如何為聊天機器人新增工具

先決條件

本指南假設您熟悉以下概念

本節將涵蓋如何建立對話式代理：可以使用工具與其他系統和 API 互動的聊天機器人。

注意

本操作指南先前使用 RunnableWithMessageHistory 建立聊天機器人。您可以在 v0.2 文件中存取本指南的舊版本。

從 LangChain v0.3 版本開始，我們建議 LangChain 使用者利用 LangGraph 持續性，將 memory 納入新的 LangChain 應用程式中。

如果您的程式碼已經依賴 RunnableWithMessageHistory 或 BaseChatMessageHistory，則您不需要進行任何變更。我們不打算在近期內棄用此功能，因為它適用於簡單的聊天應用程式，且任何使用 RunnableWithMessageHistory 的程式碼都將繼續如預期般運作。

請參閱如何遷移到 LangGraph 記憶體以取得更多詳細資訊。

設定

在本指南中，我們將使用工具調用代理，其中包含用於搜尋網路的單一工具。預設情況下，它將由 Tavily 提供支援，但您可以將其替換為任何類似的工具。本節的其餘部分將假設您正在使用 Tavily。

您需要前往 Tavily 網站註冊帳戶，並安裝以下套件

%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: ········

您還需要將您的 OpenAI 金鑰設定為 OPENAI_API_KEY，並將您的 Tavily API 金鑰設定為 TAVILY_API_KEY。

建立代理

我們的最終目標是建立一個代理，該代理可以對使用者的問題進行對話式回應，同時根據需要查找資訊。

首先，讓我們初始化 Tavily 和一個能夠進行工具調用的 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 參考文件：TavilySearchResults | ChatOpenAI

為了使我們的代理具有對話性，我們還可以指定提示。以下是一個範例

prompt = (
    "You are a helpful assistant. "
    "You may not need to use tools for every query - the user may just want to chat!"
)

太棒了！現在讓我們使用 LangGraph 的預建 create_react_agent 組裝我們的代理，它允許您建立一個工具調用代理

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 參考文件：create_react_agent

執行代理

現在我們已經設定好代理，讓我們嘗試與其互動！它可以處理不需要查找的簡單查詢

from langchain_core.messages import HumanMessage

agent.invoke({"messages": [HumanMessage(content="I'm Nemo!")]})

API 參考文件：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，它可以視需要使用傳遞的搜尋工具來取得最新的資訊

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})]}

對話式回應

由於我們的提示包含聊天記錄訊息的預留位置，因此我們的代理也可以將先前的互動納入考量，並像標準聊天機器人一樣進行對話式回應

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 參考文件：AIMessage | HumanMessage

{'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})]}

如果需要，您也可以將記憶體新增到 LangGraph 代理，以管理訊息的歷史記錄。讓我們以這種方式重新宣告它

from langgraph.checkpoint.memory import MemorySaver

memory = MemorySaver()
agent = create_react_agent(model, tools, prompt=prompt, checkpointer=memory)

API 參考文件：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})]}

然後，如果我們重新執行包裝的代理執行器

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 追蹤顯示了幕後發生的情況。

延伸閱讀

如需更多關於如何建立代理的資訊，請查看這些 LangGraph 指南

如需更多關於工具使用的資訊，您也可以查看此使用案例章節。

設定​

建立代理​

執行代理​

對話式回應​

延伸閱讀​

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設定

建立代理

執行代理

對話式回應

延伸閱讀