RagHost

What are the core features of RagHost?

• Simple API for uploading and querying documents without managing databases.
• Configurable document chunking (size and overlap) for optimized retrieval quality.
• Built-in streaming for real-time responses via the /ask endpoint.
• Support for multiple LLM models, starting with GPT 3.5 Turbo and expanding to Claude 2.
• Cost-effective alternative to OpenAI Assistants API with transparent pricing.

What are the benefits of using RagHost?

• Saves time by handling document parsing, chunking, and embeddings automatically.
• Improves answer accuracy with customizable chunking strategies for RAG.
• Enhances user experience with streaming responses for faster interactions.
• Offers flexibility by supporting non-OpenAI models like Claude 2.
• Reduces costs compared to OpenAI’s per-GB and per-assistant pricing.

What is the core purpose and selling point of RagHost?

• Simplifies RAG (Retrieval-Augmented Generation) implementation for private/internal data.
• Eliminates the need to manage databases or fine-tune models from scratch.
• Focuses on ease of use with a single API for embedding and querying documents.
• Prioritizes cost efficiency and performance with configurable settings.
• Future-proof with plans to expand model options beyond OpenAI.

What are typical use cases for RagHost?

• Building internal tools for searching company documents or knowledge bases.
• Creating customer-facing apps that answer questions using proprietary data.
• Enhancing chatbots with context from private databases via RAG.
• Streamlining workflows by integrating LLMs with internal documentation.
• Prototyping AI-powered tools quickly without infrastructure overhead.