Always match the right content to the right request
Stop searching, and start finding. Search helps you build advanced RAG systems to tackle sophisticated and precise information retrieval scenarios – all without breaking a sweat.
- Retrieves answers fast, with average response times of <100 milliseconds for one leading e-commerce company
- Significantly improves the relevance of answers by adding context and capturing semantic relationships
- Eliminates the need to rework systems to handle new scenarios by adapting to diverse data and retrieval tasks
Retrieval
Key Features
- Uses LLMs to reduce ambiguity, revising queries to more closely match specific vector stores.
- References past interactions to enrich and add additional context to queries.
- Provides control over what is retrieved from query history and how far back to go.
- Supports multiple retrievers and advanced retrieval mechanisms.
- Provides cross-encoders for assigning relevance scores to chunks, prioritizing k chunks that score the highest.
- Fine-tune and train encoders for specific domains or tasks to improve relevance assessments.
