Adds base agent and base infra

agent/agent.py

@@ -0,0 +1,81 @@
+from langchain_core.tools import tool
+from langchain.agents.output_parsers import ReActJsonSingleInputOutputParser
+from langchain_core.prompts import ChatPromptTemplate,MessagesPlaceholder, PromptTemplate
+from langchain_core.messages import HumanMessage,AIMessage
+from langchain_core.tools import render_text_description
+from langchain.chains import create_history_aware_retriever
+from langchain.chains.combine_documents import create_stuff_documents_chain
+import langchain.chains
+from langchain.agents.format_scratchpad import format_log_to_str
+from langchain.chains import create_retrieval_chain
+from langchain_aws import ChatBedrock
+from langchain_aws.retrievers import AmazonKnowledgeBasesRetriever
+from langchain.chains import ConversationalRetrievalChain
+from typing import Union
+from langchain_core.agents import AgentAction, AgentFinish
+from langchain.agents.output_parsers import ReActSingleInputOutputParser
+from langchain.tools import Tool
+import os
+llm = ChatBedrock(
+    model_id="arn:aws:bedrock:us-east-1:654654422992:application-inference-profile/d9blf0g3fzqz",
+    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": 0.2, 'max_tokens': 1000,},
+    provider='anthropic'
+)
+retriever = AmazonKnowledgeBasesRetriever(
+    knowledge_base_id="RBD9TI5HYU",
+    region_name="us-east-1",
+    retrieval_config={"vectorSearchConfiguration": {"numberOfResults": 4}},
+    
+)
+def find_tool_by_name(tools: list[Tool],tool_name:str):
+    for tool in tools:
+        if tool.name==tool_name:
+            return tool
+    raise ValueError(f"Tool with name {tool_name} not found")
+if __name__=="__main__":
+    print("Hello React Langhain")
+    tools=[retriever.as_tool()]
+    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.
+Além disso leve em consideração o chat history fornecido.
+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":""}})
+    #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 auxiliomoradia?","agent_scratchpad":intermediate_steps,"chat_history":{"role":"user","content":""}})
+    print(agent_step)