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Zapier 自然语言操作

已弃用 此 API 将于 2023-11-17 弃用：https://nla.zapier.com/start/

Zapier 自然语言操作 通过自然语言 API 接口，让您可以访问 Zapier 平台上的 5000 多个应用和 20000 多个操作。

NLA 支持 Gmail、Salesforce、Trello、Slack、Asana、HubSpot、Google Sheets、Microsoft Teams 等应用，以及数千个其他应用：https://zapier.com/apps Zapier NLA 负责所有的底层 API 认证以及从自然语言到底层 API 调用的转换，并返回简化的输出供 LLM 使用。核心思想是，您或您的用户通过类似的 OAuth 设置窗口公开一组操作，然后您可以通过 REST API 查询和执行这些操作。

NLA 提供 API 密钥和 OAuth 来签名 NLA API 请求。

  1. 服务器端（API 密钥）：用于快速入门、测试以及 LangChain 只使用开发者 Zapier 账户中公开的操作（并将使用开发者在 Zapier.com 上连接的账户）的生产场景。

  2. 面向用户（OAuth）：用于部署面向最终用户的应用程序的生产场景，并且 LangChain 需要访问用户在 Zapier.com 上公开的操作和连接的账户。

为了简洁起见，本快速入门主要介绍服务器端用例。请跳转至 使用 OAuth 访问令牌的示例 查看设置面向用户场景的 Zapier 的简短示例。请查阅 完整文档 以获得完整的面向用户的 OAuth 开发者支持。

本示例将介绍如何将 Zapier 集成与 SimpleSequentialChain 以及 Agent 结合使用。 下方的代码中展示了具体实现：

import os

# get from https://platform.openai.com/
os.environ["OPENAI_API_KEY"] = os.environ.get("OPENAI_API_KEY", "")

# get from https://nla.zapier.com/docs/authentication/ after logging in):
os.environ["ZAPIER_NLA_API_KEY"] = os.environ.get("ZAPIER_NLA_API_KEY", "")

Agent 示例​

Zapier 工具可以与代理一起使用。请参阅下面的示例。

from langchain.agents import AgentType, initialize_agent
from langchain_community.agent_toolkits import ZapierToolkit
from langchain_community.utilities.zapier import ZapierNLAWrapper
from langchain_openai import OpenAI
## step 0. expose gmail 'find email' and slack 'send channel message' actions

# first go here, log in, expose (enable) the two actions: https://nla.zapier.com/demo/start -- for this example, can leave all fields "Have AI guess"
# in an oauth scenario, you'd get your own <provider> id (instead of 'demo') which you route your users through first
llm = OpenAI(temperature=0)
zapier = ZapierNLAWrapper()
toolkit = ZapierToolkit.from_zapier_nla_wrapper(zapier)
agent = initialize_agent(
    toolkit.get_tools(), llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True
)
agent.run(
    "Summarize the last email I received regarding Silicon Valley Bank. Send the summary to the #test-zapier channel in slack."
)


