LangChain - 建立代理

本文翻譯整理自：Build an Agent
https://python.langchain.com/v0.2/docs/tutorials/agents/

文章目錄

- 一、說明
- - 概念
- 二、定義工具
- - 1、Tavily
  - - API參考：
  - 2、Retriever
  - - API參考：
    - API參考：
  - 3、工具
- 三、使用語言模型
- 四、創建代理
- 五、運行代理
- 六、Streaming Messages
- 七、Streaming tokens
- 八、添加到內存
- 九、總結

一、說明

語言模型本身無法采取行動——它們只是輸出文本。 LangChain 的一個重要用例是創建代理。

代理是使用 LLM 作為推理工程師來確定要采取哪些操作，以及這些操作的輸入應該是什么的系統。

然后，這些操作的結果可以反饋給代理，并確定是否需要更多操作，或者是否可以完成。

在本教程中，我們將構建一個可以與多種不同工具交互的代理：一個是本地數據庫，另一個是搜索引擎。

您將能夠向該代理詢問問題、觀看它調用工具并與其進行對話。

概念

我們將涵蓋的概念是：

使用語言模型，特別是它們的工具調用能力
創建檢索器以向我們的代理公開特定信息
使用搜索工具在線查找內容
使用LangGraph Agents，它使用 LLM 來思考要做什么，然后執行該操作
使用 LangSmith調試和跟蹤你的應用程序

項目設置可參考：https://blog.csdn.net/lovechris00/article/details/139130091#_33

二、定義工具

我們首先需要創建要使用的工具。我們將使用兩個工具：Tavily（用于在線搜索），以及我們將創建的本地索引檢索器

1、Tavily

https://python.langchain.com/v0.2/docs/integrations/tools/tavily_search/

我們在 LangChain 中有一個內置工具，可以輕松使用 Tavily 搜索引擎作為工具。

請注意，這需要 API 密鑰 - 他們有一個免費套餐，但如果您沒有或不想創建一個，您可以隨時忽略此步驟。

創建 API 密鑰后，您需要將其導出為：

export TAVILY_API_KEY="..."

from langchain_community.tools.tavily_search import TavilySearchResults

API參考：

TavilySearchResults

search = TavilySearchResults(max_results=2)

search.invoke("what is the weather in SF")

[{'url': 'https://weather.com/weather/tenday/l/San Francisco CA USCA0987:1:US','content': "Comfy & Cozy\nThat's Not What Was Expected\nOutside\n'No-Name Storms' In Florida\nGifts From On High\nWhat To Do For Wheezing\nSurviving The Season\nStay Safe\nAir Quality Index\nAir quality is considered satisfactory, and air pollution poses little or no risk.\n Health & Activities\nSeasonal Allergies and Pollen Count Forecast\nNo pollen detected in your area\nCold & Flu Forecast\nFlu risk is low in your area\nWe recognize our responsibility to use data and technology for good. recents\nSpecialty Forecasts\n10 Day Weather-San Francisco, CA\nToday\nMon 18 | Day\nConsiderable cloudiness. Tue 19\nTue 19 | Day\nLight rain early...then remaining cloudy with showers in the afternoon. Wed 27\nWed 27 | Day\nOvercast with rain showers at times."},{'url': 'https://www.accuweather.com/en/us/san-francisco/94103/hourly-weather-forecast/347629','content': 'Hourly weather forecast in San Francisco, CA. Check current conditions in San Francisco, CA with radar, hourly, and more.'}]

2、Retriever

我們還將針對我們自己的一些數據創建一個檢索器。

有關此處每個步驟的更詳細說明，請參閱本教程。

from langchain_community.document_loaders import WebBaseLoader
from langchain_community.vectorstores import FAISS
from langchain_openai import OpenAIEmbeddings
from langchain_text_splitters import RecursiveCharacterTextSplitterloader = WebBaseLoader("https://docs.smith.langchain.com/overview")
docs = loader.load()
documents = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200
).split_documents(docs)
vector = FAISS.from_documents(documents, OpenAIEmbeddings())
retriever = vector.as_retriever()

