new worker == vortex with primitives

swarms/hivemind/hivemind.py

@@ -1,16 +1,17 @@
 #  workers in unison
 #kye gomez jul 13 4:01pm, can scale up the number of swarms working on a probkem with `hivemind(swarms=4, or swarms=auto which will scale the agents depending on the complexity)`
+#this needs to change, we need to specify exactly what needs to be imported
+# add typechecking, documentation, and deeper error handling 
+# TODO: MANY WORKERS
 
 import concurrent.futures
 import logging
 
-#this needs to change, we need to specify exactly what needs to be imported
+
 from swarms.swarms.swarms import HierarchicalSwarm
 
 logging.basicConfig(level=logging.DEBUG, format='%(asctime)s - %(levelname)s - %(message)s')
 
-# add typechecking, documentation, and deeper error handling 
-# TODO: MANY WORKERS
 class HiveMind:
     def __init__(self, openai_api_key="", num_swarms=1, max_workers=None):
         self.openai_api_key = openai_api_key

swarms/workers/vortex_worker.py

@@ -0,0 +1,127 @@
+#aug 10
+#Vortex is the name of my Duck friend, ILY Vortex
+#Kye
+
+from swarms.agents.base import Agent
+
+import logging
+import faiss
+from typing import List, Optional, Union
+
+from langchain.agents import Tool
+from langchain.chat_models import ChatOpenAI
+from langchain.docstore import InMemoryDocstore
+from langchain.embeddings import OpenAIEmbeddings
+
+from langchain.vectorstores import FAISS
+
+from swarms.agents.tools.autogpt import (
+    FileChatMessageHistory,
+    ReadFileTool,
+    WebpageQATool,
+    WriteFileTool,
+    load_qa_with_sources_chain,
+    process_csv,
+    web_search,
+)
+from swarms.agents.base import Agent
+
+logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
+
+ROOT_DIR = "./data/"
+
+class VortexWorkerAgent:
+    """An autonomous agent instance that accomplishes various language tasks like summarization, text generation of any kind, data analysis, websearch and much more"""
+
+    def __init__(self,
+                openai_api_key: str,
+                llm: Optional[Union[InMemoryDocstore, ChatOpenAI]] = None,
+                tools: Optional[List[Tool]] = None,
+                embedding_size: Optional[int] = 8192,
+                worker_name: Optional[str] = "Vortex Worker Agent",
+                worker_role: Optional[str] = "Assistant",
+                human_in_the_loop: Optional[bool] = False,
+                search_kwargs: dict = {},
+                verbose: Optional[bool] = False,
+                chat_history_file: str = "chat_history.text"):
+        if not openai_api_key:
+            raise ValueError("openai_api_key cannot be None, try placing in ENV")
+        
+        self.openai_api_key = openai_api_key
+        self.worker_name = worker_name
+        self.worker_role = worker_role
+        
+        self.embedding_size = embedding_size
+        self.human_in_the_loop = human_in_the_loop
+        self.search_kwargs = search_kwargs
+        
+        self.verbose = verbose
+        self.chat_history_file = chat_history_file
+        self.llm = llm or self.init_llm(ChatOpenAI)
+        
+        self.tools = tools or self.init_tools()
+        self.vectorstore = self.init_vectorstore()
+        self.agent = self.create_agent()
+
+    def init_llm(self, llm_class, temperature=1.0):
+        try:
+            return llm_class(openai_api_key=self.openai_api_key, temperature=temperature)
+        except Exception:
+            logging.error("Failed to init the language model, make sure the llm function matches the llm abstract type")
+            raise
+    
+    def init_tools(self):
+        try:
+            logging.info("Initializing tools for VortexWorkerAgent")
+            tools = [
+                web_search,
+                WriteFileTool,
+                ReadFileTool,
+                process_csv,
+                WebpageQATool(qa_chain=load_qa_with_sources_chain(self.llm))
+            ]
+            return tools
+        except Exception as error:
+            logging.error(f"Failed to initialize tools: {error}")
+            raise
+    
+    def init_vectorstore(self):
+        try:
+            embeddings_model = OpenAIEmbeddings(openai_api_key=self.openai_api_key)
+            index = faiss.IndexFlatL2(embedding_size=self.embedding_size)
+            return FAISS(embeddings_model, index, InMemoryDocstore({}), {})
+        except Exception as error:
+            logging.error(f"Failed to initialize vector store: {error}")
+            raise
+
+    def create_agent(self):
+        logging.info("Creating agent in VortexWorkerAgent")
+        try:
+            Agent.from_llm_and_tools(
+                ai_name=self.worker_name,
+                ai_role=self.worker_role,
+                tools=self.tools,
+                llm=self.llm,
+                memory=self.vectorstore,
+                human_in_the_loop=self.human_in_the_loop,
+                chat_history_memory=FileChatMessageHistory(self.chat_history_file)
+            )
+        except Exception as error:
+            logging.error(f"Failed while creating agent {str(error)}")
+            raise error
+    
+    def add_tool(self, tool: Tool):
+        if not isinstance(tool, Tool):
+            logging.error("Tools must be an instant of Tool")
+            raise TypeError("Tool must be an instance of Tool, try wrapping your tool with the Tool decorator and fill in the requirements")
+        self.tools.append(tool)
+
+    def run(self, prompt) -> str:
+        if not isinstance(prompt, str) or not prompt:
+            raise ValueError("Prompt must be a non empty string")
+        try:
+            self.agent.run([prompt])
+            return "Task completed by VortexWorkerAgent"
+        except Exception as error:
+            logging.error(f"While running the agent: {str(error)}")
+            raise error

swarms/workers/worker_node.py

@@ -8,7 +8,6 @@ from langchain.docstore import InMemoryDocstore
 from langchain.embeddings import OpenAIEmbeddings
 from langchain_experimental.autonomous_agents import AutoGPT
 from langchain.vectorstores import FAISS
-
 from swarms.agents.tools.autogpt import (
     FileChatMessageHistory,
     ReadFileTool,
@@ -116,7 +115,7 @@ class WorkerNode:
             llm: Optional[Union[InMemoryDocstore, ChatOpenAI]] = None, 
             tools: Optional[List[Tool]] = None, 
             # vectorstore: Optional[FAISS] = None,
-            # embedding_size: Optional[int] = 4026,
+            embedding_size: Optional[int] = 4026,
             worker_name: Optional[str] = "Swarm Worker AI Assistant", 
             worker_role: Optional[str] = "Assistant", 
             human_in_the_loop: Optional[bool] = False, 
@@ -131,7 +130,7 @@ class WorkerNode:
         self.worker_node_initializer = WorkerNodeInitializer(openai_api_key)
         self.name = worker_name  # Added a name attribute
         self.description = "A worker node that executes tasks"  # Added a description attribute
-        self.embedding_size = embedding_size
+        self.embedding_size = self.embedding_size
 
 
     def initialize_llm(self, llm_class, temperature):