clean up

Former-commit-id: ec8b8be

Author: Kye

README.md

@@ -1,4 +1,4 @@
-## Swarming AI Agents (Swarms)
+## Swarms of Autonomous AI Agents 
 
 ![Swarming banner](images/swarms.png)
 

swarms/agents/prompts/chains/llm.py

@@ -24,7 +24,7 @@
     NoOpOutputParser,
     PromptValue,
 )
-from langchain.schema.language_model import BaseLanguageModel
+from langchain.base_language import BaseLanguageModel
 
 
 class LLMChain(Chain):

swarms/agents/tools/core/code_interpreter/main.py

@@ -7,8 +7,6 @@
 from io import BytesIO
 from typing import Any, List, Optional, Sequence, Tuple, Union
 
-from pydantic import BaseModel, BaseSettings, root_validator
-
 from langchain.agents import AgentExecutor, BaseSingleActionAgent
 from langchain.base_language import BaseLanguageModel
 from langchain.callbacks import AsyncIteratorCallbackHandler
@@ -36,8 +34,7 @@
 )
 from langchain.tools import BaseTool, StructuredTool
 from langchain.tools.convert_to_openai import format_tool_to_openai_function
-
-
+from pydantic import BaseModel, BaseSettings, root_validator
 
 remove_dl_link_prompt = ChatPromptTemplate(
     input_variables=["input_response"],
@@ -295,7 +292,7 @@ def __repr__(self):
         return f"File(name={self.name})"
 
 
-from langchain.schema import HumanMessage, AIMessage  # type: ignore
+from langchain.schema import AIMessage, HumanMessage  # type: ignore
 
 
 class UserRequest(HumanMessage):

swarms/agents/utils/Agent.py

@@ -8,9 +8,8 @@
 from typing import Any, Dict, List, Optional, Sequence, Tuple, Union
 
 import yaml
-from pydantic import BaseModel, root_validator
-
 from langchain.agents.agent_types import AgentType
+from langchain.base_language import BaseLanguageModel
 from langchain.callbacks.base import BaseCallbackManager
 from langchain.callbacks.manager import (
     AsyncCallbackManagerForToolRun,
@@ -26,9 +25,9 @@
     BaseOutputParser,
     BasePromptTemplate,
 )
-from langchain.schema.language_model import BaseLanguageModel
 from langchain.schema.messages import BaseMessage
 from langchain.tools.base import BaseTool
+from pydantic import BaseModel, root_validator
 
 logger = logging.getLogger(__name__)
 

swarms/agents/utils/Calback.py

@@ -1,12 +1,13 @@
 from typing import Any, Dict, List, Optional, Union
 
+from celery import Task
 from langchain.callbacks.base import BaseCallbackHandler
 from langchain.schema import AgentAction, AgentFinish, LLMResult
-from celery import Task
+
+from swarms.utils.logger import logger
 
 # from ansi import ANSI, Color, Style, dim_multiline
 from swarms.utils.main import ANSI, Color, Style, dim_multiline
-from swarms.utils.logger import logger
 
 
 class EVALCallbackHandler(BaseCallbackHandler):

swarms/agents/utils/ChatOpenAI.py

@@ -1,13 +1,12 @@
 """OpenAI chat wrapper."""
 from __future__ import annotations
 
-import os
 import logging
+import os
 import sys
 from typing import Any, Callable, Dict, List, Mapping, Optional, Tuple
 
 import openai
-
 from langchain.chat_models.base import BaseChatModel
 from langchain.schema import (
     AIMessage,
@@ -18,9 +17,7 @@
     HumanMessage,
     SystemMessage,
 )
-
 from langchain.utils import get_from_dict_or_env
-from swarms.utils.logger import logger
 from pydantic import BaseModel, Extra, Field, root_validator
 from tenacity import (
     before_sleep_log,
@@ -30,10 +27,11 @@
     wait_exponential,
 )
 
+from swarms.utils.logger import logger
 
 # from ansi import ANSI, Color, Style
 from swarms.utils.main import ANSI, Color, Style
-import os
+
 
 def _create_retry_decorator(llm: ChatOpenAI) -> Callable[[Any], Any]:
     import openai

swarms/agents/utils/ConversationalChatAgent.py

@@ -1,10 +1,10 @@
-from typing import Any, List, Optional, Sequence, Tuple
 import logging
+from typing import Any, List, Optional, Sequence, Tuple
 
-from swarms.agents.utils.Agent import Agent
+from langchain.agents.agent import AgentOutputParser
+from langchain.base_language import BaseLanguageModel
 from langchain.callbacks.base import BaseCallbackManager
 from langchain.chains import LLMChain
-from langchain.schema import BaseOutputParser
 from langchain.prompts.base import BasePromptTemplate
 from langchain.prompts.chat import (
     ChatPromptTemplate,
@@ -15,20 +15,14 @@
 from langchain.schema import (
     AgentAction,
     AIMessage,
-    BaseLanguageModel,
     BaseMessage,
+    BaseOutputParser,
     HumanMessage,
 )
 from langchain.tools.base import BaseTool
 
-
-from langchain.agents.agent import AgentOutputParser
-from langchain.schema import AgentAction
-
-
 from swarms.agents.prompts.prompts import EVAL_TOOL_RESPONSE
-
-
+from swarms.agents.utils.Agent import Agent
 
 logging.basicConfig(level=logging.DEBUG, format='%(asctime)s - %(levelname)s - %(message)s')
 

swarms/agents/utils/agent_setup.py

@@ -1,21 +1,19 @@
 import os
 
-from swarms.agents.prompts.prompts import EVAL_PREFIX, EVAL_SUFFIX
-from swarms.agents.tools.main import BaseToolSet
-from swarms.agents.tools.main import ToolsFactory
-
+from langchain.callbacks.base import BaseCallbackManager
 
+# from .ChatOpenAI import ChatOpenAI
+from langchain.chat_models import ChatOpenAI
 from langchain.chat_models.base import BaseChatModel
 from langchain.schema import BaseOutputParser
-from langchain.callbacks.base import BaseCallbackManager
+
+from swarms.agents.prompts.prompts import EVAL_PREFIX, EVAL_SUFFIX
+from swarms.agents.tools.main import BaseToolSet, ToolsFactory
 
 from .ConversationalChatAgent import ConversationalChatAgent
-# from .ChatOpenAI import ChatOpenAI
-from langchain.chat_models import ChatOpenAI
 from .output_parser import EvalOutputParser
 
 
-
 class AgentSetup:
     def __init__(self, toolsets: list[BaseToolSet] = [], openai_api_key: str = None, serpapi_api_key: str = None, bing_search_url: str = None, bing_subscription_key: str = None):
         self.llm: BaseChatModel = None

swarms/workers/generative_worker.py

@@ -3,16 +3,14 @@
 from datetime import datetime
 from typing import Any, Dict, List, Optional
 
+from langchain import LLMChain
+from langchain.base_language import BaseLanguageModel
+
 ############
 from langchain.prompts import PromptTemplate
 from langchain.retrievers import TimeWeightedVectorStoreRetriever
 from langchain.schema import BaseMemory, Document
-
-from langchain.schema.language_model import BaseLanguageModel
 from langchain.utils import mock_now
-from langchain import LLMChain
-from langchain.schema.language_model import BaseLanguageModel
-
 
 logger = logging.getLogger(__name__)