AutoRAG is a production-grade, privacy-focused personal AI assistant and retrieval-augmented generation (RAG) agent. It provides a local-first search and answer engine that indexes federated datasources (Slack, Discord, KakaoTalk, GitHub, email, Obsidian, RSS, cloud drives, and more) via external CLI connectors, without migrating data. The system exposes a CLI, MCP server, TypeScript API, and a TUI (Pi interactive host), and powers a separate desktop Electron app. Its architecture is modular: a configurable agent (`AutoRAGAgent`) orchestrates multiple retrieval methods (BM25, semantic vectors, hybrid RRF, file-name search, web search), document parsing (PDF, DOCX, HWP, images via OCR), embedding (local Qwen3 model or external APIs), reranking (OpenRouter/Dazzi), and evidence judgment. A peer-to-peer layer (SimpleX) enables policy-controlled file sharing with injection and PII gates. The repository also contains a legacy Python codebase (`legacy/`) focused on automated RAG pipeline evaluation and QA dataset generation, which is in maintenance mode but still provides reusable metrics and pipeline components. This is a hybrid project where the dominant character is a modern TypeScript application, with significant data/research heritage in the legacy Python portion.
Promptify is a Python library for performing NLP tasks using large language models (LLMs) with structured, type-safe outputs. It provides a scikit-learn-like interface where users instantiate task classes (e.g., NER, Classify, QA, Summarize) and call them with plain text. The library uses LiteLLM as a universal backend to support 100+ providers (OpenAI, Anthropic, Ollama, etc.), Pydantic v2 for output schemas, Jinja2 for prompt templates, and tenacity for retries. Architecture follows a clean separation: Task classes (promptify/tasks) build prompts via PromptBuilder, send them via LLMEngine (which wraps LiteLLM), and parse responses via a safe Parser that never uses eval(). The package also includes a built-in evaluation framework (promptify/eval) with metrics for precision, recall, F1, accuracy, exact match, and ROUGE, plus dataset loading utilities. The code is well-structured, typed, and includes comprehensive tests. The primary artifact is the "promptify" Python package, installable via pip. The repository is not a data research project; it is a software library for developers.
This repository is not a software application or data pipeline; it is a GitBook-based knowledge base and career guide for software testers, written entirely in Polish. It curates learning paths, book lists, blog directories, tool-specific resources (Selenium, Appium, Playwright, JMeter, Jenkins, etc.), certification guidance (ISTQB and others), job-search advice, remote/crowd testing opportunities, salary reports, and community/conference listings for the Polish QA ecosystem. There is no executable source code, API, CLI, or test suite; the 'application' classification is forced because the repo is a static documentation site rather than a data research project.