Build Secure, Accurate RAG for Regulated Industries with GroundX on OpenShift
GroundX makes deploying RAG with enterprise grade security, accuracy and scalability easy.
The world’s most important information doesn’t live on the public Internet and never will. That’s why EyeLevel.ai designed their next generation RAG (retrieval augmented generation) platform to run in the most secure data centers, including air-gapped. In this use case, we’ll explain how to set up a private RAG (“talk” to your docs application) that lives near your secure data in just a few simple steps.
GroundX turns advanced RAG into three simple calls: ingest, search and complete with the LLM of your choice. GroundX handles document ingest, parsing, chunking, storage, search and reranking without any extra work for developers.
GroundX is particularly good at understanding complex documents, which cause most RAG systems to fail.
The performance comes from a unique approach to RAG ingest. GroundX ingest combines a vision model and a VLM (visual language model) fine tuned on nearly 1M pages of enterprise documents across many verticals including health, insurance, finance, supply chain, construction and more.
Key Benefits
The key benefits of of this use-case and chosen components to highlight include:
- Advanced RAG in three simple steps
- Deploy your RAG near your data, in your secure on-premises data center or cloud
- Enterprise grade security and scalability
- Best in class document ingest model
- Connect to the LLM of your choice
- Integrates with many agentic frameworks including as MCP, Crew.ai, SmolAgents, AutoGPT and others
- Advanced RAG with GroundX
GroundX is an end-to-end RAG platform that is designed to handle all major operations required in retrieval augmented generation (RAG) workflows. GroundX handles ingest, parsing, storage, and retrieval, allowing you to upload large amounts of complex documents and retrieve relevant context via natural language queries. This context can be fed to LLM powered applications to provide greater context and promote heightened accuracy.
- Document parsing with GroundX
GroundX provides three major services, the first one is parsing. GroundX will automatically parse the content of complex PDFs, scans of documents, tables of information, slide shows, JSON data, html data, and a variety of other data types. You can read about GroundX document Ingest here.
The first point of interfacing with GroundX is uploading documents via the API, which is typically performed by Data Engineers or Application Developers.
- Data storing with GroundX
GroundX contains microservices running MySQL, Redis, and OpenSearch which automatically store the parsed representation of input documents into a queryable representation which is designed for RAG style search.
- Context retrieval with GroundX
Once data has been uploaded to GroundX, application developers can interface with that data using simple search functionality. You can read about GroundX search here. We also have guides to how GroundX can be integrated into RAG, agents, and other workflows, which you can read here.
- RAG AI application
An AI application is used for the core paradigm of interfacing with GroundX, which is to send a natural language query and then receive a textual response describing data that is relevant to that query. This allows for the following design paradigms:
- GroundX context can be retrieved on every user query and Injected throughout the continuum of chat between a user and an LLM, allowing the LLM to leverage this context when answering user queries
- GroundX can be framed as a tool which can be provided to an agent. The agent can then decide to employ GroundX to search a knowledge base and get additional contextual information.
- GroundX can be used to automatically retrieve context based on programmatic inputs, allowing GroundX to aid in automatic reporting or batch processing jobs.
AI applications can be implemented using popular frameworks like LangChain and LangGraph, or they can be simply implemented from scratch to serve more bespoke solutions.
NOTE: LangChain, LangGraph, and CrewAI are not a Red Hat certified or partner validated product, but are popular choices in building RAG and agentic applications. GroundX can also be employed, with great effect, without any external orchestration or frameworks.
“The EyeLevel.ai platform delivered truly impressive results and the bot’s ability to improve over such a short period was amazing.”
Karin OskamKnowledge Management Manager, Air France/KLM