Kye
1 year ago
parent
42b4eb32dc
commit
2c27f5e796
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from swarms import Orchestrator, Worker
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# Instantiate the Orchestrator with 10 agents
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orchestrator = Orchestrator(
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Worker,
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agent_list=[Worker]*10,
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task_queue=[]
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)
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# Agent 1 sends a message to Agent 2
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orchestrator.chat(sender_id=1, receiver_id=2, message="Hello, Agent 2!")
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@ -0,0 +1,213 @@
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import logging
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from enum import Enum
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from typing import Any
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from chromadb.utils import embedding_functions
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from swarms.workers.worker import Worker
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class TaskStatus(Enum):
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QUEUED = 1
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RUNNING = 2
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COMPLETED = 3
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FAILED = 4
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class ScalableGroupChat:
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"""
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The Orchestrator takes in an agent, worker, or boss as input
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then handles all the logic for
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- task creation,
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- task assignment,
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- and task compeletion.
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And, the communication for millions of agents to chat with eachother through
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a vector database that each agent has access to chat with.
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Each LLM agent chats with the orchestrator through a dedicated
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communication layer. The orchestrator assigns tasks to each LLM agent,
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which the agents then complete and return.
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This setup allows for a high degree of flexibility, scalability, and robustness.
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In the context of swarm LLMs, one could consider an **Omni-Vector Embedding Database
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for communication. This database could store and manage
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the high-dimensional vectors produced by each LLM agent.
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Strengths: This approach would allow for similarity-based lookup and matching of
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LLM-generated vectors, which can be particularly useful for tasks that involve finding similar outputs or recognizing patterns.
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Weaknesses: An Omni-Vector Embedding Database might add complexity to the system in terms of setup and maintenance.
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It might also require significant computational resources,
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depending on the volume of data being handled and the complexity of the vectors.
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The handling and transmission of high-dimensional vectors could also pose challenges
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in terms of network load.
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# Orchestrator
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* Takes in an agent class with vector store,
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then handles all the communication and scales
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up a swarm with number of agents and handles task assignment and task completion
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from swarms import OpenAI, Orchestrator, Swarm
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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
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Objective = "Make a business website for a marketing consultancy"
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Swarms = Swarms(orchestrated, auto=True, Objective))
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```
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In terms of architecture, the swarm might look something like this:
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```
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(Orchestrator)
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/ \
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Tools + Vector DB -- (LLM Agent)---(Communication Layer) (Communication Layer)---(LLM Agent)-- Tools + Vector DB
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/ | | \
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(Task Assignment) (Task Completion) (Task Assignment) (Task Completion)
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###Usage
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```
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from swarms import Orchestrator
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# Instantiate the Orchestrator with 10 agents
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orchestrator = Orchestrator(llm, agent_list=[llm]*10, task_queue=[])
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# Add tasks to the Orchestrator
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tasks = [{"content": f"Write a short story about a {animal}."} for animal in ["cat", "dog", "bird", "fish", "lion", "tiger", "elephant", "giraffe", "monkey", "zebra"]]
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orchestrator.assign_tasks(tasks)
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# Run the Orchestrator
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orchestrator.run()
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# Retrieve the results
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for task in tasks:
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print(orchestrator.retrieve_result(id(task)))
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```
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"""
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def __init__(
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self,
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worker_count: int = 5,
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collection_name: str = "swarm",
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api_key: str = None,
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model_name: str = None,
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worker = None
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):
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self.workers = []
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self.worker_count = worker_count
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# Create a list of Worker instances with unique names
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for i in range(worker_count):
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self.workers.append(Worker(openai_api_key="", ai_name=f"Worker-{i}"))
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def embed(self, input, api_key, model_name):
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openai = embedding_functions.OpenAIEmbeddingFunction(
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api_key=api_key,
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model_name=model_name
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)
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embedding = openai(input)
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return embedding
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# @abstractmethod
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def retrieve_results(
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self,
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agent_id: int
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) -> Any:
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"""Retrieve results from a specific agent"""
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try:
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#Query the vector database for documents created by the agents
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results = self.collection.query(
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query_texts=[str(agent_id)],
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n_results=10
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)
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return results
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except Exception as e:
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logging.error(f"Failed to retrieve results from agent {agent_id}. Error {e}")
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raise
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# @abstractmethod
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def update_vector_db(self, data) -> None:
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"""Update the vector database"""
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try:
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self.collection.add(
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embeddings=[data["vector"]],
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documents=[str(data["task_id"])],
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ids=[str(data["task_id"])]
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)
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except Exception as e:
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logging.error(f"Failed to update the vector database. Error: {e}")
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raise
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# @abstractmethod
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def get_vector_db(self):
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"""Retrieve the vector database"""
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return self.collection
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def append_to_db(
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self,
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result: str
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):
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"""append the result of the swarm to a specifici collection in the database"""
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try:
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self.collection.add(
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documents=[result],
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ids=[str(id(result))]
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)
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except Exception as e:
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logging.error(f"Failed to append the agent output to database. Error: {e}")
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raise
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def chat(
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self,
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sender_id: int,
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receiver_id: int,
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message: str
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):
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"""
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Allows the agents to chat with eachother thrught the vectordatabase
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# Instantiate the Orchestrator with 10 agents
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orchestrator = Orchestrator(
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llm,
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agent_list=[llm]*10,
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task_queue=[]
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)
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# Agent 1 sends a message to Agent 2
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orchestrator.chat(sender_id=1, receiver_id=2, message="Hello, Agent 2!")
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"""
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if sender_id < 0 or sender_id >= self.worker_count or receiver_id < 0 or receiver_id >= self.worker_count:
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raise ValueError("Invalid sender or receiver ID")
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message_vector = self.embed(
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message,
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self.api_key,
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self.model_name
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)
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#store the mesage in the vector database
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self.collection.add(
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embeddings=[message_vector],
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documents=[message],
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ids=[f"{sender_id}_to_{receiver_id}"]
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)
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self.run(
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objective=f"chat with agent {receiver_id} about {message}"
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)
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