diff --git a/.env.example b/.env.example
new file mode 100644
index 0000000..62efd1e
--- /dev/null
+++ b/.env.example
@@ -0,0 +1 @@
+OPENAI_API_KEY='sk-...'
\ No newline at end of file
diff --git a/examples/uso_basico.ipynb b/examples/uso_basico.ipynb
index e04aeb8..94caf41 100644
--- a/examples/uso_basico.ipynb
+++ b/examples/uso_basico.ipynb
@@ -20,7 +20,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -41,12 +41,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
     "import pandas as pd\n",
     "from cluster_facil import ClusterFacil\n",
+    "from dotenv import load_dotenv\n",
+    "load_dotenv(override=True)  # Isso carrega as variáveis do .env para o ambiente\n",
     "\n",
     "# Criando um DataFrame de exemplo simples com textos\n",
     "data = {'id': [1, 2, 3, 4, 5, 6, 7, 8],\n",
@@ -64,9 +66,102 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "DataFrame de Exemplo:\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>id</th>\n",
+       "      <th>texto_documento</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>O juiz determinou o pagamento da indenização.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>Recurso de apelação foi interposto pela defesa.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>A sentença julgou procedente o pedido do autor.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>A corte negou provimento ao recurso especial.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>Processo arquivado por falta de movimentação.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>6</td>\n",
+       "      <td>Acordo homologado entre as partes.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>7</td>\n",
+       "      <td>Indenização por danos morais foi concedida.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>8</td>\n",
+       "      <td>Extinção do processo sem resolução de mérito.</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   id                                  texto_documento\n",
+       "0   1    O juiz determinou o pagamento da indenização.\n",
+       "1   2  Recurso de apelação foi interposto pela defesa.\n",
+       "2   3  A sentença julgou procedente o pedido do autor.\n",
+       "3   4    A corte negou provimento ao recurso especial.\n",
+       "4   5    Processo arquivado por falta de movimentação.\n",
+       "5   6               Acordo homologado entre as partes.\n",
+       "6   7      Indenização por danos morais foi concedida.\n",
+       "7   8    Extinção do processo sem resolução de mérito."
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "df_exemplo = pd.DataFrame(data)\n",
     "\n",
@@ -86,9 +181,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2025-04-21 22:59:39,165 - INFO - ClusterFacil iniciado com um DataFrame já existente.\n",
+      "2025-04-21 22:59:39,169 - INFO - Próxima rodada de clusterização definida como: 1\n"
+     ]
+    }
+   ],
    "source": [
     "cf = ClusterFacil(df_exemplo, random_state=42)"
    ]
@@ -108,9 +212,63 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2025-04-21 22:59:39,175 - INFO - Iniciando preparação dos textos para agrupamento (rodada 1). Coluna de texto: 'texto_documento', limite de grupos para teste: 5.\n",
+      "2025-04-21 22:59:39,175 - INFO - Analisando características de 8 textos (TF-IDF)...\n",
+      "2025-04-21 22:59:39,182 - INFO - Análise TF-IDF concluída. 8 textos processados, 30 características (palavras/termos) identificadas.\n",
+      "2025-04-21 22:59:39,182 - INFO - Avaliando diferentes números de grupos (método do cotovelo)...\n",
+      "2025-04-21 22:59:39,183 - INFO - Calculando opções de agrupamento (inércias) para K de 1 a 5...\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iniciando a preparação e geração do gráfico do cotovelo...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Avaliando número de grupos (K): 100%|██████████| 5/5 [00:00<00:00, 20.80K/s]\n",
+      "2025-04-21 22:59:39,425 - INFO - Cálculo das opções de agrupamento (inércias) concluído.\n",
+      "2025-04-21 22:59:39,427 - WARNING - O gráfico do cotovelo pode estar incompleto devido a erros no cálculo de alguns valores de K (exibindo 4 pontos).\n",
+      "2025-04-21 22:59:39,865 - INFO - Gerando gráfico do cotovelo para visualização...\n",
+      "2025-04-21 22:59:39,885 - INFO - Exibindo gráfico do cotovelo...\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2025-04-21 22:59:39,968 - INFO - Preparação da rodada 1 concluída. Analise o gráfico do cotovelo (baseado em 8 textos) para escolher o número de grupos (K).\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Preparação concluída.\n"
+     ]
+    }
+   ],
    "source": [
     "print(\"Iniciando a preparação e geração do gráfico do cotovelo...\")\n",
     "cf.preparar(coluna_textos='texto_documento', limite_k=5)\n",
@@ -141,9 +299,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2025-04-21 22:59:39,975 - INFO - Iniciando agrupamento (clusterização) em 3 grupos para a rodada 1 (prefixo: 'cluster_').\n",
+      "2025-04-21 22:59:39,975 - INFO - Agrupando 8 textos em 3 grupos (K-Means)...\n",
+      "2025-04-21 22:59:39,975 - INFO - Agrupamento K-Means concluído. 8 textos foram atribuídos a grupos.\n",
+      "2025-04-21 22:59:39,983 - INFO - Coluna de resultados 'cluster_1' adicionada/atualizada.\n",
+      "2025-04-21 22:59:39,983 - INFO - Agrupamento da rodada 1 (prefixo 'cluster_') concluído com sucesso. Resultados na coluna 'cluster_1'.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Executando a clusterização com K=3...\n",
+      "Clusterização concluída. Coluna 'cluster_1' adicionada ao DataFrame.\n"
+     ]
+    }
+   ],
    "source": [
     "k_escolhido = 3\n",
     "print(f\"Executando a clusterização com K={k_escolhido}...\")\n",
@@ -162,9 +340,111 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "DataFrame com a coluna de cluster:\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>id</th>\n",
+       "      <th>cluster_1</th>\n",
+       "      <th>texto_documento</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>O juiz determinou o pagamento da indenização.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Recurso de apelação foi interposto pela defesa.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>A sentença julgou procedente o pedido do autor.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>A corte negou provimento ao recurso especial.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Processo arquivado por falta de movimentação.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>6</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Acordo homologado entre as partes.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>7</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Indenização por danos morais foi concedida.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>8</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Extinção do processo sem resolução de mérito.</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   id  cluster_1                                  texto_documento\n",
+       "0   1          2    O juiz determinou o pagamento da indenização.\n",
+       "1   2          2  Recurso de apelação foi interposto pela defesa.\n",
+       "2   3          0  A sentença julgou procedente o pedido do autor.\n",
+       "3   4          2    A corte negou provimento ao recurso especial.\n",
+       "4   5          1    Processo arquivado por falta de movimentação.\n",
+       "5   6          2               Acordo homologado entre as partes.\n",
+       "6   7          2      Indenização por danos morais foi concedida.\n",
+       "7   8          1    Extinção do processo sem resolução de mérito."