API參考：

WebBaseLoader
FAISS
OpenAIEmbeddings
RecursiveCharacterTextSplitter

retriever.invoke("how to upload a dataset")[0]

Document(page_content='import Clientfrom langsmith.evaluation import evaluateclient = Client()# Define dataset: these are your test casesdataset_name = "Sample Dataset"dataset = client.create_dataset(dataset_name, description="A sample dataset in LangSmith.")client.create_examples(    inputs=[        {"postfix": "to LangSmith"},        {"postfix": "to Evaluations in LangSmith"},    ],    outputs=[        {"output": "Welcome to LangSmith"},        {"output": "Welcome to Evaluations in LangSmith"},    ],    dataset_id=dataset.id,)# Define your evaluatordef exact_match(run, example):    return {"score": run.outputs["output"] == example.outputs["output"]}experiment_results = evaluate(    lambda input: "Welcome " + input[\'postfix\'], # Your AI system goes here    data=dataset_name, # The data to predict and grade over    evaluators=[exact_match], # The evaluators to score the results    experiment_prefix="sample-experiment", # The name of the experiment    metadata={      "version": "1.0.0",      "revision_id":', metadata={'source': 'https://docs.smith.langchain.com/overview', 'title': 'Getting started with LangSmith | 🦜?🛠? LangSmith', 'description': 'Introduction', 'language': 'en'})

現在我們已經填充了我們將進行檢索的索引，我們可以輕松地將其變成一個工具（代理正確使用它所需的格式）

from langchain.tools.retriever import create_retriever_tool

API參考：

create_retriever_tool

retriever_tool = create_retriever_tool(retriever,"langsmith_search","Search for information about LangSmith. For any questions about LangSmith, you must use this tool!",
)

3、工具

現在我們已經創建了兩者，我們可以創建將在下游使用的工具列表。

tools = [search, retriever_tool]

三、使用語言模型

接下來我們通過調用工具來學習如何使用語言模型。 LangChain支持多種不同的語言模型：
OpenAI, Anthropic, Google, Cohere, FireworksAI, MistralAI, TogetherAI
這里以 OpenAI 為例，其他模型調用可參閱 https://python.langchain.com/v0.2/docs/tutorials/agents/#using-language-models

pip install -qU langchain-openai

import getpass
import osos.environ["OPENAI_API_KEY"] = getpass.getpass()from langchain_openai import ChatOpenAImodel = ChatOpenAI(model="gpt-4")

您可以通過傳入消息列表來調用語言模型。默認情況下，響應是一個content字符串。

from langchain_core.messages import HumanMessageresponse = model.invoke([HumanMessage(content="hi!")])
response.content

API參考：

HumanMessage

'Hello! How can I assist you today?'

現在我們可以看到讓這個模型進行工具調用是什么樣子的。為了使我們能夠使用.bind_tools這些工具來賦予語言模型知識

model_with_tools = model.bind_tools(tools)

我們現在可以調用該模型。我們首先用一條普通的消息來調用它，看看它如何響應。我們既可以看content場，也可以看tool_calls場。

response = model_with_tools.invoke([HumanMessage(content="Hi!")])print(f"ContentString: {response.content}")
print(f"ToolCalls: {response.tool_calls}")

ContentString: Hello! How can I assist you today?
ToolCalls: []

現在，讓我們嘗試使用一些期望調用工具的輸入來調用它。

response = model_with_tools.invoke([HumanMessage(content="What's the weather in SF?")])print(f"ContentString: {response.content}")
print(f"ToolCalls: {response.tool_calls}")

ContentString: 
ToolCalls: [{'name': 'tavily_search_results_json', 'args': {'query': 'current weather in SF'}, 'id': 'call_nfE1XbCqZ8eJsB8rNdn4MQZQ'}]

