raghost

RAG as a service.

Visit Website
raghost

Introduction

What is RagHost?

RagHost is a game-changer in the world of internal tool development, allowing you to rapidly build RAG-powered tools that can search documents and answer questions with a single API. It's the perfect solution for those who want to leverage the power of Large Language Models (LLMs) without the hassle of training from scratch or fine-tuning models.

Key Features of RagHost

  • Easy Document Embedding: Upload your documents and let RagHost handle the heavy lifting of document parsing, chunking, and vector embeddings.

  • Configurable Chunking: Choose your chunk size and chunk overlap to optimize the quality of answers.

  • Built-In Streaming: Get instant responses with our /ask endpoint that streams LLM responses.

  • Model Flexibility: Use non-OpenAI models, including Anthropic's Claude 2, and more to come.

  • Fair Pricing: Say goodbye to OpenAI's expensive pricing model and hello to a more affordable solution.

How to Use RagHost

Using RagHost is a breeze. Simply embed your files using the /embed endpoint and ask questions using the /ask endpoint. Our API takes care of the rest, providing you with accurate and relevant answers.

Why Choose RagHost Over OpenAI Assistants API?

  • More Model Options: RagHost offers a range of models, including non-OpenAI options, giving you more flexibility and choice.

  • Customizable Chunking: Optimize your chunking strategy to get the best possible answers.

  • Cost-Effective: RagHost's pricing model is designed to be fair and affordable, so you can focus on building your app without breaking the bank.

Helpful Tips

  • Experiment with Chunking Strategies: Find the perfect chunk size and overlap to get the most accurate answers.

  • Try Different Models: See how different models perform with your specific use case.

  • Integrate with Your App: Seamlessly integrate RagHost with your existing app to provide a seamless user experience.

Frequently Asked Questions

  • What is Retrieval-Augmented Generation?: RAG is a technique that involves feeding an LLM with relevant context from a database to generate more accurate answers.

  • How Does RagHost Handle Document Parsing?: We take care of document parsing, chunking, and vector embeddings, so you don't have to.

  • Can I Use RagHost for Customer-Facing Apps?: Absolutely! RagHost is perfect for building customer-facing apps that require accurate and relevant answers.