Tech GPT

 In this blog, we'll break down the provided Python code that creates a streamlit-based AI-powered SQL assistant . This assistant enables users to interact with a database, write optimized SQL queries, and retrieve results—all powered by OpenAI's GPT-4-turbo model. By the end of this blog, you'll understand how the code works and how you can use or modify it for your own projects. Introduction The code builds a user-friendly web app that: Connects to a PostgreSQL database. Uses OpenAI's GPT-4 model to generate SQL queries. Executes the queries on the database. Displays the results, SQL query, and explanations. The app is beginner-friendly and uses Streamlit for the interface, SQLAlchemy for database interaction, and LangChain for managing the AI functionality. Breaking Down the Code 1. Setup and Dependencies Before diving into the code, the following libraries are used: Streamlit : For creating the web-based user interface. SQLAlchemy : For managing ...

When working with large CSV files and querying them using a Language Model (LLM), optimizing your approach to minimize token usage is crucial. This helps reduce costs, improve performance, and make your system more efficient. Here’s a beginner-friendly guide to help you understand how to achieve this. What Are Tokens, and Why Do They Matter? Tokens are the building blocks of text that LLMs process. A single word like "cat" or punctuation like "." counts as a token. Longer texts mean more tokens, which can lead to higher costs and slower query responses. By optimizing how you query CSV data, you can significantly reduce token usage. Key Strategies to Optimize LLM Queries for CSV Files 1. Preprocess and Filter Data Before sending data to the LLM, filter and preprocess it to retrieve only the relevant rows and columns. This minimizes the size of the input text. How to Do It: Use Python or database tools to preprocess the CSV file. Filter for only the rows an...

When processing software requirements or testing documents using large language models (LLMs) , ensuring the accuracy and reliability of the results is a critical challenge. One effective approach is using a multi-agent system that leverages multiple LLMs to cross-validate outputs, compare results, and improve overall accuracy. This approach reduces reliance on a single model and ensures robustness in processing, especially for tasks like test case generation , requirement validation , and data summarization . This blog explores how to use multi-agent systems with different LLMs to process software requirements, compare results, and measure accuracy. What is a Multi-Agent System? A multi-agent system involves multiple independent AI agents (in this case, LLMs) working together to: Perform the same task independently. Cross-validate or refine each other’s outputs. Provide diverse perspectives to improve the quality and reliability of the final result. In our case, the agen...

 Software requirements or testing documents often contain structured data, and querying or processing these documents effectively can make tasks like test case generation , requirement analysis , and data summarization much easier. In this blog, we’ll explore how to use Python and Generative AI to process software requirements or testing documents stored in CSV files. We’ll cover: Reading and preparing the CSV file. Writing queries for Generative AI using prompt engineering . Using Generative AI to extract, process, and generate additional data based on the CSV. Saving results back to a CSV file. 1. Understanding the Example CSV Here’s an example of a software requirements CSV file ( requirements.csv ): ID,Requirement,Type,Priority,Status 1,Users must be able to register and log in using their email and password,Functional,High,Approved 2,Search functionality must return relevant results within 2 seconds,Functional,Medium,Pending 3,The platform must handle 500 concurre...