05_sqlite-openai-vanna-vannadb.ipynb

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    "# Generating SQL for SQLite using OpenAI via Vanna.AI (Recommended), Vanna Hosted Vector DB (Recommended)\n",
    "This notebook runs through the process of using the `vanna` Python package to generate SQL using AI (RAG + LLMs) including connecting to a database and training. If you're not ready to train on your own database, you can still try it using a sample [SQLite database](app.md)."
   ]
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   "source": [
    "\n",
    "<h3 class=\"mb-5 text-lg font-medium text-gray-900 dark:text-white\">Which LLM do you want to use?</h3>\n",
    "<ul class=\"grid w-full gap-6 md:grid-cols-2\">\n",
    "    \n",
    "  <li>\n",
    "    <span class=\"inline-flex w-full cursor-pointer items-center justify-between rounded-lg border bg-white p-5 border-blue-600 text-blue-600 dark:bg-gray-800 dark:text-blue-500\">\n",
    "      <div class=\"block\">\n",
    "        <div class=\"w-full text-lg font-semibold\"><span class=\"hidden\">[Selected]</span> OpenAI via Vanna.AI (Recommended)</div>\n",
    "        <small class=\"w-full\">Use Vanna.AI for free to generate your queries</small>\n",
    "      </div>\n",
    "    </span>\n",
    "  </li>\n",
    "  \n",
    "  <li>\n",
    "    <a href=\"../sqlite-openai-standard-vannadb/\" class=\"inline-flex w-full cursor-pointer items-center justify-between rounded-lg border border-gray-200 bg-white p-5 text-gray-500 hover:bg-gray-100 hover:text-gray-600 peer-checked:border-blue-600 peer-checked:text-blue-600 dark:border-gray-700 dark:bg-gray-800 dark:text-gray-400 dark:hover:bg-gray-700 dark:hover:text-gray-300 dark:peer-checked:text-blue-500\">\n",
    "      <div class=\"block\">\n",
    "        <div class=\"w-full text-lg font-semibold\">OpenAI</div>\n",
    "        <small class=\"w-full\">Use OpenAI with your own API key</small>\n",
    "      </div>\n",
    "    </a>\n",
    "  </li>\n",
    "    \n",
    "  <li>\n",
    "    <a href=\"../sqlite-openai-azure-vannadb/\" class=\"inline-flex w-full cursor-pointer items-center justify-between rounded-lg border border-gray-200 bg-white p-5 text-gray-500 hover:bg-gray-100 hover:text-gray-600 peer-checked:border-blue-600 peer-checked:text-blue-600 dark:border-gray-700 dark:bg-gray-800 dark:text-gray-400 dark:hover:bg-gray-700 dark:hover:text-gray-300 dark:peer-checked:text-blue-500\">\n",
    "      <div class=\"block\">\n",
    "        <div class=\"w-full text-lg font-semibold\">Azure OpenAI</div>\n",
    "        <small class=\"w-full\">If you have OpenAI models deployed on Azure</small>\n",
    "      </div>\n",
    "    </a>\n",
    "  </li>\n",
    "    \n",
    "  <li>\n",
    "    <a href=\"../sqlite-ollama-vannadb/\" class=\"inline-flex w-full cursor-pointer items-center justify-between rounded-lg border border-gray-200 bg-white p-5 text-gray-500 hover:bg-gray-100 hover:text-gray-600 peer-checked:border-blue-600 peer-checked:text-blue-600 dark:border-gray-700 dark:bg-gray-800 dark:text-gray-400 dark:hover:bg-gray-700 dark:hover:text-gray-300 dark:peer-checked:text-blue-500\">\n",
    "      <div class=\"block\">\n",
    "        <div class=\"w-full text-lg font-semibold\">Ollama</div>\n",
    "        <small class=\"w-full\">Use Ollama locally for free. Requires additional setup.</small>\n",
    "      </div>\n",
    "    </a>\n",
    "  </li>\n",
    "    \n",
    "  <li>\n",
    "    <a href=\"../sqlite-mistral-vannadb/\" class=\"inline-flex w-full cursor-pointer items-center justify-between rounded-lg border border-gray-200 bg-white p-5 text-gray-500 hover:bg-gray-100 hover:text-gray-600 peer-checked:border-blue-600 peer-checked:text-blue-600 dark:border-gray-700 dark:bg-gray-800 dark:text-gray-400 dark:hover:bg-gray-700 dark:hover:text-gray-300 dark:peer-checked:text-blue-500\">\n",
    "      <div class=\"block\">\n",
    "        <div class=\"w-full text-lg font-semibold\">Mistral via Mistral API</div>\n",
    "        <small class=\"w-full\">If you have a Mistral API key</small>\n",
    "      </div>\n",
    "    </a>\n",
    "  </li>\n",
    "    \n",
    "  <li>\n",
    "    <a href=\"../sqlite-other-llm-vannadb/\" class=\"inline-flex w-full cursor-pointer items-center justify-between rounded-lg border border-gray-200 bg-white p-5 text-gray-500 hover:bg-gray-100 hover:text-gray-600 peer-checked:border-blue-600 peer-checked:text-blue-600 dark:border-gray-700 dark:bg-gray-800 dark:text-gray-400 dark:hover:bg-gray-700 dark:hover:text-gray-300 dark:peer-checked:text-blue-500\">\n",
    "      <div class=\"block\">\n",
    "        <div class=\"w-full text-lg font-semibold\">Other LLM</div>\n",
    "        <small class=\"w-full\">If you have a different LLM model</small>\n",
    "      </div>\n",
    "    </a>\n",
    "  </li>\n",
    "    \n",
    "</ul>\n",
    "    "
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    "\n",
    "<h3 class=\"mb-5 text-lg font-medium text-gray-900 dark:text-white\">Where do you want to store the 'training' data?</h3>\n",
    "<ul class=\"grid w-full gap-6 md:grid-cols-2\">\n",
    "    \n",
    "  <li>\n",
    "    <span class=\"inline-flex w-full cursor-pointer items-center justify-between rounded-lg border bg-white p-5 border-blue-600 text-blue-600 dark:bg-gray-800 dark:text-blue-500\">\n",
    "      <div class=\"block\">\n",
    "        <div class=\"w-full text-lg font-semibold\"><span class=\"hidden\">[Selected]</span> Vanna Hosted Vector DB (Recommended)</div>\n",
    "        <small class=\"w-full\">Use Vanna.AIs hosted vector database (pgvector) for free. This is usable across machines with no additional setup.</small>\n",
    "      </div>\n",
    "    </span>\n",
    "  </li>\n",
    "  \n",
    "  <li>\n",
