clean up for swarms

swarms/swarms.py

@@ -7,6 +7,13 @@ from swarms.tools.main import RequestsGet
 
 logging.basicConfig(level=logging.DEBUG, format='%(asctime)s - %(levelname)s - %(message)s')
 
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 class Swarms:
     def __init__(self, openai_api_key):
         self.openai_api_key = openai_api_key
@@ -26,10 +33,6 @@ class Swarms:
 
             process_csv,
             WebpageQATool(qa_chain=load_qa_with_sources_chain(llm)),
-
-            # RequestsGet()
-            Tool(name="RequestsGet", func=RequestsGet.get, description="A portal to the internet, Use this when you need to get specific content from a website. Input should be a  url (i.e. https://www.google.com). The output will be the text response of the GET request."),
-
             
             # CodeEditor,
             # Terminal,
@@ -37,7 +40,6 @@ class Swarms:
             # ExitConversation
 
             #code editor + terminal editor + visual agent
-            # Give the worker node itself as a tool
 
         ]
         assert tools is not None, "tools is not initialized"
@@ -77,7 +79,7 @@ class Swarms:
         return BossNode(llm, vectorstore, agent_executor, max_iterations=5)
 
 
-    def run_swarms(self, objective, run_as=None):
+    def run_swarms(self, objective):
         try:
             # Run the swarm with the given objective
             worker_tools = self.initialize_tools(OpenAI)
@@ -86,13 +88,10 @@ class Swarms:
             vectorstore = self.initialize_vectorstore()
             worker_node = self.initialize_worker_node(worker_tools, vectorstore)
 
-            if run_as.lower() == 'worker':
-                tool_input = {'prompt': objective}
-                return worker_node.run(tool_input)
-            else:
-                boss_node = self.initialize_boss_node(vectorstore, worker_node)
-                task = boss_node.create_task(objective)
-                return boss_node.execute_task(task)
+            boss_node = self.initialize_boss_node(vectorstore, worker_node)
+
+            task = boss_node.create_task(objective)
+            return boss_node.execute_task(task)
         except Exception as e:
             logging.error(f"An error occurred in run_swarms: {e}")
             raise
@@ -116,6 +115,125 @@ class Swarms:
 
 
 
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+# class Swarms:
+#     def __init__(self, openai_api_key):
+#         self.openai_api_key = openai_api_key
+
+#     def initialize_llm(self, llm_class, temperature=0.5):
+#         # Initialize language model
+#         return llm_class(openai_api_key=self.openai_api_key, temperature=temperature)
+
+#     def initialize_tools(self, llm_class):
+#         llm = self.initialize_llm(llm_class)
+#         # Initialize tools
+#         web_search = DuckDuckGoSearchRun()
+#         tools = [
+#             web_search,
+#             WriteFileTool(root_dir=ROOT_DIR),
+#             ReadFileTool(root_dir=ROOT_DIR),
+
+#             process_csv,
+#             WebpageQATool(qa_chain=load_qa_with_sources_chain(llm)),
+
+#             # RequestsGet()
+#             Tool(name="RequestsGet", func=RequestsGet.get, description="A portal to the internet, Use this when you need to get specific content from a website. Input should be a  url (i.e. https://www.google.com). The output will be the text response of the GET request."),
+
+            
+#             # CodeEditor,
+#             # Terminal,
+#             # RequestsGet,
+#             # ExitConversation
+
+#             #code editor + terminal editor + visual agent
+#             # Give the worker node itself as a tool
+
+#         ]
+#         assert tools is not None, "tools is not initialized"
+#         return tools
+
+#     def initialize_vectorstore(self):
+#         # Initialize vector store
+#         embeddings_model = OpenAIEmbeddings(openai_api_key=self.openai_api_key)
+#         embedding_size = 1536
+#         index = faiss.IndexFlatL2(embedding_size)
+#         return FAISS(embeddings_model.embed_query, index, InMemoryDocstore({}), {})
+
+#     def initialize_worker_node(self, worker_tools, vectorstore):
+#         # Initialize worker node
+#         llm = self.initialize_llm(ChatOpenAI)
+#         worker_node = WorkerNode(llm=llm, tools=worker_tools, vectorstore=vectorstore)
+#         worker_node.create_agent(ai_name="Swarm Worker AI Assistant", ai_role="Assistant", human_in_the_loop=False, search_kwargs={})
+#         worker_node_tool = Tool(name="WorkerNode AI Agent", func=worker_node.run, description="Input: an objective with a todo list for that objective. Output: your task completed: Please be very clear what the objective and task instructions are. The Swarm worker agent is Useful for when you need to spawn an autonomous agent instance as a worker to accomplish any complex tasks, it can search the internet or write code or spawn child multi-modality models to process and generate images and text or audio and so on")
+#         return worker_node_tool
+
+#     def initialize_boss_node(self, vectorstore, worker_node):
+#         # Initialize boss node
+#         llm = self.initialize_llm(OpenAI)
+#         todo_prompt = PromptTemplate.from_template("You are a boss planer in a swarm who is an expert at coming up with a todo list for a given objective and then creating an worker to help you accomplish your task. Come up with a todo list for this objective: {objective} and then spawn a worker agent to complete the task for you. Always spawn an worker agent after creating a plan and pass the objective and plan to the worker agent.")
+#         todo_chain = LLMChain(llm=llm, prompt=todo_prompt)
+#         tools = [
+#             Tool(name="TODO", func=todo_chain.run, description="useful for when you need to come up with todo lists. Input: an objective to create a todo list for. Output: a todo list for that objective. Please be very clear what the objective is!"),
+#             worker_node
+#         ]
+#         suffix = """Question: {task}\n{agent_scratchpad}"""
+#         prefix = """You are an Boss in a swarm who performs one task based on the following objective: {objective}. Take into account these previously completed tasks: {context}.\n """
+#         prompt = ZeroShotAgent.create_prompt(tools, prefix=prefix, suffix=suffix, input_variables=["objective", "task", "context", "agent_scratchpad"],)
+#         llm_chain = LLMChain(llm=llm, prompt=prompt)
+#         agent = ZeroShotAgent(llm_chain=llm_chain, allowed_tools=[tool.name for tool in tools])
+#         agent_executor = AgentExecutor.from_agent_and_tools(agent=agent, tools=tools, verbose=True)
+#         # return BossNode(return BossNode(llm, vectorstore, agent_executor, max_iterations=5)
+#         return BossNode(llm, vectorstore, agent_executor, max_iterations=5)
+
+
+#     def run_swarms(self, objective, run_as=None):
+#         try:
+#             # Run the swarm with the given objective
+#             worker_tools = self.initialize_tools(OpenAI)
+#             assert worker_tools is not None, "worker_tools is not initialized"
+
+#             vectorstore = self.initialize_vectorstore()
+#             worker_node = self.initialize_worker_node(worker_tools, vectorstore)
+
+#             if run_as.lower() == 'worker':
+#                 tool_input = {'prompt': objective}
+#                 return worker_node.run(tool_input)
+#             else:
+#                 boss_node = self.initialize_boss_node(vectorstore, worker_node)
+#                 task = boss_node.create_task(objective)
+#                 return boss_node.execute_task(task)
+#         except Exception as e:
+#             logging.error(f"An error occurred in run_swarms: {e}")
+#             raise
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