我們可以看到現在沒有內容，但是有一個工具調用！它希望我們調用 Tavily Search 工具。

這還沒有調用該工具——它只是告訴我們這樣做。為了實際調用它，我們需要創建我們的代理。

四、創建代理

現在我們已經定義了工具和 LLM，我們可以創建代理。

我們將使用LangGraph來構建代理。

目前我們使用高級接口來構建代理，但 LangGraph 的好處在于，這個高級接口由低級、高度可控的 API 支持，以防您想要修改代理邏輯。

現在，我們可以使用 LLM 和工具來初始化代理。

請注意，我們傳入的是model，而不是model_with_tools。這是因為create_tool_calling_executor會在后臺為我們調用 .bind_tools。

from langgraph.prebuilt import chat_agent_executoragent_executor = chat_agent_executor.create_tool_calling_executor(model, tools)

五、運行代理

我們現在可以針對一些查詢運行代理！

請注意，目前這些都是無狀態查詢（它不會記住以前的交互）。

請注意，代理將在交互結束時返回最終狀態（其中包括任何輸入，我們稍后將看到如何僅獲取輸出）。

首先，讓我們看看當不需要調用工具時它如何響應：

response = agent_executor.invoke({"messages": [HumanMessage(content="hi!")]})response["messages"]

[HumanMessage(content='hi!', id='1535b889-10a5-45d0-a1e1-dd2e60d4bc04'),AIMessage(content='Hello! How can I assist you today?', response_metadata={'token_usage': {'completion_tokens': 10, 'prompt_tokens': 129, 'total_tokens': 139}, 'model_name': 'gpt-4', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-2c94c074-bdc9-4f01-8fd7-71cfc4777d55-0')]

為了準確了解幕后發生的情況（并確保它沒有調用工具），我們可以查看LangSmith 跟蹤

現在讓我們嘗試一下應該調用檢索器的示例

response = agent_executor.invoke({"messages": [HumanMessage(content="how can langsmith help with testing?")]}
)
response["messages"]

