Our project revolutionizes database interaction by integrating cutting-edge NLP algorithms with Python scripting to enable users, including data scientists and software engineers, to articulate complex database queries naturally. By interpreting user intent from plain language inputs, the system dynamically formulates precise SQL commands tailored to diverse database schemas and operations. Key features include syntactic and semantic parsing for query validation, ensuring accuracy and efficiency in query execution. This innovation not only accelerates development cycles but also empowers users to focus more on data analysis rather than query formulation complexities.

Codestral Hackathon: 24 Hours Challenge

An advanced AI-driven system utilizing genAI, NLP, Codestral model and Python to autonomously generate SQL queries based on a user-provided MySQL connection.

Neural Ninjas

The AI/ML API provides advanced features like text completion, image inference, speech-to-text, and text-to-speech with seamless integration and secure API key management.

AI/ML API

A Gradio web UI for running Large Language Models like LLaMA, llama.cpp, GPT-J, Pythia, OPT, and GALACTICA. It provides a user-friendly interface to interact with these models and generate text, with features such as model switching, notebook mode, chat mode, and more.

Text Generation Web UI

QueryMaster - AI driven SQL composer

Text-to-SQL (Structured Query Language) is a fascinating area of natural language processing where the goal is to generate SQL queries from human-readable questions. This technology has significant implications for database querying, making it more accessible to users who may not be proficient in writing SQL code. One of the key challenges in developing effective Text-to-SQL systems is ensuring that the generated queries accurately capture the user's intent while also adhering to the structure and constraints of the underlying database schema.

To enhance the accuracy and precision of Text-to-SQL systems, developers often incorporate various techniques and strategies. One such approach involves providing the model with additional contextual information in the form of prompts. These prompts typically include details such as the database schema, links to relevant tables, information about columns, and sample data. By supplying this supplementary information, the model gains a deeper understanding of the database structure and the relationships between different entities, enabling it to generate more precise queries.

The inclusion of schema links, column details, and sample data in the prompts serves several purposes. Firstly, it helps the model to grasp the semantics of the database schema, including the names of tables, their attributes, and the relationships between them. This understanding is crucial for accurately interpreting user queries and mapping them to appropriate SQL statements. Additionally, providing examples of actual data helps the model to learn patterns and correlations, enabling it to generate more contextually relevant queries.

However, even with the provision of comprehensive prompts, there is still the possibility of the model generating incorrect or suboptimal queries. To address this challenge, developers often implement a feedback loop mechanism.

GPT-4 Powered App Creation and Evals Hackathon

Input is natural language question , and we have written prompt , based on the prompt and question GPT interpretes the most accurate SQL query for that question.

AI Geeks

GPT-3.5 stands for Generative Pre-trained Transformer and is fine-tuned version of GPT-3. It is an autoregressive language model that uses deep learning to produce human-like text. There are many models in GPT-3.5 family as gpt-3.5-turbo or gpt-3.5-turbo.

GPT-3.5

OpenAI is an AI research and deployment company

OpenAI

Text-to-SQL model based on GPT

Query Optimization has been a crucial and headache issue for SQL storage engines. Databricks, Snowflake, Microsoft, and Oracle all have big teams dedicated to this field. Meanwhile, insufficient SQL queries are still everywhere and wasting tons of time and computing resources. Currently, there are various ways to rewrite an inefficient SQL query, there are even startups just to help you write good queries. But their approaches are still mostly by rules and heuristics. ChatGPT is able to rewrite some queries, but firstly the current results are not good enough, and secondly, it would be hard to incorporate data schema information such as table, index, etc. However, these obstacles are not unsolvable. We should believe LLM can play a bigger role in this field. This project leverages the power of PaLM and LangChain, demonstrating the first step towards this goal.

Google Vertex AI Hackathon

This is a query optimization tool that incorporates both state-of-the-art technology and various domain knowledge. So that engineers and data scientists can feel truly excited using this tool.

SuperCouch

Discover PaLM 2, Google's groundbreaking large language model, excelling in complex reasoning, multilingual translation, and coding expertise. Experience the next-generation AI technology that is setting new standards in the field of artificial intelligence.

PaLM

Discover Alphabet Inc.'s gem: Google LLC! A global multi-tech marvel offering internet-focused services – search engines, online ads, cloud prowess, software savvy, and hardware innovation.

Google

Large language models (LLMs) have transformed the way we approach machine learning and artificial intelligence. These models are incredibly powerful, but their true potential lies in their ability to work in harmony with other sources of data and computation. This library has been developed to assist developers in building applications that combine LLMs with other technologies, allowing them to create truly intelligent and innovative products.

LangChain

Langchain

SQL Query Optimizer by LLM

SQLGenius is a smart tool that assists C-Suite executives in querying databases in the plain English language and returns retrieved results with an option to download the data in csv format and a smart visualization engine which takes call on if a plot also needs to be generated or not for the given user query.

Current features:
1. Transform User Query to Snowflake Query using AI21 LLM model
2. Format that query to fix it if any formatting issues
3. Retrieve results from the database for selected tables
4. Convert that result back to plain English and intelligently decide based on the query result what kind of response needs to be given
5. Retrieved data can be downloaded as a CSV file as well

Future Scope:
1. Integrate visualization decision engine
2. Add voice assistant
3. Reduce response time
4. Add multi-table support for each user query

Plug into AI with AI21

SQLGenius is a smart tool that assists C-Suite executives in querying databases in the plain English language and returns retrieved results with an option to download the data in CSV format and visualization if needed.

SQLGenius

AI21 Labs is a platform for building AI applications. It provides a suite of tools and guides for building AI applications and training models. AI21 Labs helps developers and businesses use cutting-edge language models, with no NLP expertise required. Access powerful language models using an API that makes you smile

AI21 Labs

SQLGenius- Smart Virtual Assistant

Our solution generates SQL Queries based on plaine English language commands.

OpenAI GPT-3 Online Hackathon 2022 #1

CodeConsultant

GPT-3 stands for Generative Pre-trained Transformer 3 and it is an autoregressive language model that uses deep learning to produce human-like text. It is the third-generation language prediction model in the GPT-n series created by OpenAI. GPT-3 is currently in open beta.

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