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "print(\"DataFrame com a coluna de cluster:\")\n",
     "display(cf.df[['id', nome_coluna_cluster, 'texto_documento']])"
@@ -181,9 +461,49 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2025-04-21 22:59:40,007 - INFO - Iniciando processo para salvar resultados (Opção: 'ambos', Prefixo: 'cluster_')...\n",
+      "2025-04-21 22:59:40,010 - INFO - Usando caminho padrão para o arquivo completo: .\\resultados_cluster_cluster_rodada1.csv\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Salvando os resultados...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2025-04-21 22:59:40,395 - INFO - Usando caminho padrão para o arquivo de amostras: .\\resultados_cluster_cluster_amostras_rodada1.xlsx\n",
+      "2025-04-21 22:59:40,395 - INFO - Salvando dados em 'resultados_cluster_cluster_rodada1.csv' (Formato: csv)...\n",
+      "2025-04-21 22:59:40,399 - INFO - Dados salvos com sucesso em: 'c:\\Users\\julio\\OneDrive\\Documentos\\insper\\cluster-facil\\examples\\resultados_cluster_cluster_rodada1.csv'.\n",
+      "2025-04-21 22:59:40,400 - INFO - Gerando e salvando arquivo de exemplos (até 10 por grupo) em 'resultados_cluster_cluster_amostras_rodada1.xlsx' (Formato: xlsx)...\n",
+      "2025-04-21 22:59:40,402 - INFO - Salvando dados em 'resultados_cluster_cluster_amostras_rodada1.xlsx' (Formato: xlsx)...\n",
+      "2025-04-21 22:59:40,415 - INFO - Dados salvos com sucesso em: 'c:\\Users\\julio\\OneDrive\\Documentos\\insper\\cluster-facil\\examples\\resultados_cluster_cluster_amostras_rodada1.xlsx'.\n",
+      "2025-04-21 22:59:40,417 - INFO - Processo de salvamento concluído. Status: {'tudo_salvo': True, 'caminho_tudo': 'c:\\\\Users\\\\julio\\\\OneDrive\\\\Documentos\\\\insper\\\\cluster-facil\\\\examples\\\\resultados_cluster_cluster_rodada1.csv', 'amostras_salvas': True, 'caminho_amostras': 'c:\\\\Users\\\\julio\\\\OneDrive\\\\Documentos\\\\insper\\\\cluster-facil\\\\examples\\\\resultados_cluster_cluster_amostras_rodada1.xlsx'}\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Status do salvamento:\n",
+      "{'tudo_salvo': True, 'caminho_tudo': 'c:\\\\Users\\\\julio\\\\OneDrive\\\\Documentos\\\\insper\\\\cluster-facil\\\\examples\\\\resultados_cluster_cluster_rodada1.csv', 'amostras_salvas': True, 'caminho_amostras': 'c:\\\\Users\\\\julio\\\\OneDrive\\\\Documentos\\\\insper\\\\cluster-facil\\\\examples\\\\resultados_cluster_cluster_amostras_rodada1.xlsx'}\n",
+      "- DataFrame completo salvo em: c:\\Users\\julio\\OneDrive\\Documentos\\insper\\cluster-facil\\examples\\resultados_cluster_cluster_rodada1.csv\n",
+      "- Amostras salvas em: c:\\Users\\julio\\OneDrive\\Documentos\\insper\\cluster-facil\\examples\\resultados_cluster_cluster_amostras_rodada1.xlsx\n"
+     ]
+    }
+   ],
    "source": [
     "print(\"Salvando os resultados...\")\n",
     "status_salvar = cf.salvar(\n",
@@ -217,9 +537,143 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2025-04-21 22:59:40,425 - INFO - Iniciando atribuição da classificação 'Procedente/Indenização' para o(s) grupo(s) [0, 2] da última rodada.\n",
+      "2025-04-21 22:59:40,427 - INFO - Coluna de classificação 'classificacao' não encontrada. Criando coluna para futuras classificações.\n",
+      "2025-04-21 22:59:40,427 - INFO - Aplicando classificação 'Procedente/Indenização' a 6 texto(s) na coluna 'classificacao' correspondente(s) ao(s) grupo(s) [0, 2] da coluna 'cluster_1'.\n",
+      "2025-04-21 22:59:40,427 - INFO - Atribuição de classificação concluída.\n",
+      "2025-04-21 22:59:40,427 - INFO - Iniciando atribuição da classificação 'Recurso/Extinção' para o(s) grupo(s) 1 da última rodada.\n",
+      "2025-04-21 22:59:40,436 - INFO - Aplicando classificação 'Recurso/Extinção' a 2 texto(s) na coluna 'classificacao' correspondente(s) ao(s) grupo(s) [1] da coluna 'cluster_1'.\n",
+      "2025-04-21 22:59:40,437 - INFO - Atribuição de classificação concluída.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "DataFrame com a coluna de classificação:\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>id</th>\n",
+       "      <th>cluster_1</th>\n",
+       "      <th>classificacao</th>\n",
+       "      <th>texto_documento</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Procedente/Indenização</td>\n",
+       "      <td>O juiz determinou o pagamento da indenização.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Procedente/Indenização</td>\n",
+       "      <td>Recurso de apelação foi interposto pela defesa.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Procedente/Indenização</td>\n",
+       "      <td>A sentença julgou procedente o pedido do autor.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Procedente/Indenização</td>\n",
+       "      <td>A corte negou provimento ao recurso especial.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Recurso/Extinção</td>\n",
+       "      <td>Processo arquivado por falta de movimentação.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>6</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Procedente/Indenização</td>\n",
+       "      <td>Acordo homologado entre as partes.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>7</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Procedente/Indenização</td>\n",
+       "      <td>Indenização por danos morais foi concedida.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>8</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Recurso/Extinção</td>\n",
+       "      <td>Extinção do processo sem resolução de mérito.</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   id  cluster_1           classificacao  \\\n",
+       "0   1          2  Procedente/Indenização   \n",
+       "1   2          2  Procedente/Indenização   \n",
+       "2   3          0  Procedente/Indenização   \n",
