Verba is a retrieval-augmented generation interface and chatbot that uses Weaviate to provide factual answers based on private datasets. It functions as a vector database knowledge base, combining a hybrid search engine with an orchestration interface to connect various large language model providers and embedding services.
The system differentiates itself through a RAG pipeline manager for adjusting text chunking rules and retrieval settings, alongside a 3D vector space visualization tool for analyzing the spatial organization and clustering of high-dimensional embeddings. It employs a modular provider system that allows for swapping between different local and cloud text generation and embedding services.
The platform covers multi-modal data ingestion, processing unstructured documents, audio transcriptions, web crawls, and version control repositories into a searchable knowledge base. Its retrieval capabilities combine semantic and keyword search to extract relevant context from vector stores, utilizing configurable text chunking to optimize retrieval precision.