From 556d3e8b721e162ae014b63757f367479cf6d935 Mon Sep 17 00:00:00 2001
From: Kye <kye@apacmediasolutions.com>
Date: Thu, 28 Sep 2023 20:49:36 -0400
Subject: [PATCH] clean up
Former-commit-id: 2c27f5e796ee1be198e384384cfe70556419d3e1
---
chat.py | 11 ++
swarms/__init__.py | 3 +-
swarms/agents/__init__.py | 7 +-
swarms/swarms/orchestrate.py | 4 +-
swarms/swarms/scable_groupchat.py | 213 ++++++++++++++++++++++++++++++
5 files changed, 231 insertions(+), 7 deletions(-)
create mode 100644 chat.py
create mode 100644 swarms/swarms/scable_groupchat.py
diff --git a/chat.py b/chat.py
new file mode 100644
index 00000000..134f7f16
--- /dev/null
+++ b/chat.py
@@ -0,0 +1,11 @@
+from swarms import Orchestrator, Worker
+
+# Instantiate the Orchestrator with 10 agents
+orchestrator = Orchestrator(
+ Worker,
+ agent_list=[Worker]*10,
+ task_queue=[]
+)
+
+# Agent 1 sends a message to Agent 2
+orchestrator.chat(sender_id=1, receiver_id=2, message="Hello, Agent 2!")
\ No newline at end of file
diff --git a/swarms/__init__.py b/swarms/__init__.py
index 8c105356..fcd31eb2 100644
--- a/swarms/__init__.py
+++ b/swarms/__init__.py
@@ -4,6 +4,7 @@ print(logo2)
# worker
import swarms.workers
+from swarms.workers.worker import Worker
#boss
# from swarms.boss.boss_node import Boss
@@ -16,6 +17,6 @@ import swarms.structs
# swarms
import swarms.swarms
-
+from swarms.swarms.orchestrate import Orchestrator
#agents
import swarms.agents
\ No newline at end of file
diff --git a/swarms/agents/__init__.py b/swarms/agents/__init__.py
index 6a1cfea3..24c0ae9e 100644
--- a/swarms/agents/__init__.py
+++ b/swarms/agents/__init__.py
@@ -2,7 +2,6 @@
###########
-# #tools
# from swarms.tools.base import BaseTool, Tool, StructuredTool, ToolWrapper, BaseToolSet, ToolCreator, GlobalToolsCreator, SessionToolsCreator, ToolsFactory
# from swarms.tools.autogpt import pushd, process_csv, async_load_playwright, run_async, browse_web_page, WebpageQATool, web_search, query_website_tool
# from swarms.tools.exit_conversation import ExitConversation
@@ -15,7 +14,7 @@
#agents
-from swarms.agents.profitpilot import ProfitPilot
+# from swarms.agents.profitpilot import ProfitPilot
from swarms.agents.aot import AoTAgent
-from swarms.agents.multi_modal_agent import MultiModalVisualAgent
-from swarms.agents.omni_modal_agent import OmniModalAgent
\ No newline at end of file
+# from swarms.agents.multi_modal_agent import MultiModalVisualAgent
+# from swarms.agents.omni_modal_agent import OmniModalAgent
\ No newline at end of file
diff --git a/swarms/swarms/orchestrate.py b/swarms/swarms/orchestrate.py
index fde746b7..083a8f7b 100644
--- a/swarms/swarms/orchestrate.py
+++ b/swarms/swarms/orchestrate.py
@@ -2,8 +2,8 @@ import logging
import queue
import threading
from concurrent.futures import ThreadPoolExecutor
-from typing import Any, Dict, List
from enum import Enum
+from typing import Any, Dict, List
import chromadb
from chromadb.utils import embedding_functions
@@ -71,7 +71,7 @@ class Orchestrator:
###Usage
```
- from exa import Orchestrator
+ from swarms import Orchestrator
# Instantiate the Orchestrator with 10 agents
orchestrator = Orchestrator(llm, agent_list=[llm]*10, task_queue=[])
diff --git a/swarms/swarms/scable_groupchat.py b/swarms/swarms/scable_groupchat.py
new file mode 100644
index 00000000..9c59882e
--- /dev/null
+++ b/swarms/swarms/scable_groupchat.py
@@ -0,0 +1,213 @@
+import logging
+from enum import Enum
+from typing import Any
+
+from chromadb.utils import embedding_functions
+
+from swarms.workers.worker import Worker
+
+
+class TaskStatus(Enum):
+ QUEUED = 1
+ RUNNING = 2
+ COMPLETED = 3
+ FAILED = 4
+
+class ScalableGroupChat:
+ """
+ The Orchestrator takes in an agent, worker, or boss as input
+ then handles all the logic for
+ - task creation,
+ - task assignment,
+ - and task compeletion.
