Adding v2 of rag-agent

Dockerfile

@@ -11,4 +11,4 @@ RUN pip install --no-cache-dir -r requirements.txt
 
 EXPOSE 7860
 
-CMD ["streamlit", "run", "interface/chatbot.py", "--server.port=7860"]
+CMD ["streamlit", "run", "interface/app.py", "--server.port=7860"]

agente/interface/app.py

@@ -0,0 +1,32 @@
+import streamlit as st
+import sys
+import os
+
+# Configuração do caminho para incluir as pastas necessárias
+sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+
+# Importando módulos necessários
+from interface.chatbot import Chatbot
+
+
+def main():
+    app = Chatbot()
+    
+    with st.sidebar:
+        app.create_sidebar()
+
+    app.mount_chatbot()
+
+    if app.response:
+        app.add_to_history(app.response, role="user")
+        app.send_message_for_ai(app.response)
+
+        app.response = ""  # Zerando a variável após uso
+
+    for message in st.session_state.chat_history:
+        st.chat_message(message["role"]).write(message["content"])
+
+
+
+if __name__ == "__main__":
+    main()

agente/interface/chatbot.py

@@ -16,7 +16,6 @@ st.set_page_config(page_title="Carlos AI Agent")
 
 load_dotenv()
 
-
 @st.cache_resource
 def connect_to_services():
     oa_client = OpenAI(
@@ -32,119 +31,122 @@ def connect_to_services():
 
     return oa_client, qdrant_client, edgedb_client
 
+class Chatbot():
+    def __init__(self):
+        # Configuração inicial do histórico de chat
+        if "chat_history" not in st.session_state:
+            st.session_state.chat_history = [{
+                "role": "assistant",
+                "content": "Como eu posso ajudar?"
+            }]
+        
+        # Conteúdo dos botões do sidebar
+        if "topics" not in st.session_state:
+            st.session_state["topics"] = [
+                "Niveis da conta govbr.",
+                "Dúvidas no reconhecimento facial.",
+                "Como recuperar minha conta gov.br",
+                "Dúvidas para aumentar o nível com a cin."
+            ]
+        
+        # Pergunta do usuário no chatbot
+        self.response = ""
 
-def send_message_for_ai(prompt, oa_client, qdrant_client, edgedb_client):
-    embedding = oa_client.embeddings.create(
-        input=[prompt],
-        model=os.environ.get("OPENAI_MODEL_EMBEDDING")
-    ).data[0].embedding
-
-    child_texts = qdrant_client.search(
-        collection_name=os.environ.get("COLLECTION_NAME"), 
-        query_vector=embedding,
-        limit=3
-    )
+        self.oa_client, self.qdrant_client, self.edgedb_client =  connect_to_services()
 
-    contexts = []
+    
+    def mount_chatbot(self):
+        # Exibição do título e subtítulo
+        st.title("Bem-vindo à ajuda do gov.br")
+        st.caption("💬 Qual a sua dificuldade hoje? Estou aqui para ajudar!")
+
+        # Exibição do espaço para mandar mensagem
+        if user_query := st.chat_input(placeholder="Digite sua mensagem"):
+            self.add_to_history(user_query, role="user")
+
+            # Simulação de resposta do assistente
+            self.send_message_for_ai(user_query)
+            # response = self.send_message_for_ai(user_query)
+            # self.add_to_history(response, role="assistant")
+    
+            
+    def create_sidebar(self):
+        st.image('https://www.gov.br/++theme++padrao_govbr/img/govbr-logo-large.png', width=200)
+        st.header("Tópicos frequentes")
+        
+        for topic in st.session_state.topics:
+            if st.button(topic, key=topic):
+                self.response = topic
 
-    for child_text in child_texts:
-        parent_text = edgedb_client.query('''
-            SELECT Pattern {
-                content,
-                url,
-                parent_id
+        
+        # Espaços em branco para organização
+        for _ in range(5):
+            st.write("")
+        
+        # Botão centralizado
+        col1, col2, col3 = st.columns([1, 2, 1])
+        with col2:
+            if st.button("LIMPAR HISTÓRICO"):
+                st.session_state.chat_history = [{
+                    "role": "assistant",
+                    "content": "Como eu posso ajudar?",
+                }]
+
+    def send_message_for_ai(self, prompt):
+        embedding = self.oa_client.embeddings.create(
+            input=[prompt],
+            model=os.environ.get("OPENAI_MODEL_EMBEDDING")
+        ).data[0].embedding
+    
+        child_texts = self.qdrant_client.search(
+            collection_name=os.environ.get("COLLECTION_NAME"), 
+            query_vector=embedding,
+            limit=3
+        )
+    
+        contexts = []
+    
+        for child_text in child_texts:
+            parent_text = self.edgedb_client.query('''
+                SELECT Pattern {
+                    content,
+                    url,
+                    parent_id
+                }
+                FILTER .id = <uuid>$parent_id
+                ''', parent_id=child_text.payload["parent_id"])[0]
+            context = {
+                "content": parent_text.content,
+                "url": parent_text.url,
+                "parent_id": parent_text.parent_id
             }
-            FILTER .id = <uuid>$parent_id
-            ''', parent_id=child_text.payload["parent_id"])[0]
-        context = {
-            "content": parent_text.content,
-            "url": parent_text.url,
-            "parent_id": parent_text.parent_id
-        }
-        contexts.append(context)
-
-    # system_msg.write(
-    #    f"""Contexto: {contexts}""")
+            contexts.append(context)
     
