Feat: adds front and infra

agent/agent.py

@@ -18,6 +18,8 @@ from typing import Union
 from langchain_core.agents import AgentAction, AgentFinish
 from langchain.agents.output_parsers import ReActSingleInputOutputParser
 from langchain.tools import Tool
+from langgraph.checkpoint.memory import MemorySaver
+from langgraph.prebuilt import create_react_agent
 import os
 
 def find_tool_by_name(tools: list[Tool],tool_name:str):
@@ -27,15 +29,14 @@ def find_tool_by_name(tools: list[Tool],tool_name:str):
             print("\n\n")
             return tool
     raise ValueError(f"Tool with name {tool_name} not found")
-def agent_call(event,context):
-
+def hello(event,context):
     llm = ChatBedrock(
-    model_id="arn:aws:bedrock:us-east-1:654654422992:application-inference-profile/d9blf0g3fzqz",
+    model_id="arn:aws:bedrock:us-east-1:654654422992:application-inference-profile/glvyppv11ds5",
     region_name="us-east-1",
-    aws_access_key_id=os.environ["AWS_ACCESS_KEY_ID"],
-    aws_secret_access_key=os.environ["AWS_SECRET_ACCESS_KEY"],
-    aws_session_token=os.environ["AWS_SESSION_TOKEN"],
-    model_kwargs={"temperature": 0, 'max_tokens': 1000,},
+    #aws_access_key_id=os.environ["AWS_ACCESS_KEY_ID"],
+    #aws_secret_access_key=os.environ["AWS_SECRET_ACCESS_KEY"],
+    #aws_session_token=os.environ["AWS_SESSION_TOKEN"],
+    model_kwargs={"temperature": 0.1, 'max_tokens': 1000,},
     provider='anthropic'
 )
     retriever = AmazonKnowledgeBasesRetriever(
@@ -44,55 +45,22 @@ def agent_call(event,context):
     retrieval_config={"vectorSearchConfiguration": {"numberOfResults": 4}},
 
 )
-
-# Cria o retrieval chain
-    retrievertool=retriever.as_tool()
-    retrievertool.description="Baseando se numa query retorna trechos de editais de campus diferentes do instituto são paulo"
-    tools=[retrievertool]
-    template="""Você é um assistente para alunos de diversos campus diferentes do instituto federal de são paulo, sua função é responder perguntas 
-    de forma mais eficiente possivel sobre editais, que pode ser acessados pela tool fornecida:
-
-{tools}
-
-Note que dependendo do Campus os editais são diferentes, então é mandatório saber o campus do aluno antes de devolver alguma informação.
+    memory = MemorySaver()
+    model = llm
+    tools = [retriever.as_tool()]
+    prompt="""Você é um assistente para alunos de diversos campus diferentes do instituto federal de são paulo, sua função é responder perguntas de forma mais eficiente possivel sobre editais, que pode ser acessados pela tool fornecida. 
+        Note que dependendo do Campus os editais são diferentes, então é mandatório saber o campus do aluno antes de devolver alguma informação.
 Além disso existem editais válidos apenas para alunos do ensino médio, apaenas para ensino superior e para ambos.
 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.
-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.
-Use the following format:
-
-Question: the input question you must answer
-Thought: you should always think about what to do and take your previous thougths into consideration
-Action: the action to take, should be one of [{tool_names}]
-Action Input: the input to the action
-Observation: the result of the action
-... (this Thought/Action/Action Input/Observation can repeat N times)
-Thought: I now know the final answer
-Final Answer: the final answer to the original input question
-
-Begin!
-Question: {input}
-Chat history:{chat_history}
-Thought: {agent_scratchpad}
-"""
-    prompt=PromptTemplate.from_template(template=template).partial(tools=render_text_description(tools), tool_names=','.join([t.name for t in tools]))
-    #llm=ChatOpenAI(model="gpt-4o-mini",temperature=0,stop_sequences=["\nObservation:"])
-    intermediate_steps=[]
-
-    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
-    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"}})
-    #print(agent_step)
-    if isinstance(agent_step,AgentAction):
-        tool_name=agent_step.tool
-        tool_to_use=find_tool_by_name(tools,tool_name)
-        tool_input=agent_step.tool_input
-        observation=tool_to_use.func(str(tool_input))
-        print(f"{observation=}")
-        intermediate_steps.append((agent_step,str(observation)))
-    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"}})
-    return agent_step
-def hello(event,context):
-    return{
-        "statusCode":200,
-        "body":json.dumps("hello_world")
-    }
-print(agent_call("",""))
+Além disso leve em consideração o chat history fornecido.  Não crie informações de editais, apenas faça perguntas para o contexto e busque dos editais quando estiver com informações suficientes. Retorne o titulo do documento consultado, não o nome do arquivo. 
+Documentos com a sigla MTO são do campus matão. Documentos com a sigla SPO são do campus são paulo. É imperativo não obeter documentos de outros campus que não sejam o solicitado, não use informações deles. Os valores, se estiverem em formato de tabela, se encontrarão após o tipo da modaldie (moradia,transporte e etc), nunca antes. Não é nescessário buscar informações dos editais se uma dúvida não for apresentada ou se houver uma informação importante faltando.
+Use sempre os documentos mais recentes, atualmente estamos no segundo semestre de 2025.
+Caso seja mencionado que um dos pais não é registrado isso significa que é trabalhador informal."""
+    agent_executor = create_react_agent(model, tools, checkpointer=memory, prompt=prompt)
+    config = {"configurable": {"thread_id": "abc123"}}
+    input_message = event["message"]
+    #input_message=[{"role":"user","content":"aluno superior, nunca recebi auxilio, campus são paulo, Meu pai não é registrado, como faço para ganhar auxilio?"}]
+    response=""
+    for step in agent_executor.stream({"messages": input_message}, config, stream_mode="values"):
+        response={"json":(step["messages"][-1].text())}
+    return (response)

