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Swarms Documentation
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====================
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Worker Node
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-----------
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The `WorkerNode` class is a powerful component of the Swarms framework. It is designed to spawn an autonomous agent instance as a worker to accomplish complex tasks. It can search the internet, spawn child multi-modality models to process and generate images, text, audio, and so on.
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### WorkerNodeInitializer
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The `WorkerNodeInitializer` class is used to initialize a worker node.
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#### Initialization
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```
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WorkerNodeInitializer(openai_api_key: str,
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llm: Optional[Union[InMemoryDocstore, ChatOpenAI]] = None,
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tools: Optional[List[Tool]] = None,
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worker_name: Optional[str] = "Swarm Worker AI Assistant",
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worker_role: Optional[str] = "Assistant",
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human_in_the_loop: Optional[bool] = False,
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search_kwargs: dict = {},
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verbose: Optional[bool] = False,
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chat_history_file: str = "chat_history.txt")
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```
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Copy code
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##### Parameters
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- `openai_api_key` (str): The OpenAI API key.
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- `llm` (Union[InMemoryDocstore, ChatOpenAI], optional): The language model to use. Default is `ChatOpenAI`.
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- `tools` (List[Tool], optional): The tools to use.
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- `worker_name` (str, optional): The name of the worker. Default is "Swarm Worker AI Assistant".
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- `worker_role` (str, optional): The role of the worker. Default is "Assistant".
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- `human_in_the_loop` (bool, optional): Whether to include a human in the loop. Default is False.
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- `search_kwargs` (dict, optional): The keyword arguments for the search.
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- `verbose` (bool, optional): Whether to print verbose output. Default is False.
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- `chat_history_file` (str, optional): The file to store the chat history. Default is "chat_history.txt".
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##### Example
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```
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from swarms.agents.tools.autogpt import DuckDuckGoSearchRun
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worker_node_initializer = WorkerNodeInitializer(openai_api_key="your_openai_api_key",
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tools=[DuckDuckGoSearchRun()],
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worker_name="My Worker",
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worker_role="Assistant",
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human_in_the_loop=True)
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```
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Copy code
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### WorkerNode
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The `WorkerNode` class is used to create a worker node.
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#### Initialization
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```
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WorkerNode(openai_api_key: str,
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temperature: int,
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llm: Optional[Union[InMemoryDocstore, ChatOpenAI]] = None,
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tools: Optional[List[Tool]] = None,
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worker_name: Optional[str] = "Swarm Worker AI Assistant",
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worker_role: Optional[str] = "Assistant",
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human_in_the_loop: Optional[bool] = False,
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search_kwargs: dict = {},
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verbose: Optional[bool] = False,
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chat_history_file: str = "chat_history.txt")
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```
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Copy code
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##### Parameters
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- `openai_api_key` (str): The OpenAI API key.
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- `temperature` (int): The temperature for the language model.
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- `llm` (Union[InMemoryDocstore, ChatOpenAI], optional): The language model to use. Default is `ChatOpenAI`.
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- `tools` (List[Tool], optional): The tools to use.
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- `worker_name` (str, optional): The name of the worker. Default is "Swarm Worker AI Assistant".
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- `worker_role` (str, optional): The role of the worker. Default is "Assistant".
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- `human_in_the_loop` (bool, optional): Whether to include a human in the loop. Default is False.
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- `search_kwargs` (dict, optional): The keyword arguments for the search.
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- `verbose` (bool, optional): Whether to print verbose output. Default is False.
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- `chat_history_file` (str, optional): The file to store the chat history. Default is "chat_history.txt".
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##### Example
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```
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worker_node = WorkerNode(openai_api_key="your_openai_api_key",
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temperature=0.8,
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tools=[DuckDuckGoSearchRun()],
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worker_name="My Worker",
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worker_role="As```
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tools=[DuckDuckGoSearchRun()],
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worker_name="My Worker",
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worker_role="Assistant",
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human_in_the_loop=True)
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# Create a worker node
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worker_node = WorkerNode(openai_api_key="your_openai_api_key",
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temperature=0.8,
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tools=[DuckDuckGoSearchRun()],
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worker_name="My Worker",
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worker_role="Assistant",
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human_in_the_loop=True)
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# Add a tool to the worker node
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worker_node_initializer.add_tool(DuckDuckGoSearchRun())
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# Initialize the language model and tools for the worker node
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worker_node.initialize_llm(ChatOpenAI, temperature=0.8)
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worker_node.initialize_tools(ChatOpenAI)
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# Create the worker node
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worker_node.create_worker_node(worker_name="My Worker Node",
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worker_role="Assistant",
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human_in_the_loop=True,
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llm_class=ChatOpenAI,
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search_kwargs={})
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# Run the worker node
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`worker_node.run("Hello, world!")`
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In this example, we first initialize a `WorkerNodeInitializer` and a `WorkerNode`. We then add a tool to the `WorkerNodeInitializer` and initialize the language model and tools for the `WorkerNode`. Finally, we create the worker node and run it with a given prompt.
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This example shows how you can use the `WorkerNode` and `WorkerNodeInitializer` classes to create a worker node, add tools to it, initialize its language model and tools, and run it with a given prompt. The parameters of these classes can be customized to suit your specific needs.
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Thanks for becoming an alpha build user, email kye@apac.ai with all complaintssistant",
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human_in_the_loop=True)
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```
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Copy code
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### Full Example
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Here is a full example of how to use the `WorkerNode` and `WorkerNodeInitializer` classes:
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```python
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from swarms.agents.tools.autogpt import DuckDuckGoSearchRun
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from swarms.worker_node import WorkerNode, WorkerNodeInitializer
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# Initialize a worker node
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worker_node_initializer = WorkerNodeInitializer(openai_api_key="your_openai_api_key",
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tools=[DuckDuckGoSearchRun()],
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worker_name="My Worker",
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worker_role="Assistant",
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human_in_the_loop=True)
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# Create a worker node
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worker_node = WorkerNode(openai_api_key="your_openai_api_key",
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temperature=0.8,
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tools=[DuckDuckGoSearchRun()],
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worker_name="My Worker",
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worker_role="Assistant",
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human_in_the_loop=True)
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# Add a tool to the worker node
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worker_node_initializer.add_tool(DuckDuckGoSearchRun())
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# Initialize the language model and tools for the worker node
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worker_node.initialize_llm(ChatOpenAI, temperature=0.8)
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worker_node.initialize_tools(ChatOpenAI)
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# Create the worker node
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worker_node.create_worker_node(worker_name="My Worker Node",
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worker_role="Assistant",
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human_in_the_loop=True,
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llm_class=ChatOpenAI,
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search_kwargs={})
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# Run the worker node
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worker_node.run("Hello, world!")
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```
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In this example, we first initialize a `WorkerNodeInitializer` and a `WorkerNode`. We then add a tool to the `WorkerNodeInitializer` and initialize the language model and tools for the `WorkerNode`. Finally, we create the worker node and run it with a given prompt.
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This example shows how you can use the `WorkerNode` and `WorkerNodeInitializer` classes to create a worker node, add tools to it, initialize its language model and tools, and run it with a given prompt. The parameters of these classes can be customized to suit your specific needs.
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