Chat with SQL and Tabular Databases using LLM Agents (DON'T USE RAG!)

Introduction

In this detailed tutorial, we will explore how to effectively interact with SQL and tabular databases using Large Language Model (LLM) agents. This approach provides a better alternative compared to Retrieval-Augmented Generation (RAG) techniques, enabling more accurate and precise answers from your databases.

Understanding the Concepts

When working with databases, two distinct methodologies can be employed:

1. RAG (Retrieval-Augmented Generation): This method utilizes an embedding model to convert user queries into vectors. These vectors are then used to perform searches in a vector database, retrieving relevant content which is finally interpreted via an LLM model.

2. Q&A with LLM Agents: In contrast, this method converts user queries into database-understandable queries directly, allowing for greater accuracy in results.

The key distinction lies in the fact that while RAG looks for semantic relationships, Q&A with LLM agents aims to create and execute precise queries, thus providing more reliable insights and allowing for complex query handling.

Overview of the Series

This article presents the first part of an Advanced RAG series, focusing on LLM agents applied to databases derived from CSV files and Excel spreadsheets. As part of this endeavor, we will convert and manage different data formats, utilizing frameworks like Microsoft Azure and LangChain.

Project Breakdown

The project involves several key stages:

1. Data Input: Uploading data in the form of CSV and Excel files, or directly linking to SQL databases.
2. Database Integration: Using SQLite to convert the uploaded data into SQL databases.
3. Vector Database Creation: Transforming existing tabular data into a vector database, allowing for more advanced queries leveraging RAG techniques.
4. Handling and Querying: Formulating queries against SQL databases and extracting desired information with LLM agents.

Setting Up the Environment

To implement the project, you need:

• Knowledge of SQL and Python.
• Libraries such as LangChain, SQLAlchemy, and relevant configuration for connecting to OpenAI or Azure models.
• Proper setup of database files (CSV, Excel, and SQL).

Implementing the Solution

Here’s a concise breakdown of the implementation steps:

1. Database Creation:

   • Convert the datasets from CSV/Excel to SQL databases using SQLite.
   • For SQL files, ensure they are appropriately formatted and placed in the specified directories.

2. Creating LLM Agents:

   • Leverage LangChain to build agents that can query your databases effectively.
   • Implement an agent that can create SQL queries based on user prompts.

3. Performing Queries:

   • Test the agents to ensure they provide relevant answers from the SQL databases.
   • The queries can range from simple counts to averages and more specific dataset inquiries.

4. Interacting via the Chatbot:

   • Implement a user-friendly chatbot interface that allows users to type in queries or upload files for immediate database interaction.
   • Ensure the chatbot can seamlessly switch between different database types for Q&A and retrieval.

Limitations of RAG

While RAG provides a method for interaction with databases, the inherent limitations, especially for complex queries, reveal that utilizing LLM agents is a more effective strategy. For example, asking questions like "What is the average age of survivors?" may yield approximated results but lacks precision compared to clarity gained through SQL queries via LLM agents.

Conclusion

In conclusion, using LLM agents to interact with SQL and tabular databases presents a far superior way of obtaining results. By forming structured queries and harnessing the capabilities of language models, developers can build robust systems capable of addressing user inquiries accurately.

The power of LLM agents is underscored by their ability to refine their queries dynamically based on context and user input, which is critical for any system handling complex or extensive datasets.

Keywords

Keywords: SQL databases, LLM agents, tabular data, RAG, Azure, LangChain, embeddings, queries, chatbot interface.

FAQ

Q1: What is the main advantage of using LLM agents over RAG?
A1: LLM agents provide more accurate and precise answers by directly generating database queries, unlike RAG which relies on semantic search.

Q2: What types of databases can be integrated using this method?
A2: You can integrate SQL databases created from CSV files, Excel files, and even from direct SQL data.

Q3: What libraries are required to implement this solution?
A3: You will need libraries such as LangChain, SQLAlchemy, and any relevant API for connecting with LLMs like Azure or OpenAI.

Q4: Can LLM agents handle complex queries?
A4: Yes, LLM agents can handle complex queries by creating specific SQL commands based on user prompts, which RAG may struggle to resolve accurately.

Q5: Is a chatbot interface necessary for interaction with databases?
A5: While not necessary, a chatbot interface simplifies user interaction, allowing for easy question submission and dataset uploads.