    "    <a href=\"../sqlite-openai-standard-chromadb/\" class=\"inline-flex w-full cursor-pointer items-center justify-between rounded-lg border border-gray-200 bg-white p-5 text-gray-500 hover:bg-gray-100 hover:text-gray-600 peer-checked:border-blue-600 peer-checked:text-blue-600 dark:border-gray-700 dark:bg-gray-800 dark:text-gray-400 dark:hover:bg-gray-700 dark:hover:text-gray-300 dark:peer-checked:text-blue-500\">\n",
    "      <div class=\"block\">\n",
    "        <div class=\"w-full text-lg font-semibold\">ChromaDB</div>\n",
    "        <small class=\"w-full\">Use ChromaDBs open-source vector database for free locally. No additional setup is necessary -- all database files will be created and stored locally.</small>\n",
    "      </div>\n",
    "    </a>\n",
    "  </li>\n",
    "    \n",
    "  <li>\n",
    "    <a href=\"../sqlite-openai-standard-marqo/\" class=\"inline-flex w-full cursor-pointer items-center justify-between rounded-lg border border-gray-200 bg-white p-5 text-gray-500 hover:bg-gray-100 hover:text-gray-600 peer-checked:border-blue-600 peer-checked:text-blue-600 dark:border-gray-700 dark:bg-gray-800 dark:text-gray-400 dark:hover:bg-gray-700 dark:hover:text-gray-300 dark:peer-checked:text-blue-500\">\n",
    "      <div class=\"block\">\n",
    "        <div class=\"w-full text-lg font-semibold\">Marqo</div>\n",
    "        <small class=\"w-full\">Use Marqo locally for free. Requires additional setup. Or use their hosted option.</small>\n",
    "      </div>\n",
    "    </a>\n",
    "  </li>\n",
    "    \n",
    "  <li>\n",
    "    <a href=\"../sqlite-openai-standard-other-vectordb/\" class=\"inline-flex w-full cursor-pointer items-center justify-between rounded-lg border border-gray-200 bg-white p-5 text-gray-500 hover:bg-gray-100 hover:text-gray-600 peer-checked:border-blue-600 peer-checked:text-blue-600 dark:border-gray-700 dark:bg-gray-800 dark:text-gray-400 dark:hover:bg-gray-700 dark:hover:text-gray-300 dark:peer-checked:text-blue-500\">\n",
    "      <div class=\"block\">\n",
    "        <div class=\"w-full text-lg font-semibold\">Other VectorDB</div>\n",
    "        <small class=\"w-full\">Use any other vector database. Requires additional setup.</small>\n",
    "      </div>\n",
    "    </a>\n",
    "  </li>\n",
    "    \n",
    "</ul>\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee059407-58ac-50fa-843a-7b876328df13",
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   "source": [
    "## Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b9b77362-c049-5500-b502-08811fcd4dce",
   "metadata": {},
   "outputs": [],
   "source": [
    "%pip install vanna"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6160c274-caf4-537e-9a02-f6a1d7022a2c",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import vanna\n",
    "from vanna.remote import VannaDefault"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7cd78528-b0b0-5428-901c-6b5dc2158ef9",
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   "source": [
    "api_key = # Your API key from https://vanna.ai/account/profile \n",
    "\n",
    "vanna_model_name = # Your model name from https://vanna.ai/account/profile \n",
    "vn = VannaDefault(model=vanna_model_name, api_key=api_key)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "fa676dd4-dd64-4673-b42c-8d2241c304bf",
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   "source": [
    "api_key = ''\n",
    "\n",
    "vanna_model_name = 'vanna_test_model'\n",
    "vn = VannaDefault(model=vanna_model_name, api_key=api_key)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b69b91bc-5cf5-5549-b01a-1c5c263ebd00",
   "metadata": {},
   "source": [
    "\n",
    "<h3 class=\"mb-5 text-lg font-medium text-gray-900 dark:text-white\">Which database do you want to query?</h3>\n",
    "<ul class=\"grid w-full gap-6 md:grid-cols-2\">\n",
    "    \n",
    "  <li>\n",
    "    <a href=\"../postgres-openai-vanna-vannadb/\" class=\"inline-flex w-full cursor-pointer items-center justify-between rounded-lg border border-gray-200 bg-white p-5 text-gray-500 hover:bg-gray-100 hover:text-gray-600 peer-checked:border-blue-600 peer-checked:text-blue-600 dark:border-gray-700 dark:bg-gray-800 dark:text-gray-400 dark:hover:bg-gray-700 dark:hover:text-gray-300 dark:peer-checked:text-blue-500\">\n",
    "      <div class=\"block\">\n",
    "        <div class=\"w-full text-lg font-semibold\">Postgres</div>\n",
    "        <small class=\"w-full\"></small>\n",
    "      </div>\n",
    "    </a>\n",
    "  </li>\n",
    "    \n",
    "  <li>\n",
    "    <a href=\"../mssql-openai-vanna-vannadb/\" class=\"inline-flex w-full cursor-pointer items-center justify-between rounded-lg border border-gray-200 bg-white p-5 text-gray-500 hover:bg-gray-100 hover:text-gray-600 peer-checked:border-blue-600 peer-checked:text-blue-600 dark:border-gray-700 dark:bg-gray-800 dark:text-gray-400 dark:hover:bg-gray-700 dark:hover:text-gray-300 dark:peer-checked:text-blue-500\">\n",
    "      <div class=\"block\">\n",
    "        <div class=\"w-full text-lg font-semibold\">Microsoft SQL Server</div>\n",
    "        <small class=\"w-full\"></small>\n",
    "      </div>\n",
    "    </a>\n",
    "  </li>\n",
    "    \n",
    "  <li>\n",
    "    <a href=\"../duckdb-openai-vanna-vannadb/\" class=\"inline-flex w-full cursor-pointer items-center justify-between rounded-lg border border-gray-200 bg-white p-5 text-gray-500 hover:bg-gray-100 hover:text-gray-600 peer-checked:border-blue-600 peer-checked:text-blue-600 dark:border-gray-700 dark:bg-gray-800 dark:text-gray-400 dark:hover:bg-gray-700 dark:hover:text-gray-300 dark:peer-checked:text-blue-500\">\n",
    "      <div class=\"block\">\n",
    "        <div class=\"w-full text-lg font-semibold\">DuckDB</div>\n",
    "        <small class=\"w-full\"></small>\n",
    "      </div>\n",
    "    </a>\n",
    "  </li>\n",
    "    \n",
    "  <li>\n",
