287 lines
9.6 KiB
Markdown
287 lines
9.6 KiB
Markdown
<!-- START doctoc generated TOC please keep comment here to allow auto update -->
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<!-- DON'T EDIT THIS SECTION, INSTEAD RE-RUN doctoc TO UPDATE -->
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**Table of Contents** *generated with [DocToc](https://github.com/thlorenz/doctoc)*
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- [Skyvern Langchain](#skyvern-langchain)
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- [Installation](#installation)
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- [Basic Usage](#basic-usage)
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- [Run a task(sync) locally in your local environment](#run-a-tasksync-locally-in-your-local-environment)
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- [Run a task(async) locally in your local environment](#run-a-taskasync-locally-in-your-local-environment)
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- [Get a task locally in your local environment](#get-a-task-locally-in-your-local-environment)
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- [Run a task(sync) by calling skyvern APIs](#run-a-tasksync-by-calling-skyvern-apis)
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- [Run a task(async) by calling skyvern APIs](#run-a-taskasync-by-calling-skyvern-apis)
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- [Get a task by calling skyvern APIs](#get-a-task-by-calling-skyvern-apis)
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- [Agent Usage](#agent-usage)
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- [Run a task(async) locally in your local environment and wait until the task is finished](#run-a-taskasync-locally-in-your-local-environment-and-wait-until-the-task-is-finished)
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- [Run a task(async) by calling skyvern APIs and wait until the task is finished](#run-a-taskasync-by-calling-skyvern-apis-and-wait-until-the-task-is-finished)
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<!-- END doctoc generated TOC please keep comment here to allow auto update -->
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# Skyvern Langchain
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This is a langchain integration for Skyvern.
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## Installation
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```bash
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pip install skyvern-langchain
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```
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To run the example scenarios, you might need to install other langchain dependencies.
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```bash
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pip install langchain-openai
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pip install langchain-community
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```
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## Basic Usage
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This is the only basic usage of skyvern langchain tool. If you want a full langchain integration experience, please refer to the [Agent Usage](#agent-usage) section to play with langchain agent.
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Go to [Langchain Tools](https://python.langchain.com/v0.1/docs/modules/tools/) to see more advanced langchain tool usage.
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### Run a task(sync) locally in your local environment
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> sync task won't return until the task is finished.
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:warning: :warning: if you want to run this code block, you need to run `skyvern init --openai-api-key <your_openai_api_key>` command in your terminal to set up skyvern first.
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```python
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import asyncio
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from skyvern_langchain.agent import RunTask
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run_task = RunTask()
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async def main():
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# to run skyvern agent locally, must run `skyvern init` first
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print(await run_task.ainvoke("Navigate to the Hacker News homepage and get the top 3 posts."))
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if __name__ == "__main__":
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asyncio.run(main())
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```
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### Run a task(async) locally in your local environment
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> async task will return immediately and the task will be running in the background.
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:warning: :warning: if you want to run the task in the background, you need to keep the script running until the task is finished, otherwise the task will be killed when the script is finished.
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:warning: :warning: if you want to run this code block, you need to run `skyvern init --openai-api-key <your_openai_api_key>` command in your terminal to set up skyvern first.
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```python
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import asyncio
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from skyvern_langchain.agent import DispatchTask
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dispatch_task = DispatchTask()
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async def main():
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# to run skyvern agent locally, must run `skyvern init` first
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print(await dispatch_task.ainvoke("Navigate to the Hacker News homepage and get the top 3 posts."))
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# keep the script running until the task is finished
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await asyncio.sleep(600)
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if __name__ == "__main__":
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asyncio.run(main())
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```
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### Get a task locally in your local environment
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:warning: :warning: if you want to run this code block, you need to run `skyvern init --openai-api-key <your_openai_api_key>` command in your terminal to set up skyvern first.
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```python
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import asyncio
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from skyvern_langchain.agent import GetTask
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get_task = GetTask()
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async def main():
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# to run skyvern agent locally, must run `skyvern init` first
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print(await get_task.ainvoke("<task_id>"))
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if __name__ == "__main__":
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asyncio.run(main())
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```
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### Run a task(sync) by calling skyvern APIs
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> sync task won't return until the task is finished.
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no need to run `skyvern init` command in your terminal to set up skyvern before using this integration.
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```python
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import asyncio
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from skyvern_langchain.client import RunTask
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run_task = RunTask(
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api_key="<your_organization_api_key>",
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)
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# or you can load the api_key from SKYVERN_API_KEY in .env
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# run_task = RunTask()
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async def main():
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print(await run_task.ainvoke("Navigate to the Hacker News homepage and get the top 3 posts."))
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if __name__ == "__main__":
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asyncio.run(main())
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```
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### Run a task(async) by calling skyvern APIs
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> async task will return immediately and the task will be running in the background.
