In the world of Business Intelligence (BI), generating accurate and efficient SQL queries is a crucial step in obtaining insightful data reports. Automating this process can significantly streamline workflows and improve productivity. This technical story explores the development of a system that automates SQL generation using Oracle documentation and a Retrieval-Augmented Generation (RAG) model.
Step 1: Data Extraction from Oracle DocumentationThe first step in our pipeline involves extracting data from Oracle's comprehensive documentation, which lists all the details of Oracle ERP tables. This is where our Python script, extractData.py, comes into play. By providing a URL to the Oracle documentation, the script automates the extraction of metadata, including table structures and summaries.
The Process:- Initialization: The user provides the URL of the Oracle documentation page.
- Extraction:
extractData.py scrapes the page for relevant data on Oracle ERP tables. This includes details such as column names, data types, constraints, and a brief description of each table's purpose. - Output: The script organizes the extracted information into a structured format. Each table's details are stored in a separate folder named after the table. Inside each folder, a CSV file contains the table's schema, while another file provides a summary of the table's purpose and key characteristics.
This structured data serves as the foundation for the next steps, providing a rich dataset for model training.
Step 2: Building the Dataset for SQL GenerationWith the extracted data organized, we create a custom dataset to train our SQL generation model. The key to this process is using Retrieval-Augmented Generation (RAG), an advanced technique that enhances the model's ability to generate accurate SQL queries by incorporating external knowledge.
The Process:- Data Preparation: The extracted table structures and summaries are processed to create input-output pairs for training. For example, a table's schema might be used as input, while the desired SQL query output could involve selecting specific columns or filtering based on certain criteria.
- RAG Implementation: The RAG model leverages both the dataset and additional retrieval mechanisms. It retrieves relevant documents or snippets from the dataset as context and uses this information to generate SQL queries. This dual approach helps the model generate more accurate and contextually appropriate SQL.
Step 3: Training the ModelThe final step involves training the RAG model using the prepared dataset. The model learns to understand the relationships between the inputs (table structures and summaries) and the outputs (SQL queries). This training process involves fine-tuning the model's parameters to minimize errors and optimize performance.
The Process:- Model Initialization: The RAG model is initialized with a pre-trained language model as its backbone.
- Training Loop: The model iteratively processes the dataset, adjusting its parameters based on the accuracy of the generated SQL queries compared to the expected outputs.
- Evaluation and Fine-Tuning: The model's performance is evaluated using a separate validation set. Fine-tuning adjustments are made to improve accuracy and generalization.
WIP
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