Introduction
Creating a simple application to chat with an SQL database is relatively straightforward. However, a significant hurdle arises when dealing with large – scale databases. When the database is massive, including the entire list of columns and tables in the prompt context becomes impractical. This article delves into a solution for this problem by presenting a method to build an AI application that can effectively chat with massive SQL databases.
Creating an AI Application to Chat with Massive SQL Databases
The following is the code to initiate a basic Streamlit application. It allows users to connect to an SQL database and start a chat. This application makes use of various libraries such as Streamlit, Psycopg2, and LangChain, along with OpenAI for language – related tasks.
The application first creates necessary folders like ‘csvs’ to store database – related information in CSV format. It then loads the OpenAI API key from the environment variables and initializes language models and embeddings.
There are functions to get basic table details from the database, save these details as CSV files, generate SQL query templates, and execute SQL solutions. The Streamlit interface provides instructions to the user on how to connect to the database and start the chat.
How to Simplify the Prompt
The key strategy to simplify the prompt is to send only the relevant table and column names related to the user’s query. To achieve this, we can use vector databases like ChromaDB. First, we install ChromaDB using ‘pip install chromadb’.
An additional folder named ‘vectors’ is created to store embeddings of table and column names, along with other relevant database details. There are functions to generate embeddings, check the user’s intent (whether they are asking about the general schema or a specific SQL query), and generate SQL query prompts accordingly.
The complete code up to this point includes all the necessary setup and functions to handle user queries, connect to the database, and generate appropriate responses. The application checks if the user’s query is about general schema information or a specific SQL query and then proceeds to generate the relevant prompt and response.
After that, we perform vector retrieval to find the most relevant tables. Once these tables are identified, we gather all the column details and compile this information into a string to be included in the prompt. This ensures that the prompt contains only the most relevant information for the user’s query.
We can further enhance the application by including information about foreign keys in the prompt. This additional information can help the language model generate more accurate SQL queries.
Conclusion
This article has introduced a new approach to developing an AI – chat application for massive SQL databases. By dynamically retrieving relevant table and column names based on user queries and using vector databases for embedding generation and retrieval, we overcome the challenge of dealing with large – scale databases. The step – by – step implementation and code examples demonstrate how to streamline the interaction process. With potential further enhancements like adding more fallbacks and including more database details, this application shows great promise for various database interaction scenarios.
