如何向聊天机器人添加工具
本节将介绍如何创建对话代理：可以使用工具与其他系统和 API 交互的聊天机器人。
注意
本操作指南之前使用 RunnableWithMessageHistory 构建了一个聊天机器人。您可以在 v0.2 文档中访问此版本的指南。
从 LangChain v0.3 版本开始，我们建议 LangChain 用户利用 LangGraph 持久化来整合memory导入到新的 LangChain 应用程序中。
如果您的代码已经依赖于RunnableWithMessageHistory或BaseChatMessageHistory，则无需进行任何更改。我们不打算在不久的将来弃用此功能，因为它适用于简单的聊天应用程序和任何使用RunnableWithMessageHistory将继续按预期工作。
有关更多详细信息，请参阅如何迁移到 LangGraph 内存。
设置
在本指南中，我们将使用一个工具调用代理和一个用于搜索 Web 的工具。默认将由 Tavily 提供支持，但您可以将其切换为任何类似工具。本节的其余部分将假定您使用的是 Tavily。
您需要在 Tavily 网站上注册一个帐户，并安装以下软件包：
%pip install --upgrade --quiet langchain-community langchain-openai tavily-python langgraph
import getpass
import os
if not os.environ.get("OPENAI_API_KEY"):
os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")
if not os.environ.get("TAVILY_API_KEY"):
os.environ["TAVILY_API_KEY"] = getpass.getpass("Tavily API Key:")
OpenAI API Key: ········
Tavily API Key: ········
您还需要将 OpenAI 密钥设置为OPENAI_API_KEY并将您的 Tavily API 密钥设置为TAVILY_API_KEY.
创建代理
我们的最终目标是创建一个代理，它可以在根据需要查找信息的同时以对话方式回答用户问题。
首先，让我们初始化 Tavily 和能够进行工具调用的 OpenAI 聊天模型：
from langchain_community.tools.tavily_search import TavilySearchResults
from langchain_openai import ChatOpenAI
tools = [TavilySearchResults(max_results=1)]
# Choose the LLM that will drive the agent
# Only certain models support this
model = ChatOpenAI(model="gpt-4o-mini", temperature=0)
API 参考：TavilySearchResults | 聊天OpenAI
要使我们的代理具有对话性，我们还可以指定一个提示。下面是一个示例：
prompt = (
"You are a helpful assistant. "
"You may not need to use tools for every query - the user may just want to chat!"
)
伟大！现在让我们使用 LangGraph 的预构建create_react_agent组装我们的代理，它允许您创建一个工具调用代理：
from langgraph.prebuilt import create_react_agent
# prompt allows you to preprocess the inputs to the model inside ReAct agent
# in this case, since we're passing a prompt string, we'll just always add a SystemMessage
# with this prompt string before any other messages sent to the model
agent = create_react_agent(model, tools, prompt=prompt)
API 参考：create_react_agent
运行代理
现在我们已经设置了代理，让我们尝试与它交互！它可以处理不需要查找的琐碎查询：
from langchain_core.messages import HumanMessage
agent.invoke({"messages": [HumanMessage(content="I'm Nemo!")]})
API 参考：HumanMessage
{'messages': [HumanMessage(content="I'm Nemo!", additional_kwargs={}, response_metadata={}, id='39e715c7-bd1c-426f-8e14-c05586b3d221'),
AIMessage(content='Hi Nemo! How can I assist you today?', additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 11, 'prompt_tokens': 107, 'total_tokens': 118, 'completion_tokens_details': {'reasoning_tokens': 0}}, 'model_name': 'gpt-4o-mini-2024-07-18', 'system_fingerprint': 'fp_1bb46167f9', 'finish_reason': 'stop', 'logprobs': None}, id='run-6937c944-d702-40bb-9a9f-4141ddde9f78-0', usage_metadata={'input_tokens': 107, 'output_tokens': 11, 'total_tokens': 118})]}
或者，如果需要，它可以使用传递的搜索工具来获取最新信息：
agent.invoke(
{
"messages": [
HumanMessage(
content="What is the current conservation status of the Great Barrier Reef?"
