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92 lines
3.6 KiB
92 lines
3.6 KiB
import os
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from langchain.callbacks.base import BaseCallbackManager
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# from .ChatOpenAI import ChatOpenAI
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from langchain.chat_models import ChatOpenAI
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from langchain.chat_models.base import BaseChatModel
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from langchain.schema import BaseOutputParser
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from swarms.agents.models.prompts.prompts import EVAL_PREFIX, EVAL_SUFFIX
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from swarms.agents.tools.main import BaseToolSet, ToolsFactory
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from .ConversationalChatAgent import ConversationalChatAgent
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from .output_parser import EvalOutputParser
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class AgentSetup:
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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):
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self.llm: BaseChatModel = None
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self.parser: BaseOutputParser = None
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self.global_tools: list = None
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self.toolsets = toolsets
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self.openai_api_key = openai_api_key or os.getenv("OPENAI_API_KEY")
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self.serpapi_api_key = serpapi_api_key or os.getenv('SERPAPI_API_KEY')
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self.bing_search_url = bing_search_url or os.getenv('BING_SEARCH_URL')
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self.bing_subscription_key = bing_subscription_key or os.getenv('BING_SUBSCRIPTION_KEY')
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if not self.openai_api_key:
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raise ValueError("OpenAI key is missing, it should either be set as an environment variable or passed as a parameter")
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def setup_llm(self, callback_manager: BaseCallbackManager = None, openai_api_key: str = None):
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if openai_api_key is None:
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openai_api_key = os.getenv('OPENAI_API_KEY')
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if openai_api_key is None:
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raise ValueError("OpenAI API key is missing. It should either be set as an environment variable or passed as a parameter.")
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self.llm = ChatOpenAI(openai_api_key=openai_api_key, temperature=0.5, callback_manager=callback_manager, verbose=True)
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def setup_parser(self):
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self.parser = EvalOutputParser()
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def setup_global_tools(self):
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if self.llm is None:
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raise ValueError("LLM must be initialized before tools")
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toolnames = ["wikipedia"]
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if self.serpapi_api_key:
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toolnames.append("serpapi")
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if self.bing_search_url and self.bing_subscription_key:
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toolnames.append("bing-search")
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self.global_tools = [
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*ToolsFactory.create_global_tools_from_names(toolnames, llm=self.llm),
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*ToolsFactory.create_global_tools(self.toolsets),
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]
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def get_parser(self):
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if self.parser is None:
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raise ValueError("Parser is not initialized yet")
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return self.parser
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def get_global_tools(self):
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if self.global_tools is None:
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raise ValueError("Global tools are not initialized yet")
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return self.global_tools
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def get_agent(self):
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if self.llm is None:
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raise ValueError("LLM must be initialized before agent")
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if self.parser is None:
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raise ValueError("Parser must be initialized before agent")
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if self.global_tools is None:
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raise ValueError("Global tools must be initialized before agent")
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return ConversationalChatAgent.from_llm_and_tools(
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llm=self.llm,
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tools=[
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*self.global_tools,
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*ToolsFactory.create_per_session_tools(
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self.toolsets
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), # for names and descriptions
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],
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system_message=EVAL_PREFIX.format(bot_name=os.environ["BOT_NAME"] or 'WorkerUltraNode'),
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human_message=EVAL_SUFFIX.format(bot_name=os.environ["BOT_NAME"] or 'WorkerUltraNode'),
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output_parser=self.parser,
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max_iterations=30,
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) |