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@cjnolet
cjnolet committed Sep 8, 2023
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Expand Up @@ -11,49 +11,49 @@ IVF-flat uses an inverted-file index, which partitions the vectors into a series

IVF-flat is a simple algorithm which won't save any space, but it provides competitive search times even at higher levels of recall.

| Parameter | Type | Required | Data Type | Default | Description |
|-----------------------|------------------|----------|---------------------|----------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| `nlists` | `build_param` | Y | Positive Integer >0 | | Number of clusters to partition the vectors into. Larger values will put less points into each cluster but this will impact index build time as more clusters need to be trained. |
| `niter` | `build_param` | N | Positive Integer >0 | 20 | Number of clusters to partition the vectors into. Larger values will put less points into each cluster but this will impact index build time as more clusters need to be trained. |
| `ratio` | `build_param` | N | Positive Integer >0 | 2 | `1/ratio` is the number of training points which should be used to train the clusters. |
| `dataset_memory_type` | `build_param` | N | "device", "host", "mmap" | "device" | What memory type should the dataset reside? |
| `query_memory_type` | `search_params` | N | "device", "host", "mmap" | "device | What memory type should the queries reside? |
| `nprobe` | `search_params` | Y | Positive Integer >0 | | The closest number of clusters to search for each query vector. Larger values will improve recall but will search more points in the index. |
| Parameter | Type | Required | Data Type | Default | Description |
|-----------------------|------------------|----------|----------------------------|----------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| `nlists` | `build_param` | Y | Positive Integer >0 | | Number of clusters to partition the vectors into. Larger values will put less points into each cluster but this will impact index build time as more clusters need to be trained. |
| `niter` | `build_param` | N | Positive Integer >0 | 20 | Number of clusters to partition the vectors into. Larger values will put less points into each cluster but this will impact index build time as more clusters need to be trained. |
| `ratio` | `build_param` | N | Positive Integer >0 | 2 | `1/ratio` is the number of training points which should be used to train the clusters. |
| `dataset_memory_type` | `build_param` | N | ["device", "host", "mmap"] | "device" | What memory type should the dataset reside? |
| `query_memory_type` | `search_params` | N | ["device", "host", "mmap"] | "device | What memory type should the queries reside? |
| `nprobe` | `search_params` | Y | Positive Integer >0 | | The closest number of clusters to search for each query vector. Larger values will improve recall but will search more points in the index. |


### `raft_ivf_pq`

IVF-pq is an inverted-file index, which partitions the vectors into a series of clusters, or lists, in a similar way to IVF-flat above. The difference is that IVF-PQ uses product quantization to also compress the vectors, giving the index a smaller memory footprint. Unfortunately, higher levels of compression can also shrink recall, which a refinement step can improve when the original vectors are still available.

