpenAI
from pydantic import BaseModel
from typing import Optional
import json
client = OpenAI()“例程”的概念没有严格定义，而是旨在捕捉一系列步骤的想法。具体而言，让我们将例程定义为自然语言（我们将用系统提示表示）中的一系列指令，以及完成这些指令所需的工具。
让我们看一个例子。下面，我们为客户服务代理定义了一个例程，指示它对用户问题进行分类，然后建议修复或提供退款。我们还定义了必要的函数 execute_refund 和 look_up_item。我们可以称之为客户服务例程、代理、助手等——但其核心思想是相同的：一系列步骤和执行这些步骤的工具。
# Customer Service Routine
system_message = (
"You are a customer support agent for ACME Inc."
"Always answer in a sentence or less."
"Follow the following routine with the user:"
"1. First, ask probing questions and understand the user's problem deeper.\n"
" - unless the user has already provided a reason.\n"
"2. Propose a fix (make one up).\n"
"3. ONLY if not satesfied, offer a refund.\n"
"4. If accepted, search for the ID and then execute refund."
""
)
def look_up_item(search_query):
"""Use to find item ID.
Search query can be a description or keywords."""
# return hard-coded item ID - in reality would be a lookup
return "item_132612938"
def execute_refund(item_id, reason="not provided"):
print("Summary:", item_id, reason) # lazy summary
return "success"
例程的主要优势在于其简单性和稳健性。请注意，这些指令包含条件语句，很像状态机或代码中的分支。对于中小型例程，LLM 实际上可以非常稳健地处理这些情况，并且具有“软”依从性的额外好处——LLM 可以自然地引导对话，而不会陷入死胡同。
要执行例程，让我们实现一个简单的循环，该循环执行以下操作：
messages。messages。def run_full_turn(system_message, messages):
response = client.chat.completions.create(
model="gpt-4o-mini",
messages=[{"role": "system", "content": system_message}] + messages,
)
message = response.choices[0].message
messages.append(message)
if message.content: print("Assistant:", message.content)
return message
messages = []
while True:
user = input("User: ")
messages.append({"role": "user", "content": user})
run_full_turn(system_message, messages)正如您所看到的，这目前忽略了函数调用，因此让我们添加它。
模型要求函数格式化为函数模式。为了方便起见，我们可以定义一个辅助函数，将 python 函数转换为相应的函数模式。
import inspect
def function_to_schema(func) -> dict:
type_map = {
str: "string",
int: "integer",
float: "number",
bool: "boolean",
list: "array",
dict: "object",
type(None): "null",
}
try:
signature = inspect.signature(func)
except ValueError as e:
raise ValueError(
f"Failed to get signature for function {func.__name__}: {str(e)}"
)
parameters = {}
for param in signature.parameters.values():
try:
param_type = type_map.get(param.annotation, "string")
except KeyError as e:
raise KeyError(
f"Unknown type annotation {param.annotation} for parameter {param.name}: {str(e)}"
)
parameters[param.name] = {"type": param_type}
required = [
param.name
for param in signature.parameters.values()
if param.default == inspect._empty
]
return {
"type": "function",
"function": {
"name": func.__name__,
"description": (func.__doc__ or "").strip(),
"parameters": {
"type": "object",
"properties": parameters,
"required": required,
},
},
}例如
def sample_function(param_1, param_2, the_third_one: int, some_optional="John Doe"):
"""
This is my docstring. Call this function when you want.