agent/poetry.lock

@@ -446,6 +446,70 @@ files = [
 [package.dependencies]
 langchain-core = ">=0.3.72,<1.0.0"
 
+[[package]]
+name = "langgraph"
+version = "0.6.7"
+description = "Building stateful, multi-actor applications with LLMs"
+optional = false
+python-versions = ">=3.9"
+files = [
+    {file = "langgraph-0.6.7-py3-none-any.whl", hash = "sha256:c724dd8c24806b70faf4903e8e20c0234f8c0a356e0e96a88035cbecca9df2cf"},
+    {file = "langgraph-0.6.7.tar.gz", hash = "sha256:ba7fd17b8220142d6a4269b6038f2b3dcbcef42cd5ecf4a4c8d9b60b010830a6"},
+]
+
+[package.dependencies]
+langchain-core = ">=0.1"
+langgraph-checkpoint = ">=2.1.0,<3.0.0"
+langgraph-prebuilt = ">=0.6.0,<0.7.0"
+langgraph-sdk = ">=0.2.2,<0.3.0"
+pydantic = ">=2.7.4"
+xxhash = ">=3.5.0"
+
+[[package]]
+name = "langgraph-checkpoint"
+version = "2.1.1"
+description = "Library with base interfaces for LangGraph checkpoint savers."
+optional = false
+python-versions = ">=3.9"
+files = [
+    {file = "langgraph_checkpoint-2.1.1-py3-none-any.whl", hash = "sha256:5a779134fd28134a9a83d078be4450bbf0e0c79fdf5e992549658899e6fc5ea7"},
+    {file = "langgraph_checkpoint-2.1.1.tar.gz", hash = "sha256:72038c0f9e22260cb9bff1f3ebe5eb06d940b7ee5c1e4765019269d4f21cf92d"},
+]
+
+[package.dependencies]
+langchain-core = ">=0.2.38"
+ormsgpack = ">=1.10.0"
+
+[[package]]
+name = "langgraph-prebuilt"
+version = "0.6.4"
+description = "Library with high-level APIs for creating and executing LangGraph agents and tools."
+optional = false
+python-versions = ">=3.9"
+files = [
+    {file = "langgraph_prebuilt-0.6.4-py3-none-any.whl", hash = "sha256:819f31d88b84cb2729ff1b79db2d51e9506b8fb7aaacfc0d359d4fe16e717344"},
+    {file = "langgraph_prebuilt-0.6.4.tar.gz", hash = "sha256:e9e53b906ee5df46541d1dc5303239e815d3ec551e52bb03dd6463acc79ec28f"},
+]
+
+[package.dependencies]
+langchain-core = ">=0.3.67"
+langgraph-checkpoint = ">=2.1.0,<3.0.0"
+
+[[package]]
+name = "langgraph-sdk"
+version = "0.2.6"
+description = "SDK for interacting with LangGraph API"
+optional = false
+python-versions = ">=3.9"
+files = [
+    {file = "langgraph_sdk-0.2.6-py3-none-any.whl", hash = "sha256:477216b573b8177bbd849f4c754782a81279fbbd88bfadfeda44422d14b18b08"},
+    {file = "langgraph_sdk-0.2.6.tar.gz", hash = "sha256:7db27cd86d1231fa614823ff416fcd2541b5565ad78ae950f31ae96d7af7c519"},
+]
+
+[package.dependencies]
+httpx = ">=0.25.2"
+orjson = ">=3.10.1"
+
 [[package]]
 name = "langsmith"
 version = "0.4.20"
@@ -648,6 +712,56 @@ files = [
     {file = "orjson-3.11.3.tar.gz", hash = "sha256:1c0603b1d2ffcd43a411d64797a19556ef76958aef1c182f22dc30860152a98a"},
 ]
 
