Advanced NLP-to-SQL Chatbot System for Efficient Data Querying

Key Features

• The system processes user input, generating SQL queries using Langchain’s SQL Agent, and executes them on a PostgreSQL database to retrieve relevant data.
• Embeddings are created for user queries using Azure Embedding Model, ensuring that the system can match user intent with stored data accurately.
• FAISS is used for efficient vector management, allowing the system to search and retrieve similar queries or historical examples from a large dataset.
• The system integrates fine-tuned language models, such as GPT-3.5 and GPT-4, which provide detailed and relevant responses based on user queries, ensuring high-quality answers.

Technologies Used

Challenges

The main challenge was converting free-form natural language into precise SQL queries, which required training the system to understand complex user inputs and generate accurate results. Additionally, maintaining query accuracy across various contexts was a significant challenge.

Solution

The solution utilized Langchain’s framework, which enabled the dynamic generation of SQL queries from user input. Fine-tuning of language models ensured that the system could handle a wide range of query types, providing accurate and contextually appropriate results. The integration of Azure’s embedding model helped improve the system's understanding of user intent and enhanced the system’s accuracy.

Results

Application significantly improved data querying for real estate professionals by allowing them to access relevant data quickly and efficiently using natural language. It helped save time on manual queries and provided insights that would have otherwise required deep technical expertise to extract.