DB-conv is a lightweight Python library designed to enable conversational AI over SQL databases. After installation, developers configure it with database connection details and LLM provider credentials. DB-conv handles schema introspection, constructs optimized SQL from user prompts, executes queries, and returns results in tables or charts. It supports multiple database engines, caching, query logging, and custom prompt templates. By abstracting prompt engineering and SQL generation, DB-conv simplifies building chatbots, voice assistants, or web interfaces for self-service data exploration.

MindSearch provides a modular Retrieval-Augmented Generation architecture designed to enhance large language models with real-time knowledge access. By connecting to various data sources including local file systems, document stores, and cloud-based vector databases, MindSearch indexes and embeds documents using configurable embedding models. During runtime, it retrieves the most relevant context, re-ranks results using customizable scoring functions, and composes a comprehensive prompt for LLMs to generate accurate responses. It also supports caching, multi-modal data types, and pipelines combining multiple retrievers. MindSearch’s flexible API allows developers to tinker with embedding parameters, retrieval strategies, chunking methods, and prompt templates. Whether building conversational AI assistants, question-answering systems, or domain-specific chatbots, MindSearch simplifies the integration of external knowledge into LLM-driven applications.