+[[package]]
+name = "ormsgpack"
+version = "1.10.0"
+description = "Fast, correct Python msgpack library supporting dataclasses, datetimes, and numpy"
+optional = false
+python-versions = ">=3.9"
+files = [
+    {file = "ormsgpack-1.10.0-cp310-cp310-macosx_10_12_x86_64.macosx_11_0_arm64.macosx_10_12_universal2.whl", hash = "sha256:8a52c7ce7659459f3dc8dec9fd6a6c76f855a0a7e2b61f26090982ac10b95216"},
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+    {file = "ormsgpack-1.10.0.tar.gz", hash = "sha256:7f7a27efd67ef22d7182ec3b7fa7e9d147c3ad9be2a24656b23c989077e08b16"},
+]
+
 [[package]]
 name = "packaging"
 version = "25.0"
@@ -1107,6 +1221,138 @@ h2 = ["h2 (>=4,<5)"]
 socks = ["pysocks (>=1.5.6,!=1.5.7,<2.0)"]
 zstd = ["zstandard (>=0.18.0)"]
 
+[[package]]
+name = "xxhash"
+version = "3.5.0"
+description = "Python binding for xxHash"
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "xxhash-3.5.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:ece616532c499ee9afbb83078b1b952beffef121d989841f7f4b3dc5ac0fd212"},
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 [[package]]
 name = "zstandard"
 version = "0.24.0"
@@ -1221,4 +1467,4 @@ cffi = ["cffi (>=1.17)"]
 [metadata]
 lock-version = "2.0"
 python-versions = "^3.12"
-content-hash = "b264be7efe32a8f9be5fa414232cfa31533b0f7c5c441ebf62ea60ab23ac2951"
+content-hash = "0bf45e6e01cf95fb297c7674fadb6700e0f7b47a530c2dfafc7ab912432bfb5a"

agent/pyproject.toml

@@ -12,6 +12,7 @@ langchain = "^0.3.27"
 boto3 = "^1.40.19"
 langchain-aws = "^0.2.31"
 dotenv = "^0.9.9"
+langgraph = "^0.6.7"
 
 
 [build-system]

agent/requirements.txt

@@ -1,4 +1,5 @@
 langchain_core
 langchain
 langchain_aws
+langgraph