boss node fix cleanup

.readthedocs.yaml

@@ -1,32 +0,0 @@
-# .readthedocs.yaml
-# Read the Docs configuration file
-# See https://docs.readthedocs.io/en/stable/config-file/v2.html for details
-
-# Required
-version: 2
-
-# Set the OS, Python version and other tools you might need
-build:
-  os: ubuntu-22.04
-  tools:
-    python: "3.11"
-    # You can also specify other tool versions:
-    # nodejs: "19"
-    # rust: "1.64"
-    # golang: "1.19"
-
-# Build documentation in the "docs/" directory with Sphinx
-sphinx:
-   configuration: docs/conf.py
-
-# Optionally build your docs in additional formats such as PDF and ePub
-# formats:
-#    - pdf
-#    - epub
-
-# Optional but recommended, declare the Python requirements required
-# to build your documentation
-# See https://docs.readthedocs.io/en/stable/guides/reproducible-builds.html
-# python:
-#    install:
-#    - requirements: docs/requirements.txt

swarms/agents/base.py

@@ -7,20 +7,18 @@ from pydantic import ValidationError
 
 from swarms.agents.memory.base import VectorStoreRetriever
 from swarms.agents.memory.base_memory import BaseChatMessageHistory, ChatMessageHistory
-
 from swarms.agents.memory.document import Document
 from swarms.agents.models.base import AbstractModel
-
-from swarms.agents.models.prompts.base import (
-    AIMessage,
-    HumanMessage,
-    SystemMessage,
-)
 from swarms.agents.models.prompts.agent_prompt_auto import (
     MessageFormatter,
     PromptConstructor,
 )
 from swarms.agents.models.prompts.agent_prompt_generator import FINISH_NAME
+from swarms.agents.models.prompts.base import (
+    AIMessage,
+    HumanMessage,
+    SystemMessage,
+)
 from swarms.agents.tools.base import BaseTool
 from swarms.agents.utils.Agent import AgentOutputParser
 from swarms.agents.utils.human_input import HumanInputRun

swarms/boss/boss_node.py

@@ -104,18 +104,19 @@ class BossNodeInitializer:
 
 class BossNode:
     def __init__(self,
-                #  vectorstore,
+                 llm=None,
+                 vectorstore=None,
+                 agent_executor=None,
+                 max_iterations=5,
+                 human_in_the_loop=None,
                  objective: Optional[str] = None,
                  boss_system_prompt: Optional[str] = "You are a boss planner in a swarm...",
                  api_key=None,
                  worker_node=None,
                  llm_class=OpenAI,
-                 max_iterations=5,
                  verbose=False,
                  ):
-        
         self.api_key = api_key or os.getenv("OPENAI_API_KEY")
-        # self.vectorstore = vectorstore
         self.worker_node = worker_node
         self.boss_system_prompt = boss_system_prompt
         self.llm_class = llm_class
@@ -123,46 +124,54 @@ class BossNode:
         self.verbose = verbose
 
         if not self.api_key:
-            raise ValueError("[BossNode][ValueError][API KEY must be provided either as an argument or as an environment variable API_KEY]")
+            raise ValueError("[MasterBossNode][ValueError][API KEY must be provided either as an argument or as an environment variable API_KEY]")
 
-        self.llm = self.initialize_llm(self.llm_class)
+        # Initialize components if not provided
+        self.llm = llm if llm else self._initialize_llm(self.llm_class)
+        self.vectorstore = vectorstore if vectorstore else self._initialize_vectorstore()
 
+        # Setting up todo_chain and agent_executor
         todo_prompt = PromptTemplate.from_template(boss_system_prompt)
         todo_chain = LLMChain(llm=self.llm, prompt=todo_prompt)
-
         tools = [
             Tool(name="TODO", func=todo_chain.run, description="useful for when you need to come up with todo lists..."),
             self.worker_node
         ]
         suffix = """Question: {task}\n{agent_scratchpad}"""
         prefix = """You are a 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=self.llm, prompt=prompt)
         agent = ZeroShotAgent(llm_chain=llm_chain, allowed_tools=[tool.name for tool in tools])
+        self.agent_executor = agent_executor if agent_executor else AgentExecutor.from_agent_and_tools(agent=agent, tools=tools, verbose=self.verbose)
 
-        self.agent_executor = AgentExecutor.from_agent_and_tools(agent=agent, tools=tools, verbose=self.verbose)
-
-        vectorstore = self.initialize_vectorstore()
-
-        self.boss_initializer = BossNodeInitializer(
+        # Setup BabyAGI
+        try:
+            self.baby_agi = BabyAGI.from_llm(
                 llm=self.llm,
-            vectorstore=vectorstore, 
-            agent_executor=self.agent_executor, 
+                vectorstore=self.vectorstore,
+                task_execution_chain=self.agent_executor,
                 max_iterations=self.max_iterations,
+                human_in_the_loop=human_in_the_loop
             )
-        self.task = self.boss_initializer.create_task(objective)
+        except ValidationError as e:
+            logging.error(f"Validation Error while initializing BabyAGI: {e}")
+            raise
+        except Exception as e:
+            logging.error(f"Unexpected Error while initializing BabyAGI: {e}")
+            raise
 
-    def initialize_llm(self, llm_class, temperature=0.5):
+        self.task = self._create_task(objective)
+
+    def _initialize_llm(self, llm_class, temperature=0.5):
         try:
             return llm_class(openai_api_key=self.api_key, temperature=temperature)
         except Exception as e:
             logging.error(f"Failed to initialize language model: {e}")
             raise e
 
-    def initialize_vectorstore(self):
+    def _initialize_vectorstore(self):
         try:
-            embeddings_model = OpenAIEmbeddings(openai_api_key=self.openai_api_key)
+            embeddings_model = OpenAIEmbeddings(openai_api_key=self.api_key)
             embedding_size = 8192
             index = faiss.IndexFlatL2(embedding_size)
             return FAISS(embeddings_model.embed_query, index, InMemoryDocstore({}), {})
@@ -170,7 +179,18 @@ class BossNode:
             logging.error(f"Failed to initialize vector store: {e}")
             return None
 
+    def _create_task(self, objective):
+        if not objective:
+            logging.error("Objective cannot be empty.")
+            raise ValueError("Objective cannot be empty.")
+        return {"objective": objective}
 
     def run(self):
-        self.boss_initializer.run(self.task)
-
+        if not self.task:
+            logging.error("Task cannot be empty.")
+            raise ValueError("Task cannot be empty.")
+        try:
+            self.baby_agi(self.task)
+        except Exception as e:
+            logging.error(f"Error while executing task: {e}")
+            raise

swarms/workers/vortex_worker.py

@@ -58,7 +58,7 @@ class VortexWorkerAgent:
         self.chat_history_file = chat_history_file
         self.llm = llm or self.init_llm(ChatOpenAI)
         
-        self.tools = self.init_tools()
+        self.tools = tools or self.init_tools()
         self.vectorstore = self.init_vectorstore()
         self.agent = self.create_agent()