    "    <a href=\"../snowflake-openai-vanna-vannadb/\" class=\"inline-flex w-full cursor-pointer items-center justify-between rounded-lg border border-gray-200 bg-white p-5 text-gray-500 hover:bg-gray-100 hover:text-gray-600 peer-checked:border-blue-600 peer-checked:text-blue-600 dark:border-gray-700 dark:bg-gray-800 dark:text-gray-400 dark:hover:bg-gray-700 dark:hover:text-gray-300 dark:peer-checked:text-blue-500\">\n",
    "      <div class=\"block\">\n",
    "        <div class=\"w-full text-lg font-semibold\">Snowflake</div>\n",
    "        <small class=\"w-full\"></small>\n",
    "      </div>\n",
    "    </a>\n",
    "  </li>\n",
    "    \n",
    "  <li>\n",
    "    <a href=\"../bigquery-openai-vanna-vannadb/\" class=\"inline-flex w-full cursor-pointer items-center justify-between rounded-lg border border-gray-200 bg-white p-5 text-gray-500 hover:bg-gray-100 hover:text-gray-600 peer-checked:border-blue-600 peer-checked:text-blue-600 dark:border-gray-700 dark:bg-gray-800 dark:text-gray-400 dark:hover:bg-gray-700 dark:hover:text-gray-300 dark:peer-checked:text-blue-500\">\n",
    "      <div class=\"block\">\n",
    "        <div class=\"w-full text-lg font-semibold\">BigQuery</div>\n",
    "        <small class=\"w-full\"></small>\n",
    "      </div>\n",
    "    </a>\n",
    "  </li>\n",
    "    \n",
    "  <li>\n",
    "    <span class=\"inline-flex w-full cursor-pointer items-center justify-between rounded-lg border bg-white p-5 border-blue-600 text-blue-600 dark:bg-gray-800 dark:text-blue-500\">\n",
    "      <div class=\"block\">\n",
    "        <div class=\"w-full text-lg font-semibold\"><span class=\"hidden\">[Selected]</span> SQLite</div>\n",
    "        <small class=\"w-full\"></small>\n",
    "      </div>\n",
    "    </span>\n",
    "  </li>\n",
    "  \n",
    "  <li>\n",
    "    <a href=\"../other-database-openai-vanna-vannadb/\" class=\"inline-flex w-full cursor-pointer items-center justify-between rounded-lg border border-gray-200 bg-white p-5 text-gray-500 hover:bg-gray-100 hover:text-gray-600 peer-checked:border-blue-600 peer-checked:text-blue-600 dark:border-gray-700 dark:bg-gray-800 dark:text-gray-400 dark:hover:bg-gray-700 dark:hover:text-gray-300 dark:peer-checked:text-blue-500\">\n",
    "      <div class=\"block\">\n",
    "        <div class=\"w-full text-lg font-semibold\">Other Database</div>\n",
    "        <small class=\"w-full\">Use Vanna to generate queries for any SQL database</small>\n",
    "      </div>\n",
    "    </a>\n",
    "  </li>\n",
    "    \n",
    "</ul>\n",
    "    "
   ]
  },
  {
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   "execution_count": 5,
   "id": "4bb60e4c-1036-5c5d-84c6-11c9f2e9c8d1",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "vn.connect_to_sqlite('sakila.db')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f06c0e89-83f7-5ad1-8f6e-a64cf5bd8e60",
   "metadata": {},
   "source": [
    "## Training\n",
    "You only need to train once. Do not train again unless you want to add more training data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "068a891d-bbab-5462-9767-ebf7211fe423",
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   "outputs": [
    {
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\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>type</th>\n",
       "      <th>sql</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>table</td>\n",
       "      <td>CREATE TABLE actor (\\n  actor_id integer PRIMA...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>table</td>\n",
       "      <td>CREATE TABLE sqlite_sequence(name,seq)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>index</td>\n",
       "      <td>CREATE INDEX idx_actor_last_name ON actor(last...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>trigger</td>\n",
       "      <td>CREATE TRIGGER actor_trigger_ai AFTER INSERT O...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>trigger</td>\n",
       "      <td>CREATE TRIGGER actor_trigger_au AFTER UPDATE O...</td>\n",
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       "    <tr>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>view</td>\n",
       "      <td>CREATE VIEW customer_list\\nAS\\nSELECT cu.custo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>view</td>\n",
       "      <td>CREATE VIEW film_list\\nAS\\nSELECT film.film_id...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>view</td>\n",
       "      <td>CREATE VIEW staff_list\\nAS\\nSELECT s.staff_id ...</td>\n",
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       "    <tr>\n",
       "      <th>74</th>\n",
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       "      <td>CREATE VIEW sales_by_store\\nAS\\nSELECT\\n  s.st...</td>\n",
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       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>view</td>\n",
       "      <td>CREATE VIEW sales_by_film_category\\nAS\\nSELECT...</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>76 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       type                                                sql\n",
       "0     table  CREATE TABLE actor (\\n  actor_id integer PRIMA...\n",
       "1     table             CREATE TABLE sqlite_sequence(name,seq)\n",
       "2     index  CREATE INDEX idx_actor_last_name ON actor(last...\n",
       "3   trigger  CREATE TRIGGER actor_trigger_ai AFTER INSERT O...\n",
       "4   trigger  CREATE TRIGGER actor_trigger_au AFTER UPDATE O...\n",
       "..      ...                                                ...\n",
       "71     view  CREATE VIEW customer_list\\nAS\\nSELECT cu.custo...\n",
       "72     view  CREATE VIEW film_list\\nAS\\nSELECT film.film_id...\n",
       "73     view  CREATE VIEW staff_list\\nAS\\nSELECT s.staff_id ...\n",
       "74     view  CREATE VIEW sales_by_store\\nAS\\nSELECT\\n  s.st...\n",
