Tutorial on enforcing JSON output with Llama.cpp or the Gemini’s API for structured data generation from LLMs.
A study investigating whether format restrictions like JSON or XML impact the performance of large language models (LLMs) in tasks like reasoning and domain knowledge comprehension.
Introduction to Pkl, a programming language designed for generating configuration files.
Addresses limitations of static languages and general-purpose languages for configuration purposes.
Provides safety by catching validation errors before deployment.
Scales from simple to complex use-cases.
Enhanced with capabilities borrowed from general-purpose languages.
Familiar syntax and easy learning curve.
Built-in validation using type annotations.
Ability to publish packages and import them as dependencies in a project.
Language bindings for Swift, Go, Java, and Kotlin.
Editor support for IntelliJ, VS Code, and Neovim.
Addresses limitations of static languages and general-purpose languages for configuration purposes.
Provides safety by catching validation errors before deployment.
Scales from simple to complex use-cases.
Enhanced with capabilities borrowed from general-purpose languages.
Familiar syntax and easy learning curve.
Built-in validation using type annotations.
Ability to publish packages and import them as dependencies in a project.
Language bindings for Swift, Go, Java, and Kotlin.
Editor support for IntelliJ, VS Code, and Neovim.
The llama-cpp-agent framework is a tool designed for easy interaction with Large Language Models (LLMs). It provides a simple yet robust interface using llama-cpp-python, allowing users to chat with LLM models, execute structured function calls and get structured output.
How to unnest / extract nested JSON data in BigQuery
