Update docs

src/content/docs/client-apis/python.mdx

@@ -35,30 +35,30 @@ curl -L -o ./data/lives-in.csv https://raw.githubusercontent.com/lbugdb/lbug/ref
 The synchronous API is the default and is a common way to work with Ladybug in Python.
 
 ```python
-import lbug
+import real_ladybug as lb
 
 def main() -> None:
     # Create an empty on-disk database and connect to it
-    db = lbug.Database("example.lbug")
-    conn = lbug.Connection(db)
+    db = lb.Database("example.lbug")
+    conn = lb.Connection(db)
 
     create_tables(conn)
     copy_data(conn)
     query(conn)
 
-def create_tables(conn: lbug.Connection) -> None:
+def create_tables(conn: lb.Connection) -> None:
     conn.execute("CREATE NODE TABLE User(name STRING PRIMARY KEY, age INT64)")
     conn.execute("CREATE NODE TABLE City(name STRING PRIMARY KEY, population INT64)")
     conn.execute("CREATE REL TABLE Follows(FROM User TO User, since INT64)")
     conn.execute("CREATE REL TABLE LivesIn(FROM User TO City)")
 
-def copy_data(conn: lbug.Connection) -> None:
+def copy_data(conn: lb.Connection) -> None:
     conn.execute('COPY User FROM "./data/user.csv"')
     conn.execute('COPY City FROM "./data/city.csv"')
     conn.execute('COPY Follows FROM "./data/follows.csv"')
     conn.execute('COPY LivesIn FROM "./data/lives-in.csv"')
 
-def query(conn: lbug.Connection) -> None:
+def query(conn: lb.Connection) -> None:
     results = conn.execute("""
         MATCH (a:User)-[f:Follows]->(b:User)
         RETURN a.name, b.name, f.since;
@@ -79,33 +79,33 @@ such as in web frameworks like FastAPI or cases where you need to concurrently r
 
 ```python
 import asyncio
-import lbug
+import real_ladybug as lb
 
 async def main():
     # Create an empty on-disk database and connect to it
-    db = lbug.Database("example.lbug")
+    db = lb.Database("example.lbug")
     # The underlying connection pool will be automatically created and managed by the async connection
-    conn = lbug.AsyncConnection(db, max_concurrent_queries=4)
+    conn = lb.AsyncConnection(db, max_concurrent_queries=4)
 
     await create_tables(conn)
     await copy_data(conn)
     await query(conn)
 
-async def create_tables(conn: lbug.AsyncConnection) -> None:
+async def create_tables(conn: lb.AsyncConnection) -> None:
     await conn.execute("CREATE NODE TABLE User(name STRING PRIMARY KEY, age INT64)")
     await conn.execute(
         "CREATE NODE TABLE City(name STRING PRIMARY KEY, population INT64)"
     )
     await conn.execute("CREATE REL TABLE Follows(FROM User TO User, since INT64)")
     await conn.execute("CREATE REL TABLE LivesIn(FROM User TO City)")
 
-async def copy_data(conn: lbug.AsyncConnection) -> None:
+async def copy_data(conn: lb.AsyncConnection) -> None:
     await conn.execute("COPY User FROM './data/user.csv'")
     await conn.execute("COPY City FROM './data/city.csv'")
     await conn.execute("COPY Follows FROM './data/follows.csv'")
     await conn.execute("COPY LivesIn FROM './data/lives-in.csv'")
 