       "75     view  CREATE VIEW sales_by_film_category\\nAS\\nSELECT...\n",
       "\n",
       "[76 rows x 2 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
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    }
   ],
   "source": [
    "\n",
    "df_ddl = vn.run_sql(\"SELECT type, sql FROM sqlite_master WHERE sql is not null\")\n",
    "df_ddl"
   ]
  },
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   "cell_type": "code",
   "execution_count": 14,
   "id": "bc0b3099-ab4e-4dfd-b66e-7656b8c201a2",
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Adding ddl: CREATE TABLE actor (\n",
      "  actor_id integer PRIMARY KEY AUTOINCREMENT NOT NULL,\n",
      "  first_name VARCHAR(45) NOT NULL,\n",
      "  last_name VARCHAR(45) NOT NULL,\n",
      "  last_update TIMESTAMP NOT NULL DEFAULT CURRENT_TIMESTAMP\n",
      "  )\n",
      "Adding ddl: CREATE TABLE sqlite_sequence(name,seq)\n",
      "Adding ddl: CREATE INDEX idx_actor_last_name ON actor(last_name)\n",
      "\n",
      "Adding ddl: CREATE TRIGGER actor_trigger_ai AFTER INSERT ON actor\n",
      " BEGIN\n",
      "  UPDATE actor SET last_update = DATETIME('NOW')  WHERE rowid = new.rowid;\n",
      " END\n",
      "Adding ddl: CREATE TRIGGER actor_trigger_au AFTER UPDATE ON actor\n",
      " BEGIN\n",
      "  UPDATE actor SET last_update = DATETIME('NOW')  WHERE rowid = new.rowid;\n",
      " END\n",
      "Adding ddl: CREATE TABLE country (\n",
      "  country_id SMALLINT NOT NULL,\n",
      "  country VARCHAR(50) NOT NULL,\n",
      "  last_update TIMESTAMP,\n",
      "  PRIMARY KEY  (country_id)\n",
      ")\n",
      "Adding ddl: CREATE TRIGGER country_trigger_ai AFTER INSERT ON country\n",
      " BEGIN\n",
      "  UPDATE country SET last_update = DATETIME('NOW')  WHERE rowid = new.rowid;\n",
      " END\n",
      "Adding ddl: CREATE TRIGGER country_trigger_au AFTER UPDATE ON country\n",
      " BEGIN\n",
      "  UPDATE country SET last_update = DATETIME('NOW')  WHERE rowid = new.rowid;\n",
      " END\n",
      "Adding ddl: CREATE TABLE city (\n",
      "  city_id int NOT NULL,\n",
      "  city VARCHAR(50) NOT NULL,\n",
      "  country_id SMALLINT NOT NULL,\n",
      "  last_update TIMESTAMP NOT NULL,\n",
      "  PRIMARY KEY  (city_id),\n",
      "  CONSTRAINT fk_city_country FOREIGN KEY (country_id) REFERENCES country (country_id) ON DELETE NO ACTION ON UPDATE CASCADE\n",
      ")\n",
      "Adding ddl: CREATE INDEX idx_fk_country_id ON city(country_id)\n",
      "\n",
      "Adding ddl: CREATE TRIGGER city_trigger_ai AFTER INSERT ON city\n",
      " BEGIN\n",
      "  UPDATE city SET last_update = DATETIME('NOW')  WHERE rowid = new.rowid;\n",
      " END\n",
      "Adding ddl: CREATE TRIGGER city_trigger_au AFTER UPDATE ON city\n",
      " BEGIN\n",
      "  UPDATE city SET last_update = DATETIME('NOW')  WHERE rowid = new.rowid;\n",
      " END\n",
      "Adding ddl: CREATE TABLE address (\n",
      "  address_id int NOT NULL,\n",
      "  address VARCHAR(50) NOT NULL,\n",
      "  address2 VARCHAR(50) DEFAULT NULL,\n",
      "  district VARCHAR(20) NOT NULL,\n",
      "  city_id INT  NOT NULL,\n",
      "  postal_code VARCHAR(10) DEFAULT NULL,\n",
      "  phone VARCHAR(20) NOT NULL,\n",
      "  last_update TIMESTAMP NOT NULL,\n",
      "  PRIMARY KEY  (address_id),\n",
      "  CONSTRAINT fk_address_city FOREIGN KEY (city_id) REFERENCES city (city_id) ON DELETE NO ACTION ON UPDATE CASCADE\n",
      ")\n",
      "Adding ddl: CREATE INDEX idx_fk_city_id ON address(city_id)\n",
      "\n",
      "Adding ddl: CREATE TRIGGER address_trigger_ai AFTER INSERT ON address\n",
      " BEGIN\n",
      "  UPDATE address SET last_update = DATETIME('NOW')  WHERE rowid = new.rowid;\n",
      " END\n",
      "Adding ddl: CREATE TRIGGER address_trigger_au AFTER UPDATE ON address\n",
      " BEGIN\n",
      "  UPDATE address SET last_update = DATETIME('NOW')  WHERE rowid = new.rowid;\n",
      " END\n",
      "Adding ddl: CREATE TABLE language (\n",
      "  language_id SMALLINT NOT NULL ,\n",
      "  name CHAR(20) NOT NULL,\n",
      "  last_update TIMESTAMP NOT NULL,\n",
      "  PRIMARY KEY (language_id)\n",
      ")\n",
      "Adding ddl: CREATE TRIGGER language_trigger_ai AFTER INSERT ON language\n",
      " BEGIN\n",
      "  UPDATE language SET last_update = DATETIME('NOW')  WHERE rowid = new.rowid;\n",
      " END\n",
      "Adding ddl: CREATE TRIGGER language_trigger_au AFTER UPDATE ON language\n",
      " BEGIN\n",
      "  UPDATE language SET last_update = DATETIME('NOW')  WHERE rowid = new.rowid;\n",
      " END\n",
      "Adding ddl: CREATE TABLE category (\n",
      "  category_id SMALLINT NOT NULL,\n",
      "  name VARCHAR(25) NOT NULL,\n",
      "  last_update TIMESTAMP NOT NULL,\n",
      "  PRIMARY KEY  (category_id)\n",
      ")\n",
      "Adding ddl: CREATE TRIGGER category_trigger_ai AFTER INSERT ON category\n",
      " BEGIN\n",
      "  UPDATE category SET last_update = DATETIME('NOW')  WHERE rowid = new.rowid;\n",
      " END\n",
      "Adding ddl: CREATE TRIGGER category_trigger_au AFTER UPDATE ON category\n",
      " BEGIN\n",
      "  UPDATE category SET last_update = DATETIME('NOW')  WHERE rowid = new.rowid;\n",
      " END\n",
      "Adding ddl: CREATE TABLE customer (\n",
      "  customer_id INT NOT NULL,\n",
      "  store_id INT NOT NULL,\n",
      "  first_name VARCHAR(45) NOT NULL,\n",
      "  last_name VARCHAR(45) NOT NULL,\n",
      "  email VARCHAR(50) DEFAULT NULL,\n",
      "  address_id INT NOT NULL,\n",
      "  active CHAR(1) DEFAULT 'Y' NOT NULL,\n",
      "  create_date TIMESTAMP NOT NULL,\n",
      "  last_update TIMESTAMP NOT NULL,\n",
      "  PRIMARY KEY  (customer_id),\n",
      "  CONSTRAINT fk_customer_store FOREIGN KEY (store_id) REFERENCES store (store_id) ON DELETE NO ACTION ON UPDATE CASCADE,\n",
