Project Title

Verba — A Community-Driven RAG Chatbot for Data Interaction

Overview

Verba, also known as the Golden RAGtriever, is an open-source, community-driven application that offers an end-to-end Retrieval-Augmented Generation (RAG) chatbot experience. It is designed to provide a user-friendly interface for querying and interacting with data, either locally or via cloud deployment, using Weaviate's context-aware database. This project stands out for its flexibility in choosing RAG frameworks, data types, and LLM providers, catering to various individual use-cases.

Key Features

Integration with Weaviate for context-aware data interaction
Support for multiple RAG frameworks and LLM providers (e.g., Ollama, HuggingFace, Cohere)
Customizable personal assistant for querying and interacting with datasets
Local and cloud deployment options

Use Cases

Data Scientists: Extract insights and cross-reference data points within large datasets
Enterprises: Build custom chatbots for internal knowledge bases and customer support
Researchers: Resolve questions and gain insights from existing knowledge bases

Advantages

Flexibility in choosing RAG frameworks and LLM providers
Easy deployment with pip, source, or Docker
Community-driven development for continuous improvement

Limitations / Considerations

Community-driven project, which may result in slower issue resolution compared to commercial software
Customization and setup may require a certain level of technical expertise

RAG-TensorFlow: A TensorFlow implementation of Retrieval-Augmented Generation, differing in its focus on the TensorFlow ecosystem.
LangChain: A framework for building LLM applications, offering a different approach to chatbot development.
GPT-3-Chatbot: A simpler chatbot project leveraging GPT-3, which may be more suitable for beginners or those looking for a more straightforward implementation.