Rerank documents based on a query
ModernBERT for reasoning and zero-shot classification
Submit model predictions and view leaderboard results
Upload a table to predict basalt source lithology, temperature, and pressure
Compare different tokenizers in char-level and byte-level.
Generate relation triplets from text
Choose to summarize text or answer questions from context
Easily visualize tokens for any diffusion model.
Test SEO effectiveness of your content
Track, rank and evaluate open LLMs and chatbots
Analyze similarity of patent claims and responses
Determine emotion from text
Compare LLMs by role stability
RAG - augment is a text analysis tool designed to rerank documents based on a query. It leverages advanced AI algorithms to enhance the relevance and accuracy of search results, ensuring users receive the most pertinent information for their queries. By focusing on improving search efficiency, RAG - augment is particularly useful for applications requiring precise and context-aware document retrieval.
What does RAG - augment do exactly?
RAG - augment reranks a set of documents based on a specific query, improving the relevance of search results by leveraging AI algorithms.
What types of documents can RAG - augment process?
RAG - augment supports a wide range of document formats, including plain text, PDF, Word documents, and more.
Can I customize the ranking criteria in RAG - augment?
Yes, users can customize the relevance models in RAG - augment to align with specific requirements, such as prioritizing certain keywords or adjusting ranking weights.