| Parameter | Type | Required | Data Type | Default | Description |
|-------------------------|----------------|---|------------------------------|---------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| `nlists` | `build_param` | Y | Positive Integer >0 | | Number of clusters to partition the vectors into. Larger values will put less points into each cluster but this will impact index build time as more clusters need to be trained. |
| `niter` | `build_param` | N | Positive Integer >0 | 20 | Number of k-means iterations to use when training the clusters. |
| `ratio` | `build_param` | N | Positive Integer >0 | 2 | `1/ratio` is the number of training points which should be used to train the clusters. |
| Parameter | Type | Required | Data Type | Default | Description |
|-------------------------|----------------|---|----------------------------------|---------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| `nlists` | `build_param` | Y | Positive Integer >0 | | Number of clusters to partition the vectors into. Larger values will put less points into each cluster but this will impact index build time as more clusters need to be trained. |
| `niter` | `build_param` | N | Positive Integer >0 | 20 | Number of k-means iterations to use when training the clusters. |
| `ratio` | `build_param` | N | Positive Integer >0 | 2 | `1/ratio` is the number of training points which should be used to train the clusters. |
| `pq_dim` | `build_param` | N | Positive Integer. Multiple of 8. | 0 | Dimensionality of the vector after product quantization. When 0, a heuristic is used to select this value. `pq_dim` * `pq_bits` must be a multiple of 8. |
| `pq_bits` | `build_param` | N | Positive Integer. [4-8] | 8 | Bit length of the vector element after quantization. |
| `codebook_kind` | `build_param` | N | ["cluster", "subspace"] | "subspace" | Type of codebook. See the [API docs](https://docs.rapids.ai/api/raft/nightly/cpp_api/neighbors_ivf_pq/#_CPPv412codebook_gen) for more detail |
| `dataset_memory_type` | `build_param` | N | "device", "host", "mmap" | "device" | What memory type should the dataset reside? |
| `query_memory_type` | `search_params` | N | "device", "host", "mmap" | "device | What memory type should the queries reside? |
| `nprobe` | `search_params` | Y | Positive Integer >0 | | The closest number of clusters to search for each query vector. Larger values will improve recall but will search more points in the index. |
| `internalDistanceDtype` | `search_params` | N | [`float`, `half`] | `half` | The precision to use for the distance computations. Lower precision can increase performance at the cost of accuracy. |
| `smemLutDtype` | `search_params` | N | [`float`, `half`, `fp8`] | `half` | The precision to use for the lookup table in shared memory. Lower precision can increase performance at the cost of accuracy. |
| `refine_ratio` | `search_params` | N| Positive Number >=0 | 0 | `refine_ratio * k` nearest neighbors are queried from the index initially and an additional refinement step improves recall by selecting only the best `k` neighbors. |
| `pq_bits` | `build_param` | N | Positive Integer. [4-8] | 8 | Bit length of the vector element after quantization. |
| `codebook_kind` | `build_param` | N | ["cluster", "subspace"] | "subspace" | Type of codebook. See the [API docs](https://docs.rapids.ai/api/raft/nightly/cpp_api/neighbors_ivf_pq/#_CPPv412codebook_gen) for more detail |
| `dataset_memory_type` | `build_param` | N | ["device", "host", "mmap"] | "device" | What memory type should the dataset reside? |
| `query_memory_type` | `search_params` | N | ["device", "host", "mmap"] | "device | What memory type should the queries reside? |
| `nprobe` | `search_params` | Y | Positive Integer >0 | | The closest number of clusters to search for each query vector. Larger values will improve recall but will search more points in the index. |
| `internalDistanceDtype` | `search_params` | N | [`float`, `half`] | `half` | The precision to use for the distance computations. Lower precision can increase performance at the cost of accuracy. |
| `smemLutDtype` | `search_params` | N | [`float`, `half`, `fp8`] | `half` | The precision to use for the lookup table in shared memory. Lower precision can increase performance at the cost of accuracy. |
| `refine_ratio` | `search_params` | N| Positive Number >=0 | 0 | `refine_ratio * k` nearest neighbors are queried from the index initially and an additional refinement step improves recall by selecting only the best `k` neighbors. |


### `raft_cagra`
CAGRA uses a graph-based index, which creates an intermediate, approximate kNN graph using IVF-PQ and then further refining and optimizing to create a final kNN graph. This kNN graph is used by CAGRA as an index for search.

| Parameter | Type | Required | Data Type | Default | Description |
|-----------------------------|----------------|----------|---------------------|---------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| `graph_degree` | `build_param` | N | Positive Integer >0 | 64 | Degree of the final kNN graph index. |
| `intermediate_graph_degree` | `build_param` | N | Positive Integer >0 | 128 | Degree of the intermediate kNN graph. |
| `dataset_memory_type` | `build_param` | N | "device", "host", "mmap" | "device" | What memory type should the dataset reside? |
| `query_memory_type` | `search_params` | N | "device", "host", "mmap" | "device | What memory type should the queries reside? |
| `itopk` | `search_wdith` | N | Positive Integer >0 | 64 | Number of intermediate search results retained during the search. Higher values improve search accuracy at the cost of speed. |
| `search_width` | `search_param` | N | Positive Integer >0 | 1 | Number of graph nodes to select as the starting point for the search in each iteration. |
| `max_iterations` | `search_param` | N | Integer >=0 | 0 | Upper limit of search iterations. Auto select when 0. |
| `algo` | `search_param` | N | string | "auto" | Algorithm to use for search. Possible values: {"auto", "single_cta", "multi_cta", "multi_kernel"} |
| Parameter | Type | Required | Data Type | Default | Description |
|-----------------------------|----------------|----------|----------------------------|---------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| `graph_degree` | `build_param` | N | Positive Integer >0 | 64 | Degree of the final kNN graph index. |
| `intermediate_graph_degree` | `build_param` | N | Positive Integer >0 | 128 | Degree of the intermediate kNN graph. |
| `dataset_memory_type` | `build_param` | N | ["device", "host", "mmap"] | "device" | What memory type should the dataset reside? |
| `query_memory_type` | `search_params` | N | ["device", "host", "mmap"] | "device | What memory type should the queries reside? |
| `itopk` | `search_wdith` | N | Positive Integer >0 | 64 | Number of intermediate search results retained during the search. Higher values improve search accuracy at the cost of speed. |
| `search_width` | `search_param` | N | Positive Integer >0 | 1 | Number of graph nodes to select as the starting point for the search in each iteration. |
| `max_iterations` | `search_param` | N | Integer >=0 | 0 | Upper limit of search iterations. Auto select when 0. |
| `algo` | `search_param` | N | string | "auto" | Algorithm to use for search. Possible values: {"auto", "single_cta", "multi_cta", "multi_kernel"} |


## FAISS Indexes
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