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no need to run `skyvern init` command in your terminal to set up skyvern before using this integration.
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the task is actually running in the skyvern cloud service, so you don't need to keep your script running until the task is finished.
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```python
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import asyncio
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from skyvern_langchain.client import DispatchTask
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dispatch_task = DispatchTask(
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api_key="<your_organization_api_key>",
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)
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# or you can load the api_key from SKYVERN_API_KEY in .env
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# dispatch_task = DispatchTask()
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async def main():
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print(await dispatch_task.ainvoke("Navigate to the Hacker News homepage and get the top 3 posts."))
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if __name__ == "__main__":
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asyncio.run(main())
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```
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### Get a task by calling skyvern APIs
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> async task will return immediately and the task will be running in the background.
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no need to run `skyvern init` command in your terminal to set up skyvern before using this integration.
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the task is actually running in the skyvern cloud service, so you don't need to keep your script running until the task is finished.
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```python
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import asyncio
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from skyvern_langchain.client import GetTask
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get_task = GetTask(
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api_key="<your_organization_api_key>",
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)
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# or you can load the api_key from SKYVERN_API_KEY in .env
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# get_task = GetTask()
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async def main():
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print(await get_task.ainvoke("<task_id>"))
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if __name__ == "__main__":
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asyncio.run(main())
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```
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## Agent Usage
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Langchain is more powerful when used with [Langchain Agents](https://python.langchain.com/v0.1/docs/modules/agents/).
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The following two examples show how to build an agent that executes a specified task, waits for its completion, and then returns the results. For example, the agent is tasked with navigating to the Hacker News homepage and retrieving the top three posts.
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### Run a task(async) locally in your local environment and wait until the task is finished
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> async task will return immediately and the task will be running in the background. You can use `GetTask` tool to poll the task information until the task is finished.
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:warning: :warning: if you want to run this code block, you need to run `skyvern init --openai-api-key <your_openai_api_key>` command in your terminal to set up skyvern first.
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```python
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import asyncio
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from dotenv import load_dotenv
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from langchain_openai import ChatOpenAI
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from langchain.agents import initialize_agent, AgentType
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from skyvern_langchain.agent import DispatchTask, GetTask
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from langchain_community.tools.sleep.tool import SleepTool
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# load OpenAI API key from .env
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load_dotenv()
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llm = ChatOpenAI(model="gpt-4o", temperature=0)
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dispatch_task = DispatchTask()
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get_task = GetTask()
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agent = initialize_agent(
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llm=llm,
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tools=[
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dispatch_task,
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get_task,
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SleepTool(),
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],
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verbose=True,
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agent=AgentType.STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION,
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)
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async def main():
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# use sleep tool to set up the polling logic until the task is completed, if you only want to dispatch a task, you can remove the sleep tool
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print(await agent.ainvoke("Run a task with Skyvern. The task is about 'Navigate to the Hacker News homepage and get the top 3 posts.' Then, get this task information until it's completed. The task information re-get interval should be 60s."))
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if __name__ == "__main__":
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asyncio.run(main())
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```
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### Run a task(async) by calling skyvern APIs and wait until the task is finished
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> async task will return immediately and the task will be running in the background. You can use `GetTask` tool to poll the task information until the task is finished.
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no need to run `skyvern init` command in your terminal to set up skyvern before using this integration.
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```python
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import asyncio
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from dotenv import load_dotenv
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from langchain_openai import ChatOpenAI
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from langchain.agents import initialize_agent, AgentType
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from skyvern_langchain.client import DispatchTask, GetTask
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from langchain_community.tools.sleep.tool import SleepTool
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# load OpenAI API key from .env
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load_dotenv()
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llm = ChatOpenAI(model="gpt-4o", temperature=0)
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dispatch_task = DispatchTask(
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api_key="<your_organization_api_key>",
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)
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# or you can load the api_key from SKYVERN_API_KEY in .env
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# dispatch_task = DispatchTask()
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get_task = GetTask(
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api_key="<your_organization_api_key>",
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)
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# or you can load the api_key from SKYVERN_API_KEY in .env
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# get_task = GetTask()
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agent = initialize_agent(
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llm=llm,
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tools=[
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dispatch_task,
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get_task,
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SleepTool(),
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],
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verbose=True,
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agent=AgentType.STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION,
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)
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async def main():
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# use sleep tool to set up the polling logic until the task is completed, if you only want to dispatch a task, you can remove the sleep tool
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print(await agent.ainvoke("Run a task with Skyvern. The task is about 'Navigate to the Hacker News homepage and get the top 3 posts.' Then, get this task information until it's completed. The task information re-get interval should be 60s."))
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if __name__ == "__main__":
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asyncio.run(main())
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``` |