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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. |