> Entering new AgentExecutor chain...
 I need to find the email and summarize it.
Action: Gmail: Find Email
Action Input: Find the latest email from Silicon Valley Bank
Observation: {"from__name": "Silicon Valley Bridge Bank, N.A.", "from__email": "sreply@svb.com", "body_plain": "Dear Clients, After chaotic, tumultuous & stressful days, we have clarity on path for SVB, FDIC is fully insuring all deposits & have an ask for clients & partners as we rebuild. Tim Mayopoulos <https://eml.svb.com/NjEwLUtBSy0yNjYAAAGKgoxUeBCLAyF_NxON97X4rKEaNBLG", "reply_to__email": "sreply@svb.com", "subject": "Meet the new CEO Tim Mayopoulos", "date": "Tue, 14 Mar 2023 23:42:29 -0500 (CDT)", "message_url": "https://mail.google.com/mail/u/0/#inbox/186e393b13cfdf0a", "attachment_count": "0", "to__emails": "ankush@langchain.dev", "message_id": "186e393b13cfdf0a", "labels": "IMPORTANT, CATEGORY_UPDATES, INBOX"}
Thought: I need to summarize the email and send it to the #test-zapier channel in Slack.
Action: Slack: Send Channel Message
Action Input: Send a slack message to the #test-zapier channel with the text "Silicon Valley Bank has announced that Tim Mayopoulos is the new CEO. FDIC is fully insuring all deposits and they have an ask for clients and partners as they rebuild."
Observation: {"message__text": "Silicon Valley Bank has announced that Tim Mayopoulos is the new CEO. FDIC is fully insuring all deposits and they have an ask for clients and partners as they rebuild.", "message__permalink": "https://langchain.slack.com/archives/C04TSGU0RA7/p1678859932375259", "channel": "C04TSGU0RA7", "message__bot_profile__name": "Zapier", "message__team": "T04F8K3FZB5", "message__bot_id": "B04TRV4R74K", "message__bot_profile__deleted": "false", "message__bot_profile__app_id": "A024R9PQM", "ts_time": "2023-03-15T05:58:52Z", "message__bot_profile__icons__image_36": "https://avatars.slack-edge.com/2022-08-02/3888649620612_f864dc1bb794cf7d82b0_36.png", "message__blocks[]block_id": "kdZZ", "message__blocks[]elements[]type": "['rich_text_section']"}
Thought: I now know the final answer.
Final Answer: I have sent a summary of the last email from Silicon Valley Bank to the #test-zapier channel in Slack.

> Finished chain.
'I have sent a summary of the last email from Silicon Valley Bank to the #test-zapier channel in Slack.'

SimpleSequentialChain 示例​

如果您需要更明确的控制，可以使用链，如下所示。

from langchain.chains import LLMChain, SimpleSequentialChain, TransformChain
from langchain_community.tools.zapier.tool import ZapierNLARunAction
from langchain_community.utilities.zapier import ZapierNLAWrapper
from langchain_core.prompts import PromptTemplate
from langchain_openai import OpenAI
## step 0. expose gmail 'find email' and slack 'send direct message' actions

# first go here, log in, expose (enable) the two actions: https://nla.zapier.com/demo/start -- for this example, can leave all fields "Have AI guess"
# in an oauth scenario, you'd get your own <provider> id (instead of 'demo') which you route your users through first

actions = ZapierNLAWrapper().list()
## step 1. gmail find email

GMAIL_SEARCH_INSTRUCTIONS = "Grab the latest email from Silicon Valley Bank"


def nla_gmail(inputs):
    action = next(
        (a for a in actions if a["description"].startswith("Gmail: Find Email")), None
    )
    return {
        "email_data": ZapierNLARunAction(
            action_id=action["id"],
            zapier_description=action["description"],
            params_schema=action["params"],
        ).run(inputs["instructions"])
    }


gmail_chain = TransformChain(
    input_variables=["instructions"],
    output_variables=["email_data"],
    transform=nla_gmail,
)
## step 2. generate draft reply

template = """You are an assisstant who drafts replies to an incoming email. Output draft reply in plain text (not JSON).

Incoming email:
{email_data}

Draft email reply:"""

prompt_template = PromptTemplate(input_variables=["email_data"], template=template)
reply_chain = LLMChain(llm=OpenAI(temperature=0.7), prompt=prompt_template)
## step 3. send draft reply via a slack direct message

SLACK_HANDLE = "@Ankush Gola"


def nla_slack(inputs):
    action = next(
        (
            a
            for a in actions
            if a["description"].startswith("Slack: Send Direct Message")
        ),
        None,
    )
    instructions = f'Send this to {SLACK_HANDLE} in Slack: {inputs["draft_reply"]}'
    return {
        "slack_data": ZapierNLARunAction(
            action_id=action["id"],
            zapier_description=action["description"],
            params_schema=action["params"],
        ).run(instructions)
    }


slack_chain = TransformChain(
    input_variables=["draft_reply"],
    output_variables=["slack_data"],
    transform=nla_slack,
)
## finally, execute

overall_chain = SimpleSequentialChain(
    chains=[gmail_chain, reply_chain, slack_chain], verbose=True
)
overall_chain.run(GMAIL_SEARCH_INSTRUCTIONS)


