clean up

infra/Docker/Dockerfile.cpu

@@ -22,7 +22,7 @@ ENV EVAL_PORT=8000 \
     USE_GPU=False \
     PLAYGROUND_DIR=playground \
     LOG_LEVEL=INFO \
-    BOT_NAME=Orca \
+    BOT_NAME=Swarm \
     # You will need to set these environment variables to your actual keys in production
     OPENAI_API_KEY=your_openai_api_key \
     WINEDB_HOST=your_winedb_host \

swarms/__init__.py

@@ -1,5 +1,5 @@
 # from swarms import Swarms, swarm
 from swarms.swarms import HierarchicalSwarm, swarm
 from swarms.workers.worker_ultra_node import WorkerUltraNode, WorkerUltra, worker_ultra_node
-from swarms.workers.WorkerNode import WorkerNode, worker_node
+from swarms.workers.worker_node import WorkerNode, worker_node
 from swarms.boss.boss_node import BossNode

swarms/orchestrate.py

@@ -1,5 +1,39 @@
-#input agent or multiple: => it handles multi agent communication, it handles task assignment, task execution, report back with a status, auto scaling,  number of agent nodes, 
+#input agent or multiple: => it handles multi agent communication, it handles task assignment, task execution, report back with a status, auto scaling,  number of agent nodes,
+#make it optional to have distributed communication protocols, trco, rdp, http, microsoervice
 """
+# Orchestrator
+* Takes in an agent class with vector store, then handles all the communication and scales up a swarm with number of agents and handles task assignment and task completion
+
+```python
+
+from swarms import OpenAI, Orchestrator, Swarm
+
+orchestrated = Orchestrate(OpenAI, nodes=40) #handles all the task assignment and allocation and agent communication using a vectorstore as a universal communication layer and also handlles the task completion logic
+
+Objective = "Make a business website for a marketing consultancy"
+
+Swarms = (Swarms(orchestrated, auto=True, Objective))
+```
+
+In terms of architecture, the swarm might look something like this:
+
+```
+                                           (Orchestrator)
+                                             /        \
+           Tools + Vector DB -- (LLM Agent)---(Communication Layer)       (Communication Layer)---(LLM Agent)-- Tools + Vector DB 
+              /                  |                                           |                 \
+(Task Assignment)      (Task Completion)                    (Task Assignment)       (Task Completion)
+```
+
+Each LLM agent communicates with the orchestrator through a dedicated communication layer. The orchestrator assigns tasks to each LLM agent, which the agents then complete and return. This setup allows for a high degree of flexibility, scalability, and robustness.
+
+In the context of swarm LLMs, one could consider an **Omni-Vector Embedding Database** for communication. This database could store and manage the high-dimensional vectors produced by each LLM agent.
+
+- Strengths: This approach would allow for similarity-based lookup and matching of LLM-generated vectors, which can be particularly useful for tasks that involve finding similar outputs or recognizing patterns.
+
+- Weaknesses: An Omni-Vector Embedding Database might add complexity to the system in terms of setup and maintenance. It might also require significant computational resources, depending on the volume of data being handled and the complexity of the vectors. The handling and transmission of high-dimensional vectors could also pose challenges in terms of network load.
+
+
 from swarms import WorkerNode, Orchestrate
 
 Orchestrate(WorkerNode, autoscale=True, nodes=int, swarm_type="flat")

swarms/swarms.py

@@ -3,7 +3,7 @@ import asyncio
 
 # from swarms.agents.tools.agent_tools import *
 from swarms.agents.tools.agent_tools import *
-from swarms.workers.WorkerNode import WorkerNodeInitializer, worker_node
+from swarms.workers.worker_node import WorkerNodeInitializer, worker_node
 from swarms.boss.boss_node import BossNodeInitializer as BossNode
 from swarms.workers.worker_ultra_node import WorkerUltra
 
@@ -11,7 +11,6 @@ from swarms.utils.task import Task
 from swarms.agents.models.hf import HuggingFaceLLM
 
 # from langchain import LLMMathChain
-
 logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
 
 
@@ -19,6 +18,8 @@ logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(
 # TODO: ADD Universal Communication Layer, a ocean vectorstore instance
 # TODO: BE MORE EXPLICIT ON TOOL USE, TASK DECOMPOSITION AND TASK COMPLETETION AND ALLOCATION
 # TODO: Add RLHF Data collection, ask user how the swarm is performing
+# TODO: Create an onboarding process if not settings are preconfigured like `from swarms import Swarm, Swarm()` => then initiate onboarding name your swarm + provide purpose + etc 
+# TODO: Off
 
 class HierarchicalSwarm:
     def __init__(self, model_id: str = None, 

swarms/workers/__init__.py

@@ -1,2 +1,2 @@
-from .WorkerNode import worker_node
+from .worker_node import worker_node
 from .worker_ultra_node import WorkerUltraNode

tests/swarms.py

@@ -1,6 +1,6 @@
 import unittest
 import swarms
-from swarms.workers.WorkerNode import WorkerNode
+from swarms.workers.worker_node import WorkerNode
 from swarms.boss.BossNode import BossNode
 
 class TestSwarms(unittest.TestCase):