Feat: Adds base project

back/app/api.py

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+from fastapi import FastAPI
+from pydantic import BaseModel
+from .backend import orquestrador
+
+app = FastAPI()
+
+
+@app.get("/")
+def health():
+    return {"status": "ok"}
+
+
+class QueryRequest(BaseModel):
+    query: str
+    history: str = ""
+
+class QueryResponse(BaseModel):
+    response: str
+    input_tokens: int
+    output_tokens: int
+    total_tokens: int
+
+
+@app.post("/agent", response_model=QueryResponse)
+def run_agent(request: QueryRequest):
+    result = orquestrador.main(request.query, request.history)
+    return QueryResponse(
+        response=result["response"],
+        input_tokens=result["input_tokens"],
+        output_tokens=result["output_tokens"],
+        total_tokens=result["total_tokens"],
+    )

back/app/backend/agent_bedrock.py

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+import operator
+from typing import TypedDict, Annotated
+
+from langchain_aws import ChatBedrockConverse
+from langchain_core.messages import AIMessage, ToolMessage
+from langgraph.graph import StateGraph, END
+
+from .config import REGION, AWS_ACCOUNT
+
+
+class AgentState(TypedDict):
+    messages: Annotated[list, operator.add]
+    current_step: str
+
+
+def create_bedrock_llm(model_id: str, region: str = REGION, tools: list = None):
+    """
+    Create a ChatBedrock instance using a model ID.
+
+    Args:
+        model_id: Bedrock model ID (e.g., anthropic.claude-haiku-4-5-20251001-v1:0)
+        region: AWS region (default: REGION env var)
+        tools: List of LangChain tools to bind to the model
+
+    Returns:
+        ChatBedrock instance configured with the model
+    """
+    MODEL_ARNS = {
+        "anthropic.claude-haiku-4-5-20251001-v1:0": f"arn:aws:bedrock:{REGION}:{AWS_ACCOUNT}:inference-profile/us.anthropic.claude-haiku-4-5-20251001-v1:0",
+        "anthropic.claude-sonnet-4-5-20250929-v1:0": f"arn:aws:bedrock:{REGION}:{AWS_ACCOUNT}:inference-profile/global.anthropic.claude-sonnet-4-5-20250929-v1:0",
+        "meta.llama4-maverick-17b-instruct-v1:0": f"arn:aws:bedrock:{REGION}:{AWS_ACCOUNT}:inference-profile/us.meta.llama4-maverick-17b-instruct-v1:0",
+        "meta.llama4-scout-17b-instruct-v1:0": f"arn:aws:bedrock:{REGION}:{AWS_ACCOUNT}:inference-profile/us.meta.llama4-scout-17b-instruct-v1:0",
+        "amazon.nova-lite-v1:0": f"arn:aws:bedrock:{REGION}:{AWS_ACCOUNT}:inference-profile/us.amazon.nova-lite-v1:0",
+        "amazon.nova-pro-v1:0": f"arn:aws:bedrock:{REGION}:{AWS_ACCOUNT}:inference-profile/us.amazon.nova-pro-v1:0",
+        "amazon.nova-2-lite-v1:0": f"arn:aws:bedrock:{REGION}:{AWS_ACCOUNT}:inference-profile/global.amazon.nova-2-lite-v1:0",
+    }
+    PROVIDER = {
+        "anthropic.claude-haiku-4-5-20251001-v1:0": "anthropic",
+        "anthropic.claude-sonnet-4-5-20250929-v1:0": "anthropic",
+        "meta.llama4-maverick-17b-instruct-v1:0": "meta",
+        "meta.llama4-scout-17b-instruct-v1:0": "meta",
+        "amazon.nova-lite-v1:0": "amazon",
+        "amazon.nova-pro-v1:0": "amazon",
+        "amazon.nova-2-lite-v1:0": "amazon",
+    }
+    prefix = {
+        "anthropic.claude-haiku-4-5-20251001-v1:0": "us",
+        "anthropic.claude-sonnet-4-5-20250929-v1:0": "global",
+        "meta.llama4-maverick-17b-instruct-v1:0": "us",
+        "meta.llama4-scout-17b-instruct-v1:0": "us",
+        "amazon.nova-lite-v1:0": "us",
+        "amazon.nova-pro-v1:0": "us",
+        "amazon.nova-2-lite-v1:0": "global",
+    }
+    llm = ChatBedrockConverse(
+        model_id=prefix[model_id] + "." + model_id,
+        region_name=region,
+        provider=PROVIDER[model_id],
+        max_tokens=2048,
+        temperature=0.7,
+    )
+    return llm.bind_tools(tools or [])
+
+
+def call_model(state: AgentState, llm) -> AgentState:
+    """Call the LLM with tools."""
+    response = llm.invoke(state["messages"])
+    state["current_step"] = "model_called"
+    return {"messages": [response]}
+
+
+def call_tools(state: AgentState, tools_map: dict) -> AgentState:
+    """Execute any tool calls from the LLM response."""
+    last_message = state["messages"][-1]
+
+    if hasattr(last_message, "tool_calls") and last_message.tool_calls:
+        tool_messages = []
+        for tool_call in last_message.tool_calls:
+            result = tools_map[tool_call["name"]].invoke(tool_call["args"])
+            tool_messages.append(ToolMessage(content=str(result), tool_call_id=tool_call["id"]))
+
+        state["current_step"] = "tools_executed"
+        return {"messages": tool_messages}
+    else:
+        state["current_step"] = "no_tools"
+        return {"messages": []}
+
+
+def should_continue(state: AgentState) -> str:
+    """Determine if we should continue to tools or end."""
+    last_message = state["messages"][-1]
+    if hasattr(last_message, "tool_calls") and last_message.tool_calls:
+        return "tools"
+    return "end"
+
+
+def create_agent(inference_profile_arn: str, region: str = REGION, tools: list = None):
+    """
+    Create a LangGraph agent that uses Bedrock inference profile with tools.
+
+    Args:
+        inference_profile_arn: ARN of the cross-region inference profile
+        region: AWS region
+        tools: List of LangChain tools to bind to the model
+
+    Returns:
+        Compiled LangGraph workflow
+    """
+    tools = tools or []
+    llm = create_bedrock_llm(inference_profile_arn, region, tools)
+    tools_map = {t.name: t for t in tools}
+
+    workflow = StateGraph(AgentState)
+    workflow.add_node("model", lambda state: call_model(state, llm))
+    workflow.add_node("tools", lambda state: call_tools(state, tools_map))
+    workflow.set_entry_point("model")
+    workflow.add_conditional_edges("model", should_continue, {"tools": "tools", "end": END})
+    workflow.add_edge("tools", "model")
+
+    return workflow.compile()