[HumanMessage(content='how can langsmith help with testing?', id='04f4fe8f-391a-427c-88af-1fa064db304c'),AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_FNIgdO97wo51sKx3XZOGLHqT', 'function': {'arguments': '{\n  "query": "how can LangSmith help with testing"\n}', 'name': 'langsmith_search'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 22, 'prompt_tokens': 135, 'total_tokens': 157}, 'model_name': 'gpt-4', 'system_fingerprint': None, 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-51f6ea92-84e1-43a5-b1f2-bc0c12d8613f-0', tool_calls=[{'name': 'langsmith_search', 'args': {'query': 'how can LangSmith help with testing'}, 'id': 'call_FNIgdO97wo51sKx3XZOGLHqT'}]),ToolMessage(content="Getting started with LangSmith | 🦜?🛠? LangSmith\n\nSkip to main contentLangSmith API DocsSearchGo to AppQuick StartUser GuideTracingEvaluationProduction Monitoring & AutomationsPrompt HubProxyPricingSelf-HostingCookbookQuick StartOn this pageGetting started with LangSmithIntroduction\u200bLangSmith is a platform for building production-grade LLM applications. It allows you to closely monitor and evaluate your application, so you can ship quickly and with confidence. Use of LangChain is not necessary - LangSmith works on its own!Install LangSmith\u200bWe offer Python and Typescript SDKs for all your LangSmith needs.PythonTypeScriptpip install -U langsmithyarn add langchain langsmithCreate an API key\u200bTo create an API key head to the setting pages. Then click Create API Key.Setup your environment\u200bShellexport LANGCHAIN_TRACING_V2=trueexport LANGCHAIN_API_KEY=<your-api-key># The below examples use the OpenAI API, though it's not necessary in generalexport OPENAI_API_KEY=<your-openai-api-key>Log your first trace\u200bWe provide multiple ways to log traces\n\nLearn about the workflows LangSmith supports at each stage of the LLM application lifecycle.Pricing: Learn about the pricing model for LangSmith.Self-Hosting: Learn about self-hosting options for LangSmith.Proxy: Learn about the proxy capabilities of LangSmith.Tracing: Learn about the tracing capabilities of LangSmith.Evaluation: Learn about the evaluation capabilities of LangSmith.Prompt Hub Learn about the Prompt Hub, a prompt management tool built into LangSmith.Additional Resources\u200bLangSmith Cookbook: A collection of tutorials and end-to-end walkthroughs using LangSmith.LangChain Python: Docs for the Python LangChain library.LangChain Python API Reference: documentation to review the core APIs of LangChain.LangChain JS: Docs for the TypeScript LangChain libraryDiscord: Join us on our Discord to discuss all things LangChain!FAQ\u200bHow do I migrate projects between organizations?\u200bCurrently we do not support project migration betwen organizations. While you can manually imitate this by\n\nteam deals with sensitive data that cannot be logged. How can I ensure that only my team can access it?\u200bIf you are interested in a private deployment of LangSmith or if you need to self-host, please reach out to us at sales@langchain.dev. Self-hosting LangSmith requires an annual enterprise license that also comes with support and formalized access to the LangChain team.Was this page helpful?NextUser GuideIntroductionInstall LangSmithCreate an API keySetup your environmentLog your first traceCreate your first evaluationNext StepsAdditional ResourcesFAQHow do I migrate projects between organizations?Why aren't my runs aren't showing up in my project?My team deals with sensitive data that cannot be logged. How can I ensure that only my team can access it?CommunityDiscordTwitterGitHubDocs CodeLangSmith SDKPythonJS/TSMoreHomepageBlogLangChain Python DocsLangChain JS/TS DocsCopyright ? 2024 LangChain, Inc.", name='langsmith_search', id='f286c7e7-6514-4621-ac60-e4079b37ebe2', tool_call_id='call_FNIgdO97wo51sKx3XZOGLHqT'),AIMessage(content="LangSmith is a platform that can significantly aid in testing by offering several features:\n\n1. **Tracing**: LangSmith provides robust tracing capabilities that enable you to monitor your application closely. This feature is particularly useful for tracking the behavior of your application and identifying any potential issues.\n\n2. **Evaluation**: LangSmith allows you to perform comprehensive evaluations of your application. This can help you assess the performance of your application under various conditions and make necessary adjustments to enhance its functionality.\n\n3. **Production Monitoring & Automations**: With LangSmith, you can keep a close eye on your application when it's in active use. The platform provides tools for automatic monitoring and managing routine tasks, helping to ensure your application runs smoothly.\n\n4. **Prompt Hub**: It's a prompt management tool built into LangSmith. This feature can be instrumental when testing various prompts in your application.\n\nOverall, LangSmith helps you build production-grade LLM applications with confidence, providing necessary tools for monitoring, evaluation, and automation.", response_metadata={'token_usage': {'completion_tokens': 200, 'prompt_tokens': 782, 'total_tokens': 982}, 'model_name': 'gpt-4', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-4b80db7e-9a26-4043-8b6b-922f847f9c80-0')]

讓我們看一下LangSmith 跟蹤，看看幕后發生了什么。

請注意，我們最后返回的狀態還包含工具調用和工具響應消息。

現在讓我們嘗試一下需要調用搜索工具的地方：

response = agent_executor.invoke({"messages": [HumanMessage(content="whats the weather in sf?")]}
)
response["messages"]

