Building a Database Query Assistant

This guide explores the key challenges and interesting aspects of our Database Query Assistant.

Full code and demo can be found here

Key Challenges and Solutions

1. Natural Language to SQL Translation

One of the primary challenges is translating natural language queries into valid SQL. Our solution uses the Opper framework to generate SQL queries based on the user's input and the database structure.

Key function: generate_sql_query()

def generate_sql_query(conversation: str, db_structure: str, comment: str = "None") -> Query:
    return opper.call(
        "generate_sql_query",
        instructions="Given a conversation and a database structure and comments on previous attempts, generate an sql query to answer the question",
        input={
            "conversation": conversation,
            "db_structure": db_structure,
            "comment": comment
        },
        output_type=Query
    )

This function takes the conversation history, database structure, and any previous comments to generate an appropriate SQL query.

2. Handling Query Failures

Not all generated queries will be successful. We implemented a reflection and retry mechanism to handle query failures. Key function: reflect_on_result()

def reflect_on_result(conversation: str, db_structure: str, query: str, query_result: str) -> Reflection:
    return opper.call(
        "reflect_on_result",
        instructions="Given a conversation, a database structure, a query and a query result, reflect and decide if you are satisfied with the query or want to improve it",
        input={
            "conversation": conversation,
            "db_structure": db_structure,
            "query": query,
            "query_result": query_result
        },
        output_type=Reflection
    )

This function analyzes the query result and determines if the query needs improvement.

3. Maintaining Conversation Context

To provide coherent responses, the assistant needs to maintain the context of the conversation. We achieve this by appending each question and response to a conversation string.

4. Dynamic Database Structure Understanding

The assistant needs to understand the structure of any given database. We solve this with the get_db_structure() method in the DatabaseManager class.

This method retrieves table names, column information, and sample data to provide context for query generation.

5. Interesting Features

Tracing and Metrics

We use the Opper framework's tracing capabilities to log each step of the process and collect metrics on user feedback.

Suggestion System

The assistant can suggest potential questions based on the database structure and conversation history.

def suggest_question(conversation: str, db_structure: str) -> str:
    return opper.call(
        "suggest_question",
        instructions="Given a conversation and a database structure, suggest a likely question that the user might ask next",
        input={
            "conversation": conversation,
            "db_structure": db_structure,
        },
        output_type=str
    )

Verbose Mode

For debugging and educational purposes, we implemented a verbose mode that displays the thought process, generated SQL, and query results.

Conclusion

The Database Query Assistant demonstrates how to combine natural language processing with database operations. By addressing challenges such as query generation, error handling, and context maintenance, we've created a tool that allows users to interact with databases using natural language.

The use of the Opper framework for query generation and reflection, along with features like suggestion and verbose mode, make this assistant both powerful and educational. Developers can use this code as a starting point for building more advanced database interfaces or for learning about natural language processing in the context of databases.