"""
print("Hello, world")
schema = function_to_schema(sample_function)
print(json.dumps(schema, indent=2)){
"type": "function",
"function": {
"name": "sample_function",
"description": "This is my docstring. Call this function when you want.",
"parameters": {
"type": "object",
"properties": {
"param_1": {
"type": "string"
},
"param_2": {
"type": "string"
},
"the_third_one": {
"type": "integer"
},
"some_optional": {
"type": "string"
}
},
"required": [
"param_1",
"param_2",
"the_third_one"
]
}
}
}
现在，我们可以使用此函数在调用模型时将工具传递给模型。
messages = []
tools = [execute_refund, look_up_item]
tool_schemas = [function_to_schema(tool) for tool in tools]
response = client.chat.completions.create(
model="gpt-4o-mini",
messages=[{"role": "user", "content": "Look up the black boot."}],
tools=tool_schemas,
)
message = response.choices[0].message
message.tool_calls[0].functionFunction(arguments='{"search_query":"black boot"}', name='look_up_item')最后，当模型调用工具时，我们需要执行相应的函数并将结果提供回模型。
我们可以通过将工具名称映射到 tool_map 中的 python 函数来做到这一点，然后在 execute_tool_call 中查找并调用它。最后，我们将结果添加到对话中。
tools_map = {tool.__name__: tool for tool in tools}
def execute_tool_call(tool_call, tools_map):
name = tool_call.function.name
args = json.loads(tool_call.function.arguments)
print(f"Assistant: {name}({args})")
# call corresponding function with provided arguments
return tools_map[name](**args)
for tool_call in message.tool_calls:
result = execute_tool_call(tool_call, tools_map)
# add result back to conversation
result_message = {
"role": "tool",
"tool_call_id": tool_call.id,
"content": result,
}
messages.append(result_message)Assistant: look_up_item({'search_query': 'black boot'})
在实践中，我们还需要让模型使用结果来生成另一个响应。该响应可能也包含工具调用，因此我们可以只在一个循环中运行它，直到没有更多的工具调用为止。
如果我们将所有内容放在一起，它看起来会像这样
tools = [execute_refund, look_up_item]
def run_full_turn(system_message, tools, messages):
num_init_messages = len(messages)
messages = messages.copy()
while True:
# turn python functions into tools and save a reverse map
tool_schemas = [function_to_schema(tool) for tool in tools]
tools_map = {tool.__name__: tool for tool in tools}
# === 1. get openai completion ===
response = client.chat.completions.create(
model="gpt-4o-mini",
messages=[{"role": "system", "content": system_message}] + messages,
tools=tool_schemas or None,
)
message = response.choices[0].message
messages.append(message)
if message.content: # print assistant response
print("Assistant:", message.content)
if not message.tool_calls: # if finished handling tool calls, break
break
# === 2. handle tool calls ===
for tool_call in message.tool_calls:
result = execute_tool_call(tool_call, tools_map)
result_message = {
"role": "tool",
"tool_call_id": tool_call.id,
"content": result,
}
messages.append(result_message)
# ==== 3. return new messages =====
return messages[num_init_messages:]
def execute_tool_call(tool_call, tools_map):
name = tool_call.function.name
args = json.loads(tool_call.function.arguments)
print(f"Assistant: {name}({args})")
# call corresponding function with provided arguments
return tools_map[name](**args)
messages = []
while True:
user = input("User: ")
messages.append({"role": "user", "content": user})
new_messages = run_full_turn(system_message, tools, messages)
messages.extend(new_messages)现在我们有了一个例程，假设我们想要添加更多步骤和更多工具。我们可以在一定程度上做到这一点，但最终，如果我们尝试使用太多不同的任务来扩展例程，它可能会开始变得吃力。这就是我们可以利用多个例程概念的地方——给定用户请求，我们可以加载正确的例程，其中包含适当的步骤和工具来解决它。
动态交换系统指令和工具似乎令人望而生畏。但是，如果我们将“例程”视为“代理”，那么交接的概念使我们能够简单地表示这些交换——就像一个代理将对话移交给另一个代理一样。
让我们将交接定义为一个代理（或例程）将正在进行的对话移交给另一个代理，很像您在电话中被转移到其他人一样。不同的是，在这种情况下，代理完全了解您之前的对话！
要了解交接的实际应用，让我们首先为一个代理定义一个基本类。
class Agent(BaseModel):
name: str = "Agent"
model: str = "gpt-4o-mini"
instructions: str = "You are a helpful Agent"
tools: list = []现在为了使我们的代码支持它，我们可以更改 run_full_turn 以采用 Agent 而不是单独的 system_message 和 tools
def run_full_turn(agent, messages):
num_init_messages = len(messages)
messages = messages.copy()
while True:
# turn python functions into tools and save a reverse map
tool_schemas = [function_to_schema(tool) for tool in agent.tools]
tools_map = {tool.__name__: tool for tool in agent.tools}
