Kye
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A high-level pseudocode for creating the classes and functions for your desired system:
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1. **Swarms**
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- The main class. It initializes the swarm with a specified number of worker nodes and sets up self-scaling if required.
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- Methods include `add_worker`, `remove_worker`, `execute`, and `scale`.
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2. **WorkerNode**
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- Class for each worker node in the swarm. It has a `task_queue` and a `completed_tasks` queue.
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- Methods include `receive_task`, `complete_task`, and `communicate`.
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3. **HierarchicalSwarms**
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- Inherits from Swarms and overrides the `execute` method to execute tasks in a hierarchical manner.
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4. **CollaborativeSwarms**
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- Inherits from Swarms and overrides the `execute` method to execute tasks in a collaborative manner.
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5. **CompetitiveSwarms**
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- Inherits from Swarms and overrides the `execute` method to execute tasks in a competitive manner.
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6. **MultiAgentDebate**
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- Inherits from Swarms and overrides the `execute` method to execute tasks in a debating manner.
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To implement this in Python, you would start by setting up the base `Swarm` class and `WorkerNode` class. Here's a simplified Python example:
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```python
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class WorkerNode:
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def __init__(self, llm: BaseLLM):
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self.llm = llm
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self.task_queue = deque()
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self.completed_tasks = deque()
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def receive_task(self, task):
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self.task_queue.append(task)
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def complete_task(self):
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task = self.task_queue.popleft()
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result = self.llm.execute(task)
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self.completed_tasks.append(result)
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return result
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def communicate(self, other_node):
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# Placeholder for communication method
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pass
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class Swarms:
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def __init__(self, num_nodes: int, llm: BaseLLM, self_scaling: bool):
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self.nodes = [WorkerNode(llm) for _ in range(num_nodes)]
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self.self_scaling = self_scaling
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def add_worker(self, llm: BaseLLM):
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self.nodes.append(WorkerNode(llm))
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def remove_worker(self, index: int):
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self.nodes.pop(index)
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def execute(self, task):
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# Placeholder for main execution logic
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pass
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def scale(self):
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# Placeholder for self-scaling logic
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pass
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```
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Then, you would build out the specialized classes for each type of swarm:
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```python
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class HierarchicalSwarms(Swarms):
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def execute(self, task):
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# Implement hierarchical task execution
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pass
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class CollaborativeSwarms(Swarms):
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def execute(self, task):
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# Implement collaborative task execution
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pass
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class CompetitiveSwarms(Swarms):
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def execute(self, task):
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# Implement competitive task execution
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pass
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class MultiAgentDebate(Swarms):
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def execute(self, task):
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# Implement debate-style task execution
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pass
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```
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# WorkerNode class
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Here's the pseudocode algorithm for a `WorkerNode` class that includes a vector embedding database for communication:
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1. **WorkerNode**
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- Initialize a worker node with an LLM and a connection to the vector embedding database.
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- The worker node maintains a `task_queue` and `completed_tasks` queue. It also keeps track of the status of tasks (e.g., "pending", "completed").
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- The `receive_task` method accepts a task and adds it to the `task_queue`.
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- The `complete_task` method takes the oldest task from the `task_queue`, executes it, and then stores the result in the `completed_tasks` queue. It also updates the task status in the vector embedding database to "completed".
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- The `communicate` method uses the vector embedding database to share information with other nodes. It inserts the task result into the vector database and also queries for tasks marked as "completed".
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In Python, this could look something like:
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```python
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from langchain.vectorstores import FAISS
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from langchain.docstore import InMemoryDocstore
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from langchain.embeddings import OpenAIEmbeddings
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import faiss
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from swarms.agents.workers.auto_agent import AutoGPT
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from collections import deque
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from typing import Dict, Any
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class WorkerNode:
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def __init__(self, llm: AutoGPT, vectorstore: FAISS):
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self.llm = llm
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self.vectorstore = vectorstore
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self.task_queue = deque()
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self.completed_tasks = deque()
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self.task_status: Dict[Any, str] = {}
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def receive_task(self, task):
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self.task_queue.append(task)
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self.task_status[task] = 'pending'
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def complete_task(self):
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task = self.task_queue.popleft()
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result = self.llm.run(task)
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self.completed_tasks.append(result)
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self.task_status[task] = 'completed'
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# Insert task result into the vectorstore
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self.vectorstore.insert(task, result)
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return result
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def communicate(self):
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# Share task results and status through vectorstore
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completed_tasks = [(task, self.task_status[task]) for task in self.task_queue if self.task_status[task] == 'completed']
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for task, status in completed_tasks:
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self.vectorstore.insert(task, status)
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```
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This example assumes that tasks are hashable and can be used as dictionary keys. The `vectorstore.insert` method is used to share task results and status with other nodes, and you can use methods like `vectorstore.query` or `vectorstore.regex_search` to retrieve this information. Please remember this is a simplified implementation and might need changes according to your exact requirements.
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