      "  CONSTRAINT fk_customer_address FOREIGN KEY (address_id) REFERENCES address (address_id) ON DELETE NO ACTION ON UPDATE CASCADE\n",
      ")\n",
      "Adding ddl: CREATE INDEX idx_customer_fk_store_id ON customer(store_id)\n",
      "\n",
      "Adding ddl: CREATE INDEX idx_customer_fk_address_id ON customer(address_id)\n",
      "\n",
      "Adding ddl: CREATE INDEX idx_customer_last_name ON customer(last_name)\n",
      "\n",
      "Adding ddl: CREATE TRIGGER customer_trigger_ai AFTER INSERT ON customer\n",
      " BEGIN\n",
      "  UPDATE customer SET last_update = DATETIME('NOW')  WHERE rowid = new.rowid;\n",
      " END\n",
      "Adding ddl: CREATE TRIGGER customer_trigger_au AFTER UPDATE ON customer\n",
      " BEGIN\n",
      "  UPDATE customer SET last_update = DATETIME('NOW')  WHERE rowid = new.rowid;\n",
      " END\n",
      "Adding ddl: CREATE TABLE film (\n",
      "  film_id int NOT NULL,\n",
      "  title VARCHAR(255) NOT NULL,\n",
      "  description BLOB SUB_TYPE TEXT DEFAULT NULL,\n",
      "  release_year VARCHAR(4) DEFAULT NULL,\n",
      "  language_id SMALLINT NOT NULL,\n",
      "  original_language_id SMALLINT DEFAULT NULL,\n",
      "  rental_duration SMALLINT  DEFAULT 3 NOT NULL,\n",
      "  rental_rate DECIMAL(4,2) DEFAULT 4.99 NOT NULL,\n",
      "  length SMALLINT DEFAULT NULL,\n",
      "  replacement_cost DECIMAL(5,2) DEFAULT 19.99 NOT NULL,\n",
      "  rating VARCHAR(10) DEFAULT 'G',\n",
      "  special_features VARCHAR(100) DEFAULT NULL,\n",
      "  last_update TIMESTAMP NOT NULL,\n",
      "  PRIMARY KEY  (film_id),\n",
      "  CONSTRAINT CHECK_special_features CHECK(special_features is null or\n",
      "                                                           special_features like '%Trailers%' or\n",
      "                                                           special_features like '%Commentaries%' or\n",
      "                                                           special_features like '%Deleted Scenes%' or\n",
      "                                                           special_features like '%Behind the Scenes%'),\n",
      "  CONSTRAINT CHECK_special_rating CHECK(rating in ('G','PG','PG-13','R','NC-17')),\n",
      "  CONSTRAINT fk_film_language FOREIGN KEY (language_id) REFERENCES language (language_id) ,\n",
      "  CONSTRAINT fk_film_language_original FOREIGN KEY (original_language_id) REFERENCES language (language_id)\n",
      ")\n",
      "Adding ddl: CREATE INDEX idx_fk_language_id ON film(language_id)\n",
      "\n",
      "Adding ddl: CREATE INDEX idx_fk_original_language_id ON film(original_language_id)\n",
      "\n",
      "Adding ddl: CREATE TRIGGER film_trigger_ai AFTER INSERT ON film\n",
      " BEGIN\n",
      "  UPDATE film SET last_update = DATETIME('NOW')  WHERE rowid = new.rowid;\n",
      " END\n",
      "Adding ddl: CREATE TRIGGER film_trigger_au AFTER UPDATE ON film\n",
      " BEGIN\n",
      "  UPDATE film SET last_update = DATETIME('NOW')  WHERE rowid = new.rowid;\n",
      " END\n",
      "Adding ddl: CREATE TABLE film_actor (\n",
      "  actor_id INT NOT NULL,\n",
      "  film_id  INT NOT NULL,\n",
      "  last_update TIMESTAMP NOT NULL,\n",
      "  PRIMARY KEY  (actor_id,film_id),\n",
      "  CONSTRAINT fk_film_actor_actor FOREIGN KEY (actor_id) REFERENCES actor (actor_id) ON DELETE NO ACTION ON UPDATE CASCADE,\n",
      "  CONSTRAINT fk_film_actor_film FOREIGN KEY (film_id) REFERENCES film (film_id) ON DELETE NO ACTION ON UPDATE CASCADE\n",
      ")\n",
      "Adding ddl: CREATE INDEX idx_fk_film_actor_film ON film_actor(film_id)\n",
      "\n",
      "Adding ddl: CREATE INDEX idx_fk_film_actor_actor ON film_actor(actor_id) \n",
      "\n",
      "Adding ddl: CREATE TRIGGER film_actor_trigger_ai AFTER INSERT ON film_actor\n",
      " BEGIN\n",
      "  UPDATE film_actor SET last_update = DATETIME('NOW')  WHERE rowid = new.rowid;\n",
      " END\n",
      "Adding ddl: CREATE TRIGGER film_actor_trigger_au AFTER UPDATE ON film_actor\n",
      " BEGIN\n",
      "  UPDATE film_actor SET last_update = DATETIME('NOW')  WHERE rowid = new.rowid;\n",
      " END\n",
      "Adding ddl: CREATE TABLE film_category (\n",
      "  film_id INT NOT NULL,\n",
      "  category_id SMALLINT  NOT NULL,\n",
      "  last_update TIMESTAMP NOT NULL,\n",
      "  PRIMARY KEY (film_id, category_id),\n",
      "  CONSTRAINT fk_film_category_film FOREIGN KEY (film_id) REFERENCES film (film_id) ON DELETE NO ACTION ON UPDATE CASCADE,\n",
      "  CONSTRAINT fk_film_category_category FOREIGN KEY (category_id) REFERENCES category (category_id) ON DELETE NO ACTION ON UPDATE CASCADE\n",
      ")\n",
      "Adding ddl: CREATE INDEX idx_fk_film_category_film ON film_category(film_id)\n",
      "\n",
      "Adding ddl: CREATE INDEX idx_fk_film_category_category ON film_category(category_id)\n",
      "\n",
      "Adding ddl: CREATE TRIGGER film_category_trigger_ai AFTER INSERT ON film_category\n",
      " BEGIN\n",
      "  UPDATE film_category SET last_update = DATETIME('NOW')  WHERE rowid = new.rowid;\n",
      " END\n",
      "Adding ddl: CREATE TRIGGER film_category_trigger_au AFTER UPDATE ON film_category\n",
      " BEGIN\n",
      "  UPDATE film_category SET last_update = DATETIME('NOW')  WHERE rowid = new.rowid;\n",
      " END\n",
      "Adding ddl: CREATE TABLE film_text (\n",
      "  film_id SMALLINT NOT NULL,\n",
      "  title VARCHAR(255) NOT NULL,\n",
      "  description BLOB SUB_TYPE TEXT,\n",
      "  PRIMARY KEY  (film_id)\n",
      ")\n",
      "Adding ddl: CREATE TABLE inventory (\n",
      "  inventory_id INT NOT NULL,\n",
      "  film_id INT NOT NULL,\n",
      "  store_id INT NOT NULL,\n",
      "  last_update TIMESTAMP NOT NULL,\n",
      "  PRIMARY KEY  (inventory_id),\n",
      "  CONSTRAINT fk_inventory_store FOREIGN KEY (store_id) REFERENCES store (store_id) ON DELETE NO ACTION ON UPDATE CASCADE,\n",
