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"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from pyprojroot import here\n",
"from langchain_community.utilities import SQLDatabase\n",
"from langchain.chains import create_sql_query_chain\n",
"from langchain_community.tools.sql_database.tool import QuerySQLDataBaseTool\n",
"from langchain_core.prompts import PromptTemplate\n",
"from langchain_core.output_parsers import StrOutputParser\n",
"from langchain_core.runnables import RunnablePassthrough\n",
"from operator import itemgetter\n",
"from langchain_groq import ChatGroq\n",
"import os\n",
"from dotenv import load_dotenv\n",
"load_dotenv()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Set the environment variables and load the LLM**"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"os.environ['GROQ_API_KEY'] = os.getenv(\"GROQ_API_KEY\")\n",
"\n",
"llm = ChatGroq(model=\"openai/gpt-oss-120b\")\n",
"# llm = ChatGroq(model=\"llama3-8b-8192\")\n",
"# llm = ChatGroq(model=\"mixtral-8x7b-32768\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Load and test the sqlite db**"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"sqlite\n",
"['Album', 'Artist', 'Customer', 'Employee', 'Genre', 'Invoice', 'InvoiceLine', 'MediaType', 'Playlist', 'PlaylistTrack', 'Track']\n"
]
},
{
"data": {
"text/plain": [
"\"[('Album',), ('Artist',), ('Customer',), ('Employee',), ('Genre',), ('Invoice',), ('InvoiceLine',), ('MediaType',), ('Playlist',), ('PlaylistTrack',), ('Track',)]\""
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sqldb_directory = here(\"data/Chinook.db\")\n",
"db = SQLDatabase.from_uri(\n",
" f\"sqlite:///{sqldb_directory}\")\n",
"\n",
"print(db.dialect)\n",
"print(db.get_usable_table_names())\n",
"db.run(\"\"\" SELECT name\n",
"FROM sqlite_master\n",
"WHERE type='table'\n",
"AND name NOT LIKE 'sqlite_%'; \"\"\")\n",
"\n",
"# from sqlalchemy import create_engine, inspect\n",
"# from sqlalchemy.orm import sessionmaker\n",
"# engine = create_engine(db_path)\n",
"\n",
"# # Create a session\n",
"# Session = sessionmaker(bind=engine)\n",
"# session = Session()\n",
"\n",
"# # Use SQLAlchemy's Inspector to get database information\n",
"# inspector = inspect(engine)\n",
"\n",
"# # Get table names\n",
"# tables = inspector.get_table_names()\n",
"# print(\"Tables in the database:\", tables)\n",
"# print(len(tables))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Create the SQL agent chain and run a test query**"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"system_role = \"\"\"Given the following user question, corresponding SQL query, and SQL result, answer the user question.\\n\n",
" Question: {question}\\n\n",
" SQL Query: {query}\\n\n",
" SQL Result: {result}\\n\n",
" Answer:\n",
" \"\"\"\n",
"\n",
"execute_query = QuerySQLDataBaseTool(db=db)\n",
"write_query = create_sql_query_chain(\n",
" llm, db)\n",
"answer_prompt = PromptTemplate.from_template(\n",
" system_role)\n",
"answer = answer_prompt | llm | StrOutputParser()\n",
"chain = (\n",
" RunnablePassthrough.assign(query=write_query).assign(\n",
" result=itemgetter(\"query\") | execute_query\n",
" )\n",
" | answer\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'I’m sorry, but there’s no SQL result provided, so I can’t determine how many tables are in your database or what their names are. If you can share the query output (e.g., a list of table names), I’ll be happy to give you the answer.'"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"message = \"How many tables do I have in the database? and what are their names?\"\n",
"response = chain.invoke({\"question\": message})\n",
"response"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Travel SQL-agent Tool Design**"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"from langchain_core.tools import tool\n",
"from langchain_community.utilities import SQLDatabase\n",
"from langchain.chains import create_sql_query_chain\n",
"from langchain_community.tools.sql_database.tool import QuerySQLDataBaseTool\n",
"from langchain_core.prompts import PromptTemplate\n",
"from langchain_core.output_parsers import StrOutputParser\n",
"from langchain_core.runnables import RunnablePassthrough\n",
"from operator import itemgetter\n",