[HumanMessage(content='whats the weather in sf?', id='e6b716e6-da57-41de-a227-fee281fda588'),AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_TGDKm0saxuGKJD5OYOXWRvLe', 'function': {'arguments': '{\n  "query": "current weather in San Francisco"\n}', 'name': 'tavily_search_results_json'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 23, 'prompt_tokens': 134, 'total_tokens': 157}, 'model_name': 'gpt-4', 'system_fingerprint': None, 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-fd7d5854-2eab-4fca-ad9e-b3de8d587614-0', tool_calls=[{'name': 'tavily_search_results_json', 'args': {'query': 'current weather in San Francisco'}, 'id': 'call_TGDKm0saxuGKJD5OYOXWRvLe'}]),ToolMessage(content='[{"url": "https://www.weatherapi.com/", "content": "{\'location\': {\'name\': \'San Francisco\', \'region\': \'California\', \'country\': \'United States of America\', \'lat\': 37.78, \'lon\': -122.42, \'tz_id\': \'America/Los_Angeles\', \'localtime_epoch\': 1714426800, \'localtime\': \'2024-04-29 14:40\'}, \'current\': {\'last_updated_epoch\': 1714426200, \'last_updated\': \'2024-04-29 14:30\', \'temp_c\': 17.8, \'temp_f\': 64.0, \'is_day\': 1, \'condition\': {\'text\': \'Sunny\', \'icon\': \'//cdn.weatherapi.com/weather/64x64/day/113.png\', \'code\': 1000}, \'wind_mph\': 23.0, \'wind_kph\': 37.1, \'wind_degree\': 290, \'wind_dir\': \'WNW\', \'pressure_mb\': 1019.0, \'pressure_in\': 30.09, \'precip_mm\': 0.0, \'precip_in\': 0.0, \'humidity\': 50, \'cloud\': 0, \'feelslike_c\': 17.8, \'feelslike_f\': 64.0, \'vis_km\': 16.0, \'vis_miles\': 9.0, \'uv\': 5.0, \'gust_mph\': 27.5, \'gust_kph\': 44.3}}"}, {"url": "https://www.wunderground.com/hourly/us/ca/san-francisco/94125/date/2024-4-29", "content": "Current Weather for Popular Cities . San Francisco, CA warning 59 \\u00b0 F Mostly Cloudy; Manhattan, NY 56 \\u00b0 F Fair; Schiller Park, IL (60176) warning 58 \\u00b0 F Mostly Cloudy; Boston, MA 52 \\u00b0 F Sunny ..."}]', name='tavily_search_results_json', id='aa0d8c3d-23b5-425a-ad05-3c174fc04892', tool_call_id='call_TGDKm0saxuGKJD5OYOXWRvLe'),AIMessage(content='The current weather in San Francisco, California is sunny with a temperature of 64.0°F (17.8°C). The wind is coming from the WNW at a speed of 23.0 mph. The humidity level is at 50%. There is no precipitation and the cloud cover is 0%. The visibility is 16.0 km. The UV index is 5.0. Please note that this information is as of 14:30 on April 29, 2024, according to [Weather API](https://www.weatherapi.com/).', response_metadata={'token_usage': {'completion_tokens': 117, 'prompt_tokens': 620, 'total_tokens': 737}, 'model_name': 'gpt-4', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-2359b41b-cab6-40c3-b6d9-7bdf7195a601-0')]

我們可以檢查LangSmith 跟蹤以確保它有效地調用搜索工具。

六、Streaming Messages

我們已經了解了如何調用代理.invoke來獲取最終響應。

如果代理正在執行多個步驟，則可能需要一段時間。為了顯示中間進度，我們可以在消息發生時流回消息。

for chunk in agent_executor.stream({"messages": [HumanMessage(content="whats the weather in sf?")]}
):print(chunk)print("----")

