An Index in R2R represents a vector index structure designed to optimize similarity search operations across document chunks. Indices are a core component of R2R’s Retrieval-Augmented Generation (RAG) architecture, enabling fast and scalable semantic retrieval. By organizing vector embeddings efficiently, indices make it possible to perform high-performance vector searches across large datasets using different similarity metrics and indexing strategies.Documentation Index
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Key Capabilities
Indices in R2R provide:- Fast similarity search for vector-based retrieval.
- Multiple index methods, including HNSW (Hierarchical Navigable Small World) and IVF-Flat.
- Configurable similarity measures such as cosine similarity or inner product.
- Concurrent index building to improve throughput and scalability.
- Performance optimization for large-scale vector operations.
API Endpoints
| Method | Endpoint | Description |
|---|---|---|
| GET | /indices | List available indices with pagination. |
| GET | /indices// | Retrieve details and configuration for a specific index. |
| DELETE | /indices// | Delete an existing index. |