从用户需求生成提示¶
在此示例中，我们将创建一个聊天机器人来帮助用户生成提示。它将首先收集用户的需求，然后生成提示（并根据用户输入进行完善）。这些被分成两个独立的状态，大型语言模型 (LLM) 决定何时在它们之间转换。
系统示意图如下所示。
设置¶
首先，让我们安装所需的软件包并设置我们的 OpenAI API 密钥（我们将使用的大型语言模型）。
import getpass
import os
def _set_env(var: str):
if not os.environ.get(var):
os.environ[var] = getpass.getpass(f"{var}: ")
_set_env("OPENAI_API_KEY")
为 LangGraph 开发设置 LangSmith
注册 LangSmith 以快速发现问题并提高 LangGraph 项目的性能。LangSmith 允许您使用跟踪数据调试、测试和监控您使用 LangGraph 构建的大型语言模型应用程序 — 此处提供了更多关于如何开始的信息。
收集信息¶
首先，让我们定义图中负责收集用户需求的部分。这将是一个带有特定系统消息的大型语言模型调用。它将能够访问一个工具，以便在准备好生成提示时调用该工具。
在 LangChain 中使用 Pydantic
本笔记本使用 Pydantic v2 的 BaseModel，这需要 langchain-core >= 0.3。使用 langchain-core < 0.3 将由于 Pydantic v1 和 v2 BaseModel 的混合而导致错误。
API 参考：SystemMessage | ChatOpenAI
from typing import List
from langchain_core.messages import SystemMessage
from langchain_openai import ChatOpenAI
from pydantic import BaseModel
template = """Your job is to get information from a user about what type of prompt template they want to create.
You should get the following information from them:
- What the objective of the prompt is
- What variables will be passed into the prompt template
- Any constraints for what the output should NOT do
- Any requirements that the output MUST adhere to
If you are not able to discern this info, ask them to clarify! Do not attempt to wildly guess.
After you are able to discern all the information, call the relevant tool."""
def get_messages_info(messages):
return [SystemMessage(content=template)] + messages
class PromptInstructions(BaseModel):
"""Instructions on how to prompt the LLM."""
objective: str
variables: List[str]
constraints: List[str]
requirements: List[str]
llm = ChatOpenAI(temperature=0)
llm_with_tool = llm.bind_tools([PromptInstructions])
def info_chain(state):
messages = get_messages_info(state["messages"])
response = llm_with_tool.invoke(messages)
return {"messages": [response]}
生成提示¶
现在我们设置将生成提示的状态。这将需要一个单独的系统消息，以及一个函数来过滤掉工具调用之前的所有消息（因为上一个状态正是在此时决定生成提示）。
API 参考：AIMessage | HumanMessage | ToolMessage
from langchain_core.messages import AIMessage, HumanMessage, ToolMessage
# New system prompt
prompt_system = """Based on the following requirements, write a good prompt template:
{reqs}"""
# Function to get the messages for the prompt
# Will only get messages AFTER the tool call
def get_prompt_messages(messages: list):
tool_call = None
other_msgs = []
for m in messages:
if isinstance(m, AIMessage) and m.tool_calls:
tool_call = m.tool_calls[0]["args"]
elif isinstance(m, ToolMessage):
continue
elif tool_call is not None:
other_msgs.append(m)
return [SystemMessage(content=prompt_system.format(reqs=tool_call))] + other_msgs
def prompt_gen_chain(state):
messages = get_prompt_messages(state["messages"])
response = llm.invoke(messages)
return {"messages": [response]}
定义状态逻辑¶
这是聊天机器人所处状态的逻辑。如果最后一条消息是工具调用，那么我们处于“提示创建者”（prompt）应该响应的状态。否则，如果最后一条消息不是 HumanMessage，那么我们知道接下来应该是人类响应，因此我们处于 END 状态。如果最后一条消息是 HumanMessage，那么如果之前有工具调用，我们处于 prompt 状态。否则，我们处于“信息收集”（info）状态。
API 参考：END
from typing import Literal
from langgraph.graph import END
def get_state(state):
messages = state["messages"]
if isinstance(messages[-1], AIMessage) and messages[-1].tool_calls:
return "add_tool_message"
elif not isinstance(messages[-1], HumanMessage):
return END
return "info"
创建图¶
现在我们可以创建图了。我们将使用 SqliteSaver 来持久化对话历史。
API 参考：MemorySaver | StateGraph | START | add_messages
from langgraph.checkpoint.memory import MemorySaver
from langgraph.graph import StateGraph, START
from langgraph.graph.message import add_messages
from typing import Annotated
from typing_extensions import TypedDict
class State(TypedDict):
messages: Annotated[list, add_messages]
memory = MemorySaver()
workflow = StateGraph(State)
workflow.add_node("info", info_chain)
workflow.add_node("prompt", prompt_gen_chain)
@workflow.add_node
def add_tool_message(state: State):
return {
"messages": [
ToolMessage(
content="Prompt generated!",
tool_call_id=state["messages"][-1].tool_calls[0]["id"],
)
]
}
workflow.add_conditional_edges("info", get_state, ["add_tool_message", "info", END])
workflow.add_edge("add_tool_message", "prompt")
workflow.add_edge("prompt", END)
workflow.add_edge(START, "info")
graph = workflow.compile(checkpointer=memory)
使用图¶
现在我们可以使用创建好的聊天机器人了。
import uuid
cached_human_responses = ["hi!", "rag prompt", "1 rag, 2 none, 3 no, 4 no", "red", "q"]
cached_response_index = 0
config = {"configurable": {"thread_id": str(uuid.uuid4())}}
while True:
try:
user = input("User (q/Q to quit): ")
except:
user = cached_human_responses[cached_response_index]
cached_response_index += 1
print(f"User (q/Q to quit): {user}")
if user in {"q", "Q"}:
print("AI: Byebye")
break
output = None
for output in graph.stream(
{"messages": [HumanMessage(content=user)]}, config=config, stream_mode="updates"
):
last_message = next(iter(output.values()))["messages"][-1]
last_message.pretty_print()
if output and "prompt" in output:
print("Done!")
User (q/Q to quit): hi!
================================== Ai Message ==================================
Hello! How can I assist you today?
User (q/Q to quit): rag prompt
================================== Ai Message ==================================
Sure! I can help you create a prompt template. To get started, could you please provide me with the following information:
1. What is the objective of the prompt?
2. What variables will be passed into the prompt template?
3. Any constraints for what the output should NOT do?
4. Any requirements that the output MUST adhere to?
Once I have this information, I can assist you in creating the prompt template.
User (q/Q to quit): 1 rag, 2 none, 3 no, 4 no
================================== Ai Message ==================================
Tool Calls:
PromptInstructions (call_tcz0foifsaGKPdZmsZxNnepl)
Call ID: call_tcz0foifsaGKPdZmsZxNnepl
Args:
objective: rag
variables: ['none']
constraints: ['no']
requirements: ['no']
================================= Tool Message =================================
Prompt generated!
================================== Ai Message ==================================
Please write a response using the RAG (Red, Amber, Green) rating system.
Done!
User (q/Q to quit): red
================================== Ai Message ==================================
Response: The status is RED.
User (q/Q to quit): q
AI: Byebye