-    stream_response = send_message(
-        oa_client,
-        contexts,
-        prompt,
-        []
-    )
-
-    return stream_response
-
-
-oa_client, qdrant_client, edgedb_client = connect_to_services()
-
-
-def display_chat():
-    for msg in st.session_state.messages:
-        st.chat_message(msg["role"]).write(msg["content"])
-
-
-def sidebar_content():
-    st.image('https://www.gov.br/++theme++padrao_govbr/img/govbr-logo-large.png', width=200)
-    st.header("Tópicos frequentes")
-
-    # Botões de exemplo
-    topics = [
-        "Niveis da conta govbr.",
-        "Dúvidas no reconhecimento facial.",
-        "Dúvidas na autenticação dos bancos.",
-        "Dúvidas para aumentar o nível com a cin."
-    ]
-
-    for topic in topics:
-        st.button(topic)
-
-    # Espaços em branco para organização
-    for _ in range(5):
-        st.write("")
-
-    # Botão centralizado
-    col1, col2, col3 = st.columns([1, 1, 1])
-    with col2:
-        st.button("VOLTAR")
-
-
-# Função principal de processamento de input do chat
-def process_user_input():
-    if prompt := st.chat_input(placeholder="Digite sua mensagem"):
-        # Armazenar mensagem do usuário
-        st.session_state.messages.append({"role": "user", "content": prompt})
-        st.chat_message("user").write(prompt)
-
-        # Simulação de resposta do assistente
-        response = send_message_for_ai(
-        prompt, oa_client, qdrant_client, edgedb_client)
-
-        # Armazenar e exibir resposta do assistente
-        st.session_state.messages.append({"role": "assistant", "content": response})
-        st.chat_message("assistant").write(response)
-
         
-#system_msg = st.chat_message("system")
-
-
-# Configuração inicial
-if "messages" not in st.session_state:
-    st.session_state["messages"] = [{"role": "assistant", "content": "Como eu posso ajudar?"}]
-
-# Exibição da barra lateral
-with st.sidebar:
-    sidebar_content()
+        stream_response = send_message(
+            self.oa_client,
+            contexts,
+            prompt,
+            []
+        )
+        
+        # Se a resposta for uma lista ou um gerador, você precisa processá-la
+        response_content = []
+        for chunk in stream_response:
+            content = chunk.choices[0].delta.content if chunk.choices[0].delta.content else ""
+            response_content.append(content)
+    
+        # Junte todas as partes da resposta em uma única string
+        full_response = ''.join(response_content)
 
-# Exibição do título e subtítulo
-st.title("Bem-vindo à ajuda do gov.br")
-st.caption("💬 Lorem ipsum dolor sit amet, consectetur adipiscing elit.")
 
-# Exibição do chat
-display_chat()
+        self.add_to_history(full_response, role="assistant")
 
-# Processamento da entrada do usuário no chat
-process_user_input()
 
 
+    def add_to_history(self, message, role="user"):
+        st.session_state.chat_history.append({
+            "role": role,
+            "content": message
+            })
 

agente/interface/main.py

@@ -1,91 +0,0 @@
-import os
-import sys
-from dotenv import load_dotenv
-from openai import OpenAI
-from qdrant_client import QdrantClient
-import redis
-import streamlit as st
-
-# Add the parent directory to the Python path
-sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
-
-from pipelines.message import send_message # noqa
-
-st.set_page_config(page_title="Carlos AI Agent")
-
-
-load_dotenv()
-
-
-@st.cache_resource
-def connect_to_services():
-    oa_client = OpenAI(
-        api_key=os.environ.get("OPENAI_API_KEY")
-    )
-
-    qdrant_client = QdrantClient(
-        host=os.environ.get("QDRANT_HOST"),
-        port=os.environ.get("QDRANT_PORT")
-    )
-
-    redis_client = redis.Redis(
-        host=os.environ.get("REDIS_HOST"), 
-        port=os.environ.get("REDIS_PORT"), 
-        decode_responses=True
-    )
-
-    return oa_client, qdrant_client, redis_client
-
-
-def send_message_for_ai(prompt, oa_client, qdrant_client, redis_client):
-    embedding = oa_client.embeddings.create(
-        input=[prompt],
-        model=os.environ.get("OPENAI_MODEL_EMBEDDING")
-    ).data[0].embedding
-
-    child_texts = qdrant_client.search(
-        collection_name=os.environ.get("COLLECTION_NAME"), 
-        query_vector=embedding,
-        limit=3
-    )
-
-    contexts = []
-
-    for child_text in child_texts:
-        parent_text = redis_client.hgetall(
-            child_text.payload["parent_id"]
-        )
-        context = {
-            "content": parent_text["content"],
-            "url": parent_text["url"],
-            "parent_id": parent_text["parent_id"]
-        }
-        contexts.append(context)
-
-    system_msg.write(
-        f"""Contexto: {contexts}""")
-    
-    stream_response = send_message(
-        oa_client,
-        contexts,
-        prompt,
-        []
-    )
-
-    return stream_response
-
-
-oa_client, qdrant_client, redis_client = connect_to_services()
-
-st.write("# Carlos AI Agent")
-
-prompt = st.chat_input("Digite sua pergunta:")
-user_msg = st.chat_message("user")
-assistant_msg = st.chat_message("assistant")
-system_msg = st.chat_message("system")
-
-if prompt:
-    user_msg.write(prompt)
-    response = send_message_for_ai(
-        prompt, oa_client, qdrant_client, redis_client)
-    assistant_msg.write_stream(response)

agente/prompts/system.md

@@ -17,6 +17,6 @@ Since you will receive a list with these contexts, you will answer based on them
 
 The format of your response should be in markdown. You can include images in your response by interpreting them based on the description provided for each image and responding in markdown format.
 
-If you do not find the answer in the given context, simply respond to the user with "Não encontrei sua resposta."
+If you do not find the answer in the given context, simply respond to the user with "Esta informação não está em meu alcance."
 
 Do not use information that is not in the given context.