-async def query(conn: lbug.AsyncConnection) -> None:
+async def query(conn: lb.AsyncConnection) -> None:
     results = await conn.execute("""
         MATCH (a:User)-[f:Follows]->(b:User)
         RETURN a.name, b.name, f.since;
@@ -187,11 +187,11 @@ The following examples show how to output query results to each of these data st
 You can output the results of a Cypher query to a Pandas DataFrame using the `get_as_df()` method:
 
 ```py
-import lbug
+import real_ladybug as lb
 import pandas as pd
 
-db = lbug.Database(":memory:")
-conn = lbug.Connection(db)
+db = lb.Database(":memory:")
+conn = lb.Connection(db)
 
 conn.execute("CREATE NODE TABLE Person(name STRING PRIMARY KEY, age INT64)")
 conn.execute("CREATE (a:Person {name: 'Adam', age: 30})")
@@ -226,11 +226,11 @@ print(result.get_as_df())
 You can output the results of a Cypher query to a Polars DataFrame using the `get_as_pl()` method:
 
 ```py
-import lbug
+import real_ladybug as lb
 import polars as pl
 
-db = lbug.Database(":memory:")
-conn = lbug.Connection(db)
+db = lb.Database(":memory:")
+conn = lb.Connection(db)
 
 conn.execute("CREATE NODE TABLE Person(name STRING PRIMARY KEY, age INT64)")
 conn.execute("CREATE (a:Person {name: 'Adam', age: 30})")
@@ -277,11 +277,11 @@ shape: (3, 1)
 You can output the results of a Cypher query to a PyArrow Table using the `get_as_arrow()` method:
 
 ```py
-import lbug
+import real_ladybug as lb
 import pyarrow as pa
 
-db = lbug.Database(":memory:")
-conn = lbug.Connection(db)
+db = lb.Database(":memory:")
+conn = lb.Connection(db)
 
 conn.execute("CREATE NODE TABLE Person(name STRING PRIMARY KEY, age INT64)")
 conn.execute("CREATE (a:Person {name: 'Adam', age: 30})")
@@ -314,11 +314,11 @@ Scanning a DataFrame or Table does *not* copy the data into Ladybug, it only rea
 <TabItem label="Pandas">
 
 ```py
-import lbug
+import real_ladybug as lb
 import pandas as pd
 
-db = lbug.Database(":memory:")
-conn = lbug.Connection(db)
+db = lb.Database(":memory:")
+conn = lb.Connection(db)
 
 df = pd.DataFrame({
     "name": ["Adam", "Karissa", "Zhang"],
@@ -339,11 +339,11 @@ print(result.get_as_df())
 <TabItem label="Polars">
 
 ```py
-import lbug
+import real_ladybug as lb
 import polars as pl
 
-db = lbug.Database(":memory:")
-conn = lbug.Connection(db)
+db = lb.Database(":memory:")
+conn = lb.Connection(db)
 
 df = pl.DataFrame({
     "name": ["Adam", "Karissa", "Zhang"],
@@ -370,11 +370,11 @@ shape: (3, 2)
 <TabItem label="Arrow Table">
 
 ```py
-import lbug
+import real_ladybug as lb
 import pyarrow as pa
 
-db = lbug.Database(":memory:")
-conn = lbug.Connection(db)
+db = lb.Database(":memory:")
+conn = lb.Connection(db)
 
 tbl = pa.table({
     "name": ["Adam", "Karissa", "Zhang"],
@@ -405,11 +405,11 @@ age: [[30,40,50]]
 Copy from a Pandas DataFrame into a Ladybug table using the `COPY FROM` command:
 
 ```py
-import lbug
+import real_ladybug as lb
 import pandas as pd
 
-db = lbug.Database(":memory:")
-conn = lbug.Connection(db)
+db = lb.Database(":memory:")
+conn = lb.Connection(db)
 
 conn.execute("CREATE NODE TABLE Person(name STRING PRIMARY KEY, age INT64)")
 
@@ -436,11 +436,11 @@ print(result.get_as_df())
 Copy from a Polars DataFrame into a Ladybug table using the `COPY FROM` command:
 
 ```py
-import lbug
+import real_ladybug as lb
 import polars as pl
 
-db = lbug.Database(":memory:")
-conn = lbug.Connection(db)
+db = lb.Database(":memory:")
+conn = lb.Connection(db)
 
 conn.execute("CREATE NODE TABLE Person(name STRING PRIMARY KEY, age INT64)")
 