      "  CONSTRAINT fk_inventory_film FOREIGN KEY (film_id) REFERENCES film (film_id) ON DELETE NO ACTION ON UPDATE CASCADE\n",
      ")\n",
      "Adding ddl: CREATE INDEX idx_fk_film_id ON inventory(film_id)\n",
      "\n",
      "Adding ddl: CREATE INDEX idx_fk_film_id_store_id ON inventory(store_id,film_id)\n",
      "\n",
      "Adding ddl: CREATE TRIGGER inventory_trigger_ai AFTER INSERT ON inventory\n",
      " BEGIN\n",
      "  UPDATE inventory SET last_update = DATETIME('NOW')  WHERE rowid = new.rowid;\n",
      " END\n",
      "Adding ddl: CREATE TRIGGER inventory_trigger_au AFTER UPDATE ON inventory\n",
      " BEGIN\n",
      "  UPDATE inventory SET last_update = DATETIME('NOW')  WHERE rowid = new.rowid;\n",
      " END\n",
      "Adding ddl: CREATE TABLE staff (\n",
      "  staff_id SMALLINT NOT NULL,\n",
      "  first_name VARCHAR(45) NOT NULL,\n",
      "  last_name VARCHAR(45) NOT NULL,\n",
      "  address_id INT NOT NULL,\n",
      "  picture BLOB DEFAULT NULL,\n",
      "  email VARCHAR(50) DEFAULT NULL,\n",
      "  store_id INT NOT NULL,\n",
      "  active SMALLINT DEFAULT 1 NOT NULL,\n",
      "  username VARCHAR(16) NOT NULL,\n",
      "  password VARCHAR(40) DEFAULT NULL,\n",
      "  last_update TIMESTAMP NOT NULL,\n",
      "  PRIMARY KEY  (staff_id),\n",
      "  CONSTRAINT fk_staff_store FOREIGN KEY (store_id) REFERENCES store (store_id) ON DELETE NO ACTION ON UPDATE CASCADE,\n",
      "  CONSTRAINT fk_staff_address FOREIGN KEY (address_id) REFERENCES address (address_id) ON DELETE NO ACTION ON UPDATE CASCADE\n",
      ")\n",
      "Adding ddl: CREATE INDEX idx_fk_staff_store_id ON staff(store_id)\n",
      "\n",
      "Adding ddl: CREATE INDEX idx_fk_staff_address_id ON staff(address_id)\n",
      "\n",
      "Adding ddl: CREATE TRIGGER staff_trigger_ai AFTER INSERT ON staff\n",
      " BEGIN\n",
      "  UPDATE staff SET last_update = DATETIME('NOW')  WHERE rowid = new.rowid;\n",
      " END\n",
      "Adding ddl: CREATE TRIGGER staff_trigger_au AFTER UPDATE ON staff\n",
      " BEGIN\n",
      "  UPDATE staff SET last_update = DATETIME('NOW')  WHERE rowid = new.rowid;\n",
      " END\n",
      "Adding ddl: CREATE TABLE store (\n",
      "  store_id INT NOT NULL,\n",
      "  manager_staff_id SMALLINT NOT NULL,\n",
      "  address_id INT NOT NULL,\n",
      "  last_update TIMESTAMP NOT NULL,\n",
      "  PRIMARY KEY  (store_id),\n",
      "  CONSTRAINT fk_store_staff FOREIGN KEY (manager_staff_id) REFERENCES staff (staff_id) ,\n",
      "  CONSTRAINT fk_store_address FOREIGN KEY (address_id) REFERENCES address (address_id)\n",
      ")\n",
      "Adding ddl: CREATE INDEX idx_store_fk_manager_staff_id ON store(manager_staff_id)\n",
      "\n",
      "Adding ddl: CREATE INDEX idx_fk_store_address ON store(address_id)\n",
      "\n",
      "Adding ddl: CREATE TRIGGER store_trigger_ai AFTER INSERT ON store\n",
      " BEGIN\n",
      "  UPDATE store SET last_update = DATETIME('NOW')  WHERE rowid = new.rowid;\n",
      " END\n",
      "Adding ddl: CREATE TRIGGER store_trigger_au AFTER UPDATE ON store\n",
      " BEGIN\n",
      "  UPDATE store SET last_update = DATETIME('NOW')  WHERE rowid = new.rowid;\n",
      " END\n",
      "Adding ddl: CREATE TABLE payment (\n",
      "  payment_id int NOT NULL,\n",
      "  customer_id INT  NOT NULL,\n",
      "  staff_id SMALLINT NOT NULL,\n",
      "  rental_id INT DEFAULT NULL,\n",
      "  amount DECIMAL(5,2) NOT NULL,\n",
      "  payment_date TIMESTAMP NOT NULL,\n",
      "  last_update TIMESTAMP NOT NULL,\n",
      "  PRIMARY KEY  (payment_id),\n",
      "  CONSTRAINT fk_payment_rental FOREIGN KEY (rental_id) REFERENCES rental (rental_id) ON DELETE SET NULL ON UPDATE CASCADE,\n",
      "  CONSTRAINT fk_payment_customer FOREIGN KEY (customer_id) REFERENCES customer (customer_id) ,\n",
      "  CONSTRAINT fk_payment_staff FOREIGN KEY (staff_id) REFERENCES staff (staff_id)\n",
      ")\n",
      "Adding ddl: CREATE INDEX idx_fk_staff_id ON payment(staff_id)\n",
      "\n",
      "Adding ddl: CREATE INDEX idx_fk_customer_id ON payment(customer_id)\n",
      "\n",
      "Adding ddl: CREATE TRIGGER payment_trigger_ai AFTER INSERT ON payment\n",
      " BEGIN\n",
      "  UPDATE payment SET last_update = DATETIME('NOW')  WHERE rowid = new.rowid;\n",
      " END\n",
      "Adding ddl: CREATE TRIGGER payment_trigger_au AFTER UPDATE ON payment\n",
      " BEGIN\n",
      "  UPDATE payment SET last_update = DATETIME('NOW')  WHERE rowid = new.rowid;\n",
      " END\n",
      "Adding ddl: CREATE TABLE rental (\n",
      "  rental_id INT NOT NULL,\n",
      "  rental_date TIMESTAMP NOT NULL,\n",
      "  inventory_id INT  NOT NULL,\n",
      "  customer_id INT  NOT NULL,\n",
      "  return_date TIMESTAMP DEFAULT NULL,\n",
      "  staff_id SMALLINT  NOT NULL,\n",
      "  last_update TIMESTAMP NOT NULL,\n",
      "  PRIMARY KEY (rental_id),\n",
      "  CONSTRAINT fk_rental_staff FOREIGN KEY (staff_id) REFERENCES staff (staff_id) ,\n",
      "  CONSTRAINT fk_rental_inventory FOREIGN KEY (inventory_id) REFERENCES inventory (inventory_id) ,\n",
      "  CONSTRAINT fk_rental_customer FOREIGN KEY (customer_id) REFERENCES customer (customer_id)\n",
      ")\n",
      "Adding ddl: CREATE INDEX idx_rental_fk_inventory_id ON rental(inventory_id)\n",
      "\n",
      "Adding ddl: CREATE INDEX idx_rental_fk_customer_id ON rental(customer_id)\n",
      "\n",
      "Adding ddl: CREATE INDEX idx_rental_fk_staff_id ON rental(staff_id)\n",
      "\n",
      "Adding ddl: CREATE UNIQUE INDEX idx_rental_uq  ON rental (rental_date,inventory_id,customer_id)\n",
      "\n",
      "Adding ddl: CREATE TRIGGER rental_trigger_ai AFTER INSERT ON rental\n",
      " BEGIN\n",
      "  UPDATE rental SET last_update = DATETIME('NOW')  WHERE rowid = new.rowid;\n",
      " END\n",
      "Adding ddl: CREATE TRIGGER rental_trigger_au AFTER UPDATE ON rental\n",
      " BEGIN\n",
