Allow creating additional indexes

Author: cevian

README.md

@@ -78,24 +78,24 @@ Now you can query for similar items:
 await vec.search([1.0, 9.0])
 ```
 
-    [<Record id=UUID('1bd6a985-a837-4742-a007-d8a785e7089f') metadata={'action': 'jump', 'animal': 'fox'} contents='jumped over the' embedding=array([ 1. , 10.8], dtype=float32) distance=0.00016793422934946456>,
-     <Record id=UUID('2e52b4a4-3422-42d7-8e62-fd40731e7ffa') metadata={'animal': 'fox'} contents='the brown fox' embedding=array([1. , 1.3], dtype=float32) distance=0.14489260377438218>]
+    [<Record id=UUID('9b567b36-209e-4240-aa93-f8e7e74277cd') metadata={'action': 'jump', 'animal': 'fox'} contents='jumped over the' embedding=array([ 1. , 10.8], dtype=float32) distance=0.00016793422934946456>,
+     <Record id=UUID('2d49fd73-3db1-4061-81f3-a4ed7529eb61') metadata={'animal': 'fox'} contents='the brown fox' embedding=array([1. , 1.3], dtype=float32) distance=0.14489260377438218>]
 
 You can specify the number of records to return.
 
 ``` python
 await vec.search([1.0, 9.0], limit=1)
 ```
 
-    [<Record id=UUID('1bd6a985-a837-4742-a007-d8a785e7089f') metadata={'action': 'jump', 'animal': 'fox'} contents='jumped over the' embedding=array([ 1. , 10.8], dtype=float32) distance=0.00016793422934946456>]
+    [<Record id=UUID('9b567b36-209e-4240-aa93-f8e7e74277cd') metadata={'action': 'jump', 'animal': 'fox'} contents='jumped over the' embedding=array([ 1. , 10.8], dtype=float32) distance=0.00016793422934946456>]
 
 You can also specify a filter on the metadata as a simple dictionary
 
 ``` python
 await vec.search([1.0, 9.0], limit=1, filter={"action": "jump"})
 ```
 
-    [<Record id=UUID('1bd6a985-a837-4742-a007-d8a785e7089f') metadata={'action': 'jump', 'animal': 'fox'} contents='jumped over the' embedding=array([ 1. , 10.8], dtype=float32) distance=0.00016793422934946456>]
+    [<Record id=UUID('9b567b36-209e-4240-aa93-f8e7e74277cd') metadata={'action': 'jump', 'animal': 'fox'} contents='jumped over the' embedding=array([ 1. , 10.8], dtype=float32) distance=0.00016793422934946456>]
 
 You can also specify a list of filter dictionaries, where an item is
 returned if it matches any dict
@@ -104,8 +104,8 @@ returned if it matches any dict
 await vec.search([1.0, 9.0], limit=2, filter=[{"action": "jump"}, {"animal": "fox"}])
 ```
 
-    [<Record id=UUID('1bd6a985-a837-4742-a007-d8a785e7089f') metadata={'action': 'jump', 'animal': 'fox'} contents='jumped over the' embedding=array([ 1. , 10.8], dtype=float32) distance=0.00016793422934946456>,
-     <Record id=UUID('2e52b4a4-3422-42d7-8e62-fd40731e7ffa') metadata={'animal': 'fox'} contents='the brown fox' embedding=array([1. , 1.3], dtype=float32) distance=0.14489260377438218>]
+    [<Record id=UUID('9b567b36-209e-4240-aa93-f8e7e74277cd') metadata={'action': 'jump', 'animal': 'fox'} contents='jumped over the' embedding=array([ 1. , 10.8], dtype=float32) distance=0.00016793422934946456>,
+     <Record id=UUID('2d49fd73-3db1-4061-81f3-a4ed7529eb61') metadata={'animal': 'fox'} contents='the brown fox' embedding=array([1. , 1.3], dtype=float32) distance=0.14489260377438218>]
 
 You can access the fields as follows
 
@@ -114,7 +114,7 @@ records = await vec.search([1.0, 9.0], limit=1, filter={"action": "jump"})
 records[0][client.SEARCH_RESULT_ID_IDX]
 ```
 
-    UUID('1bd6a985-a837-4742-a007-d8a785e7089f')
+    UUID('9b567b36-209e-4240-aa93-f8e7e74277cd')
 
 ``` python
 records[0][client.SEARCH_RESULT_METADATA_IDX]
@@ -176,7 +176,7 @@ You can create an ivfflat index with the following command after the
 table has been populated.
 
 ``` python
-await vec.create_ivfflat_index()
+await vec.create_embedding_index(client.IvfflatIndex())
 ```
 
 Please note it is very important to do this only after you have data in