RagHost
Build RAG-powered applications through simple API integration
Target Audience
- Developers building RAG applications
- Companies needing internal document search
- Customer support teams creating chatbots
Hashtags
Overview
RagHost lets developers quickly create document search tools and question-answering apps without managing complex infrastructure. Simply upload PDFs or text files via API, and let RagHost handle document parsing, chunking, and AI-powered responses. Focus on building your app while we handle the retrieval-augmented generation (RAG) pipeline.
Key Features
Configurable Chunking
Customize document chunk size/overlap for better search accuracy
Streaming Responses
Real-time answer delivery keeps users engaged
Multi-Model Support
GPT-3.5 Turbo now, Claude 2 and others coming soon
Cost Efficiency
Avoid OpenAI's per-assistant fees with optimized pricing
Simple API
Two endpoints for embedding documents and asking questions
Use Cases
Build internal document search tools
Create customer-facing Q&A chatbots
Analyze financial reports with embedded data
Pros & Cons
Pros
- Simplifies RAG implementation for developers
- Configurable document processing settings
- Real-time response streaming
- More cost-effective than OpenAI Assistants API
Cons
- Currently only supports GPT-3.5 Turbo (Claude 2 pending)
- Limited integration options mentioned
- Pricing still in beta phase
Frequently Asked Questions
Why choose RagHost over OpenAI Assistants API?
RagHost offers more cost-effective pricing and upcoming multi-model support while handling document processing infrastructure for you.
What AI models are available?
Currently supports GPT-3.5 Turbo, with Anthropic's Claude 2 and others coming soon.
Can I control how documents are processed?
Yes, configure chunk size and overlap parameters during embedding.
Reviews for RagHost
Alternatives of RagHost
Instantly find answers in your private documents using AI chat
Build and optimize Retrieval-Augmented Generation (RAG) pipelines efficiently
Connect LLM applications to real-world web data with high-quality context