Chat with BigQuery

This project is a data processing and natural language understanding tool that integrates with Google Cloud services such as BigQuery and Vertex AI. The tool allows users to generate SQL queries based on natural language input, validate these queries, and provide human-readable responses. It also includes debugging and validation mechanisms to ensure the SQL queries are syntactically and semantically correct.

Objective:
The key objective of this project is to create an intuitive tool that turns natural language inputs into SQL queries to extract relevant information from Google BigQuery datasets quickly and efficiently.

Technologies Used:

  • Google BigQuery for managing and querying large datasets.

  • Natural Language Processing (NLP) to interpret user queries.

  • Python for backend processing and API integration.

Features

Natural Language to SQL Conversion: Convert user questions in natural language into SQL queries that can be executed on BigQuery.

SQL Validation: Validate the generated SQL queries for correctness and adherence to schema definitions.

SQL Debugging: Automatically debug SQL queries by identifying and fixing errors based on BigQuery feedback.

Natural Language Responses: Generate natural language responses from the results of SQL queries, providing easy-to-understand answers to users' questions.

Modular Design: The project is structured into reusable components, making it easy to extend and maintain.

  • Dataset

    This project can work with any dataset available in Google BigQuery, including public datasets like e-commerce transactions, weather data, or company-specific datasets. For example, I used Google's bike dataset to test the interface.

  • BigQuery Connection

    The project uses the official BigQuery Python client to establish a connection with Google BigQuery. This connection allows us to query the database in real-time using the generated SQL statements.

  • Authentication

    Authentication is handled via OAuth 2.0, ensuring secure access to the dataset. Users need to authenticate through their Google accounts to connect to the BigQuery API and perform queries.

How it works

Architecture Overview:
The system is built on a simple architecture where the user inputs natural language queries into a chat interface. These inputs are then processed using Natural Language Processing (NLP) models that interpret the query’s intent. Once processed, the NLP model translates the input into SQL, which is executed on BigQuery. The results are then fetched and displayed back to the user in the chat interface.

Natural Language Processing:
The system uses NLP to understand user queries, including the detection of keywords and context that are essential to formulating a proper SQL query. Libraries like spaCy or transformers help in parsing the queries.

SQL Conversion:
To convert the natural language input to SQL, the project utilizes a combination of predefined templates and rule-based logic. Depending on the complexity of the query, the system may also leverage machine learning models to generate more nuanced SQL commands.

Next?

Challenges:
One of the key challenges in building this project was ensuring that the GenAI model can handle a wide variety of query types, especially more complex or ambiguous questions resulting in a correct SQL. There were also difficulties in scaling the project to work with extremely large datasets without incurring performance issues.

Future Enhancements:

  • Improved NLP Understanding: Future iterations of the project will involve training the NLP model on more specific domain datasets to improve its accuracy in query understanding.

  • Complex Query Handling: Adding functionality to handle more complex queries, such as those involving subqueries or advanced SQL clauses.

  • User Authentication: Enhancing security by implementing role-based access control, ensuring that only authorized users can access certain datasets.

Next

AB Testing