Update python doc for MultiVector data type (#1713)

### What problem does this PR solve?

Update python doc for MultiVector data type

Issue link:#1679

### Type of change

- [x] Documentation Update

---------

Co-authored-by: writinwaters <93570324+writinwaters@users.noreply.github.com>

Author: yangzq50

docs/references/pysdk_api_reference.md

@@ -386,6 +386,8 @@ db_object.create_table("my_table", {"c1": {"type": "int", "default": 1}})
 # The `create_table`method supports creating float columns in the following data types:
 # - float/float32
 # - double/float64
+# - float16
+# - bfloat16
 db_object.create_table("my_table", {"c1": {"type": "float64"}})
 ```
 
@@ -403,17 +405,40 @@ db_object.create_table("my_table", {"c1": {"type": "bool"}})
 
 #### Create a table with a vector column only
 
+:::tip NOTE
+You can build a HNSW index on the vector column to speed up the match_dense search.
+:::
+
 ```python
 # Create a table with a vector column only:  
 # - `vector`: The column is a vector column
 # - `128`: The vector dimension
-# - `float`: The primitive data type of the vectors. Can be `float`/`float32` or `double`/`float64`
+# - `float`: The primitive data type of the vectors. Can be `float`/`float32`, `float16`, `bfloat16`, `uint8` or `int8`
 db_object.create_table("my_table", {"c1": {"type": "vector,128,float"}}, None)
 
 ```
 
+#### Create a table with a multi-vector column only
+
+:::tip NOTE
+You can build an HNSW index on the multi-vector column to accelerate match_dense search.
+:::
+
+```python
+# Create a table with a multi-vector column only:  
+# - `multivector`: The column is a multi-vector column
+# - `128`: The basic vector dimension
+# - `float`: The primitive data type of the basic vectors. Can be `float`/`float32`, `float16`, `bfloat16`, `uint8` or `int8`
+db_object.create_table("my_table", {"c1": {"type": "multivector,128,float"}}, None)
+
+```
+
 #### Create a table with a sparse vector column only
 
+:::tip NOTE
+You can build a BMP index on the sparse vector column to speed up the match_sparse search.
+:::
+
 ```python
 from infinity.common import ConflictType
 # Create a table with a vector column only:  
@@ -431,8 +456,8 @@ from infinity.common import ConflictType
 # Create a table with a tensor column only:  
 # - `tensor`: The column is a tensor column
 # - `4`: Dimension of each vector unit in the tensor
-# - `float64`: The primitive data type of the tensors. Can be `float`/`float32` or `double`/`float64`
-db_object.create_table("my_table", {"c1": {"type": "tensor,4,float64"}}, ConflictType.Ignore)
+# - `float`: The primitive data type of the tensors. Can be `float`/`float32`, `float16`, `bfloat16` or `bit`
+db_object.create_table("my_table", {"c1": {"type": "tensor,4,float"}}, ConflictType.Ignore)
 ```
 
 #### Create a table with a tensor array column only
@@ -442,7 +467,7 @@ from infinity.common import ConflictType
 # Create a table with a tensor array column only:  
 # - `tensorarray`: The column is a tensor array column
 # - `6`: Dimension of each vector unit in the tensor arrays
-# - `float`: The primitive data type of the tensor arrays. Can be `float`/`float32` or `double`/`float64`
+# - `float`: The primitive data type of the tensor arrays. Can be `float`/`float32`, `float16`, `bfloat16` or `bit`
 db_object.create_table("my_table", {"c1": {"type": "tensorarray,6,float"}}, ConflictType.Ignore)
 ```
 
@@ -1469,7 +1494,7 @@ table_object.output(["*"]).filter("c2 = 3").to_pl()
 table_object.match_dense(vector_column_name, embedding_data, embedding_data_type, distance_type, topn, knn_params = None)
 ```
 
-Creates a dense vector search expression to identify the top n closest rows to the given dense vector. Suitable for working with dense vectors (dense embeddings).
+Creates a dense vector search expression to identify the top n closest rows to the given dense vector. Suitable for working with dense vectors (dense embeddings) or multi-vectors (multiple dense embeddings in one row).
 
 :::tip NOTE
 To display your query results, you must chain this method with `output(columns)`, which specifies the columns to output, and a method such as `to_pl()`, `to_df()`, or `to_arrow()` to format the query results.
@@ -1479,7 +1504,7 @@ To display your query results, you must chain this method with `output(columns)`
 
 #### vector_column_name: `str`, *Required*
 
-A non-empty string indicating the name of the vector column to search on.
+A non-empty string indicating the name of the vector column or multi-vector column to search on.
 
 #### embedding_data: `list/np.ndarray`, *Required*