This video is a step-by-step tutorial to build from scratch AI-powered database agents to use with your own data.
Code:
!pip install pyodbc==5.1.0
!pip install tabulate==0.9.0
!pip install openai==1.12.0
!pip install langchain==0.1.6
!pip install langchain-community==0.0.20
!pip install langchain-core==0.1.23
!pip install langchain-experimental==0.0.49
!pip install langchain-openai==0.0.5
!pip install pandas==2.2.2
import os
from IPython.display import Markdown, HTML, display
from langchain_openai import OpenAI
from langchain.agents import create_sql_agent
from langchain.agents.agent_toolkits import SQLDatabaseToolkit
from langchain.sql_database import SQLDatabase
from langchain_openai import AzureChatOpenAI
from sqlalchemy import create_engine
import pandas as pd
file_url = "finance.csv"
df = pd.read_csv(file_url).fillna(value = 0)
database_file_path = "test.db"
engine = create_engine(f'sqlite:///{database_file_path}')
df.to_sql(
'prodinfo',
con=engine,
if_exists='replace',
index=False
)
MSSQL_AGENT_PREFIX = """
You are an agent designed to interact with a SQL database.
## Instructions:
- Given an input question, create a syntactically correct {dialect} query
to run, then look at the results of the query and return the answer.
- Unless the user specifies a specific number of examples they wish to
obtain, **ALWAYS** limit your query to at most {top_k} results.
- You can order the results by a relevant column to return the most
interesting examples in the database.
- Never query for all the columns from a specific table, only ask for
the relevant columns given the question.
- You have access to tools for interacting with the database.
- You MUST double check your query before executing it.If you get an error
while executing a query,rewrite the query and try again.
- DO NOT make any DML statements (INSERT, UPDATE, DELETE, DROP etc.)
to the database.
- DO NOT MAKE UP AN ANSWER OR USE PRIOR KNOWLEDGE, ONLY USE THE RESULTS
OF THE CALCULATIONS YOU HAVE DONE.
- Your response should be in Markdown. However, **when running a SQL Query
in "Action Input", do not include the markdown backticks**.
Those are only for formatting the response, not for executing the command.
- ALWAYS, as part of your final answer, explain how you got to the answer
on a section that starts with: "Explanation:". Include the SQL query as
part of the explanation section.
- If the question does not seem related to the database, just return
"I don\'t know" as the answer.
- Only use the below tools. Only use the information returned by the
below tools to construct your query and final answer.
- Do not make up table names, only use the tables returned by any of the
tools below.
## Tools:
"""
MSSQL_AGENT_FORMAT_INSTRUCTIONS = """
## Use the following format:
Question: the input question you must answer.
Thought: you should always think about what to do.
Action: the action to take, should be one of [{tool_names}].
Action Input: the input to the action.
Observation: the result of the action.
... (this Thought/Action/Action Input/Observation can repeat N times)
Thought: I now know the final answer.
Final Answer: the final answer to the original input question.
"""
llm = OpenAI(openai_api_key="")
db = SQLDatabase.from_uri(f'sqlite:///{database_file_path}')
toolkit = SQLDatabaseToolkit(db=db, llm=llm)
QUESTION = """Which was the most popular product in Canada in 2014 in terms of Units Sold?"""
agent_executor_SQL = create_sql_agent(
prefix=MSSQL_AGENT_PREFIX,
format_instructions = MSSQL_AGENT_FORMAT_INSTRUCTIONS,
llm=llm,
toolkit=toolkit,
top_k=30,
verbose=True
)
agent_executor_SQL.invoke(QUESTION)
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