Learn how to connect several essential tools to develop a simple yet intuitive dashboard using Streamlit, Plotly, DuckDB, and Pandas to visualize data from a JSON file.
Retrospect is a system for scalable data analysis that combines the simplicity of Python with built‑in map‑reduce, a fast abstract machine, and a framework for distributed computation, aiming to provide easy‑to‑use, expressive, and scalable data analysis.
This video course introduces DuckDB, an open-source database for data analytics in Python. It covers creating databases from files (Parquet, CSV, JSON), querying with SQL and the Python API, concurrent access, and integration with pandas and Polars.
This tutorial provides a comprehensive guide to Apache Doris, a high-performance, real-time analytical database. It covers installation, basic operations, data loading, querying, and performance optimization.
Apache Spark 4.0 marks a major milestone with advancements in SQL language enhancements, Spark Connect, reliability, Python capabilities, and structured streaming. It's designed to be more powerful, ANSI-compliant, and user-friendly while maintaining compatibility.
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.
