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add documentation for metric

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Chang She
2023-04-24 22:42:30 -07:00
parent 89e6232aeb
commit b0e578c609
1 changed files with 6 additions and 5 deletions
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docs/src/ann_indexes.md
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@@ -28,11 +28,11 @@ tbl.create_index(num_partitions=256, num_sub_vectors=96)
Since `create_index` has a training step, it can take a few minutes to finish for large tables. You can control the index
creation by providing the following parameters:
- **num_partitions** (default: 256): The number of partitions of the index. The number of partitions should be configured so each partition has 3-5K vectors. For example, a table
with ~1M vectors should use 256 partitions. You can specify arbitrary number of partitions but powers of 2 is most conventional.
A higher number leads to faster queries, but it makes index generation slower.
- **num_partitions** (default: 256): The number of partitions of the index. The number of partitions should be configured so each partition has 3-5K vectors. For example, a table
with ~1M vectors should use 256 partitions. You can specify arbitrary number of partitions but powers of 2 is most conventional.
A higher number leads to faster queries, but it makes index generation slower.
- **num_sub_vectors** (default: 96): The number of subvectors (M) that will be created during Product Quantization (PQ). A larger number makes
search more accurate, but also makes the index larger and slower to build.
search more accurate, but also makes the index larger and slower to build.
## Querying an ANN Index
@@ -41,8 +41,9 @@ Querying vector indexes is done via the [search](https://lancedb.github.io/lance
There are a couple of parameters that can be used to fine-tune the search:
- **limit** (default: 10): The amount of results that will be returned
- **metric** (default: "l2"): The distance metric to use. By default we use euclidean distance. We also support cosine distance.
- **nprobes** (default: 20): The number of probes used. A higher number makes search more accurate but also slower.
- **refine_factor** (default: None): Refine the results by reading extra elements and re-ranking them in memory. A higher number makes
- **refine_factor** (default: None): Refine the results by reading extra elements and re-ranking them in memory. A higher number makes
search more accurate but also slower.
```python