+       "3   4          2  Procedente/Indenização   \n",
+       "4   5          1        Recurso/Extinção   \n",
+       "5   6          2  Procedente/Indenização   \n",
+       "6   7          2  Procedente/Indenização   \n",
+       "7   8          1        Recurso/Extinção   \n",
+       "\n",
+       "                                   texto_documento  \n",
+       "0    O juiz determinou o pagamento da indenização.  \n",
+       "1  Recurso de apelação foi interposto pela defesa.  \n",
+       "2  A sentença julgou procedente o pedido do autor.  \n",
+       "3    A corte negou provimento ao recurso especial.  \n",
+       "4    Processo arquivado por falta de movimentação.  \n",
+       "5               Acordo homologado entre as partes.  \n",
+       "6      Indenização por danos morais foi concedida.  \n",
+       "7    Extinção do processo sem resolução de mérito.  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# Supondo que, após análise, o cluster 0 e 2 são 'Procedente/Indenização'\n",
     "# e o cluster 1 é 'Recurso/Extinção'\n",
@@ -231,6 +685,165 @@
     "display(cf.df[['id', nome_coluna_cluster, cf.nome_coluna_classificacao, 'texto_documento']])"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Também é possível anotar um cluster automaticamente com LLMs:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2025-04-21 23:01:45,683 - INFO - HTTP Request: POST https://api.openai.com/v1/responses \"HTTP/1.1 200 OK\"\n",
+      "2025-04-21 23:01:47,365 - INFO - HTTP Request: POST https://api.openai.com/v1/responses \"HTTP/1.1 200 OK\"\n",
+      "2025-04-21 23:01:48,702 - INFO - HTTP Request: POST https://api.openai.com/v1/responses \"HTTP/1.1 200 OK\"\n",
+      "2025-04-21 23:01:50,940 - INFO - HTTP Request: POST https://api.openai.com/v1/responses \"HTTP/1.1 200 OK\"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{2: 'Decisões e Procedimentos Judiciais', 0: 'Decisões Favoráveis ao Autor', 1: 'Arquivamento e Extinção de Processos'}\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>id</th>\n",
+       "      <th>texto_documento</th>\n",
+       "      <th>cluster_1</th>\n",
+       "      <th>classificacao</th>\n",
+       "      <th>cluster_1_classificacao</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>O juiz determinou o pagamento da indenização.</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Procedente/Indenização</td>\n",
+       "      <td>Decisões e Procedimentos Judiciais</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>Recurso de apelação foi interposto pela defesa.</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Procedente/Indenização</td>\n",
+       "      <td>Decisões e Procedimentos Judiciais</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>A sentença julgou procedente o pedido do autor.</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Procedente/Indenização</td>\n",
+       "      <td>Decisões Favoráveis ao Autor</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>A corte negou provimento ao recurso especial.</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Procedente/Indenização</td>\n",
+       "      <td>Decisões e Procedimentos Judiciais</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>Processo arquivado por falta de movimentação.</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Recurso/Extinção</td>\n",
+       "      <td>Arquivamento e Extinção de Processos</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>6</td>\n",
+       "      <td>Acordo homologado entre as partes.</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Procedente/Indenização</td>\n",
+       "      <td>Decisões e Procedimentos Judiciais</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>7</td>\n",
+       "      <td>Indenização por danos morais foi concedida.</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Procedente/Indenização</td>\n",
+       "      <td>Decisões e Procedimentos Judiciais</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>8</td>\n",
+       "      <td>Extinção do processo sem resolução de mérito.</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Recurso/Extinção</td>\n",
+       "      <td>Arquivamento e Extinção de Processos</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   id                                  texto_documento  cluster_1  \\\n",
+       "0   1    O juiz determinou o pagamento da indenização.          2   \n",
+       "1   2  Recurso de apelação foi interposto pela defesa.          2   \n",
+       "2   3  A sentença julgou procedente o pedido do autor.          0   \n",
+       "3   4    A corte negou provimento ao recurso especial.          2   \n",
+       "4   5    Processo arquivado por falta de movimentação.          1   \n",
+       "5   6               Acordo homologado entre as partes.          2   \n",
+       "6   7      Indenização por danos morais foi concedida.          2   \n",
+       "7   8    Extinção do processo sem resolução de mérito.          1   \n",
+       "\n",
+       "            classificacao               cluster_1_classificacao  \n",
+       "0  Procedente/Indenização    Decisões e Procedimentos Judiciais  \n",
+       "1  Procedente/Indenização    Decisões e Procedimentos Judiciais  \n",
+       "2  Procedente/Indenização          Decisões Favoráveis ao Autor  \n",
+       "3  Procedente/Indenização    Decisões e Procedimentos Judiciais  \n",
+       "4        Recurso/Extinção  Arquivamento e Extinção de Processos  \n",
+       "5  Procedente/Indenização    Decisões e Procedimentos Judiciais  \n",
+       "6  Procedente/Indenização    Decisões e Procedimentos Judiciais  \n",
+       "7        Recurso/Extinção  Arquivamento e Extinção de Processos  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Supondo que cf seja uma instância de ClusterFacil já clusterizada\n",
