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docs: search param explanation for OSS doc (#1815)

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@@ -140,13 +140,15 @@ 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
- **nprobes** (default: 20): The number of probes used. A higher number makes search more accurate but also slower.<br/>
Most of the time, setting nprobes to cover 5-10% of the dataset should achieve high recall with low latency.<br/>
e.g., for 1M vectors divided up into 256 partitions, nprobes should be set to ~20-40.<br/>
Note: nprobes is only applicable if an ANN index is present. If specified on a table without an ANN index, it is ignored.
Most of the time, setting nprobes to cover 5-15% of the dataset should achieve high recall with low latency.<br/>
- _For example_, For a dataset of 1 million vectors divided into 256 partitions, `nprobes` should be set to ~20-40. This value can be adjusted to achieve the optimal balance between search latency and search quality. <br/>
- **refine_factor** (default: None): Refine the results by reading extra elements and re-ranking them in memory.<br/>
A higher number makes search more accurate but also slower. If you find the recall is less than ideal, try refine_factor=10 to start.<br/>
e.g., for 1M vectors divided into 256 partitions, if you're looking for top 20, then refine_factor=200 reranks the whole partition.<br/>
Note: refine_factor is only applicable if an ANN index is present. If specified on a table without an ANN index, it is ignored.
- _For example_, For a dataset of 1 million vectors divided into 256 partitions, setting the `refine_factor` to 200 will initially retrieve the top 4,000 candidates (top k * refine_factor) from all searched partitions. These candidates are then reranked to determine the final top 20 results.<br/>
!!! note
Both `nprobes` and `refine_factor` are only applicable if an ANN index is present. If specified on a table without an ANN index, those parameters are ignored.
=== "Python"