clean up

swarms/agents/agents.py

@@ -1,10 +1,143 @@
 from __future__ import annotations
+from enum import Enum
+from typing import Callable, Tuple
+
+from langchain.agents.agent import AgentExecutor
+from langchain.agents.tools import BaseTool, Tool
+
+
+class ToolScope(Enum):
+    GLOBAL = "global"
+    SESSION = "session"
+
+SessionGetter = Callable[[], Tuple[str, AgentExecutor]]
+
+
+def tool(
+    name: str,
+    description: str,
+    scope: ToolScope = ToolScope.GLOBAL,
+):
+    def decorator(func):
+        func.name = name
+        func.description = description
+        func.is_tool = True
+        func.scope = scope
+        return func
+
+    return decorator
+
+
+class ToolWrapper:
+    def __init__(self, name: str, description: str, scope: ToolScope, func):
+        self.name = name
+        self.description = description
+        self.scope = scope
+        self.func = func
+
+    def is_global(self) -> bool:
+        return self.scope == ToolScope.GLOBAL
+
+    def is_per_session(self) -> bool:
+        return self.scope == ToolScope.SESSION
+
+    def to_tool(
+        self,
+        get_session: SessionGetter = lambda: [],
+    ) -> BaseTool:
+        func = self.func
+        if self.is_per_session():
+            func = lambda *args, **kwargs: self.func(
+                *args, **kwargs, get_session=get_session
+            )
+
+        return Tool(
+            name=self.name,
+            description=self.description,
+            func=func,
+        )
+
+
+class BaseToolSet:
+    def tool_wrappers(cls) -> list[ToolWrapper]:
+        methods = [
+            getattr(cls, m) for m in dir(cls) if hasattr(getattr(cls, m), "is_tool")
+        ]
+        return [ToolWrapper(m.name, m.description, m.scope, m) for m in methods]
+
+
+#=====================================>
+from typing import Optional
+
+from langchain.agents import load_tools
+from langchain.agents.tools import BaseTool
+from langchain.llms.base import BaseLLM
+
+
+class ToolsFactory:
+    @staticmethod
+    def from_toolset(
+        toolset: BaseToolSet,
+        only_global: Optional[bool] = False,
+        only_per_session: Optional[bool] = False,
+        get_session: SessionGetter = lambda: [],
+    ) -> list[BaseTool]:
+        tools = []
+        for wrapper in toolset.tool_wrappers():
+            if only_global and not wrapper.is_global():
+                continue
+            if only_per_session and not wrapper.is_per_session():
+                continue
+            tools.append(wrapper.to_tool(get_session=get_session))
+        return tools
+
+    @staticmethod
+    def create_global_tools(
+        toolsets: list[BaseToolSet],
+    ) -> list[BaseTool]:
+        tools = []
+        for toolset in toolsets:
+            tools.extend(
+                ToolsFactory.from_toolset(
+                    toolset=toolset,
+                    only_global=True,
+                )
+            )
+        return tools
+
+    @staticmethod
+    def create_per_session_tools(
+        toolsets: list[BaseToolSet],
+        get_session: SessionGetter = lambda: [],
+    ) -> list[BaseTool]:
+        tools = []
+        for toolset in toolsets:
+            tools.extend(
+                ToolsFactory.from_toolset(
+                    toolset=toolset,
+                    only_per_session=True,
+                    get_session=get_session,
+                )
+            )
+        return tools
+
+    @staticmethod
+    def create_global_tools_from_names(
+        toolnames: list[str],
+        llm: Optional[BaseLLM],
+    ) -> list[BaseTool]:
+        return load_tools(toolnames, llm=llm)
+
+#=====================================>
+#=====================================>
+
+
+
 
 
 ################
-from core.prompts.input import EVAL_PREFIX, EVAL_SUFFIX
-from core.tools.base import BaseToolSet
-from core.tools.factory import ToolsFactory
+# from core.prompts.input import EVAL_PREFIX, EVAL_SUFFIX
+from swarms.prompts.prompts import EVAL_PREFIX, EVAL_SUFFIX
 ############
 
 
@@ -312,6 +445,7 @@ from langchain.schema import (
 from langchain.tools.base import BaseTool
 
 # from core.prompts.input import EVAL_TOOL_RESPONSE
+from swarms.prompts.prompts import EVAL_TOOL_RESPONSE
 
 from swarms.prompts.prompts import EVAL_FORMAT_INSTRUCTIONS
 
@@ -453,7 +587,7 @@ from tenacity import (
 )
 
 from env import settings
-from ansi import ANSI, Color, Style
+
 
 
 def _create_retry_decorator(llm: ChatOpenAI) -> Callable[[Any], Any]:
@@ -785,7 +919,6 @@ class ChatOpenAI(BaseChatModel, BaseModel):
 
 
 from typing import Dict, Optional
-from celery import Task
 
 from langchain.agents.agent import AgentExecutor
 from langchain.callbacks.base import CallbackManager
@@ -879,7 +1012,9 @@ from typing import Dict
 
 from langchain.schema import BaseOutputParser
 
-from core.prompts.input import EVAL_FORMAT_INSTRUCTIONS
+# from core.prompts.input import EVAL_FORMAT_INSTRUCTIONS
+
+from swarms.prompts.prompts import EVAL_FORMAT_INSTRUCTIONS
 
 
 class EvalOutputParser(BaseOutputParser):