> Entering new SimpleSequentialChain chain...
{"from__name": "Silicon Valley Bridge Bank, N.A.", "from__email": "sreply@svb.com", "body_plain": "Dear Clients, After chaotic, tumultuous & stressful days, we have clarity on path for SVB, FDIC is fully insuring all deposits & have an ask for clients & partners as we rebuild. Tim Mayopoulos <https://eml.svb.com/NjEwLUtBSy0yNjYAAAGKgoxUeBCLAyF_NxON97X4rKEaNBLG", "reply_to__email": "sreply@svb.com", "subject": "Meet the new CEO Tim Mayopoulos", "date": "Tue, 14 Mar 2023 23:42:29 -0500 (CDT)", "message_url": "https://mail.google.com/mail/u/0/#inbox/186e393b13cfdf0a", "attachment_count": "0", "to__emails": "ankush@langchain.dev", "message_id": "186e393b13cfdf0a", "labels": "IMPORTANT, CATEGORY_UPDATES, INBOX"}

Dear Silicon Valley Bridge Bank, 

Thank you for your email and the update regarding your new CEO Tim Mayopoulos. We appreciate your dedication to keeping your clients and partners informed and we look forward to continuing our relationship with you. 

Best regards, 
[Your Name]
{"message__text": "Dear Silicon Valley Bridge Bank, \n\nThank you for your email and the update regarding your new CEO Tim Mayopoulos. We appreciate your dedication to keeping your clients and partners informed and we look forward to continuing our relationship with you. \n\nBest regards, \n[Your Name]", "message__permalink": "https://langchain.slack.com/archives/D04TKF5BBHU/p1678859968241629", "channel": "D04TKF5BBHU", "message__bot_profile__name": "Zapier", "message__team": "T04F8K3FZB5", "message__bot_id": "B04TRV4R74K", "message__bot_profile__deleted": "false", "message__bot_profile__app_id": "A024R9PQM", "ts_time": "2023-03-15T05:59:28Z", "message__blocks[]block_id": "p7i", "message__blocks[]elements[]elements[]type": "[['text']]", "message__blocks[]elements[]type": "['rich_text_section']"}

> Finished chain.
'{"message__text": "Dear Silicon Valley Bridge Bank, \\n\\nThank you for your email and the update regarding your new CEO Tim Mayopoulos. We appreciate your dedication to keeping your clients and partners informed and we look forward to continuing our relationship with you. \\n\\nBest regards, \\n[Your Name]", "message__permalink": "https://langchain.slack.com/archives/D04TKF5BBHU/p1678859968241629", "channel": "D04TKF5BBHU", "message__bot_profile__name": "Zapier", "message__team": "T04F8K3FZB5", "message__bot_id": "B04TRV4R74K", "message__bot_profile__deleted": "false", "message__bot_profile__app_id": "A024R9PQM", "ts_time": "2023-03-15T05:59:28Z", "message__blocks[]block_id": "p7i", "message__blocks[]elements[]elements[]type": "[[\'text\']]", "message__blocks[]elements[]type": "[\'rich_text_section\']"}'

使用 OAuth 访问令牌的示例​

下面的代码段展示了如何使用获取到的 OAuth 访问令牌来初始化包装器。请注意这里是直接传递参数，而不是设置环境变量。有关完整的面向用户的 OAuth 开发人员支持，请查阅 身份验证文档。

开发人员的任务是处理 OAuth 握手，以获取和刷新访问令牌。

llm = OpenAI(temperature=0)
zapier = ZapierNLAWrapper(zapier_nla_oauth_access_token="<fill in access token here>")
toolkit = ZapierToolkit.from_zapier_nla_wrapper(zapier)
agent = initialize_agent(
    toolkit.get_tools(), llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True
)

agent.run(
    "Summarize the last email I received regarding Silicon Valley Bank. Send the summary to the #test-zapier channel in slack."
)