Verta RAG System
Enhances AI with real-time data retrieval and no-code ease.. [Contact for Pricing]
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What is Verta RAG System?
Verta RAG System is a retrieval-augmented generation (RAG) platform that combines pre-trained large language models (LLMs) with your private, trusted data sources to generate accurate, up-to-date AI answers. It enables users to go from dataset to a custom RAG prototype in just 5 minutes with no-code setup, making it accessible to both technical experts and non-technical domain experts.
Key features include embedded RLHF (Reinforcement Learning from Human Feedback) and automated evaluation for benchmarking as you build, automatic citations with every output, hallucination detection and warnings, support for multiple vectorDBs and embedding models, configurable chunking parameters, a playground environment for testing, a GenAI Leaderboard to compare model and prompt configurations across quality/cost/latency dimensions, and the ability to easily update your knowledge base (PDFs, spreadsheets) via the UI. The system provides a dummy-proof endpoint at integration time and supports deployment on AWS, Azure, GCP, and on-premise.
Verta RAG System is designed for GenAI product builders of all technical levels, enterprise data science and ML teams, domain experts who need to steer application results, and organizations that need accurate, domain-specific AI responses from their proprietary data. It's ideal for customer support chatbots, internal knowledge bases, research assistants, and any use case requiring timely, relevant, and accurate LLM outputs grounded in your business data.
Verta RAG System pricing
Pricing model: Freemium
Contact for pricing - specific pricing details are not publicly listed on the website. Users can get started for free by signing up at https://app.verta.ai to create a project and load documents for a prototype. The pricing model typically involves subscription tiers based on usage volume and access to advanced features. The Verta GenAI Workbench is available on AWS, Azure, GCP, and on-premise deployments.
Verta RAG System pros
- Go from dataset to custom RAG prototype in 5 minutes
- No-code setup - no environment nightmares
- No model training time or money needed
- Embedded RLHF for continuous improvement
- Automated evaluation benchmarking as you build
- Automatic citations with every output
- Built-in hallucination detection and warnings
- Configure vectorDBs, embedding models, chunking or skip it
- Easy maintenance and scaling via UI
- Give SMEs access to update knowledge base
- Support for leading foundational LLMs (Llama 2, Mistral, Falcon, GPT, PaLM)
- GenAI Leaderboard compares quality, cost, and latency
- Deploy on AWS, Azure, GCP, or on-premise
- Dummy-proof endpoint at integration time
- Upload evaluation dataset for scale testing
- Human evaluation support with feedback labeling
- Leaderboard feature to compare experiments
- Automatic vectorization of uploaded documents
- Standard question generation from documents for testing
- Privacy-focused - answers from your trusted private data
Verta RAG System cons
- Specific pricing details not publicly listed - contact for pricing
- May require technical expertise for optimal configuration
- Performance depends on quality of external data uploaded
- Smaller teams or individual users may find pricing prohibitive
- Limited documentation on advanced customization options
- Requires signing up to access full features
- VectorDB configuration complexity for advanced users
- Evaluation dataset preparation requires manual effort
Frequently asked questions about Verta RAG System
What is Verta RAG System?
Verta RAG System is a retrieval-augmented generation platform that combines pre-trained large language models with your trusted, private data sources. It generates AI answers from your knowledge base (PDFs, spreadsheets, etc.), ensuring outputs are up-to-date, relevant, and accurate by retrieving information from your proprietary data rather than relying solely on the LLM's training.
How long does it take to set up Verta RAG?
You can go from dataset to custom RAG prototype in just 5 minutes with no code needed. Simply sign up, load your documents, and get a prototype. The no-code setup eliminates environment nightmares and requires no ML expertise.
Do I need ML expertise to use Verta RAG?
No, you don't need ML expertise. Verta emphasizes no-code setup where you can leverage this industry-defining technique without knowing what embedding or chunking means. Domain experts can use the UI to update the knowledge base and label benchmark datasets.
How does Verta handle hallucinations?
Every output comes with automatic hallucination detection and warnings. If hallucinations are suspected, the system will warn you. Additionally, every answer comes with embedded citations so you can verify the source information.
What data sources can I use with Verta RAG?
You can upload PDFs, spreadsheets, product documentation, and any relevant documents for your chatbot. These documents are automatically vectorized to serve as your knowledge base. You can easily update your knowledge base anytime via the UI.
What LLMs does Verta support?
Verta supports an always up-to-date variety of open-source LLMs like Llama 2, Mistral, and Falcon alongside proprietary models like OpenAI's GPT models and Google's PaLM. You can choose from leading foundational models and experiment with different configurations.
How does the GenAI Leaderboard work?
The GenAI Leaderboard ranks model and prompt configurations on different dimensions like application-specific quality, cost, and latency. It uses the leaderboard feature to compare different experiments, allowing you to analyze results and determine which configurations yield the best outcomes for your specific application.
Can I evaluate my RAG application?
Yes, Verta provides comprehensive evaluation capabilities. The system automatically evaluates responses using quality metrics like guideline adherence. You can upload an evaluation dataset comprising various queries to test at scale, and also involve human evaluators to label data and provide feedback for comprehensive quality assessment.
Where can I deploy Verta RAG?
The Verta GenAI Workbench is available on AWS, Azure, and GCP, as well as on-premise deployments. It offers a cloud-native, portable, and consistent experience everywhere, allowing you to deploy any model anywhere with enterprise-grade performance and scale.
How do I get support for Verta RAG?
You can ping the team at [email protected] and one of them will help you problem solve and tinker with your use case. They also provide documentation with step-by-step guidance on popular tasks and offer free Cloudera AI training to help you start projects.