The article explores techniques to improve Large Language Model (LLM) accuracy, focusing on Lamini Memory Tuning. It discusses fine-tuning methods like Low-Rank Adaptation (LoRA), the advantages and disadvantages of fine-tuning, and practical steps using Lamini to achieve higher precision in SQL query generation. The author demonstrates a step-by-step approach to creating a high-quality dataset, fine-tuning, and evaluating model accuracy.
This article discusses methods to measure and improve the accuracy of Large Language Model (LLM) applications, focusing on building an SQL Agent where precision is crucial. It covers setting up the environment, creating a prototype, evaluating accuracy, and using techniques like self-reflection and retrieval-augmented generation (RAG) to enhance performance.
A new plugin for sqlite-utils CLI tool called sqlite-utils-ask allows users to ask human-language questions directly of SQLite databases and CSV/JSON files, using an LLM to generate SQL queries and execute them.
Learn how to automate AI embedding creation using PostgreSQL with pgai Vectorizer. Streamline your AI workflow with simple SQL commands.
ntegration: PGAI Vectorizer integrates AI capabilities into PostgreSQL, enabling users to generate AI embeddings directly within the database.
Ease of Use: It simplifies the process of creating embeddings using a single SQL command, eliminating the need for multiple tools and complex pipelines.
Automatic Sync: Embeddings are automatically updated as data changes, ensuring that embeddings stay current without manual intervention.
Model Flexibility: Users can quickly switch between different AI models without reprocessing data.
Scalability: Optimizes search performance with vector indexes, making it suitable for large datasets.
Customization: Allows users to define chunking and formatting rules to tailor embeddings to their specific needs.
ntegration: PGAI Vectorizer integrates AI capabilities into PostgreSQL, enabling users to generate AI embeddings directly within the database.
Ease of Use: It simplifies the process of creating embeddings using a single SQL command, eliminating the need for multiple tools and complex pipelines.
Automatic Sync: Embeddings are automatically updated as data changes, ensuring that embeddings stay current without manual intervention.
Model Flexibility: Users can quickly switch between different AI models without reprocessing data.
Scalability: Optimizes search performance with vector indexes, making it suitable for large datasets.
Customization: Allows users to define chunking and formatting rules to tailor embeddings to their specific needs.
This article discusses how to overcome limitations of retrieval-augmented generation (RAG) models by creating an AI assistant using advanced SQL vector queries. The author uses tools such as MyScaleDB, OpenAI, LangChain, Hugging Face and the HackerNews API to develop an application that enhances the accuracy and efficiency of data retrieval process.
Quadratic is a modern spreadsheet that combines the familiarity of a spreadsheet with the power of code, allowing you to work with data and code collaboratively in real-time. It supports popular programming languages like Python, SQL, and JavaScript, and offers features such as dynamic charts, APIs, multi-line formulas, and AI integration.
A prototype tool powered by Large Language Models to make querying your databases as easy as saying the word.
- Introduction to QueryGPT, a tool using Large Language Models (LLMs) for natural language database queries
- Focus on implementing a basic iteration of the system, with potential for significant enhancements
- Aim is to provide the LLM with the database schema and have it answer questions based on that context
- Discussion on prompt engineering, which is designing inputs for generative AI tools to produce optimal results
- Introduction to QueryGPT, a tool using Large Language Models (LLMs) for natural language database queries
- Focus on implementing a basic iteration of the system, with potential for significant enhancements
- Aim is to provide the LLM with the database schema and have it answer questions based on that context
- Discussion on prompt engineering, which is designing inputs for generative AI tools to produce optimal results
Tune a base LLama2 LLM to output SQL code. with Parameter Efficient Fine-Tuning techniques to optimise the process.
training GPT to query with few-shot prompting.
