294 lines
10 KiB
Markdown
294 lines
10 KiB
Markdown
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<!-- 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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- [Skyvern LlamaIndex](#skyvern-llamaindex)
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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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- [Advanced Usage](#advanced-usage)
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- [Dispatch a task(async) locally in your local environment and wait until the task is finished](#dispatch-a-taskasync-locally-in-your-local-environment-and-wait-until-the-task-is-finished)
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- [Dispatch a task(async) by calling skyvern APIs and wait until the task is finished](#dispatch-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 LlamaIndex
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This is a LlamaIndex integration for Skyvern.
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## Installation
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```bash
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pip install skyvern-llamaindex
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```
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## Basic 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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from dotenv import load_dotenv
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from llama_index.agent.openai import OpenAIAgent
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from llama_index.llms.openai import OpenAI
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from skyvern_llamaindex.agent import SkyvernTool
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# load OpenAI API key from .env
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load_dotenv()
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skyvern_tool = SkyvernTool()
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agent = OpenAIAgent.from_tools(
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tools=[skyvern_tool.run_task()],
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llm=OpenAI(model="gpt-4o"),
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verbose=True,
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)
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response = agent.chat("Run a task with Skyvern. The task is about 'Navigate to the Hacker News homepage and get the top 3 posts.'")
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print(response)
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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 agent running until the task is finished, otherwise the task will be killed when the agent finished the chat.
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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 llama_index.agent.openai import OpenAIAgent
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from llama_index.llms.openai import OpenAI
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from skyvern_llamaindex.agent import SkyvernTool
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from llama_index.core.tools import FunctionTool
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# load OpenAI API key from .env
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load_dotenv()
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async def sleep(seconds: int) -> str:
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await asyncio.sleep(seconds)
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return f"Slept for {seconds} seconds"
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# define a sleep tool to keep the agent running until the task is finished
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sleep_tool = FunctionTool.from_defaults(
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async_fn=sleep,
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description="Sleep for a given number of seconds",
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name="sleep",
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)
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skyvern_tool = SkyvernTool()
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agent = OpenAIAgent.from_tools(
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tools=[skyvern_tool.dispatch_task(), sleep_tool],
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llm=OpenAI(model="gpt-4o"),
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verbose=True,
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)
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response = agent.chat("Run a task with Skyvern. The task is about 'Navigate to the Hacker News homepage and get the top 3 posts.' Then, sleep for 10 minutes.")
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print(response)
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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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from dotenv import load_dotenv
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from llama_index.agent.openai import OpenAIAgent
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from llama_index.llms.openai import OpenAI
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from skyvern_llamaindex.agent import SkyvernTool
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# load OpenAI API key from .env
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load_dotenv()
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skyvern_tool = SkyvernTool()
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agent = OpenAIAgent.from_tools(
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tools=[skyvern_tool.get_task()],
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llm=OpenAI(model="gpt-4o"),
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verbose=True,
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)
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response = agent.chat("Get the task information with Skyvern. The task id is '<task_id>'.")
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print(response)
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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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from dotenv import load_dotenv
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from llama_index.agent.openai import OpenAIAgent
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from llama_index.llms.openai import OpenAI
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from skyvern_llamaindex.client import SkyvernTool
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# load OpenAI API key from .env
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load_dotenv()
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skyvern_tool = SkyvernTool(api_key="<your_organization_api_key>")
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# or you can load the api_key from SKYVERN_API_KEY in .env
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# skyvern_tool = SkyvernTool()
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agent = OpenAIAgent.from_tools(
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tools=[skyvern_tool.run_task()],
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llm=OpenAI(model="gpt-4o"),
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verbose=True,
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)
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response = agent.chat("Run a task with Skyvern. The task is about 'Navigate to the Hacker News homepage and get the top 3 posts.'")
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print(response)
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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 agent running until the task is finished.