back/app/backend/config.py

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+import os
+
+TABLE = os.environ["TABLE"]
+REGION = os.environ["REGION"]
+AWS_ACCOUNT = os.environ["AWS_ACCOUNT"]
+SECRET_NAME = os.environ["SECRET_NAME"]

back/app/backend/dynamo.py

@@ -0,0 +1,53 @@
+import boto3
+import json
+import os
+from botocore.exceptions import ClientError
+from langfuse import Langfuse
+
+from .config import REGION, TABLE, SECRET_NAME
+
+dynamodb = boto3.resource("dynamodb", region_name=REGION)
+
+
+def get_secret() -> str:
+    session = boto3.session.Session()
+    client = session.client(service_name="secretsmanager", region_name=REGION)
+    try:
+        response = client.get_secret_value(SecretId=SECRET_NAME)
+    except ClientError as e:
+        raise e
+    return response["SecretString"]
+
+
+secrets = json.loads(get_secret())
+langfuse = Langfuse(
+    public_key=secrets["LANGFUSE-PUBLIC-KEY"],
+    secret_key=secrets["LANGFUSE-SECRET-KEY"],
+    host=os.environ["LANGFUSE_HOST"],
+)
+
+
+def get_contexto(dashboard: str) -> dict:
+    """
+    Get contexto, filter, and items_disponiveis from DynamoDB for a given dashboard.
+
+    Returns:
+        Dict with 'contexto', 'filter', and 'items_disponiveis' keys
+    """
+    try:
+        table = dynamodb.Table(TABLE)
+        response = table.get_item(Key={"id": dashboard + "_contexto"})
+
+        if "Item" not in response:
+            return {"contexto": "", "filter": "", "items_disponiveis": {}}
+
+        item = response["Item"]
+        return {
+            "contexto": item.get("contexto", ""),
+            "filter": item.get("filter_key", ""),
+            "items_disponiveis": item.get("itens_disponiveis", {}),
+        }
+
+    except ClientError as e:
+        error_message = e.response["Error"]["Message"]
+        return {"contexto": f"Error: {error_message}", "filter": "", "items_disponiveis": {}}

back/app/backend/orquestrador.py

@@ -0,0 +1,48 @@
+from langchain_core.messages import HumanMessage, AIMessage, SystemMessage
+from langfuse.langchain import CallbackHandler
+
+from .config import REGION
+from .dynamo import langfuse, get_contexto
+from .agent_bedrock import create_agent
+from .tools import ReportTools
+
+
+def main(user_query, history, model, base):
+    """Main execution function."""
+    report_tools = []
+
+
+    SYSTEM_PROMPT = """"""
+    langfuse_handler = CallbackHandler()
+    agent = create_agent(model, REGION, tools=report_tools)
+
+    initial_state = {
+        "messages": [
+            SystemMessage(content=SYSTEM_PROMPT),
+            HumanMessage(content=user_query),
+        ],
+        "current_step": "init",
+    }
+
+    config = {"callbacks": [langfuse_handler], "tags": [base]}
+    final_state = agent.invoke(initial_state, config=config)
+
+    total_input_tokens = 0
+    total_output_tokens = 0
+    for msg in final_state["messages"]:
+        if isinstance(msg, AIMessage) and hasattr(msg, "usage_metadata") and msg.usage_metadata:
+            total_input_tokens += msg.usage_metadata.get("input_tokens", 0)
+            total_output_tokens += msg.usage_metadata.get("output_tokens", 0)
+
+    langfuse.flush()
+    return {
+        "response": final_state["messages"][-1].content,
+        "input_tokens": total_input_tokens,
+        "output_tokens": total_output_tokens,
+        "total_tokens": total_input_tokens + total_output_tokens,
+    }
+
+
+if __name__ == "__main__":
+    main(
+    )

