Add TaskQueueSwarm documentation

docs/swarms/structs/taskqueue_swarm.md

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+# TaskQueueSwarm Documentation
+
+The `TaskQueueSwarm` class is designed to manage and execute tasks using multiple agents concurrently. This class allows for the orchestration of multiple agents processing tasks from a shared queue, facilitating complex workflows where tasks can be distributed and processed in parallel by different agents.
+
+## Attributes
+
+| Attribute | Type | Description |
+|-----------|------|-------------|
+| `agents` | `List[Agent]` | The list of agents in the swarm. |
+| `task_queue` | `queue.Queue` | A queue to store tasks for processing. |
+| `lock` | `threading.Lock` | A lock for thread synchronization. |
+| `autosave_on` | `bool` | Whether to automatically save the swarm metadata. |
+| `save_file_path` | `str` | The file path for saving swarm metadata. |
+| `workspace_dir` | `str` | The directory path of the workspace. |
+| `return_metadata_on` | `bool` | Whether to return the swarm metadata after running. |
+| `max_loops` | `int` | The maximum number of loops to run the swarm. |
+| `metadata` | `SwarmRunMetadata` | Metadata about the swarm run. |
+
+## Methods
+
+### `__init__(self, agents: List[Agent], name: str = "Task-Queue-Swarm", description: str = "A swarm that processes tasks from a queue using multiple agents on different threads.", autosave_on: bool = True, save_file_path: str = "swarm_run_metadata.json", workspace_dir: str = os.getenv("WORKSPACE_DIR"), return_metadata_on: bool = False, max_loops: int = 1, *args, **kwargs)`
+
+The constructor initializes the `TaskQueueSwarm` object.
+
+- **Parameters:**
+  - `agents` (`List[Agent]`): The list of agents in the swarm.
+  - `name` (`str`, optional): The name of the swarm. Defaults to "Task-Queue-Swarm".
+  - `description` (`str`, optional): The description of the swarm. Defaults to "A swarm that processes tasks from a queue using multiple agents on different threads.".
+  - `autosave_on` (`bool`, optional): Whether to automatically save the swarm metadata. Defaults to True.
+  - `save_file_path` (`str`, optional): The file path to save the swarm metadata. Defaults to "swarm_run_metadata.json".
+  - `workspace_dir` (`str`, optional): The directory path of the workspace. Defaults to os.getenv("WORKSPACE_DIR").
+  - `return_metadata_on` (`bool`, optional): Whether to return the swarm metadata after running. Defaults to False.
+  - `max_loops` (`int`, optional): The maximum number of loops to run the swarm. Defaults to 1.
+  - `*args`: Variable length argument list.
+  - `**kwargs`: Arbitrary keyword arguments.
+
+### `add_task(self, task: str)`
+
+Adds a task to the queue.
+
+- **Parameters:**
+  - `task` (`str`): The task to be added to the queue.
+
+### `run(self)`
+
+Runs the swarm by having agents pick up tasks from the queue.
+
+- **Returns:**
+  - `str`: JSON string of the swarm run metadata if `return_metadata_on` is True.
+
+- **Usage Example:**
+  ```python
+  from swarms import Agent, TaskQueueSwarm
+  from swarms.models import OpenAIChat
+
+  # Initialize the language model
+  llm = OpenAIChat()
+
+  # Initialize agents
+  agent1 = Agent(agent_name="Agent1", llm=llm)
+  agent2 = Agent(agent_name="Agent2", llm=llm)
+
+  # Create the TaskQueueSwarm
+  swarm = TaskQueueSwarm(agents=[agent1, agent2], max_loops=5)
+
+  # Add tasks to the swarm
+  swarm.add_task("Analyze the latest market trends")
+  swarm.add_task("Generate a summary report")
+
+  # Run the swarm
+  result = swarm.run()
+  print(result)  # Prints the swarm run metadata
+  ```
+
+  This example initializes a `TaskQueueSwarm` with two agents, adds tasks to the queue, and runs the swarm.
+
+### `save_json_to_file(self)`
+
+Saves the swarm run metadata to a JSON file.
+
+### `export_metadata(self)`
+
+Exports the swarm run metadata as a JSON string.
+
+- **Returns:**
+  - `str`: JSON string of the swarm run metadata.
+
+## Additional Notes
+
+- The `TaskQueueSwarm` uses threading to process tasks concurrently, which can significantly improve performance for I/O-bound tasks.
+- The `reliability_checks` method ensures that the swarm is properly configured before running.
+- The swarm automatically handles task distribution among agents and provides detailed metadata about the run.
+- Error handling and logging are implemented to track the execution flow and capture any issues during task processing.