How to Handle Ambiguous or Missing Data in LangChain SQL Agent Queries?

[SyedNusrath]

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Example Code

from langchain.utilities import SQLDatabase
from langchain.llms import OpenAI
from langchain.agents import create_sql_agent
from langchain.agents.agent_toolkits import SQLDatabaseToolkit
from langchain.agents.agent_types import AgentType

db = SQLDatabase.from_uri("sqlite:///chinook.db")
llm = OpenAI(temperature=0, verbose=True, openai_api_key=openai_api_key)

agent_executor = create_sql_agent(
    llm=llm,
    toolkit=SQLDatabaseToolkit(db=db, llm=llm),
    verbose=True,
    prefix=prefix_prompt,
    format_instructions=format_prompt,
    agent_type=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
)

agent_executor.invoke("What is the profit on iPhone in 2022?")

Description

I am using LangChain SQL Agent to query a database, but I am encountering challenges in handling ambiguous column names and missing values when users ask questions.

Example Scenario:
I asked my bot the following question:

Question: What is the profit on iPhone in 2022?

Here are two key problems that arise:

1. Ambiguous Column Names:

• The database schema contains multiple columns related to profit, such as:

  • total_profit
  • actual_profit
  • net_profit
  • gross_profit

• Ideally, the agent should detect this ambiguity and respond with:

"Multiple` profit-related columns found. Please specify one of the following: total_profit, actual_profit, net_profit, or gross_profit."

2. Missing or Inexact Values:

• The database might not contain an exact match for "iPhone" but has related values like:

  • "iPhone 4"
  • "iPhone 5"
  • "iPhone 6"

• In this case, the agent should suggest:

"No exact match for 'iPhone'. Did you mean one of the following: iPhone 4, iPhone 5, iPhone 6?"

System Info

pip install langchain