LlamaIndex is a developer-focused Python library designed to bridge the gap between large language models and private or domain-specific data. It offers multiple index types—such as vector, tree, and keyword indices—along with adapters for databases, file systems, and web APIs. The framework includes tools for slicing documents into nodes, embedding those nodes via popular embedding models, and performing smart retrieval to supply context to an LLM. With built-in caching, query schemas, and node management, LlamaIndex streamlines building retrieval-augmented generation, enabling highly accurate, context-rich responses in applications like chatbots, QA services, and analytics pipelines.

Rhippo revolutionizes the way teams collaborate with their LLM chatbots. By creating a 'brain' that injects relevant context into your prompts and maintains an updating knowledge database, it ensures that only important project information is shared. The setup is swift, taking less than 10 minutes, and includes integrations with Slack and Google Drive for seamless communication. Rhippo promises improved responses with state-of-the-art embedding models, guaranteeing data transparency through Google Drive.