The KEY to Building Smarter RAG Database Agents (n8n)

Key Concepts

AI Agent, Database Querying, Natural Language Query (NLQ), SQL Injection, Postgress, Superbase, Database Schema, Foreign Keys, RAG (Retrieval Augmented Generation), Vector Database, Graph Database, System Prompt, Chain of Thought, Database Views, Prepared Queries, Read-Only User, Agentic RAG.

AI Agent for Database Querying

Introduction

The video demonstrates how to build an AI agent capable of querying databases, focusing on complex schemas and security against SQL injection attacks. The demonstration uses a Postgress database on Superbase, but the principles apply to other databases like SQL Server or MySQL. Blueprints and a database loading script are provided.

Demo and Initial Query

The agent is shown querying a database with tables like customers, orders, and products. It successfully answers the question: "Give me the total amount spent per customer with total dollar amounts and number of orders." The agent constructs and executes a complex SQL query joining data from multiple tables, a task that standard RAG agents struggle with.

How the Agent Works

1. Context Acquisition: The agent first retrieves database schema information (tables, columns, data types, foreign keys) using a tool.
2. Inference: The user's question is processed by the AI agent, which uses the database context to construct an SQL query.
3. Execution: The SQL query is executed against the database.
4. Response: The database returns the data to the agent, which then formats and presents the response to the user.

Comparison with Traditional RAG

Traditional RAG converts user questions into embeddings and performs semantic search in a vector database. This approach is unsuitable for databases because relational data stored in a vector database results in random, out-of-context chunks, leading to inaccurate calculations and potential hallucinations.

Building the AI Agent

The agent is built using a NAN AI agent connected to an OpenAI chat model. A "chain of thought" model (e.g., 04 mini) is recommended for constructing sophisticated queries. A Postgress chat memory node is used for short-term memory.

System Prompt Configuration

The system prompt is crucial for guiding the agent's behavior. Key instructions include:

• Calling the "get tables, schemas, columns, and foreign keys" tool before querying the database.
• Using foreign key references to construct join statements.
• Running SELECT DISTINCT queries on relevant fields to understand valid options before applying WHERE clauses.
• Constructing a single SQL query to join all relevant tables.
• Saying "Sorry, I don't know" if the question cannot be answered.

Tool for Retrieving Database Schema

The "get tables, schemas, and foreign keys" tool executes a workflow that queries a database view called get_list_of_tables_and_columns. This view contains table names, column names, data types, and foreign key references. The view is created using SQL code that can be generated by ChatGPT.

Handling Data Filtering

The agent demonstrates the ability to handle data filtering based on user input. It identifies potential misspellings or incorrect values in the user's query and prompts for clarification before executing the SQL query. For example, when asked to show products from the "clothes" category, the agent responds that it doesn't see a category named "clothes" and suggests "clothing" instead.

Optimization: Hardcoding Schema Information

For static database schemas, the schema information retrieved by the "get tables, schemas, and foreign keys" tool can be hardcoded directly into the system prompt. This eliminates the need to call the tool, resulting in faster retrieval.

Alternative Approach: Using Database Views

For complex databases, creating a database view that flattens multiple tables into a single view can simplify querying. This approach denormalizes data, introducing redundancy but making it easier to query from the AI agent. The video demonstrates creating a "full order details" view that combines data from customer, order, and product tables.

Deterministic Approach: Prepared Queries

For highly controlled scenarios, prepared queries can be hardcoded into the agent. This involves creating specific SQL queries for different tasks and assigning them to different tools. The AI agent can then call these tools with specific parameters, limiting its ability to construct arbitrary queries.

Security: Preventing SQL Injection Attacks

The video emphasizes the importance of securing the agent against SQL injection attacks. It recommends creating a read-only user within the Postgress database with restricted permissions. The video will include a read-only user script for the test database within the system blueprints in the community.

Combining with Other Retrieval Methods

The video suggests combining database querying with other retrieval methods like vector databases or graph databases. This allows the agent to leverage the strengths of each method, using vector databases for semantic search and relational databases for precise quantitative results. The system prompt can be configured to prioritize different retrieval methods based on the nature of the question.

Conclusion

The video provides a comprehensive guide to building an AI agent for database querying, covering various approaches, optimization techniques, and security considerations. It emphasizes the importance of providing the agent with accurate database context, using appropriate AI models, and implementing security measures to prevent SQL injection attacks. The video also highlights the potential for combining database querying with other retrieval methods to create more powerful and versatile AI agents.