@@ -473,11 +473,11 @@ shape: (3, 2)
 Copy from a PyArrow Table into a Ladybug table using the `COPY FROM` command:
 
 ```py
-import lbug
+import real_ladybug as lb
 import pyarrow as pa
 
-db = lbug.Database(":memory:")
-conn = lbug.Connection(db)
+db = lb.Database(":memory:")
+conn = lb.Connection(db)
 
 conn.execute("CREATE NODE TABLE Person(name STRING PRIMARY KEY, age INT64)")
 
@@ -541,20 +541,20 @@ difference between two numbers, and then apply it in a Cypher query.
 ### Register the UDF
 
 ```py
-import lbug
+import real_ladybug as lb
 
-db = lbug.Database(":memory:")
-conn = lbug.Connection(db)
+db = lb.Database(":memory:")
+conn = lb.Connection(db)
 
 # define your function
 def difference(a, b):
     return a - b
 
 # define the expected type of your parameters
-parameters = [lbug.Type.INT64, lbug.Type.INT64]
+parameters = [lb.Type.INT64, lb.Type.INT64]
 
 # define expected type of the returned value
-return_type = lbug.Type.INT64
+return_type = lb.Type.INT64
 
 # register the UDF
 conn.create_function("difference", difference, parameters, return_type)
@@ -579,10 +579,10 @@ The UDF API's `create_function` provides the following additional parameters:
 
 - `name: str` : The name of the function to be invoked in cypher.
 - `udf: Callable[[...], Any]` : The function to be executed.
-- `params_type: Optional[list[Type | str]]` : A list whose elements can either be `lbug.Type` or `str`. `lbug.Type`
+- `params_type: Optional[list[Type | str]]` : A list whose elements can either be `lb.Type` or `str`. `lb.Type`
 can be used to denote nonnested parameter types, while `str` can be used to denote both nested and nonnested parameter types.
 Details on how to denote types are in the [type notation](#type-notation) section.
-- `return_type: Optional[Type | str]` : Either a `lbug.Type` enum or `str`. Details on how to denote types are in the [type notation](#type-notation) section.
+- `return_type: Optional[Type | str]` : Either a `lb.Type` enum or `str`. Details on how to denote types are in the [type notation](#type-notation) section.
 - `default_null_handling: Optional[bool]` : True by default. When true, if any one of the inputs is null, function execution is skipped and the output is resolved to null.
 - `catch_exceptions: Optional[bool]` : False by default. When true, if the UDF raises an exception, the output is resolved to null. Otherwise the Exception is rethrown.
 
@@ -641,7 +641,7 @@ def calculate_discounted_price(price: float, has_discount: bool) -> float:
     # Assume 10% discount on all items for simplicity
     return float(price) * 0.9 if has_discount else price
 
-parameters = ['DECIMAL(7, 2)', lbug.Type.BOOL]
+parameters = ['DECIMAL(7, 2)', lb.Type.BOOL]
 
 return_type = 'DECIMAL(7, 2)'
 
@@ -666,6 +666,6 @@ print(result.get_as_df())
 0    90.00      100.00
 ```
 
-The second parameter is a built-in native type in Ladybug, i.e., `lbug.Type.BOOL`. For the first parameter,
+The second parameter is a built-in native type in Ladybug, i.e., `lb.Type.BOOL`. For the first parameter,
 we need to specify a string, i.e. `DECIMAL(7,2)` that is then parsed and used by Ladybug
 to map to the internal decimal representation.

src/content/docs/concurrency.md

@@ -43,12 +43,12 @@ Here's a simple example application in Python that demonstrates these two steps
 database and a connection. The same principles apply to other language APIs as well:
 
