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@Pringled
Pringled committed Nov 18, 2024
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Expand Up @@ -75,29 +75,36 @@ Vicinity provides the following features:
## Supported Backends
The following backends are supported:
- `BASIC`: A simple flat index for vector storage and search.
- `HNSW`: Hierarchical Navigable Small World Graph for approximate nearest neighbor search.
- `FAISS`: All FAISS indexes for approximate nearest neighbor search are supported.
- `ANNOY`: "Approximate Nearest Neighbors Oh Yeah" for approximate nearest neighbor search.
- `PYNNDescent`: Approximate nearest neighbor search using PyNNDescent.
- [HNSW](https://github.com/nmslib/hnswlib): Hierarchical Navigable Small World Graph (HNSW) for ANN search using hnswlib.
- [FAISS](https://github.com/facebookresearch/faiss): ANN search using FAISS. All FAISS indexes are supported.
- [ANNOY](https://github.com/spotify/annoy): "Approximate Nearest Neighbors Oh Yeah" for approximate nearest neighbor search.
- [PYNNDescent](https://github.com/lmcinnes/pynndescent): ANN search using PyNNDescent.
- [USEARCH](https://github.com/unum-cloud/usearch): ANN search using Usearch. This uses a highly optimized version of the HNSW algorithm.

### Backend Parameters

| Backend | Parameter | Description | Default Value |
|----------------|--------------------|-----------------------------------------------------------------------------------------------|---------------------|
| **Annoy** | `metric` | Similarity metric to use (`dot`, `euclidean`, `cosine`). | `"cosine"` |
| | `trees` | Number of trees to use for indexing. | `100` |
| | `length` | Optional length of the dataset. | `None` |
| **FAISS** | `index_type` | Type of FAISS index (`flat`, `ivf`, `hnsw`, `lsh`, `scalar`, `pq`, `ivf_scalar`, `ivfpq`, `ivfpqr`). | `"hnsw"` |
| | `metric` | Similarity metric to use (`cosine`, `l2`). | `"cosine"` |
| | `nlist` | Number of cells for IVF indexes. | `100` |
| | `m` | Number of subquantizers for PQ and HNSW indexes. | `8` |
| | `nbits` | Number of bits for LSH and PQ indexes. | `8` |
| | `refine_nbits` | Number of bits for the refinement stage in IVFPQR indexes. | `8` |
| **HNSW** | `space` | Similarity space to use (`cosine`, `l2`). | `"cosine"` |
| | `ef_construction` | Size of the dynamic list during index construction. | `200` |
| | `m` | Number of connections per layer. | `16` |
| **PyNNDescent**| `n_neighbors` | Number of neighbors to use for search. | `15` |
| | `metric` | Similarity metric to use (`cosine`, `euclidean`, `manhattan`). | `"cosine"` |

| Backend | Parameter | Description | Default Value |
|-----------------|---------------------|-----------------------------------------------------------------------------------------------|---------------------|
| **Annoy** | `metric` | Similarity metric to use (`dot`, `euclidean`, `cosine`). | `"cosine"` |
| | `trees` | Number of trees to use for indexing. | `100` |
| | `length` | Optional length of the dataset. | `None` |
| **FAISS** | `metric` | Similarity metric to use (`cosine`, `l2`). | `"cosine"` |
| | `index_type` | Type of FAISS index (`flat`, `ivf`, `hnsw`, `lsh`, `scalar`, `pq`, `ivf_scalar`, `ivfpq`, `ivfpqr`). | `"hnsw"` |
| | `nlist` | Number of cells for IVF indexes. | `100` |
| | `m` | Number of subquantizers for PQ and HNSW indexes. | `8` |
| | `nbits` | Number of bits for LSH and PQ indexes. | `8` |
| | `refine_nbits` | Number of bits for the refinement stage in IVFPQR indexes. | `8` |
| **HNSW** | `metric` | Similarity space to use (`cosine`, `l2`). | `"cosine"` |
| | `ef_construction` | Size of the dynamic list during index construction. | `200` |
| | `m` | Number of connections per layer. | `16` |
| **PyNNDescent** | `metric` | Similarity metric to use (`cosine`, `euclidean`, `manhattan`). | `"cosine"` |
| | `n_neighbors` | Number of neighbors to use for search. | `15` |
| **Usearch** | `metric` | Similarity metric to use (`cos`, `ip`, `l2sq`, `hamming`, `tanimoto`). | `"cos"` |
| | `connectivity` | Number of connections per node in the graph. | `16` |
| | `expansion_add` | Number of candidates considered during graph construction. | `128` |
| | `expansion_search` | Number of candidates considered during search. | `64` |



## Usage
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