# === 1. get openai completion ===
response = client.chat.completions.create(
model=agent.model,
messages=[{"role": "system", "content": agent.instructions}] + messages,
tools=tool_schemas or None,
)
message = response.choices[0].message
messages.append(message)
if message.content: # print assistant response
print("Assistant:", message.content)
if not message.tool_calls: # if finished handling tool calls, break
break
# === 2. handle tool calls ===
for tool_call in message.tool_calls:
result = execute_tool_call(tool_call, tools_map)
result_message = {
"role": "tool",
"tool_call_id": tool_call.id,
"content": result,
}
messages.append(result_message)
# ==== 3. return new messages =====
return messages[num_init_messages:]
def execute_tool_call(tool_call, tools_map):
name = tool_call.function.name
args = json.loads(tool_call.function.arguments)
print(f"Assistant: {name}({args})")
# call corresponding function with provided arguments
return tools_map[name](**args)我们现在可以轻松运行多个代理
def execute_refund(item_name):
return "success"
refund_agent = Agent(
name="Refund Agent",
instructions="You are a refund agent. Help the user with refunds.",
tools=[execute_refund],
)
def place_order(item_name):
return "success"
sales_assistant = Agent(
name="Sales Assistant",
instructions="You are a sales assistant. Sell the user a product.",
tools=[place_order],
)
messages = []
user_query = "Place an order for a black boot."
print("User:", user_query)
messages.append({"role": "user", "content": user_query})
response = run_full_turn(sales_assistant, messages) # sales assistant
messages.extend(response)
user_query = "Actually, I want a refund." # implicitly refers to the last item
print("User:", user_query)
messages.append({"role": "user", "content": user_query})
response = run_full_turn(refund_agent, messages) # refund agentUser: Place an order for a black boot.
Assistant: place_order({'item_name': 'black boot'})
Assistant: Your order for a black boot has been successfully placed! If you need anything else, feel free to ask!
User: Actually, I want a refund.
Assistant: execute_refund({'item_name': 'black boot'})
Assistant: Your refund for the black boot has been successfully processed. If you need further assistance, just let me know!
太棒了！但是我们在这里手动进行了交接——我们希望代理本身来决定何时执行交接。一种简单但出奇有效的方法是给他们一个 transfer_to_XXX 函数，其中 XXX 是某个代理。模型足够聪明，知道何时调用此函数以进行交接！
现在代理可以表达进行交接的意图，我们必须使其真正发生。有很多方法可以做到这一点，但有一种特别简洁的方法。
对于我们到目前为止定义的代理函数，例如 execute_refund 或 place_order，它们返回一个字符串，该字符串将提供给模型。如果相反，我们返回一个 Agent 对象来指示我们想要转移到哪个代理，会怎么样？像这样
refund_agent = Agent(
name="Refund Agent",
instructions="You are a refund agent. Help the user with refunds.",
tools=[execute_refund],
)
def transfer_to_refunds():
return refund_agent
sales_assistant = Agent(
name="Sales Assistant",
instructions="You are a sales assistant. Sell the user a product.",
tools=[place_order],
)然后，我们可以更新我们的代码以检查函数响应的返回类型，如果它是 Agent，则更新正在使用的代理！此外，现在 run_full_turn 将需要返回正在使用的最新代理，以防发生交接。（我们可以在 Response 类中执行此操作以保持整洁。）
class Response(BaseModel):
agent: Optional[Agent]
messages: list现在是更新后的 run_full_turn
def run_full_turn(agent, messages):
current_agent = agent
num_init_messages = len(messages)
messages = messages.copy()
while True:
# turn python functions into tools and save a reverse map
tool_schemas = [function_to_schema(tool) for tool in current_agent.tools]
tools = {tool.__name__: tool for tool in current_agent.tools}
# === 1. get openai completion ===
response = client.chat.completions.create(
model=agent.model,
messages=[{"role": "system", "content": current_agent.instructions}]
+ messages,
tools=tool_schemas or None,
)
message = response.choices[0].message
messages.append(message)
if message.content: # print agent response
print(f"{current_agent.name}:", message.content)
if not message.tool_calls: # if finished handling tool calls, break
break
# === 2. handle tool calls ===
for tool_call in message.tool_calls:
result = execute_tool_call(tool_call, tools, current_agent.name)
if type(result) is Agent: # if agent transfer, update current agent
current_agent = result
result = (
f"Transfered to {current_agent.name}. Adopt persona immediately."