      "  UPDATE rental SET last_update = DATETIME('NOW')  WHERE rowid = new.rowid;\n",
      " END\n",
      "Adding ddl: CREATE VIEW customer_list\n",
      "AS\n",
      "SELECT cu.customer_id AS ID,\n",
      "       cu.first_name||' '||cu.last_name AS name,\n",
      "       a.address AS address,\n",
      "       a.postal_code AS zip_code,\n",
      "       a.phone AS phone,\n",
      "       city.city AS city,\n",
      "       country.country AS country,\n",
      "       case when cu.active=1 then 'active' else '' end AS notes,\n",
      "       cu.store_id AS SID\n",
      "FROM customer AS cu JOIN address AS a ON cu.address_id = a.address_id JOIN city ON a.city_id = city.city_id\n",
      "    JOIN country ON city.country_id = country.country_id\n",
      "Adding ddl: CREATE VIEW film_list\n",
      "AS\n",
      "SELECT film.film_id AS FID,\n",
      "       film.title AS title,\n",
      "       film.description AS description,\n",
      "       category.name AS category,\n",
      "       film.rental_rate AS price,\n",
      "       film.length AS length,\n",
      "       film.rating AS rating,\n",
      "       actor.first_name||' '||actor.last_name AS actors\n",
      "FROM category LEFT JOIN film_category ON category.category_id = film_category.category_id LEFT JOIN film ON film_category.film_id = film.film_id\n",
      "        JOIN film_actor ON film.film_id = film_actor.film_id\n",
      "    JOIN actor ON film_actor.actor_id = actor.actor_id\n",
      "Adding ddl: CREATE VIEW staff_list\n",
      "AS\n",
      "SELECT s.staff_id AS ID,\n",
      "       s.first_name||' '||s.last_name AS name,\n",
      "       a.address AS address,\n",
      "       a.postal_code AS zip_code,\n",
      "       a.phone AS phone,\n",
      "       city.city AS city,\n",
      "       country.country AS country,\n",
      "       s.store_id AS SID\n",
      "FROM staff AS s JOIN address AS a ON s.address_id = a.address_id JOIN city ON a.city_id = city.city_id\n",
      "    JOIN country ON city.country_id = country.country_id\n",
      "Adding ddl: CREATE VIEW sales_by_store\n",
      "AS\n",
      "SELECT\n",
      "  s.store_id\n",
      " ,c.city||','||cy.country AS store\n",
      " ,m.first_name||' '||m.last_name AS manager\n",
      " ,SUM(p.amount) AS total_sales\n",
      "FROM payment AS p\n",
      "INNER JOIN rental AS r ON p.rental_id = r.rental_id\n",
      "INNER JOIN inventory AS i ON r.inventory_id = i.inventory_id\n",
      "INNER JOIN store AS s ON i.store_id = s.store_id\n",
      "INNER JOIN address AS a ON s.address_id = a.address_id\n",
      "INNER JOIN city AS c ON a.city_id = c.city_id\n",
      "INNER JOIN country AS cy ON c.country_id = cy.country_id\n",
      "INNER JOIN staff AS m ON s.manager_staff_id = m.staff_id\n",
      "GROUP BY  \n",
      "  s.store_id\n",
      ", c.city||','||cy.country\n",
      ", m.first_name||' '||m.last_name\n",
      "Adding ddl: CREATE VIEW sales_by_film_category\n",
      "AS\n",
      "SELECT\n",
      "c.name AS category\n",
      ", SUM(p.amount) AS total_sales\n",
      "FROM payment AS p\n",
      "INNER JOIN rental AS r ON p.rental_id = r.rental_id\n",
      "INNER JOIN inventory AS i ON r.inventory_id = i.inventory_id\n",
      "INNER JOIN film AS f ON i.film_id = f.film_id\n",
      "INNER JOIN film_category AS fc ON f.film_id = fc.film_id\n",
      "INNER JOIN category AS c ON fc.category_id = c.category_id\n",
      "GROUP BY c.name\n"
     ]
    }
   ],
   "source": [
    "for ddl in df_ddl['sql'].to_list():\n",
    "    vn.train(ddl=ddl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7c421f88-42ea-567c-8581-3dcac96c36a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# The following are methods for adding training data. Make sure you modify the examples to match your database.\n",
    "\n",
    "# DDL statements are powerful because they specify table names, colume names, types, and potentially relationships\n",
    "vn.train(ddl=\"\"\"\n",
    "    CREATE TABLE IF NOT EXISTS my-table (\n",
    "        id INT PRIMARY KEY,\n",
    "        name VARCHAR(100),\n",
    "        age INT\n",
    "    )\n",
    "\"\"\")\n",
    "\n",
    "# Sometimes you may want to add documentation about your business terminology or definitions.\n",
    "vn.train(documentation=\"Our business defines OTIF score as the percentage of orders that are delivered on time and in full\")\n",
    "\n",
    "# You can also add SQL queries to your training data. This is useful if you have some queries already laying around. You can just copy and paste those from your editor to begin generating new SQL.\n",
    "vn.train(sql=\"SELECT * FROM my-table WHERE name = 'John Doe'\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "59fcb3b1-4434-583d-82be-ed8e9b04d699",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>training_data_type</th>\n",
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       "      <td>45486-ddl</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>45519-ddl</td>\n",
       "      <td>ddl</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CREATE INDEX idx_fk_staff_id ON payment(staff_...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>45479-ddl</td>\n",
       "      <td>ddl</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CREATE TRIGGER category_trigger_ai AFTER INSER...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>45465-ddl</td>\n",
       "      <td>ddl</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CREATE TRIGGER country_trigger_ai AFTER INSERT...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>45468-ddl</td>\n",
       "      <td>ddl</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CREATE INDEX idx_fk_country_id ON city(country...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>45494-ddl</td>\n",