back/app/backend/tools.py

@@ -0,0 +1,85 @@
+from botocore.exceptions import ClientError
+from langchain_core.tools import StructuredTool
+
+from .config import TABLE
+from .dynamo import dynamodb
+
+
+class ReportTools:
+    def __init__(self, id_mapping: dict[str, str]):
+        self.id_mapping = id_mapping
+
+    def get_variable_value(self, id: str, variable: str) -> str:
+        """
+        Get a specific variable's value from DynamoDB for a specific id.
+
+        Args:
+            id: The id of the data
+            variable: The variable/column name to retrieve from the table
+
+        Returns:
+            The content of the specified variable for the given id
+        """
+        real_id = self.id_mapping.get(id, id)
+        try:
+            table = dynamodb.Table(TABLE)
+            response = table.get_item(Key={"id": real_id})
+
+            if "Item" not in response:
+                return f"No report found for month: {id}"
+
+            item = response["Item"]
+            content = item.get(variable, "")
+
+            if not content:
+                return f"Variable '{variable}' not found for month: {id}"
+
+            return f"<{id}>\n{content}\n</{id}>"
+
+        except ClientError as e:
+            error_message = e.response["Error"]["Message"]
+            return f"Error fetching report: {error_message}"
+
+    def get_variables_list(self, id: str) -> str:
+        """
+        Get the list of variables available in the table for a specific month.
+
+        Args:
+            id: The id of the data
+
+        Returns:
+            The list of available variables/keys for the specified data
+        """
+        real_id = self.id_mapping.get(id, id)
+        try:
+            table = dynamodb.Table(TABLE)
+            response = table.get_item(Key={"id": real_id})
+
+            if "Item" not in response:
+                return f"No data found for month: {id}"
+
+            item = response["Item"]
+            chaves_consolidadas = item.get("chaves_consolidadas", "")
+
+            if not chaves_consolidadas:
+                return f"No consolidated keys found for id: {id}"
+
+            return chaves_consolidadas
+
+        except ClientError as e:
+            error_message = e.response["Error"]["Message"]
+            return f"Error fetching consolidated keys: {error_message}"
+
+    def as_tools(self) -> list:
+        return [
+            StructuredTool.from_function(
+                self.get_variable_value,
+                name="get_variable_value",
+                description="Get a specific variable's data from DynamoDB for a specific id.",
+            ),
+            StructuredTool.from_function(
+                self.get_variables_list,
+                name="get_variable_list",
+                description="Get the list of variables available in the table for a specific id.",
+            ),
+        ]

back/app/front.py

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+import streamlit as st
+import time
+from backend import orquestrador
+import boto3
+from boto3.dynamodb.conditions import Key
+
+# Configure the page - MUST BE FIRST
+st.set_page_config(
+    page_title="Chatbot",
+    page_icon="💬",
+    layout="centered"
+)
+
+session = boto3.Session()
+
+
+# Initialize chat history in session state
+if "messages" not in st.session_state:
+    st.session_state.messages = []
+
+# Display title
+st.title("💬 Chatbot")
+
+# Display chat history
+for message in st.session_state.messages:
+    with st.chat_message(message["role"]):
+        st.markdown(message["content"])
+
+# Chat input
+if prompt := st.chat_input("Type your message here..."):
+    # Add user message to chat history
+    st.session_state.messages.append({"role": "user", "content": prompt})
+    
+    # Display user message
+    with st.chat_message("user"):
+        st.markdown(prompt)
+    
+    # Display assistant response
+    with st.chat_message("assistant"):
+        message_placeholder = st.empty()
+        
+        # Simulate streaming response (replace with actual API call)
+
+        result = orquestrador.main(prompt,str(st.session_state.messages),selected_value,base)
+        full_response = result["response"]
+
+        # Simulate typing effect
+        displayed_response = ""
+        for char in full_response:
+            displayed_response += char
+            message_placeholder.markdown(displayed_response + "▌")
+            time.sleep(0.01)
+
+        message_placeholder.markdown(full_response)
+        st.caption(f"Tokens: {result['input_tokens']} in / {result['output_tokens']} out / {result['total_tokens']} total")
+
+    # Add assistant response to chat history
+    st.session_state.messages.append({"role": "assistant", "content": full_response})
+
+# Add a sidebar with options
+with st.sidebar:
+    st.header("Options")
+    
+    if st.button("Clear Chat History"):
+        st.session_state.messages = []
+        st.rerun()
+    
+    st.divider()
+    
+    st.markdown("""
+    ### How to use:
+    1. Type your message in the input box
+    2. Press Enter or click Send
+    3. View the conversation history
+    """)