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Effortlessly transform natural language queries into AI-powered insights with NLSQL, the leading tool for AI data analytics.

How NLSQL can help you:

Transform natural language queries into SQL effortlessly.
Enable faster, data-driven business decisions.
Integrate seamlessly with major database types and corporate messengers.
Provide a Google-like interface for healthcare BI software, aiding in rapid, accurate clinical decisions.

Why choose NLSQL: Key features

First NLP to SQL API without requiring sensitive data transfer.
Supports all main database types.
Designed to significantly benefit enterprises.
Real-time access to key data for efficient decision-making.

Who should choose NLSQL:

Businesses looking to speed up decision-making with AI analytics.
Healthcare organizations aiming for better integrated and personalized healthcare.
Enterprises requiring a secure NLP to SQL solution that integrates within their IT ecosystem.

About NLSQL

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https://nlsql.com

Release Date

March 2024

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Geeky Gadgets

Thu, 20 Mar, 10:04 AM UTC

Natural Language Processing (NLP)

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Dataconomy

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Natural Language Generation (NLG)

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Democratizing data access: Seek AI changing info retrieval with AI - SiliconANGLE

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SiliconANGLE

Tue, 16 Jul, 6:05 PM UTC

Improving the Capabilities of LLM-Based Analytics

Join the DZone community and get the full member experience. Join For Free Picture this: You're an analyst drowning in a sea of data, trying to make sense of complex attribution models and customer journeys. Wouldn't it be great if you had a super-smart AI assistant that could instantly answer your questions, generate SQL queries on the fly, and break down complex tabular data? Well, that's exactly what we're working on with Large Language Model (LLM)- based analytics copilots. But as with any cutting-edge tech, it's not all smooth sailing. Let's dive into the challenges we faced and the cool solutions we came up with to make these AI assistants truly shine. The LLM Conundrum: Brilliant, but Flawed First things first: let's talk about why we're so excited about using LLMs in analytics. These language models are like the Swiss Army knives of the AI world - they can tackle a wide range of tasks, from answering questions to generating code. For us analysts, that means: Sounds great, right? But here's the catch: LLMs aren't perfect. They've got some quirks that can make them a bit tricky to work with: So, we set out on a mission to overcome these challenges and create analytics copilots that are actually useful in the real world. Our secret weapons? Semantic search and fine-tuning. Let's break it down. Semantic Search: Teaching Our AI To Find the Right Context Imagine you're at a huge library, trying to find the answer to a specific question. You could read every book, or you could ask a librarian who knows exactly where to look. Semantic search is like giving our LLM its own super-librarian. Here's how we did it: When someone asks a question, we use the same embedding magic on their query, find the most relevant chunks in our database, and feed that context to the LLM. It's like giving the AI a cheat sheet before it answers the question. Here's a simplified Python code snippet to give you an idea of how this works: We tested this setup with different LLMs (GPT-4, Falcon-40B, and Llama-2-70b) and different embedding models. The results were pretty exciting: Fine-Tuning: Teaching Old LLMs New Tricks While semantic search helped with question-answering, we still had two big problems to solve: generating SQL queries and analyzing tabular data. This is where fine-tuning came to the rescue. Fine-tuning is like sending your LLM to a specialized training camp. We take a pre-trained model and give it additional training on specific tasks. It's like teaching a chess champion how to play poker - they already understand game strategy, but now they're learning the specific rules and tactics of a new game. SQL Query Generation: From Natural Language to Database Speak For SQL generation, we used a dataset called b-mc2/sql-create-context from Hugging Face. It's got a bunch of examples that pair natural language questions with SQL queries. Here's what a typical example looks like: We fine-tuned our models on thousands of examples like this. The results were mind-blowing: Here's a simplified look at how we did the fine-tuning: Tabular Data Analysis: Making Sense of the Numbers For tabular analysis, we created a dataset specifically for attribution. We wanted our models to explain changes in attribution credit for different touchpoints. Here's an example of what our data looked like: We then fine-tuned our models to generate explanations like this: "The display channel for the lead model has seen a significant decrease in attribution credit (-82%). This is primarily due to improved targeting quality (63%), which suggests that while fewer impressions are needed, they are more effective. However, this positive effect is partially offset by increased ad cannibalization (-33%), indicating some overlap in the audience reached by different channels. The contact frequency had a minimal impact (-4%)." The results were fascinating: By combining semantic search and fine-tuning, we've managed to supercharge our analytics copilots, making them more accurate, reliable, and useful. The journey wasn't easy, but the results speak for themselves. With these advanced techniques, we're paving the way for smarter, more efficient analytics tools.

dzone.com

Mon, 22 Jul, 4:03 PM UTC

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NLSQL

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About NLSQL

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Similar products

Ai2sql

Neuralmind

EverSQL

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H2O AI

Your one-stop AI hub

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