98 lines
5.1 KiB
Python
98 lines
5.1 KiB
Python
import json
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from langchain_core.tools import tool
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from langchain.agents.output_parsers import ReActJsonSingleInputOutputParser
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from langchain_core.prompts import ChatPromptTemplate,MessagesPlaceholder, PromptTemplate
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from langchain_core.messages import HumanMessage,AIMessage
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from langchain_core.tools import render_text_description
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from langchain.chains import create_history_aware_retriever
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from langchain.chains.combine_documents import create_stuff_documents_chain
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import langchain.chains
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from langchain.chains import create_history_aware_retriever
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from langchain.chains.combine_documents import create_stuff_documents_chain
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from langchain.agents.format_scratchpad import format_log_to_str
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from langchain.chains import create_retrieval_chain
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from langchain_aws import ChatBedrock
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from langchain_aws.retrievers import AmazonKnowledgeBasesRetriever
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from langchain.chains import ConversationalRetrievalChain
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from typing import Union
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from langchain_core.agents import AgentAction, AgentFinish
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from langchain.agents.output_parsers import ReActSingleInputOutputParser
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from langchain.tools import Tool
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import os
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def find_tool_by_name(tools: list[Tool],tool_name:str):
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for tool in tools:
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if tool.name==tool_name:
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print(tool.name)
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print("\n\n")
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return tool
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raise ValueError(f"Tool with name {tool_name} not found")
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def agent_call(event,context):
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llm = ChatBedrock(
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model_id="arn:aws:bedrock:us-east-1:654654422992:application-inference-profile/d9blf0g3fzqz",
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region_name="us-east-1",
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aws_access_key_id=os.environ["AWS_ACCESS_KEY_ID"],
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aws_secret_access_key=os.environ["AWS_SECRET_ACCESS_KEY"],
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aws_session_token=os.environ["AWS_SESSION_TOKEN"],
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model_kwargs={"temperature": 0, 'max_tokens': 1000,},
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provider='anthropic'
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)
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retriever = AmazonKnowledgeBasesRetriever(
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knowledge_base_id="RBD9TI5HYU",
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region_name="us-east-1",
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retrieval_config={"vectorSearchConfiguration": {"numberOfResults": 4}},
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)
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# Cria o retrieval chain
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retrievertool=retriever.as_tool()
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retrievertool.description="Baseando se numa query retorna trechos de editais de campus diferentes do instituto são paulo"
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tools=[retrievertool]
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template="""Você é um assistente para alunos de diversos campus diferentes do instituto federal de são paulo, sua função é responder perguntas
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de forma mais eficiente possivel sobre editais, que pode ser acessados pela tool fornecida:
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{tools}
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Note que dependendo do Campus os editais são diferentes, então é mandatório saber o campus do aluno antes de devolver alguma informação.
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Além disso existem editais válidos apenas para alunos do ensino médio, apaenas para ensino superior e para ambos.
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O edital difere também se o aluno já recebe algum auxílio ou é o primeiro. Obtenha a informação do edital mais recente que cumpra as condições.
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Além disso leve em consideração o chat history fornecido. Responda baseando-se exclusivamente nos documentos retornados pela tool amazonknowledgebase retriever. Não crie informações de editais.
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Use the following format:
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Question: the input question you must answer
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Thought: you should always think about what to do and take your previous thougths into consideration
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Action: the action to take, should be one of [{tool_names}]
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Action Input: the input to the action
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Observation: the result of the action
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... (this Thought/Action/Action Input/Observation can repeat N times)
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Thought: I now know the final answer
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Final Answer: the final answer to the original input question
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Begin!
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Question: {input}
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Chat history:{chat_history}
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Thought: {agent_scratchpad}
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"""
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prompt=PromptTemplate.from_template(template=template).partial(tools=render_text_description(tools), tool_names=','.join([t.name for t in tools]))
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#llm=ChatOpenAI(model="gpt-4o-mini",temperature=0,stop_sequences=["\nObservation:"])
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intermediate_steps=[]
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agent= {"input": lambda x:x["input"],"agent_scratchpad": lambda x:format_log_to_str(x["agent_scratchpad"]),"chat_history":lambda x:x["chat_history"]}|prompt | llm
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agent_step: Union[AgentAction,AgentFinish]=agent.invoke({"input": "Quanto é o valor do auxilio moradia?","agent_scratchpad":intermediate_steps,"chat_history":{"role":"user","content":"sou do campus sao paulo, ensino superior e não recebo auxílio ainda, estamos no primeiro semestre de 2025"}})
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#print(agent_step)
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if isinstance(agent_step,AgentAction):
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tool_name=agent_step.tool
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tool_to_use=find_tool_by_name(tools,tool_name)
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tool_input=agent_step.tool_input
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observation=tool_to_use.func(str(tool_input))
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print(f"{observation=}")
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intermediate_steps.append((agent_step,str(observation)))
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agent_step: Union[AgentAction,AgentFinish]=agent.invoke({"input": "Quanto é o valor do auxilio moradia?","agent_scratchpad":intermediate_steps,"chat_history":{"role":"user","content":"sou do campus sao paulo, ensino superior e não recebo auxílio ainda, estamos no primeiro semestre de 2025"}})
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return agent_step
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def hello(event,context):
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return{
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"statusCode":200,
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"body":json.dumps("hello_world")
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}
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print(agent_call("","")) |