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