{'agent': {'messages': [AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_50Kb8zHmFqPYavQwF5TgcOH8', 'function': {'arguments': '{\n  "query": "current weather in San Francisco"\n}', 'name': 'tavily_search_results_json'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 23, 'prompt_tokens': 134, 'total_tokens': 157}, 'model_name': 'gpt-4', 'system_fingerprint': None, 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-042d5feb-c2cc-4c3f-b8fd-dbc22fd0bc07-0', tool_calls=[{'name': 'tavily_search_results_json', 'args': {'query': 'current weather in San Francisco'}, 'id': 'call_50Kb8zHmFqPYavQwF5TgcOH8'}])]}}
----
{'action': {'messages': [ToolMessage(content='[{"url": "https://www.weatherapi.com/", "content": "{\'location\': {\'name\': \'San Francisco\', \'region\': \'California\', \'country\': \'United States of America\', \'lat\': 37.78, \'lon\': -122.42, \'tz_id\': \'America/Los_Angeles\', \'localtime_epoch\': 1714426906, \'localtime\': \'2024-04-29 14:41\'}, \'current\': {\'last_updated_epoch\': 1714426200, \'last_updated\': \'2024-04-29 14:30\', \'temp_c\': 17.8, \'temp_f\': 64.0, \'is_day\': 1, \'condition\': {\'text\': \'Sunny\', \'icon\': \'//cdn.weatherapi.com/weather/64x64/day/113.png\', \'code\': 1000}, \'wind_mph\': 23.0, \'wind_kph\': 37.1, \'wind_degree\': 290, \'wind_dir\': \'WNW\', \'pressure_mb\': 1019.0, \'pressure_in\': 30.09, \'precip_mm\': 0.0, \'precip_in\': 0.0, \'humidity\': 50, \'cloud\': 0, \'feelslike_c\': 17.8, \'feelslike_f\': 64.0, \'vis_km\': 16.0, \'vis_miles\': 9.0, \'uv\': 5.0, \'gust_mph\': 27.5, \'gust_kph\': 44.3}}"}, {"url": "https://world-weather.info/forecast/usa/san_francisco/april-2024/", "content": "Extended weather forecast in San Francisco. Hourly Week 10 days 14 days 30 days Year. Detailed \\u26a1 San Francisco Weather Forecast for April 2024 - day/night \\ud83c\\udf21\\ufe0f temperatures, precipitations - World-Weather.info."}]', name='tavily_search_results_json', id='d88320ac-3fe1-4f73-870a-3681f15f6982', tool_call_id='call_50Kb8zHmFqPYavQwF5TgcOH8')]}}
----
{'agent': {'messages': [AIMessage(content='The current weather in San Francisco, California is sunny with a temperature of 17.8°C (64.0°F). The wind is coming from the WNW at 23.0 mph. The humidity is at 50%. [source](https://www.weatherapi.com/)', response_metadata={'token_usage': {'completion_tokens': 58, 'prompt_tokens': 602, 'total_tokens': 660}, 'model_name': 'gpt-4', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-0cd2a507-ded5-4601-afe3-3807400e9989-0')]}}
----

七、Streaming tokens

除了流回消息之外，流回令牌也很有用。我們可以用.astream_events方法來做到這一點。

信息

此.astream_events方法僅適用于 Python 3.11 或更高版本。

async for event in agent_executor.astream_events({"messages": [HumanMessage(content="whats the weather in sf?")]}, version="v1"
):kind = event["event"]if kind == "on_chain_start":if (event["name"] == "Agent"):  # Was assigned when creating the agent with `.with_config({"run_name": "Agent"})`print(f"Starting agent: {event['name']} with input: {event['data'].get('input')}")elif kind == "on_chain_end":if (event["name"] == "Agent"):  # Was assigned when creating the agent with `.with_config({"run_name": "Agent"})`print()print("--")print(f"Done agent: {event['name']} with output: {event['data'].get('output')['output']}")if kind == "on_chat_model_stream":content = event["data"]["chunk"].contentif content:# Empty content in the context of OpenAI means# that the model is asking for a tool to be invoked.# So we only print non-empty contentprint(content, end="|")elif kind == "on_tool_start":print("--")print(f"Starting tool: {event['name']} with inputs: {event['data'].get('input')}")elif kind == "on_tool_end":print(f"Done tool: {event['name']}")print(f"Tool output was: {event['data'].get('output')}")print("--")