)
result_message = {
"role": "tool",
"tool_call_id": tool_call.id,
"content": result,
}
messages.append(result_message)
# ==== 3. return last agent used and new messages =====
return Response(agent=current_agent, messages=messages[num_init_messages:])
def execute_tool_call(tool_call, tools, agent_name):
name = tool_call.function.name
args = json.loads(tool_call.function.arguments)
print(f"{agent_name}:", f"{name}({args})")
return tools[name](**args) # call corresponding function with provided arguments让我们看一个包含更多代理的示例。
def escalate_to_human(summary):
"""Only call this if explicitly asked to."""
print("Escalating to human agent...")
print("\n=== Escalation Report ===")
print(f"Summary: {summary}")
print("=========================\n")
exit()
def transfer_to_sales_agent():
"""User for anything sales or buying related."""
return sales_agent
def transfer_to_issues_and_repairs():
"""User for issues, repairs, or refunds."""
return issues_and_repairs_agent
def transfer_back_to_triage():
"""Call this if the user brings up a topic outside of your purview,
including escalating to human."""
return triage_agent
triage_agent = Agent(
name="Triage Agent",
instructions=(
"You are a customer service bot for ACME Inc. "
"Introduce yourself. Always be very brief. "
"Gather information to direct the customer to the right department. "
"But make your questions subtle and natural."
),
tools=[transfer_to_sales_agent, transfer_to_issues_and_repairs, escalate_to_human],
)
def execute_order(product, price: int):
"""Price should be in USD."""
print("\n\n=== Order Summary ===")
print(f"Product: {product}")
print(f"Price: ${price}")
print("=================\n")
confirm = input("Confirm order? y/n: ").strip().lower()
if confirm == "y":
print("Order execution successful!")
return "Success"
else:
print("Order cancelled!")
return "User cancelled order."
sales_agent = Agent(
name="Sales Agent",
instructions=(
"You are a sales agent for ACME Inc."
"Always answer in a sentence or less."
"Follow the following routine with the user:"
"1. Ask them about any problems in their life related to catching roadrunners.\n"
"2. Casually mention one of ACME's crazy made-up products can help.\n"
" - Don't mention price.\n"
"3. Once the user is bought in, drop a ridiculous price.\n"
"4. Only after everything, and if the user says yes, "
"tell them a crazy caveat and execute their order.\n"
""
),
tools=[execute_order, transfer_back_to_triage],
)
def look_up_item(search_query):
"""Use to find item ID.
Search query can be a description or keywords."""
item_id = "item_132612938"
print("Found item:", item_id)
return item_id
def execute_refund(item_id, reason="not provided"):
print("\n\n=== Refund Summary ===")
print(f"Item ID: {item_id}")
print(f"Reason: {reason}")
print("=================\n")
print("Refund execution successful!")
return "success"
issues_and_repairs_agent = Agent(
name="Issues and Repairs Agent",
instructions=(
"You are a customer support agent for ACME Inc."
"Always answer in a sentence or less."
"Follow the following routine with the user:"
"1. First, ask probing questions and understand the user's problem deeper.\n"
" - unless the user has already provided a reason.\n"
"2. Propose a fix (make one up).\n"
"3. ONLY if not satesfied, offer a refund.\n"
"4. If accepted, search for the ID and then execute refund."
""
),
tools=[execute_refund, look_up_item, transfer_back_to_triage],
)最后，我们可以在循环中运行它（这不会在 python 笔记本中运行，因此您可以在单独的 python 文件中尝试此操作）
agent = triage_agent
messages = []
while True:
user = input("User: ")
messages.append({"role": "user", "content": user})
response = run_full_turn(agent, messages)
agent = response.agent
messages.extend(response.messages)作为概念验证，我们将这些想法打包到一个名为 Swarm 的示例库中。它仅作为示例，不应直接用于生产环境。但是，请随意采用这些想法和代码来构建您自己的库！