"from langchain_openai import ChatOpenAI\n",
"\n",
"\n",
"class TravelSQLAgentTool:\n",
" \"\"\"\n",
" A tool for interacting with a travel-related SQL database using an LLM (Language Model) to generate and execute SQL queries.\n",
"\n",
" This tool enables users to ask travel-related questions, which are transformed into SQL queries by a language model.\n",
" The SQL queries are executed on the provided SQLite database, and the results are processed by the language model to\n",
" generate a final answer for the user.\n",
"\n",
" Attributes:\n",
" sql_agent_llm (ChatOpenAI): An instance of a ChatOpenAI language model used to generate and process SQL queries.\n",
" system_role (str): A system prompt template that guides the language model in answering user questions based on SQL query results.\n",
" db (SQLDatabase): An instance of the SQL database used to execute queries.\n",
" chain (RunnablePassthrough): A chain of operations that creates SQL queries, executes them, and generates a response.\n",
"\n",
" Methods:\n",
" __init__: Initializes the TravelSQLAgentTool by setting up the language model, SQL database, and query-answering pipeline.\n",
" \"\"\"\n",
"\n",
" def __init__(self, llm: str, sqldb_directory: str, llm_temerature: float) -> None:\n",
" \"\"\"\n",
" Initializes the TravelSQLAgentTool with the necessary configurations.\n",
"\n",
" Args:\n",
" llm (str): The name of the language model to be used for generating and interpreting SQL queries.\n",
" sqldb_directory (str): The directory path where the SQLite database is stored.\n",
" llm_temerature (float): The temperature setting for the language model, controlling response randomness.\n",
" \"\"\"\n",
" self.sql_agent_llm = ChatGroq(\n",
" model=llm, temperature=llm_temerature)\n",
" self.system_role = \"\"\"Given the following user question, corresponding SQL query, and SQL result, answer the user question.\\n\n",
" Question: {question}\\n\n",
" SQL Query: {query}\\n\n",
" SQL Result: {result}\\n\n",
" Answer:\n",
" \"\"\"\n",
" self.db = SQLDatabase.from_uri(\n",
" f\"sqlite:///{sqldb_directory}\")\n",
" print(self.db.get_usable_table_names())\n",
"\n",
" execute_query = QuerySQLDataBaseTool(db=self.db)\n",
" write_query = create_sql_query_chain(\n",
" self.sql_agent_llm, self.db)\n",
" answer_prompt = PromptTemplate.from_template(\n",
" self.system_role)\n",
"\n",
" answer = answer_prompt | self.sql_agent_llm | StrOutputParser()\n",
" self.chain = (\n",
" RunnablePassthrough.assign(query=write_query).assign(\n",
" result=itemgetter(\"query\") | execute_query\n",
" )\n",
" | answer\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"<>:3: SyntaxWarning: invalid escape sequence '\\e'\n",
"<>:3: SyntaxWarning: invalid escape sequence '\\e'\n",
"C:\\Users\\AL-MASA\\AppData\\Local\\Temp\\ipykernel_13496\\1650904972.py:3: SyntaxWarning: invalid escape sequence '\\e'\n",
" sys.path.insert(0, os.path.abspath('F:\\end_to_end_AI_Projects\\QueryMind _ AI_Powered_Natural_Language_Interface_for_SQL_&_Vector_Databases')) # or the full path to your project root\n"
]
}
],
"source": [
"import sys\n",
"import os\n",
"sys.path.insert(0, os.path.abspath('F:\\end_to_end_AI_Projects\\QueryMind _ AI_Powered_Natural_Language_Interface_for_SQL_&_Vector_Databases')) # or the full path to your project root\n",
"\n",
"from src.agent_graph.load_tools_config import LoadToolsConfig"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"from src.agent_graph.load_tools_config import LoadToolsConfig\n",
"\n",
"TOOLS_CFG = LoadToolsConfig()\n",
"\n",
"@tool\n",
"def query_travel_sqldb(query: str) -> str:\n",
" \"\"\"Query the Swiss Airline SQL Database and access all the company's information. Input should be a search query.\"\"\"\n",
" agent = TravelSQLAgentTool(\n",
" llm=TOOLS_CFG.travel_sqlagent_llm,\n",
" sqldb_directory=TOOLS_CFG.travel_sqldb_directory,\n",
" llm_temperature=TOOLS_CFG.travel_sqlagent_llm_temperature\n",
" )\n",
" response = agent.chain.invoke({\"question\": query})\n",
" return response"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "querymind (3.12.10)",
"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": "3.12.10"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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