+    "rotulos = cf.auto_label_cluster()\n",
+    "print(rotulos)\n",
+    "display(cf.df)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -242,9 +855,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2025-04-21 21:54:10,595 - INFO - Classificações manuais únicas encontradas: ['Procedente/Indenização', 'Recurso/Extinção']\n",
+      "2025-04-21 21:54:10,602 - INFO - Contagem de textos por classificação manual na coluna 'classificacao':\n",
+      "classificacao\n",
+      "Procedente/Indenização    6\n",
+      "Recurso/Extinção          2\n",
+      "Name: count, dtype: Int64\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Classificações únicas: ['Procedente/Indenização', 'Recurso/Extinção']\n",
+      "\n",
+      "Contagem por classificação:\n",
+      "None\n"
+     ]
+    }
+   ],
    "source": [
     "print(\"Classificações únicas:\", cf.listar_classificacoes())\n",
     "print(\"\\nContagem por classificação:\")\n",
@@ -263,9 +899,106 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2025-04-21 21:54:16,635 - INFO - Criando um novo objeto ClusterFacil para analisar o subcluster da classificação: 'Procedente/Indenização'\n",
+      "2025-04-21 21:54:16,636 - INFO - Filtrando dados para criar o subcluster da classificação: 'Procedente/Indenização'\n",
+      "2025-04-21 21:54:16,637 - INFO - Subcluster criado com 6 textos correspondentes à classificação 'Procedente/Indenização'.\n",
+      "2025-04-21 21:54:16,637 - INFO - Colunas de resultados de agrupamentos anteriores removidas do subcluster: ['cluster_1']\n",
+      "2025-04-21 21:54:16,637 - INFO - Coluna de classificação original ('classificacao') renomeada para 'classificacao_origem' no subcluster.\n",
+      "2025-04-21 21:54:16,637 - INFO - ClusterFacil iniciado com um DataFrame já existente.\n",
+      "2025-04-21 21:54:16,637 - INFO - Próxima rodada de clusterização definida como: 1\n",
+      "2025-04-21 21:54:16,637 - INFO - Novo objeto ClusterFacil para o subcluster criado e configurado.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Criando subcluster para 'Procedente/Indenização'...\n",
+      "Nova instância 'sub_cf' criada com 6 linhas.\n",
+      "DataFrame do subcluster (observe a coluna 'classificacao_origem'):\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>id</th>\n",
+       "      <th>texto_documento</th>\n",
+       "      <th>classificacao_origem</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>O juiz determinou o pagamento da indenização.</td>\n",
+       "      <td>Procedente/Indenização</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>Recurso de apelação foi interposto pela defesa.</td>\n",
+       "      <td>Procedente/Indenização</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>A sentença julgou procedente o pedido do autor.</td>\n",
+       "      <td>Procedente/Indenização</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>A corte negou provimento ao recurso especial.</td>\n",
+       "      <td>Procedente/Indenização</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>6</td>\n",
+       "      <td>Acordo homologado entre as partes.</td>\n",
+       "      <td>Procedente/Indenização</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   id                                  texto_documento    classificacao_origem\n",
+       "0   1    O juiz determinou o pagamento da indenização.  Procedente/Indenização\n",
+       "1   2  Recurso de apelação foi interposto pela defesa.  Procedente/Indenização\n",
+       "2   3  A sentença julgou procedente o pedido do autor.  Procedente/Indenização\n",
+       "3   4    A corte negou provimento ao recurso especial.  Procedente/Indenização\n",
+       "5   6               Acordo homologado entre as partes.  Procedente/Indenização"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "classificacao_alvo = 'Procedente/Indenização'\n",
     "print(f\"Criando subcluster para '{classificacao_alvo}'...\")\n",
@@ -287,9 +1020,150 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2025-04-21 21:54:20,544 - INFO - Iniciando preparação dos textos para agrupamento (rodada 1). Coluna de texto: 'texto_documento', limite de grupos para teste: 3.\n",
+      "2025-04-21 21:54:20,546 - INFO - Analisando características de 6 textos (TF-IDF)...\n",
+      "2025-04-21 21:54:20,547 - INFO - Análise TF-IDF concluída. 6 textos processados, 23 características (palavras/termos) identificadas.\n",
+      "2025-04-21 21:54:20,548 - INFO - Avaliando diferentes números de grupos (método do cotovelo)...\n",
+      "2025-04-21 21:54:20,548 - INFO - Calculando opções de agrupamento (inércias) para K de 1 a 3...\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Preparando o subcluster...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Avaliando número de grupos (K): 100%|██████████| 3/3 [00:00<00:00, 43.76K/s]\n",
+      "2025-04-21 21:54:20,620 - INFO - Cálculo das opções de agrupamento (inércias) concluído.\n",
+      "2025-04-21 21:54:20,621 - WARNING - O gráfico do cotovelo pode estar incompleto devido a erros no cálculo de alguns valores de K (exibindo 2 pontos).\n",
+      "2025-04-21 21:54:20,622 - INFO - Gerando gráfico do cotovelo para visualização...\n",
+      "2025-04-21 21:54:20,637 - INFO - Exibindo gráfico do cotovelo...\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2025-04-21 21:54:20,744 - INFO - Preparação da rodada 1 concluída. Analise o gráfico do cotovelo (baseado em 6 textos) para escolher o número de grupos (K).\n",
+      "2025-04-21 21:54:20,744 - INFO - Iniciando agrupamento (clusterização) em 2 grupos para a rodada 1 (prefixo: 'subcluster_').\n",
+      "2025-04-21 21:54:20,746 - INFO - Agrupando 6 textos em 2 grupos (K-Means)...\n",
