feat: initializing qa on sql databases with langchain notebook

LangChain/LangChain_Perform_QA_on_SQL_Databases.ipynb

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+    "<img width=\"10%\" alt=\"Naas\" src=\"https://landen.imgix.net/jtci2pxwjczr/assets/5ice39g4.png?w=160\"/>"
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+   "metadata": {
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+    "tags": []
+   },
+   "source": [
+    "# Langchain - QA on SQL Database"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "df7190c7-9f84-4ef9-afed-cbeec9189bdf",
+   "metadata": {
+    "papermill": {},
+    "tags": []
+   },
+   "source": [
+    "**Tags**: #langchain #toolkit #database #qa #python #sql"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cda2f1ea-c2df-45c5-9329-d832077dadfb",
+   "metadata": {
+    "papermill": {},
+    "tags": []
+   },
+   "source": [
+    "**Author:** [Léa Buendé](https://www.linkedin.com/in/leabuende)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6b607685-d026-4f09-8724-013227fcf2c5",
+   "metadata": {
+    "papermill": {},
+    "tags": []
+   },
+   "source": [
+    "**Last update:** 2023-10-19 (Created: 2023-10-08) "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "09056bc2-135e-46e8-a422-4ef3dfabf41e",
+   "metadata": {
+    "papermill": {},
+    "tags": []
+   },
+   "source": [
+    "**Description:** This notebook demonstrates how to perform queries on a SQL Database, as well as how to generate SQL queries from questions or text."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a250dcaf-66c0-40a0-a7f5-c2ff8d50a4c6",
+   "metadata": {
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+    "tags": []
+   },
+   "source": [
+    "**References:** \n",
+    "- [Langchain - SQL](https://python.langchain.com/docs/use_cases/qa_structured/sql)\n",
+    "- [Langsmith - Text-to-SQL](https://smith.langchain.com/hub/rlm/text-to-sql)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "70cb3b03-1af8-47b0-a299-2c798e4e0452",
+   "metadata": {
+    "papermill": {},
+    "tags": []
+   },
+   "source": [
+    "## Input"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "90208573-1f14-4e2b-8900-e304035a2570",
+   "metadata": {
+    "papermill": {},
+    "tags": []
+   },
+   "source": [
+    "### Import libraries\n",
+    "Libraries to be used within this notebook.<br>\n",
+    "\n",
+    "Note: You may need to restart the kernel to use updated packages."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "de70f076-2e23-4d17-9e65-d7812366aad5",
+   "metadata": {
+    "papermill": {},
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "try:\n",
+    "    import langchain\n",
+    "    import langchain_experimental\n",
+    "    import openai\n",
+    "except: \n",
+    "  !pip install langchain langchain-experimental openai --user\n",
+    "\n",
+    "from langchain.utilities import SQLDatabase\n",
+    "from langchain.llms import OpenAI\n",
+    "import naas"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0e4b4668-1cfa-4aa8-b453-2c9f863b6f29",
+   "metadata": {
+    "papermill": {},
+    "tags": []
+   },
+   "source": [
+    "### Setup variables\n",
+    "\n",
+    "- `openai_api_key`: [Get your API key here](https://openai.com/docs/api-overview/).\n",
+    "- `temp`: Default value is 0 but preferred to have 0.7. You can change this value according to your requirements\n",
+    "- `database_uri`: URI of your database. We will create one for the purpose of this notebook."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "54158c1d-3ddf-45ed-8989-9f1fd2240b6f",
+   "metadata": {
+    "papermill": {},
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "openai_api_key = naas.secret.get(\"OPENAI_API_KEY\") or \"Paste-your-key-here\"\n",
+    "temp = 0\n",
+    "sql_uri = \"sqlite:///Chinook.db\" # Replace with the URI of your database"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2f7ab5dc",
+   "metadata": {},
+   "source": [
+    "#### Creating a mock database (Optional)\n",
+    "The Chinook database is a sample database for you to run quick tests, such as trying out this notebook. It represents a digital media store, including tables for artists, albums, media tracks, invoices and customers.\n",
+    "\n",
+    "To install the Chinook database :\n",
+    "- Download the [Chinook.sql file](https://raw.githubusercontent.com/lerocha/chinook-database/master/ChinookDatabase/DataSources/Chinook_Sqlite.sql) in the same directory as this notebook, and save it as Chinook_Sqlite.sql\n",
+    "- In your terminal, run ```sqlite3 Chinook.db``` to connect to SQLite\n",
+    "- Then, run ```.read Chinook_Sqlite.sql``` to execute the script from the file\n",
+    "\n",
+    "Your database is now initialized."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3ba70fc1",
+   "metadata": {},
+   "source": [
+    "## *Text to SQL query*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9b1db6dd",
+   "metadata": {},
+   "source": [
+    "## Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "86ba23e8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.chat_models import ChatOpenAI\n",
+    "from langchain.chains import create_sql_query_chain"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4dbca1bc",
+   "metadata": {},
+   "source": [
+    "#### Build an SQL query using Create_sql_query_chain"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f66d1a9b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "db = SQLDatabase.from_uri(sql_uri) \n",
+    "chain = create_sql_query_chain(ChatOpenAI(temperature=temp, openai_api_key=openai_api_key), db)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4443a2ae",
+   "metadata": {},
+   "source": [
+    "## Output"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e1eecee9",
+   "metadata": {},
+   "source": [
+    "#### Display result"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c3e94abb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "response = chain.invoke({\"question\":\"How many songs are in the Grunge playlist ?\"})\n",
+    "print(response)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ebfef2e5",
+   "metadata": {},
+   "source": [
+    "You can test out the SQL query by running :"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cfb1d20e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "db.run(response)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a4923704",
+   "metadata": {},
+   "source": [
+    "## *Text to SQL query and execution*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4abf9e57",
+   "metadata": {},
+   "source": [
+    "## Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "df536f67",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_experimental.sql import SQLDatabaseChain"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3caef398",
+   "metadata": {},
+   "source": [
+    "#### Build and run an SQL query using SQLDatabaseChain "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "9cc14e45",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "db = SQLDatabase.from_uri(sql_uri) \n",
+    "llm = OpenAI(temperature=temp, verbose=True, openai_api_key=openai_api_key)\n",
+    "db_chain = SQLDatabaseChain.from_llm(llm, db, verbose=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "20a4e159",
+   "metadata": {},
+   "source": [
+    "## Output"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9ea4deda",
+   "metadata": {},
+   "source": [
+    "#### Display result"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "eb795f63",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "db_chain.run(\"Who is the artist with the most albums ?\")"
+   ]
+  }
+ ],
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