 ```python
-import lbug
+import real_ladybug as lb
 
 # Open the database in `READ_WRITE` mode. The below code is equivalent to:
-# db = lbug.Database("example.lbug", read_only=False)
-db = lbug.Database("example.lbug")
-conn = lbug.Connection(db)
+# db = lb.Database("example.lbug", read_only=False)
+db = lb.Database("example.lbug")
+conn = lb.Connection(db)
 conn.execute("CREATE (a:Person {name: 'Alice'});")
 ```
 

src/content/docs/cypher/query-clauses/load-from.md

@@ -139,11 +139,11 @@ inferred from the schema information of the data frame. Here is an example:
 
 ```py
 # main.py
-import lbug
+import real_ladybug as lb
 import pandas as pd
 
-db = lbug.Database(":memory:")
-conn = lbug.Connection(db)
+db = lb.Database(":memory:")
+conn = lb.Connection(db)
 
 df = pd.DataFrame({
     "name": ["Adam", "Karissa", "Zhang"],
@@ -172,11 +172,11 @@ Ladybug can also scan Polars DataFrames via the underlying PyArrow layer. The ru
 variable names and data types is identical to scanning Pandas data frames. Here is an example:
 
 ```python
-import lbug
+import real_ladybug as lb
 import polars as pl
 
-db = lbug.Database(":memory:")
-conn = lbug.Connection(db)
+db = lb.Database(":memory:")
+conn = lb.Connection(db)
 
 df = pl.DataFrame({
     "name": ["Adam", "Karissa", "Zhang"],
@@ -205,11 +205,11 @@ shape: (3, 2)
 You can scan an existing PyArrow table as follows:
 
 ```python
-import lbug
+import real_ladybug as lb
 import pyarrow as pa
 
-db = lbug.Database(":memory:")
-conn = lbug.Connection(db)
+db = lb.Database(":memory:")
+conn = lb.Connection(db)
 
 pa_table = pa.table({
     "name": ["Adam", "Karissa", "Zhang"],

src/content/docs/extensions/vector.mdx

@@ -36,11 +36,11 @@ extension to directly create the embeddings using Cypher.
 ```python
 # create_embeddings.py
 # pip install sentence-transformers
-import lbug
+import real_ladybug as lb
 import os
 
-db = lbug.Database("example.lbug")
-conn = lbug.Connection(db)
+db = lb.Database("example.lbug")
+conn = lb.Connection(db)
 
 from sentence_transformers import SentenceTransformer
 model = SentenceTransformer("all-MiniLM-L6-v2")
@@ -96,11 +96,11 @@ for title, publisher in zip(titles, publishers):
 <TabItem value="llm" label="LLM extension">
 ```python
 # create_embeddings.py
-import lbug
+import real_ladybug as lb
 import os
 
-db = lbug.Database("example.lbug")
-conn = lbug.Connection(db)
+db = lb.Database("example.lbug")
+conn = lb.Connection(db)
 
 conn.execute("INSTALL llm; LOAD llm;")
 os.environ["OPENAI_API_KEY"] = "sk-proj-key" # Replace with your own OpenAI API key
@@ -269,11 +269,11 @@ Let's run some example search queries on our newly created vector index.
 <Tabs syncKey="vector">
 <TabItem value="sentence_transformers" label="sentence_transformers">
 ```python
-import lbug
+import real_ladybug as lb
 
 # Initialize the database
-db = lbug.Database("example.lbug")
-conn = lbug.Connection(db)
+db = lb.Database("example.lbug")
+conn = lb.Connection(db)
 
 # Install and load vector extension once again
 conn.execute("INSTALL VECTOR;")
@@ -304,11 +304,11 @@ print(result.get_as_pl())
 </TabItem>
 <TabItem value="llm" label="LLM extension">
 ```python
-import lbug
+import real_ladybug as lb
 
 # Initialize the database
-db = lbug.Database("example.lbug")
-conn = lbug.Connection(db)
+db = lb.Database("example.lbug")
+conn = lb.Connection(db)
 
 # Install and load vector extension once again
 conn.execute("INSTALL VECTOR;")