+      "2025-04-21 21:54:20,749 - INFO - Agrupamento K-Means concluído. 6 textos foram atribuídos a grupos.\n",
+      "2025-04-21 21:54:20,749 - INFO - Coluna de resultados 'subcluster_1' adicionada/atualizada.\n",
+      "2025-04-21 21:54:20,752 - INFO - Agrupamento da rodada 1 (prefixo 'subcluster_') concluído com sucesso. Resultados na coluna 'subcluster_1'.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Clusterizando o subcluster com K=2...\n",
+      "Subclusterização concluída. Coluna 'subcluster_1' adicionada ao DataFrame 'sub_cf.df'.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>id</th>\n",
+       "      <th>subcluster_1</th>\n",
+       "      <th>texto_documento</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>O juiz determinou o pagamento da indenização.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Recurso de apelação foi interposto pela defesa.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>A sentença julgou procedente o pedido do autor.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>A corte negou provimento ao recurso especial.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>6</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Acordo homologado entre as partes.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>7</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Indenização por danos morais foi concedida.</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   id  subcluster_1                                  texto_documento\n",
+       "0   1             1    O juiz determinou o pagamento da indenização.\n",
+       "1   2             0  Recurso de apelação foi interposto pela defesa.\n",
+       "2   3             0  A sentença julgou procedente o pedido do autor.\n",
+       "3   4             0    A corte negou provimento ao recurso especial.\n",
+       "5   6             0               Acordo homologado entre as partes.\n",
+       "6   7             1      Indenização por danos morais foi concedida."
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "print(\"Preparando o subcluster...\")\n",
     "# Usamos a mesma coluna de texto original. limite_k pode ser menor.\n",
@@ -318,9 +1192,187 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Estado do DataFrame 'cf' ANTES do reset:\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>id</th>\n",
+       "      <th>texto_documento</th>\n",
+       "      <th>cluster_1</th>\n",
+       "      <th>classificacao</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>O juiz determinou o pagamento da indenização.</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Procedente/Indenização</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>Recurso de apelação foi interposto pela defesa.</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Procedente/Indenização</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>A sentença julgou procedente o pedido do autor.</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Procedente/Indenização</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>A corte negou provimento ao recurso especial.</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Procedente/Indenização</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>Processo arquivado por falta de movimentação.</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Recurso/Extinção</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   id                                  texto_documento  cluster_1  \\\n",
+       "0   1    O juiz determinou o pagamento da indenização.          2   \n",
+       "1   2  Recurso de apelação foi interposto pela defesa.          2   \n",
+       "2   3  A sentença julgou procedente o pedido do autor.          0   \n",
+       "3   4    A corte negou provimento ao recurso especial.          2   \n",
+       "4   5    Processo arquivado por falta de movimentação.          1   \n",
+       "\n",
+       "            classificacao  \n",
+       "0  Procedente/Indenização  \n",
+       "1  Procedente/Indenização  \n",
+       "2  Procedente/Indenização  \n",
+       "3  Procedente/Indenização  \n",
+       "4        Recurso/Extinção  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2025-04-21 21:54:30,224 - WARNING - Iniciando reinicialização do estado do ClusterFacil (Prefixo: 'cluster_', Coluna Classificação: 'classificacao')...\n",
+      "2025-04-21 21:54:30,226 - INFO - Colunas de resultados anteriores removidas: ['cluster_1', 'classificacao']\n",
+      "2025-04-21 21:54:30,226 - INFO - Estado do ClusterFacil reiniciado. Rodada definida para 1. Chame 'preparar' novamente para começar uma nova análise.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Estado do DataFrame 'cf' DEPOIS do reset (colunas de cluster/classificação removidas):\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>id</th>\n",
+       "      <th>texto_documento</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>O juiz determinou o pagamento da indenização.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>Recurso de apelação foi interposto pela defesa.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>A sentença julgou procedente o pedido do autor.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>A corte negou provimento ao recurso especial.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>Processo arquivado por falta de movimentação.</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   id                                  texto_documento\n",
+       "0   1    O juiz determinou o pagamento da indenização.\n",
+       "1   2  Recurso de apelação foi interposto pela defesa.\n",
+       "2   3  A sentença julgou procedente o pedido do autor.\n",
+       "3   4    A corte negou provimento ao recurso especial.\n",
+       "4   5    Processo arquivado por falta de movimentação."