+
+ And, the communication for millions of agents to chat with eachother through
+ a vector database that each agent has access to chat with.
+
+ Each LLM agent chats 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.
+
+ # 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
+
+ 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)
+
+
+ ###Usage
+ ```
+ from swarms import Orchestrator
+
+ # Instantiate the Orchestrator with 10 agents
+ orchestrator = Orchestrator(llm, agent_list=[llm]*10, task_queue=[])
+
+ # Add tasks to the Orchestrator
+ tasks = [{"content": f"Write a short story about a {animal}."} for animal in ["cat", "dog", "bird", "fish", "lion", "tiger", "elephant", "giraffe", "monkey", "zebra"]]
+ orchestrator.assign_tasks(tasks)
+
+ # Run the Orchestrator
+ orchestrator.run()
+
+ # Retrieve the results
+ for task in tasks:
+ print(orchestrator.retrieve_result(id(task)))
+ ```
+ """
+ def __init__(
+ self,
+ worker_count: int = 5,
+ collection_name: str = "swarm",
+ api_key: str = None,
+ model_name: str = None,
+ worker = None
+ ):
+ self.workers = []
+ self.worker_count = worker_count
+
+ # Create a list of Worker instances with unique names
+ for i in range(worker_count):
+ self.workers.append(Worker(openai_api_key="", ai_name=f"Worker-{i}"))
+
+ def embed(self, input, api_key, model_name):
+ openai = embedding_functions.OpenAIEmbeddingFunction(
+ api_key=api_key,
+ model_name=model_name
+ )
+ embedding = openai(input)
+ return embedding
+
+
+ # @abstractmethod
+ def retrieve_results(
+ self,
+ agent_id: int
+ ) -> Any:
+ """Retrieve results from a specific agent"""
+
+ try:
+ #Query the vector database for documents created by the agents
+ results = self.collection.query(
+ query_texts=[str(agent_id)],
+ n_results=10
+ )
+
+ return results
+ except Exception as e:
+ logging.error(f"Failed to retrieve results from agent {agent_id}. Error {e}")
+ raise
+
+ # @abstractmethod
+ def update_vector_db(self, data) -> None:
+ """Update the vector database"""
+
+ try:
+ self.collection.add(
+ embeddings=[data["vector"]],
+ documents=[str(data["task_id"])],
+ ids=[str(data["task_id"])]
+ )
+
+ except Exception as e:
+ logging.error(f"Failed to update the vector database. Error: {e}")
+ raise
+
+
+ # @abstractmethod
+ def get_vector_db(self):
+ """Retrieve the vector database"""
+ return self.collection
+
+
+ def append_to_db(
+ self,
+ result: str
+ ):
+ """append the result of the swarm to a specifici collection in the database"""
+
+ try:
+ self.collection.add(
+ documents=[result],
+ ids=[str(id(result))]
+ )
+
+ except Exception as e:
+ logging.error(f"Failed to append the agent output to database. Error: {e}")
+ raise
+
+
+
+ def chat(
+ self,
+ sender_id: int,
+ receiver_id: int,
+ message: str
+ ):
+ """
+
+ Allows the agents to chat with eachother thrught the vectordatabase
+
+ # Instantiate the Orchestrator with 10 agents
+ orchestrator = Orchestrator(
+ llm,
+ agent_list=[llm]*10,
+ task_queue=[]
+ )
+
+ # Agent 1 sends a message to Agent 2
+ orchestrator.chat(sender_id=1, receiver_id=2, message="Hello, Agent 2!")
+
+ """
+ if sender_id < 0 or sender_id >= self.worker_count or receiver_id < 0 or receiver_id >= self.worker_count:
+ raise ValueError("Invalid sender or receiver ID")
+
+ message_vector = self.embed(
+ message,
+ self.api_key,
+ self.model_name
+ )
+
+ #store the mesage in the vector database
+ self.collection.add(
+ embeddings=[message_vector],
+ documents=[message],
+ ids=[f"{sender_id}_to_{receiver_id}"]
+ )
+
+ self.run(
+ objective=f"chat with agent {receiver_id} about {message}"
+ )
+
+