       "      <td>ddl</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CREATE INDEX idx_fk_film_actor_actor ON film_a...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>45524-ddl</td>\n",
       "      <td>ddl</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CREATE INDEX idx_rental_fk_inventory_id ON ren...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>45474-ddl</td>\n",
       "      <td>ddl</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CREATE TRIGGER address_trigger_au AFTER UPDATE...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>45503-ddl</td>\n",
       "      <td>ddl</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CREATE TABLE inventory (\\n  inventory_id INT N...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>76 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           id training_data_type  question   \n",
       "0   45486-ddl                ddl       NaN  \\\n",
       "1   45530-ddl                ddl       NaN   \n",
       "2   45519-ddl                ddl       NaN   \n",
       "3   45479-ddl                ddl       NaN   \n",
       "4   45465-ddl                ddl       NaN   \n",
       "..        ...                ...       ...   \n",
       "71  45468-ddl                ddl       NaN   \n",
       "72  45494-ddl                ddl       NaN   \n",
       "73  45524-ddl                ddl       NaN   \n",
       "74  45474-ddl                ddl       NaN   \n",
       "75  45503-ddl                ddl       NaN   \n",
       "\n",
       "                                              content  \n",
       "0   CREATE TRIGGER customer_trigger_au AFTER UPDAT...  \n",
       "1   CREATE VIEW customer_list\\nAS\\nSELECT cu.custo...  \n",
       "2   CREATE INDEX idx_fk_staff_id ON payment(staff_...  \n",
       "3   CREATE TRIGGER category_trigger_ai AFTER INSER...  \n",
       "4   CREATE TRIGGER country_trigger_ai AFTER INSERT...  \n",
       "..                                                ...  \n",
       "71  CREATE INDEX idx_fk_country_id ON city(country...  \n",
       "72  CREATE INDEX idx_fk_film_actor_actor ON film_a...  \n",
       "73  CREATE INDEX idx_rental_fk_inventory_id ON ren...  \n",
       "74  CREATE TRIGGER address_trigger_au AFTER UPDATE...  \n",
       "75  CREATE TABLE inventory (\\n  inventory_id INT N...  \n",
       "\n",
       "[76 rows x 4 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# At any time you can inspect what training data the package is able to reference\n",
    "training_data = vn.get_training_data()\n",
    "training_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6cf17ab9-dc48-58af-8d75-4e5590a01c88",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# You can remove training data if there's obsolete/incorrect information. \n",
    "ids = training_data['id'].tolist()\n",
    "\n",
    "for id in ids:\n",
    "    vn.remove_training_data(id=id)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf2fc121-a3ab-5a2e-95b0-383271e82d5f",
   "metadata": {},
   "source": [
    "## Asking the AI\n",
    "Whenever you ask a new question, it will find the 10 most relevant pieces of training data and use it as part of the LLM prompt to generate the SQL."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "edb6679e-a102-5efc-b890-81babca8f500",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SELECT COUNT(*) AS total_cities FROM city;\n",
      "   total_cities\n",
      "0           600\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "('SELECT COUNT(*) AS total_cities FROM city;',\n",
       "    total_cities\n",
       " 0           600,\n",
       " None)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vn.ask(question='how many cities are there')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "210bacbb-21db-4a2b-9dc4-930c25916737",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SELECT f.rating AS rating, COUNT(*) AS total_movies\n",
      "FROM film AS f\n",
      "JOIN inventory AS i ON f.film_id = i.film_id\n",
      "GROUP BY f.rating\n",
      "ORDER BY total_movies DESC;\n",
      "  rating  total_movies\n",
      "0  PG-13          1018\n",
      "1  NC-17           944\n",
      "2     PG           924\n",
      "3      R           904\n",
      "4      G           791\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "('SELECT f.rating AS rating, COUNT(*) AS total_movies\\nFROM film AS f\\nJOIN inventory AS i ON f.film_id = i.film_id\\nGROUP BY f.rating\\nORDER BY total_movies DESC;',\n",
       "   rating  total_movies\n",
       " 0  PG-13          1018\n",
       " 1  NC-17           944\n",
       " 2     PG           924\n",
       " 3      R           904\n",
       " 4      G           791,\n",
       " None)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vn.ask(question='What is the total number of movies in each rating category held in inventory? sort them descendingly')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "b87d140b-ef56-5795-b489-46bb11d01459",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Your app is running at:\n",
      "http://localhost:8084\n",
      " * Serving Flask app 'vanna.flask'\n",
      " * Debug mode: off\n"
     ]
    }
   ],
   "source": [
    "from vanna.flask import VannaFlaskApp\n",
    "app = VannaFlaskApp(vn)\n",
    "app.run()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "9f33ebc8-850c-430e-ba7e-c399f7d32935",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# https://www.jooq.org/sakila\n",
    "# https://www.kaggle.com/datasets/atanaskanev/sqlite-sakila-sample-database?select=sqlite-sakila.db\n",
    "# https://www.kaggle.com/datasets/atanaskanev/sqlite-sakila-sample-database?select=sqlite-sakila.db\n",
    "# https://github.com/ivanceras/sakila/tree/master"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3324584f-a169-4c61-9d56-298b898cc08e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}