docs: update table creation examples to use 'if_exists' parameter for better clarity (#170)

Author: Mini256

src/ai/guides/auto-embedding.md

@@ -35,14 +35,14 @@ Auto embedding is a feature that allows you to automatically generate vector emb
 
     ```python hl_lines="7"
     from pytidb.schema import TableModel, Field
-    from pytidb.datatype import Text
+    from pytidb.datatype import TEXT
 
     class Chunk(TableModel):
         id: int = Field(primary_key=True)
-        text: str = Field(sa_type=Text)
+        text: str = Field(sa_type=TEXT)
         text_vec: list[float] = embed_func.VectorField(source_field="text")
 
-    table = client.create_table(schema=Chunk, mode="overwrite")
+    table = client.create_table(schema=Chunk, if_exists="overwrite")
     ```
 
     You don't need to specify the `dimensions` parameter, it will be automatically determined by the embedding model.

src/ai/guides/fulltext-search.md

@@ -35,7 +35,7 @@ TiDB provides full-text search capabilities for **massive datasets** with high p
         id: int = Field(primary_key=True)
         title: str = FullTextField(fts_parser="MULTILINGUAL")
 
-    table = client.create_table(schema=Item, mode="overwrite")
+    table = client.create_table(schema=Item, if_exists="overwrite")
     ```
 
     The `fts_parser` parameter specifies the parser for the full-text index. Supported values:

src/ai/guides/hybrid-search.md

@@ -47,7 +47,7 @@ embed_fn = EmbeddingFunction(
         text: str = FullTextField()
         text_vec: list[float] = embed_fn.VectorField(source_field="text")
 
-    table = client.create_table(schema=Chunk, mode="overwrite")
+    table = client.create_table(schema=Chunk, if_exists="overwrite")
     ```
 
     In this example, PyTiDB will automatically create a full-text index on the `text` column and a vector index on the `text_vec` column.

src/ai/guides/image-search.md

@@ -43,7 +43,7 @@ class ImageItem(TableModel):
         source_field="image_uri"
     )
 
-table = client.create_table(schema=ImageItem, mode="overwrite")
+table = client.create_table(schema=ImageItem, if_exists="overwrite")
 ```
 
 ### Step 3. Insert image data

src/ai/guides/joins.md

@@ -23,7 +23,7 @@ As a relational database, TiDB allows you to store diverse data in tables with d
         id: int = Field(primary_key=True)
         title: str = Field(max_length=255)
 
-    client.create_table(schema=Document, mode="overwrite")
+    client.create_table(schema=Document, if_exists="overwrite")
     client.table("documents").truncate()
     client.table("documents").bulk_insert([
         Document(id=1, title="The Power of Positive Thinking"),
@@ -41,7 +41,7 @@ As a relational database, TiDB allows you to store diverse data in tables with d
         text: str = Field(max_length=255)
         document_id: int = Field(foreign_key="documents.id")
 
-    client.create_table(schema=Chunk, mode="overwrite")
+    client.create_table(schema=Chunk, if_exists="overwrite")
     client.table("chunks").truncate()
     client.table("chunks").bulk_insert([
         Chunk(id=1, text="Positive thinking can change your life", document_id=1),

src/ai/guides/tables.md

@@ -37,15 +37,15 @@ In the following example, you create a table named `items` with these columns:
         embedding: list[float] = VectorField(dimensions=3)
         meta: dict = Field(sa_type=JSON, default_factory=dict)
 
-    table = client.create_table(schema=Item, mode="overwrite")
+    table = client.create_table(schema=Item, if_exists="overwrite")
     ```
 
     The `create_table` method accepts these parameters:
 
     - `schema`: The `TableModel` class that defines your table structure.
-    - `mode`: The creation mode of the table.
-        - `create` (default): Creates the table if it does not exist; raises an error if it already exists.
-        - `exists_ok`: Creates the table if it does not exist; does nothing if it already exists.
+    - `if_exists`: The creation mode of the table.
+        - `raise` (default): Creates the table if it does not exist; raises an error if it already exists.
+        - `skip`: Creates the table if it does not exist; does nothing if it already exists.
         - `overwrite`: Drops the existing table and creates a new one. This is useful for **testing and development**, but not recommended for production environments.
 
     Once the table is created, you can use the `table` object to insert, update, delete, and query data.

src/ai/guides/vector-search.md

@@ -36,7 +36,7 @@ This section shows you how to use vector search in your application in minimal s
         text_vec: list[float] = VectorField(dimensions=3)
         meta: dict = Field(sa_type=JSON, default_factory=dict)
 
-    table = client.create_table(schema=Document, mode="overwrite")
+    table = client.create_table(schema=Document, if_exists="overwrite")
     ```
 
     The `VectorField` class accepts the following parameters:
@@ -385,7 +385,7 @@ For example, you can store both text embeddings and image embeddings in the same
         image_url: str
         image_vec: list[float] = VectorField(dimensions=3)
 
-    table = client.create_table(schema=RichTextDocument, mode="overwrite")
+    table = client.create_table(schema=RichTextDocument, if_exists="overwrite")
 
     # Insert sample data ...
 

src/ai/quickstart.md

@@ -134,7 +134,7 @@ As an example, create a table named `chunks` with the following columns:
         text_vec: list[float] = text_embed.VectorField(source_field="text")
         user_id: int = Field()
 
-    table = client.create_table(schema=Chunk, mode="overwrite")
+    table = client.create_table(schema=Chunk, if_exists="overwrite")
     ```
 
 Once created, you can use the `table` object to insert data, search data, and more.