Ragdoll AI makes building Retrieval-Augmented Generation (RAG) solutions simple and efficient.
RAG, or Retrieval-Augmented Generation, is a technique that enhances large language models (LLMs) by grounding their responses in accurate, relevant information. To implement a RAG system, you typically need to prepare your data, index it, store it as embeddings, and retrieve the most relevant pieces in response to user queries.
With Ragdoll AI, setting up a RAG knowledge base is effortless. You can upload or connect your data using our intuitive no-code interface or through our API. Once your knowledge base is ready, seamlessly integrate it into your applications via our API for powerful, fact-based responses.
The key features of Ragdoll AI are:
1. Knowledge Base: The Knowledge Base is where you can seamlessly add, view, and manage the information you want to include within a specific knowledge repository. You can upload files or connect data sources to the knowledge base. Think of a knowledge base as a customizable folder: it can house departmental knowledge, content for a particular research project, FAQs tailored to customer support, or any other specific use case.
2. Sandbox: The Sandbox is where you can interact directly with your knowledge base to evaluate its robustness and effectiveness. Here, you can experiment with the system prompt, adjust generation parameters (by default, the GPT-4O-mini model powers responses), and fine-tune retrieval settings to optimize performance. Most importantly, the Sandbox lets you engage in a conversational interface with your knowledge base, while reviewing the retrieved context to assess its accuracy, relevance, and overall quality.
3. API Integration: Once your knowledge base is ready, you can seamlessly deploy it to your preferred application or workflow—such as using workflow automation tools like N8N.io—via our API endpoints. For step-by-step guidance, refer to our comprehensive API documentation.
In our help center, you can find useful resources that include:
If you have more specific questions and want to receive personalized help, contact us at info@ragdollai.io.
