Taking a Retrieval-Augmented Generation (RAG) solution from demo to full-scale production is a long and…
By Angela Chen
Composable and Customizable Advanced RAG
Pre-built, out-of-the-box, and production ready RAG framework built for enterprises, compatible with NVIDIA NIMs for GPU acceleration
Advanced RAG
Incorporating the latest research in RAG to include Query Rewriting, Re-ranking, Model Orchestration and more with support for NVIDIA NIMs
Reduced Hallucination
More data means higher quality answers. Made possible by Nexsets that merge data from across Vector DB, Databases, APIs, and Real-time events.
Future Proof
Composable design with ability to include Python gives our users the ability to tap into the latest advancements without dependency.
No Data Leaks
Rock solid security against prompt hacking built on strict user level access controls.
Multi-model
Easily route to multiple models and compare for quality, latency, and cost, or choose the best one from multiple simultaneous answers.
Agentic RAG
Agentic approach where each step can orchestrate external services and LLMs to enhance data richness and reasoning to create higher quality, more reliable outcomes across a variety of scenarios.
✕
Advanced RAG Features that are Ready-to-Use but Customizable
Query Rewriting
Re-ranking
Agentic Orchestration
Access Control
Grading
Hardware Acceleration
Model Orchestration
Embeddable API
Built-in query rewriting expands on user intent allowing for better match of information across vector database as well identifying other relevant data sources.
Modular re-ranker. Use out-of-box or any third party reranker with option for hardware-accelerated NVIDIA NIM
Agentic approach to orchestrating l allows real time data pull from API and Database sources, powered by API query and SQL generation.
Service Key based access ensures RAG flow can only access data that user has permission to
Built in evaluation for answer quality acts as a safeguard against hallucination and can help build a set of test cases.
Native integration to NVIDIA NIMs in any cloud or on-premise environment for GPU acceleration of multiple components such as inference, re-ranking, SQL generation.
Prebuilt connection to over 20+ models. Easy to customize and add setup to new models, including ones you run privately.
RAG framework available as an API to embed into your own chatbot or UI compete with citations and conversation history.
Learn More
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