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```python
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from dotenv import load_dotenv
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from llama_index.agent.openai import OpenAIAgent
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from llama_index.llms.openai import OpenAI
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from skyvern_llamaindex.client import SkyvernTool
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# load OpenAI API key from .env
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load_dotenv()
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skyvern_tool = SkyvernTool(api_key="<your_organization_api_key>")
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# or you can load the api_key from SKYVERN_API_KEY in .env
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# skyvern_tool = SkyvernTool()
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agent = OpenAIAgent.from_tools(
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tools=[skyvern_tool.dispatch_task()],
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llm=OpenAI(model="gpt-4o"),
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verbose=True,
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)
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response = agent.chat("Run a task with Skyvern. The task is about 'Navigate to the Hacker News homepage and get the top 3 posts.'")
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print(response)
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```
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### Get a task by calling skyvern APIs
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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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from dotenv import load_dotenv
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from llama_index.agent.openai import OpenAIAgent
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from llama_index.llms.openai import OpenAI
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from skyvern_llamaindex.client import SkyvernTool
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# load OpenAI API key from .env
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load_dotenv()
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skyvern_tool = SkyvernTool(api_key="<your_organization_api_key>")
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# or you can load the api_key from SKYVERN_API_KEY in .env
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# skyvern_tool = SkyvernTool()
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agent = OpenAIAgent.from_tools(
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tools=[skyvern_tool.get_task()],
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llm=OpenAI(model="gpt-4o"),
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verbose=True,
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)
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response = agent.chat("Get the task information with Skyvern. The task id is '<task_id>'.")
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print(response)
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```
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## Advanced Usage
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To provide some examples of how to integrate Skyvern with other llama-index tools in the agent.
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### Dispatch a task(async) locally in your local environment and wait until the task is finished
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> dispatch task will return immediately and the task will be running in the background. You can use `get_task` 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 llama_index.agent.openai import OpenAIAgent
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from llama_index.llms.openai import OpenAI
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from llama_index.core.tools import FunctionTool
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from skyvern_llamaindex.agent import SkyvernTool
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# load OpenAI API key from .env
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load_dotenv()
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async def sleep(seconds: int) -> str:
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await asyncio.sleep(seconds)
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return f"Slept for {seconds} seconds"
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sleep_tool = FunctionTool.from_defaults(
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async_fn=sleep,
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description="Sleep for a given number of seconds",
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name="sleep",
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)
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skyvern_tool = SkyvernTool()
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agent = OpenAIAgent.from_tools(
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tools=[skyvern_tool.dispatch_task(), skyvern_tool.get_task(), sleep_tool],
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llm=OpenAI(model="gpt-4o"),
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verbose=True,
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max_function_calls=10,
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)
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response = agent.chat("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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print(response)
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```
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### Dispatch a task(async) by calling skyvern APIs and wait until the task is finished
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> dispatch task will return immediately and the task will be running in the background. You can use `get_task` 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 llama_index.agent.openai import OpenAIAgent
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from llama_index.llms.openai import OpenAI
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from llama_index.core.tools import FunctionTool
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from skyvern_llamaindex.client import SkyvernTool
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# load OpenAI API key from .env
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load_dotenv()
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async def sleep(seconds: int) -> str:
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await asyncio.sleep(seconds)
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return f"Slept for {seconds} seconds"
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sleep_tool = FunctionTool.from_defaults(
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async_fn=sleep,
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description="Sleep for a given number of seconds",
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name="sleep",
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)
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skyvern_tool = SkyvernTool(api_key="<your_organization_api_key>")
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# or you can load the api_key from SKYVERN_API_KEY in .env
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# skyvern_tool = SkyvernTool()
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agent = OpenAIAgent.from_tools(
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tools=[skyvern_tool.dispatch_task(), skyvern_tool.get_task(), sleep_tool],
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llm=OpenAI(model="gpt-4o"),
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verbose=True,
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max_function_calls=10,
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)
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response = agent.chat("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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print(response)
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```
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