The Text2SQL CSV project is a Python-based application designed to facilitate the conversion of natural language queries into SQL queries and execute these queries on a SQLite database. This project leverages the power of Large Language Models (LLMs) to interpret user input and generate SQL queries, enabling users to interact with their data in a more natural and intuitive manner.

Clone the repo:

git clone https://github.com/LexiestLeszek/text2sql_CSV.git

Then install the necessary Python libraries, run the following command:

pip install pandas sqlite3 llmware

1. Upload CSV to SQLite: Use the upload_csv_to_sqlite function to upload a CSV file to a SQLite database. This function reads the CSV file, creates a table in the SQLite database with the same name as the CSV file, and inserts the data from the CSV file into the table.

   db_schema = upload_csv_to_sqlite('./StockRatings.csv', './StockRatings.db')

2. Text to SQL: Use the text2sql function to convert a natural language query into an SQL query. This function uses the LLM to interpret the query and generate an SQL query.

   query_sql = text2sql('Who has the best overall rating?', db_schema)

3. Query Database: Use the query_db function to execute the generated SQL query on the SQLite database and retrieve the results.

   answer = query_db(query_sql, './StockRatings.db')
   print(answer)

Note: First run will download LLMWare models and it will take some time.

Contributions are welcome! For major changes, please open an issue first to discuss what you would like to change. Please make sure to update tests as appropriate.

This project is licensed under the MIT License. See the LICENSE file for details.