--
Starting tool: tavily_search_results_json with inputs: {'query': 'current weather in San Francisco'}
Done tool: tavily_search_results_json
Tool output was: [{'url': 'https://www.weatherapi.com/', 'content': "{'location': {'name': 'San Francisco', 'region': 'California', 'country': 'United States of America', 'lat': 37.78, 'lon': -122.42, 'tz_id': 'America/Los_Angeles', 'localtime_epoch': 1714427052, 'localtime': '2024-04-29 14:44'}, 'current': {'last_updated_epoch': 1714426200, 'last_updated': '2024-04-29 14:30', 'temp_c': 17.8, 'temp_f': 64.0, 'is_day': 1, 'condition': {'text': 'Sunny', 'icon': '//cdn.weatherapi.com/weather/64x64/day/113.png', 'code': 1000}, 'wind_mph': 23.0, 'wind_kph': 37.1, 'wind_degree': 290, 'wind_dir': 'WNW', 'pressure_mb': 1019.0, 'pressure_in': 30.09, 'precip_mm': 0.0, 'precip_in': 0.0, 'humidity': 50, 'cloud': 0, 'feelslike_c': 17.8, 'feelslike_f': 64.0, 'vis_km': 16.0, 'vis_miles': 9.0, 'uv': 5.0, 'gust_mph': 27.5, 'gust_kph': 44.3}}"}, {'url': 'https://www.weathertab.com/en/c/e/04/united-states/california/san-francisco/', 'content': 'San Francisco Weather Forecast for Apr 2024 - Risk of Rain Graph. Rain Risk Graph: Monthly Overview. Bar heights indicate rain risk percentages. Yellow bars mark low-risk days, while black and grey bars signal higher risks. Grey-yellow bars act as buffers, advising to keep at least one day clear from the riskier grey and black days, guiding ...'}]
--
The| current| weather| in| San| Francisco|,| California|,| USA| is| sunny| with| a| temperature| of| |17|.|8|°C| (|64|.|0|°F|).| The| wind| is| blowing| from| the| W|NW| at| a| speed| of| |37|.|1| k|ph| (|23|.|0| mph|).| The| humidity| level| is| at| |50|%.| [|Source|](|https|://|www|.weather|api|.com|/)|

八、添加到內存

如前所述，該代理是無狀態的。這意味著它不記得以前的交互。

為了給它內存，我們需要傳入一個檢查指針。

當傳入檢查指針時，我們還必須thread_id在調用代理時傳入 a （以便它知道從哪個線程/會話恢復）。

from langgraph.checkpoint.sqlite import SqliteSavermemory = SqliteSaver.from_conn_string(":memory:")

agent_executor = chat_agent_executor.create_tool_calling_executor(model, tools, checkpointer=memory
)config = {"configurable": {"thread_id": "abc123"}}

for chunk in agent_executor.stream({"messages": [HumanMessage(content="hi im bob!")]}, config
):print(chunk)print("----")

{'agent': {'messages': [AIMessage(content='Hello Bob! How can I assist you today?', response_metadata={'token_usage': {'completion_tokens': 11, 'prompt_tokens': 131, 'total_tokens': 142}, 'model_name': 'gpt-4', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-607733e3-4b8d-4137-ae66-8a4b8ccc8d40-0')]}}
----

for chunk in agent_executor.stream({"messages": [HumanMessage(content="whats my name?")]}, config
):print(chunk)print("----")

{'agent': {'messages': [AIMessage(content='Your name is Bob. How can I assist you further?', response_metadata={'token_usage': {'completion_tokens': 13, 'prompt_tokens': 154, 'total_tokens': 167}, 'model_name': 'gpt-4', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-e1181ba6-732d-4564-b479-9f1ab6bf01f6-0')]}}
----