@githubprojects writes about Quivr-core, a Python package extracted from Quivr.com's production retrieval-augmented generation pipeline that lets developers get a working system in five lines of code. It centers on a `Brain` class that ingests files and answers questions, with a YAML-configurable workflow (filter history → rewrite → retrieve → generate) that keeps the pipeline inspectable rather than a black box. It supports OpenAI, Anthropic, Mistral, and local Ollama models, and handles PDFs, Markdown, and TXT files out of the box.
- Integrates with Megaparse for more sophisticated document ingestion without switching frameworks.
- The opinionated philosophy is the core differentiator: sensible defaults over an infinite configuration surface.
- Integrates with Megaparse for more sophisticated document ingestion without switching frameworks.
- The opinionated philosophy is the core differentiator: sensible defaults over an infinite configuration surface.
Minimalist LLM Framework in 100 Lines. Enable LLMs to Program Themselves.
Verba is an open-source application designed to offer an end-to-end, streamlined, and user-friendly interface for Retrieval-Augmented Generation (RAG) out of the box. It supports various RAG techniques, data types, LLM providers, and offers Docker support and a fully-customizable frontend.
