AI_RAG delivers a modular retrieval-augmented generation solution that combines document indexing, vector search, embedding generation, and LLM-driven response composition. Users prepare corpora of text documents, connect a vector store like FAISS or Pinecone, configure embedding and LLM endpoints, and run the indexing process. When a query arrives, AI_RAG retrieves the most relevant passages, feeds them alongside the prompt into the chosen language model, and returns a contextually grounded answer. Its extensible design allows custom connectors, multi-model support, and fine-grained control over retrieval and generation parameters, ideal for knowledge bases and advanced conversational agents.

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.