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "print(\"Estado do DataFrame 'cf' ANTES do reset:\")\n",
     "display(cf.df.head())\n",
@@ -343,7 +1395,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": ".venv",
    "language": "python",
    "name": "python3"
   },
@@ -357,7 +1409,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.7"
+   "version": "3.12.5"
   }
  },
  "nbformat": 4,
diff --git a/pyproject.toml b/pyproject.toml
index e55688a..08958cd 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -11,7 +11,7 @@ authors = [
 description = "Uma biblioteca Python intuitiva para realizar clusterização de documentos textuais. Simplifica o processo desde a preparação dos dados e análise do número ideal de clusters (método do cotovelo) até a aplicação do algoritmo e exportação dos resultados. Ideal para agrupar grandes volumes de texto, como decisões judiciais, artigos ou comentários, de forma eficiente e com poucas linhas de código."
 readme = "README.md"
 license = { text = "MIT" }
-requires-python = ">=3.8"
+requires-python = ">=3.10"
 classifiers = [
     "Programming Language :: Python :: 3",
     "License :: OSI Approved :: MIT License",
@@ -29,8 +29,10 @@ dependencies = [
     "matplotlib==3.10.1",
     "scipy==1.15.2",
     "openpyxl==3.1.5",
-    "pyarrow==19.0.1",
-    "tqdm" # Adicionado para barra de progresso
+    "pyarrow==19.0.1", # Adicionado para barra de progresso
+    "tqdm",
+    "openai>=1.75.0",
+    "python-dotenv>=1.1.0",
 ]
 
 [project.urls]
diff --git a/src/cluster_facil/cluster.py b/src/cluster_facil/cluster.py
index 325f10d..0a66bdd 100644
--- a/src/cluster_facil/cluster.py
+++ b/src/cluster_facil/cluster.py
@@ -30,6 +30,7 @@
     validar_tipo_classificacao,
     validar_opcao_salvar
 )
+from .utils_auto_label import gerar_rotulo_cluster, refinar_rotulos_clusters
 
 logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
 
@@ -255,7 +256,7 @@ def preparar(self, coluna_textos: str, limite_k: int = 10, n_init: str | int = '
 
         logging.info(f"Analisando características de {len(df_para_preparar)} textos (TF-IDF)...")
         # Define parâmetros padrão que podem ser sobrescritos pelos kwargs
-        default_tfidf_params = {'stop_words': STOPWORDS_PT}
+        default_tfidf_params = {'stop_words': list(STOPWORDS_PT)}
         final_tfidf_kwargs = {**default_tfidf_params, **self._tfidf_kwargs}
         logging.debug(f"Parâmetros finais para TfidfVectorizer: {final_tfidf_kwargs}") # Movido para DEBUG
 
@@ -594,7 +595,6 @@ def subcluster(self, classificacao_desejada: str) -> Self:
         Raises:
             KeyError: Se a coluna de classificação original não for encontrada.
             ValueError: Se a `classificacao_desejada` não existir na coluna de classificação.
-            RuntimeError: Se `preparar` não foi chamado na instância original (necessário para saber qual era a coluna de texto).
         """
         logging.info(f"Criando um novo objeto ClusterFacil para analisar o subcluster da classificação: '{classificacao_desejada}'")
 
@@ -680,3 +680,77 @@ def contar_classificacoes(self, inclui_na=False) -> pd.Series:
         contagem = self.df[self.nome_coluna_classificacao].value_counts(dropna=inclui_na)
         logging.info(f"Contagem de textos por classificação manual na coluna '{self.nome_coluna_classificacao}':\n{contagem}")
         return None
+
+    def auto_label_cluster(self, rodada: int = None, model: str = "gpt-4.1-nano", api_key: str = None, temperature: float = 0.0, cut_limit: int = 30, random_state: int = None, final_refine: bool = True, n_examples_final: int = 10) -> dict:
+        """
+        Gera rótulos automáticos para todos os clusters de uma rodada e adiciona uma coluna com esses rótulos ao DataFrame.
+        Após a rotulação inicial, faz uma passada final no LLM para sugerir nomes finais (unificando ou ajustando rótulos se necessário).
+
+        Args:
+            rodada (int, opcional): Número da rodada de clusterização (se None, usa a última rodada).
+            model (str): Nome do modelo OpenAI (default: 'gpt-4.1-nano').
+            api_key (str, opcional): Chave da API OpenAI. Se não fornecida, busca em OPENAI_API_KEY.
+            temperature (float): Temperatura do modelo.
+            cut_limit (int, opcional): Número máximo de textos a serem enviados para o LLM por cluster. Se None, usa todos. Default=30.
+            random_state (int, opcional): Semente para amostragem aleatória dos textos. Default=None (não controla aleatoriedade).
+            final_refine (bool): Se True, faz uma passada final no LLM para refinar/unificar rótulos. Default=True.
+            n_examples_final (int): Número de exemplos de texto por cluster enviados na revisão final. Default=10.
+        Returns:
+            dict: Dicionário mapeando cluster_id para rótulo final gerado.
+        """
+        import json
+        if rodada is None:
+            rodada = self.rodada_clusterizacao - 1
+        coluna_cluster = f"{self.prefixo_cluster}{rodada}"
+        if coluna_cluster not in self.df.columns:
+            raise ValueError(f"Coluna de cluster '{coluna_cluster}' não encontrada no DataFrame.")