)
],
}
)
{'messages': [HumanMessage(content='What is the current conservation status of the Great Barrier Reef?', additional_kwargs={}, response_metadata={}, id='a74cc581-8ad5-4401-b3a5-f028d69e4b21'),
AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_aKOItwvAb4DHQCwaasKphGHq', 'function': {'arguments': '{"query":"current conservation status of the Great Barrier Reef 2023"}', 'name': 'tavily_search_results_json'}, 'type': 'function'}], 'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 28, 'prompt_tokens': 116, 'total_tokens': 144, 'completion_tokens_details': {'reasoning_tokens': 0}}, 'model_name': 'gpt-4o-mini-2024-07-18', 'system_fingerprint': 'fp_1bb46167f9', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-267ff8a8-d866-4ae5-9534-ad87ebbdc954-0', tool_calls=[{'name': 'tavily_search_results_json', 'args': {'query': 'current conservation status of the Great Barrier Reef 2023'}, 'id': 'call_aKOItwvAb4DHQCwaasKphGHq', 'type': 'tool_call'}], usage_metadata={'input_tokens': 116, 'output_tokens': 28, 'total_tokens': 144}),
ToolMessage(content='[{"url": "https://www.aims.gov.au/monitoring-great-barrier-reef/gbr-condition-summary-2023-24", "content": "This report summarises the condition of coral reefs in the Northern, Central and Southern\xa0Great Barrier Reef (GBR) from the Long-Term Monitoring Program (LTMP) surveys of 94 reefs conducted between August\xa02023 and June 2024 (reported as ‘2024’). Over the past 38 years of monitoring by the Australian Institute of Marine Science (AIMS), hard coral cover on reefs of the GBR has decreased and increased in response to cycles of disturbance and recovery. It is relatively rare for GBR reefs to have 75% to 100% hard coral cover and AIMS defines >30% – 50% hard coral cover as a high value, based on historical surveys across the GBR."}]', name='tavily_search_results_json', id='05b3fab7-9ac8-42bb-9612-ff2a896dbb67', tool_call_id='call_aKOItwvAb4DHQCwaasKphGHq', artifact={'query': 'current conservation status of the Great Barrier Reef 2023', 'follow_up_questions': None, 'answer': None, 'images': [], 'results': [{'title': 'Annual Summary Report of Coral Reef Condition 2023/24', 'url': 'https://www.aims.gov.au/monitoring-great-barrier-reef/gbr-condition-summary-2023-24', 'content': 'This report summarises the condition of coral reefs in the Northern, Central and Southern\xa0Great Barrier Reef (GBR) from the Long-Term Monitoring Program (LTMP) surveys of 94 reefs conducted between August\xa02023 and June 2024 (reported as ‘2024’). Over the past 38 years of monitoring by the Australian Institute of Marine Science (AIMS), hard coral cover on reefs of the GBR has decreased and increased in response to cycles of disturbance and recovery. It is relatively rare for GBR reefs to have 75% to 100% hard coral cover and AIMS defines >30% – 50% hard coral cover as a high value, based on historical surveys across the GBR.', 'score': 0.95991266, 'raw_content': None}], 'response_time': 4.22}),
AIMessage(content='The current conservation status of the Great Barrier Reef (GBR) indicates ongoing challenges and fluctuations in coral health. According to a report from the Australian Institute of Marine Science (AIMS), the condition of coral reefs in the GBR has been monitored over the years, showing cycles of disturbance and recovery. \n\nAs of the latest surveys conducted between August 2023 and June 2024, hard coral cover on the GBR has experienced both decreases and increases. AIMS defines a hard coral cover of over 30% to 50% as high value, but it is relatively rare for GBR reefs to achieve 75% to 100% hard coral cover.\n\nFor more detailed information, you can refer to the [AIMS report](https://www.aims.gov.au/monitoring-great-barrier-reef/gbr-condition-summary-2023-24).', additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 174, 'prompt_tokens': 337, 'total_tokens': 511, 'completion_tokens_details': {'reasoning_tokens': 0}}, 'model_name': 'gpt-4o-mini-2024-07-18', 'system_fingerprint': 'fp_1bb46167f9', 'finish_reason': 'stop', 'logprobs': None}, id='run-bec32925-0dba-445d-8b55-87358ef482bb-0', usage_metadata={'input_tokens': 337, 'output_tokens': 174, 'total_tokens': 511})]}
