orchestrator scaffold

swarms/agents/agent.py

@@ -1,5 +1,7 @@
 #base toolset
 from swarms.agents.tools.agent_tools import *
+from swarms.utils.logger import logger
+
 from langchain.tools import BaseTool
 
 from langchain.callbacks.manager import (
@@ -11,12 +13,10 @@ from langchain.memory.chat_message_histories import FileChatMessageHistory
 
 import logging
 from pydantic import BaseModel, Extra
-
 from swarms.agents.models.hf import HuggingFaceLLM
 
 logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
 
-
 class AgentNodeInitializer:
     """Useful for spawning autonomous agent instances to accomplish complex tasks."""
 

swarms/orchestrate.py

@@ -44,35 +44,72 @@ import celery
 from typing import List, Dict, Any
 import numpy as np
 
-from swarms.agents.
+from swarms.agents.memory.ocean import OceanDB
+
 
 class Orchestrator(ABC):
-    def __init__(self, agent, agent_list: List[Any], task_queue: celery.Celery, vector_db: np.ndarray):
+    def __init__(self, 
+    agent, 
+    agent_list: List[Any], 
+    task_queue: celery.Celery, 
+    vector_db: OceanDB
+    ):
         self.agent = agent
-        self.agents = agent_list
+        self.agents = [agent_class() for _ in range(agent_list)]
         self.task_queue = task_queue
         self.vector_db = vector_db
         self.current_tasks = {}
+        self.lock = Lock()
         
     @abstractmethod
     def assign_task(self, agent_id: int, task: Dict[str, Any]) -> None:
         """Assign a task to a specific agent"""
-        pass
+        with self.lock:
+            if self.task_queue:
+                #get and agent and a task
+                agent = self.agents.pop(0)
+                task = self.task_queue.popleft()
+
+                #process the task and get result and vector representation
+                result, vector_representation = agent.process_task()
+
+                #store the vector representation in the database
+                self.vector_db.add_documents([vector_representation],[str(id(task))])
+
+                #put the agent back to agent slist
+                self.agents.append(agent)
+
+                logging.info(f"Task {id(str)} has been processed by agent {id(agent)} ")
+
+                return result
+            else:
+                logging.error("Task queue is empty")
     
     @abstractmethod
     def retrieve_results(self, agent_id: int) -> Any:
         """Retrieve results from a specific agent"""
-        pass
+        try:
+            #Query the vector database for documents created by the agents 
+            results = self.vector_db.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: np.ndarray) -> None:
         """Update the vector database"""
-        pass
+        try:
+            self.vector_db.add_documents([data['vector']], [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) -> np.ndarray:
         """Retrieve the vector database"""
-        pass
+        return self.vector_db