docs: query-vector float64 clause and held VectorIndex reads after DROP INDEX (#186)

docs/modules/ROOT/pages/how-to/databases-and-queries.adoc

@@ -229,7 +229,7 @@ The bindings' check counts the `?`, `$` and `:` characters of the SQL text, so i
 A query vector for `vectorNeighbors` can be a numpy array or a list. The conversion before the statement runs is the cost to watch: a query takes about 0.3 to 0.75 ms, and a vector of 1536 numbers took 0.5 to 0.6 ms to convert when it was a list.
 
 * A `float32` array crosses as one buffer on both builds: about 5 to 15 us. This is the fast path. `np.asarray(v, dtype=np.float32)` makes it from a list or from another array.
-* A list that holds only Python floats (no `int`, `bool`, `None`, `str` or numpy scalar) also crosses as one buffer, and so does a `float64` array on the JVM build. The statement reads the same list of doubles it always read: no value changes, and an `int` in a list stays an `int`. A list shorter than 32 numbers (JVM build) or 64 numbers (native build) is not worth a buffer and goes number by number. On the native build the list crosses as a buffer only when `float32` holds every number exactly, which is true of a list from a float32 model (`array.tolist()`); a list of other doubles, `NaN` or an infinity is sent as text, because a buffer would round it.
+* A list that holds only Python floats (no `int`, `bool`, `None`, `str` or numpy scalar) also crosses as one buffer. The statement reads the same list of doubles it always read: no value changes, and an `int` in a list stays an `int`. A list shorter than 32 numbers (JVM build) or 64 numbers (native build) is not worth a buffer and goes number by number. On the native build the list crosses as a buffer only when `float32` holds every number exactly, which is true of a list from a float32 model (`array.tolist()`); a list of other doubles, `NaN` or an infinity is sent as text, because a buffer would round it.
 * A `float64` array among the positional parameters (`?`) is a `float32` array to the engine on both builds, so its numbers are rounded to `float32` (a vector index stores `float32`). As a named parameter, inside `CONTENT :row` or as a property of a record you save, it keeps its doubles on both builds: `0.1` is stored as `0.1`. Such a value goes as text, so give a `float32` array when you want the buffer.
 
 === Results

docs/modules/ROOT/pages/how-to/vectors.adoc

@@ -279,6 +279,8 @@ True
 
 `build_graph_now()` rebuilds the graph at once. Call it after a bulk load, so that no search waits for a lazy build.
 
+The reads of a `VectorIndex` you hold (`get_size()`, `get_metadata()`, `get_quantization()`, `get_stats()`) ask the engine each time. After `DROP INDEX` they raise an error that names the index, and after the index is created again they report its new configuration.
+
 After you open a database, an index loads its saved graph on the first search, and that search pays the load. A service that restarts can call `warm_up()` on the index right after `open_database`, to pay it before the first query. It loads the saved graph and does not rebuild it, and it does nothing when the graph is in memory already.
 
 [source,python]