对话式响应
由于我们的提示包含聊天记录消息的占位符，因此我们的代理还可以考虑以前的交互，并像标准聊天机器人一样进行对话响应：
from langchain_core.messages import AIMessage, HumanMessage
agent.invoke(
{
"messages": [
HumanMessage(content="I'm Nemo!"),
AIMessage(content="Hello Nemo! How can I assist you today?"),
HumanMessage(content="What is my name?"),
],
}
)
API 参考：AIMessage | HumanMessage （人工消息）
{'messages': [HumanMessage(content="I'm Nemo!", additional_kwargs={}, response_metadata={}, id='2c8e58bf-ad20-45a4-940b-84393c6b3a03'),
AIMessage(content='Hello Nemo! How can I assist you today?', additional_kwargs={}, response_metadata={}, id='5e014114-7e9d-42c3-b63e-a662b3a49bef'),
HumanMessage(content='What is my name?', additional_kwargs={}, response_metadata={}, id='d92be4e1-6497-4037-9a9a-83d3e7b760d5'),
AIMessage(content='Your name is Nemo!', additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 6, 'prompt_tokens': 130, 'total_tokens': 136, 'completion_tokens_details': {'reasoning_tokens': 0}}, 'model_name': 'gpt-4o-mini-2024-07-18', 'system_fingerprint': 'fp_1bb46167f9', 'finish_reason': 'stop', 'logprobs': None}, id='run-17db96f8-8dbd-4f25-a80d-e4e872967641-0', usage_metadata={'input_tokens': 130, 'output_tokens': 6, 'total_tokens': 136})]}
如果愿意，您还可以向 LangGraph 代理添加内存以管理消息的历史记录。让我们这样重新声明它：
from langgraph.checkpoint.memory import MemorySaver
memory = MemorySaver()
agent = create_react_agent(model, tools, prompt=prompt, checkpointer=memory)
API 参考：MemorySaver
agent.invoke(
{"messages": [HumanMessage("I'm Nemo!")]},
config={"configurable": {"thread_id": "1"}},
)
{'messages': [HumanMessage(content="I'm Nemo!", additional_kwargs={}, response_metadata={}, id='117b2cfc-c6cc-449c-bba9-26fc545d0afa'),
AIMessage(content='Hi Nemo! How can I assist you today?', additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 11, 'prompt_tokens': 107, 'total_tokens': 118, 'completion_tokens_details': {'reasoning_tokens': 0}}, 'model_name': 'gpt-4o-mini-2024-07-18', 'system_fingerprint': 'fp_1bb46167f9', 'finish_reason': 'stop', 'logprobs': None}, id='run-ba16cc0b-fba1-4ec5-9d99-e010c3b702d0-0', usage_metadata={'input_tokens': 107, 'output_tokens': 11, 'total_tokens': 118})]}
然后，如果我们重新运行包装的代理执行程序：
agent.invoke(
{"messages": [HumanMessage("What is my name?")]},
config={"configurable": {"thread_id": "1"}},
)
{'messages': [HumanMessage(content="I'm Nemo!", additional_kwargs={}, response_metadata={}, id='117b2cfc-c6cc-449c-bba9-26fc545d0afa'),
AIMessage(content='Hi Nemo! How can I assist you today?', additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 11, 'prompt_tokens': 107, 'total_tokens': 118, 'completion_tokens_details': {'reasoning_tokens': 0}}, 'model_name': 'gpt-4o-mini-2024-07-18', 'system_fingerprint': 'fp_1bb46167f9', 'finish_reason': 'stop', 'logprobs': None}, id='run-ba16cc0b-fba1-4ec5-9d99-e010c3b702d0-0', usage_metadata={'input_tokens': 107, 'output_tokens': 11, 'total_tokens': 118}),
HumanMessage(content='What is my name?', additional_kwargs={}, response_metadata={}, id='53ac8d34-99bb-43a7-9103-80e26b7ee6cc'),
AIMessage(content='Your name is Nemo!', additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 6, 'prompt_tokens': 130, 'total_tokens': 136, 'completion_tokens_details': {'reasoning_tokens': 0}}, 'model_name': 'gpt-4o-mini-2024-07-18', 'system_fingerprint': 'fp_1bb46167f9', 'finish_reason': 'stop', 'logprobs': None}, id='run-b3f224a5-902a-4973-84ff-9b683615b0e2-0', usage_metadata={'input_tokens': 130, 'output_tokens': 6, 'total_tokens': 136})]}
此 LangSmith 跟踪显示了后台发生的情况。
延伸阅读
有关如何构建代理的更多信息，请查看以下 LangGraph 指南：
有关工具使用的更多信息，您还可以查看此用例部分。