+        cluster_ids = self.df[coluna_cluster].dropna().unique()
+        cluster_labels = {}
+        cluster_samples = {}
+        for cluster_id in cluster_ids:
+            textos_do_cluster = self.df.loc[self.df[coluna_cluster] == cluster_id, self.coluna_textos].astype(str).tolist()
+            if not textos_do_cluster:
+                cluster_labels[cluster_id] = None
+                continue
+            # Amostragem se necessário
+            if cut_limit is not None and len(textos_do_cluster) > cut_limit:
+                import random
+                rnd = random.Random(random_state) if random_state is not None else random
+                textos_amostrados = rnd.sample(textos_do_cluster, cut_limit)
+            else:
+                textos_amostrados = textos_do_cluster
+            label = gerar_rotulo_cluster(
+                textos_amostrados,
+                model=model,
+                api_key=api_key,
+                temperature=temperature
+            )
+            cluster_labels[cluster_id] = label
+            # Amostras para revisão final
+            if n_examples_final is not None and len(textos_do_cluster) > n_examples_final:
+                import random
+                rnd = random.Random(random_state) if random_state is not None else random
+                examples = rnd.sample(textos_do_cluster, n_examples_final)
+            else:
+                examples = textos_do_cluster[:n_examples_final]
+            cluster_samples[cluster_id] = {"label": label, "examples": examples}
+        coluna_rotulo = f"{coluna_cluster}_classificacao"
+        self.df[coluna_rotulo] = self.df[coluna_cluster].map(cluster_labels)
+
+        # Passada final de refinamento global
+        if final_refine:
+            refined_labels_struct = refinar_rotulos_clusters(
+                cluster_samples=cluster_samples,
+                model=model,
+                api_key=api_key,
+                temperature=temperature
+            )
+            # Novo formato: {'clusters': [{'cluster_id': int, 'label': str}, ...]}
+            if isinstance(refined_labels_struct, dict) and "clusters" in refined_labels_struct:
+                refined_labels = {item["cluster_id"]: item["label"] for item in refined_labels_struct["clusters"]}
+            else:
+                refined_labels = refined_labels_struct
+            # Atualiza coluna de rótulos finais
+            self.df[coluna_rotulo] = self.df[coluna_cluster].map(refined_labels)
+            return refined_labels
+        return cluster_labels
diff --git a/src/cluster_facil/utils_auto_label.py b/src/cluster_facil/utils_auto_label.py
new file mode 100644
index 0000000..9ac8bb6
--- /dev/null
+++ b/src/cluster_facil/utils_auto_label.py
@@ -0,0 +1,145 @@
+"""
+Funções utilitárias para rotulação automática de clusters usando modelos de linguagem (ex: GPT-4.1-nano via OpenAI API).
+"""
+import os
+from typing import List, Optional, Dict
+from openai import OpenAI
+import json
+
+def gerar_rotulo_cluster(
+    cluster_texts: List[str],
+    model: str = "gpt-4.1-nano",
+    api_key: Optional[str] = None,
+    temperature: float = 0.0,
+) -> str:
+    """
+    Gera um rótulo (nome/tema) para um cluster de textos usando a OpenAI Responses API.
+    Args:
+        cluster_texts (List[str]): Lista de textos pertencentes ao cluster.
+        model (str): Nome do modelo OpenAI a ser utilizado. Padrão: 'gpt-4.1-nano'.
+        api_key (Optional[str]): Chave da API OpenAI. Se não fornecida, busca em OPENAI_API_KEY.
+        temperature (float): Temperatura do modelo (criatividade).
+    Returns:
+        str: Rótulo sugerido para o cluster.
+    """
+    if not api_key:
+        api_key = os.getenv("OPENAI_API_KEY")
+    if not api_key:
+        raise ValueError("É necessário fornecer uma chave de API OpenAI via argumento ou variável de ambiente OPENAI_API_KEY.")
+
+    client_kwargs = {"api_key": api_key}
+    client = OpenAI(**client_kwargs)
+
+    sample_texts = cluster_texts[:10]
+    # Delimita cada texto com <sampleN>...</sampleN>
+    joined_texts = "\n\n".join(f"<sample{i+1}>\n{t}\n</sample{i+1}>" for i, t in enumerate(sample_texts))
+    prompt = (
+        "Dado o seguinte conjunto de textos, gere um rótulo curto (tema) que represente o cluster. "
+        "O rótulo deve ser claro, conciso e descritivo."
+    )
+    # Utiliza a Responses API mais recente com formato estruturado
+    resp_format = {
+        "format": {
+            "type": "json_schema",
+            "name": "rotulo",
+            "schema": {
+                "type": "object",
+                "properties": {
+                    "rotulo": {
+                        "type": "string",
+                        "description": "Rótulo curto, claro e descritivo para o cluster de textos."
+                    }
+                },
+                "required": ["rotulo"],
+                "additionalProperties": False
+            },
+            "strict": True
+        }
+    }
+    response = client.responses.create(
+        model=model,
+        instructions=prompt,
+        temperature=temperature,
+        input=f"Textos do cluster:\n{joined_texts}\n\nRótulo:",
+        text=resp_format
+    )
+    label_obj = json.loads(response.output_text)
+    return label_obj["rotulo"]
+
+def refinar_rotulos_clusters(
+    cluster_samples: Dict,
+    model: str = "gpt-4.1-nano",
+    api_key: str = None,
+    temperature: float = 0.0,
+) -> Dict:
+    """
+    Usa o LLM para revisar, padronizar e possivelmente agrupar rótulos de clusters, a partir de amostras e rótulos iniciais.
+    Args:
+        cluster_samples (dict): Dict {cluster_id: {"label": rótulo, "examples": [str, ...]}}
+        model (str): Nome do modelo OpenAI.
+        api_key (str): Chave da API OpenAI.
+        temperature (float): Temperatura do modelo.
+    Returns:
+        dict: Dicionário {cluster_id: rótulo_final}
+    """
+    if not api_key:
+        api_key = os.getenv("OPENAI_API_KEY")
+    if not api_key:
+        raise ValueError("É necessário fornecer uma chave de API OpenAI via argumento ou variável de ambiente OPENAI_API_KEY.")
