检查其他资源

• 我为这个问题添加了一个非常描述性的标题。
• 我使用集成搜索在LangChain文档中进行了搜索。
• 我使用GitHub搜索查找了一个类似的问题，但没有找到。
• 我确信这是LangChain中的一个bug,而不是我的代码。
• 通过更新到LangChain的最新稳定版本(或特定集成包)无法解决此bug。

示例代码

from langchain.agents import initialize_agent, AgentType
from langchain.agents import Tool
from langchain_experimental.utilities import PythonREPL
import datetime
from langchain.agents import AgentExecutor
from langchain.chains.conversation.memory import (
    ConversationBufferMemory,
)
from langchain.prompts import MessagesPlaceholder
from langchain_community.chat_models import BedrockChat
from langchain.agents import OpenAIMultiFunctionsAgent

# You can create the tool to pass to an agent
repl_tool = Tool(
    name="python_repl",
    description="A Python shell. Use this to execute python commands. Input should be a valid python command. If you want to see the output of a value, you should print it out with `print(...)`.",
    func=PythonREPL().run,
)

memory = ConversationBufferMemory(return_messages=True, k=10, memory_key="chat_history")

prompt = OpenAIMultiFunctionsAgent.create_prompt(
    system_message=SystemMessage(content="You are an helpful AI bot"),
    extra_prompt_messages=[MessagesPlaceholder(variable_name="chat_history")],
)

llm = BedrockChat(
    model_id="anthropic.claude-3-5-sonnet-20240620-v1:0",
    client=client, #initialized elsewhere
    model_kwargs={"max_tokens": 4050, "temperature": 0.5},
    verbose=True,
)

tools = [
    repl_tool,
]

agent_executor = initialize_agent(
    tools,
    llm,
    agent=AgentType.STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION,
    verbose=True,
    max_iterations=10,
    memory=memory,
    prompt=prompt,
)

res = agent_executor.invoke({
    'input': 'hi how are you?'
})
print(res['output']

# Hello! I'm an AI assistant, so I don't have feelings, but I'm functioning well and ready to help. How can I assist you today?

res=agent_executor.invoke({
"input": "what was my previous message?"
})

print(res['output']

# I'm sorry, but I don't have access to any previous messages. Each interaction starts fresh, so I don't have information about what you said before this current question. If you have a specific topic or question you'd like to discuss, please feel free to ask and I'll be happy to help.

# but when I checked the memory buffer 

print(memory.buffer)

# [HumanMessage(content='hi how are you?'), AIMessage(content="Hello! As an AI assistant, I don't have feelings, but I'm functioning well and ready to help you. How can I assist you today?"), HumanMessage(content='hi how are you?'), AIMessage(content="Hello! I'm an AI assistant, so I don't have feelings, but I'm functioning well and ready to help. How can I assist you today?"), HumanMessage(content='what was my previous message?'), AIMessage(content="I'm sorry, but I don't have access to any previous messages. Each interaction starts fresh, so I don't have information about what you said before this current question. If you have a specific topic or question you'd like to discuss, please feel free to ask and I'll be happy to help.")]

# As you can see memory is getting updated 
# so I checked the prompt template of the agent executor 

pprint(agent_executor.agent.llm_chain.prompt)

# ChatPromptTemplate(input_variables=['agent_scratchpad', 'input'], messages=[SystemMessagePromptTemplate(prompt=PromptTemplate(input_variables=[], template='Respond to the human as helpfully and accurately as possible. You have access to the following tools:\n\npython_repl: A Python shell. Use this to execute python commands. Input should be a valid python command. If you want to see the output of a value, you should print it out with `print(...)`., args: {{\'tool_input\': {{\'type\': \'string\'}}}}\n\nUse a json blob to specify a tool by providing an action key (tool name) and an action_input key (tool input).\n\nValid "action" values: "Final Answer" or python_repl\n\nProvide only ONE action per $JSON_BLOB, as shown:\n\n```\n{{\n  "action": $TOOL_NAME,\n  "action_input": $INPUT\n}}\n```\n\nFollow this format:\n\nQuestion: input question to answer\nThought: consider previous and subsequent steps\nAction:\n```\n$JSON_BLOB\n```\nObservation: action result\n... (repeat Thought/Action/Observation N times)\nThought: I know what to respond\nAction:\n```\n{{\n  "action": "Final Answer",\n  "action_input": "Final response to human"\n}}\n```\n\nBegin! Reminder to ALWAYS respond with a valid json blob of a single action. Use tools if necessary. Respond directly if appropriate. Format is Action:```$JSON_BLOB```then Observation:.\nThought:')), HumanMessagePromptTemplate(prompt=PromptTemplate(input_variables=['agent_scratchpad', 'input'], template='{input}\n\n{agent_scratchpad}'))])

# As you can see there is no input variable placeholder for `chat_memory`

错误信息和堆栈跟踪(如果适用)

• 无响应*

描述

• 我正在尝试使用内存( AgentType.STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION )的代理执行器
• 我传递了正确的提示模板，其中包含 memory_key
• 初始化的代理执行器的提示模板返回一个不包含 memory_key 占位符的默认提示模板。