+
+    client_kwargs = {"api_key": api_key}
+    client = OpenAI(**client_kwargs)
+
+    # Monta prompt e input estruturado
+    prompt = (
+        "Você receberá exemplos de clusters, cada um com um rótulo sugerido e algumas amostras de textos.\n"
+        "Sua tarefa é:\n"
+        "- Unificar rótulos semelhantes se fizer sentido,\n"
+        "- Sugerir nomes mais claros e concisos para cada grupo,\n"
+        "- Retornar um dicionário JSON com o id do cluster e o novo rótulo.\n\n"
+        "Exemplo de entrada:\n"
+        "Cluster 0 - Rótulo inicial: 'Esportes'\n<sample1>Texto exemplo</sample1>\n<sample2>Texto exemplo</sample2>\n\n"
+        "Cluster 1 - Rótulo inicial: 'Futebol'\n<sample1>Texto exemplo</sample1>\n<sample2>Texto exemplo</sample2>\n\n"
+        "Agora, siga o mesmo padrão para os clusters abaixo:\n"
+    )
+    clusters_str = ""
+    for cid, info in cluster_samples.items():
+        label = info["label"]
+        examples = "\n\n".join(f"<sample{i+1}>\n{t}\n</sample{i+1}>" for i, t in enumerate(info["examples"]))
+        clusters_str += f"Cluster {cid} - Rótulo inicial: '{label}'\n{examples}\n\n"
+    input_text = clusters_str + "Retorne APENAS um JSON no formato: {\"cluster_id\": \"novo rótulo\", ...}"
+    resp_format = {
+        "format": {
+            "type": "json_schema",
+            "name": "refino_rotulos",
+            "schema": {
+                "type": "object",
+                "properties": {
+                    "clusters": {
+                        "type": "array",
+                        "items": {
+                            "type": "object",
+                            "properties": {
+                                "cluster_id": {"type": "integer"},
+                                "label": {"type": "string"}
+                            },
+                            "required": ["cluster_id", "label"],
+                            "additionalProperties": False
+                        }
+                    }
+                },
+                "required": ["clusters"],
+                "additionalProperties": False
+            },
+            "strict": True
+        }
+    }
+    response = client.responses.create(
+        model=model,
+        instructions=prompt,
+        temperature=temperature,
+        input=input_text,
+        text=resp_format
+    )
+    refined_labels = json.loads(response.output_text)
+    return refined_labels
diff --git a/uv.lock b/uv.lock
new file mode 100644
index 0000000..f5e41b5
--- /dev/null
+++ b/uv.lock
@@ -0,0 +1,1147 @@
+version = 1
+revision = 1
+requires-python = ">=3.10"
+resolution-markers = [
+    "python_full_version >= '3.12'",
+    "python_full_version == '3.11.*'",
+    "python_full_version < '3.11'",
+]
+
+[[package]]
+name = "annotated-types"
+version = "0.7.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/ee/67/531ea369ba64dcff5ec9c3402f9f51bf748cec26dde048a2f973a4eea7f5/annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89", size = 16081 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/78/b6/6307fbef88d9b5ee7421e68d78a9f162e0da4900bc5f5793f6d3d0e34fb8/annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53", size = 13643 },
+]
+
+[[package]]
+name = "anyio"
+version = "4.9.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "exceptiongroup", marker = "python_full_version < '3.11'" },
+    { name = "idna" },
+    { name = "sniffio" },
+    { name = "typing-extensions", marker = "python_full_version < '3.13'" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/95/7d/4c1bd541d4dffa1b52bd83fb8527089e097a106fc90b467a7313b105f840/anyio-4.9.0.tar.gz", hash = "sha256:673c0c244e15788651a4ff38710fea9675823028a6f08a5eda409e0c9840a028", size = 190949 }
+wheels = [
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+]
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+[[package]]
+name = "certifi"
+version = "2025.1.31"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/1c/ab/c9f1e32b7b1bf505bf26f0ef697775960db7932abeb7b516de930ba2705f/certifi-2025.1.31.tar.gz", hash = "sha256:3d5da6925056f6f18f119200434a4780a94263f10d1c21d032a6f6b2baa20651", size = 167577 }
+wheels = [
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+]
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+[[package]]
+name = "click"
+version = "8.1.8"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "colorama", marker = "sys_platform == 'win32'" },
+]
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+wheels = [
+    { url = "https://files.pythonhosted.org/packages/7e/d4/7ebdbd03970677812aac39c869717059dbb71a4cfc033ca6e5221787892c/click-8.1.8-py3-none-any.whl", hash = "sha256:63c132bbbed01578a06712a2d1f497bb62d9c1c0d329b7903a866228027263b2", size = 98188 },
+]
+
+[[package]]
+name = "cluster-facil"
+version = "0.1.0"
+source = { editable = "." }
+dependencies = [
+    { name = "matplotlib" },
+    { name = "nltk" },
+    { name = "openai" },
+    { name = "openpyxl" },
+    { name = "pandas" },
+    { name = "pyarrow" },
+    { name = "python-dotenv" },
+    { name = "scikit-learn" },
+    { name = "scipy" },
+    { name = "tqdm" },
+]
+
+[package.metadata]
+requires-dist = [
+    { name = "matplotlib", specifier = "==3.10.1" },
+    { name = "nltk", specifier = "==3.9.1" },
+    { name = "openai", specifier = ">=1.75.0" },
+    { name = "openpyxl", specifier = "==3.1.5" },
+    { name = "pandas", specifier = "==2.2.3" },
+    { name = "pyarrow", specifier = "==19.0.1" },
+    { name = "python-dotenv", specifier = ">=1.1.0" },
+    { name = "scikit-learn", specifier = "==1.6.1" },
+    { name = "scipy", specifier = "==1.15.2" },
+    { name = "tqdm" },
+]
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+source = { registry = "https://pypi.org/simple" }
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