diff --git a/python/python/tests/docs/test_cloud.py b/python/python/tests/docs/test_cloud.py
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+++ b/python/python/tests/docs/test_cloud.py
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+# --8<-- [start:imports]
+import lancedb
+import pyarrow as pa
+import numpy as np
+
+# --8<-- [end:imports]
+# --8<-- [start:gen_data]
+def gen_data(total_rows: int, ndims: int = 1536):
+  return pa.RecordBatch.from_pylist(
+    [
+        {
+            "vector": np.random.rand(ndims).astype(np.float32).tolist(),
+            "id": i,
+            "name": "name_"+str(i),
+        }
+        for i in range(total_rows)
+    ],
+).to_pandas()
+# --8<-- [end:gen_data]
+
+def test_cloud_quickstart():
+    # --8<-- [start:connect]
+    db = lancedb.connect(
+        uri="db://your-project-slug",
+        api_key="your-api-key",
+        region="your-cloud-region"
+    )
+    # --8<-- [end:connect]
+    # --8<-- [start:create_table]
+    table_name = "myTable" 
+    table = db.create_table(table_name, data=gen_data(5000))
+    # --8<-- [end:create_table]
+    # --8<-- [start:create_index_search]
+    # create a vector index
+    table.create_index("cosine", vector_column_name="vector")
+    result = (
+        table.search([0.01, 0.02])
+        .select(["vector", "item"])
+        .limit(1)
+        .to_pandas()
+    )
+    # --8<-- [end:create_index_search]
+    # --8<-- [start:drop_table]
+    db.drop_table(table_name)
+    # --8<-- [end:drop_table]
+
+def test_ingest_data():
+    # --8<-- [start:ingest_data]
+    import lancedb
+    import pyarrow as pa
+
+    # connect to LanceDB
+    db = lancedb.connect(
+        uri="db://your-project-slug",
+        api_key="your-api-key",
+        region="us-east-1"
+    )
+
+    # create an empty table with schema
+    table_name = "myTable"
+    data = [
+        {"vector": [3.1, 4.1], "item": "foo", "price": 10.0},
+        {"vector": [5.9, 26.5], "item": "bar", "price": 20.0},
+        {"vector": [10.2, 100.8], "item": "baz", "price": 30.0},
+        {"vector": [1.4, 9.5], "item": "fred", "price": 40.0},
+    ]
+    schema = pa.schema([
+        pa.field("vector", pa.list_(pa.float32(), 2)),
+        pa.field("item", pa.utf8()),
+        pa.field("price", pa.float32()),
+    ])
+    table = db.create_table(table_name, schema=schema)
+    table.add(data)
+    # --8<-- [end:ingest_data]
+    # --8<-- [start:ingest_data_in_batch]
+    def make_batches():
+        for i in range(5):
+            yield pa.RecordBatch.from_arrays(
+                [
+                    pa.array([[3.1, 4.1], [5.9, 26.5]],
+                            pa.list_(pa.float32(), 2)),
+                    pa.array(["foo", "bar"]),
+                    pa.array([10.0, 20.0]),
+                ],
+                ["vector", "item", "price"],
+            )
+
+    schema = pa.schema([
+        pa.field("vector", pa.list_(pa.float32(), 2)),
+        pa.field("item", pa.utf8()),
+        pa.field("price", pa.float32()),
+    ])
+    db.create_table("table2", make_batches(), schema=schema)
+    # --8<-- [end:ingest_data_in_batch]
+
+def test_updates():
+    # --8<-- [start:update_data]
+    import lancedb
+
+    # connect to LanceDB
+    db = lancedb.connect(
+    uri="db://your-project-slug",
+    api_key="your-api-key",
+    region="us-east-1"
+    )
+    table_name = "myTable"
+    table = db.open_table(table_name)
+    table.update(where="price < 20.0", values={"vector": [2, 2], "item": "foo-updated"})
+    # --8<-- [end:update_data]  
+    # --8<-- [start:merge_insert]
+    table = db.open_table(table_name)
+    # upsert
+    new_data = [
+        {"vector": [1, 1], "item": 'foo-updated', "price": 50.0}
+    ]
+    table.merge_insert("item") \
+        .when_matched_update_all() \
+        .when_not_matched_insert_all() \
+        .execute(new_data)
+    # --8<-- [end:merge_insert]
+    # --8<-- [start:delete_data]
+    table_name = "myTable"
+    table = db.open_table(table_name)
+    # delete data
+    predicate = "price = 30.0"
+    table.delete(predicate)
+    # --8<-- [end:delete_data]
+
+def test_create_index():
+    # --8<-- [start:create_index]
+    import lancedb
+
+    # connect to LanceDB
+    db = lancedb.connect(
+        uri="db://your-project-slug",
+        api_key="your-api-key",
+        region="us-east-1"
+    )
+
+    table_name = "myTable"
+    table = db.open_table(table_name)
+    # the vector column only needs to be specified when there are 
+    # multiple vector columns or the column is not named as "vector"
+    # L2 is used as the default distance metric
+    table.create_index(metric="cosine", vector_column_name="vector")
+    # --8<-- [end:create_index]
+
+def test_create_scalar_index():
+    # --8<-- [start:create_scalar_index]
+    import lancedb
+
+    # connect to LanceDB
+    db = lancedb.connect(
+    uri="db://your-project-slug",
+    api_key="your-api-key",
+    region="us-east-1"
+    )
+
+    table_name = "myTable"
+    table = db.open_table(table_name)
+    # default is BTree
+    table.create_scalar_index("item", index_type="BITMAP")
+    # --8<-- [end:create_scalar_index]
+
+def test_create_fts_index():
+    # --8<-- [start:create_fts_index]
+    import lancedb
+
+    # connect to LanceDB
+    db = lancedb.connect(
+    uri="db://your-project-slug",
+    api_key="your-api-key",
+    region="us-east-1"
+    )
+
+    table_name = "myTable"
+    data = [
+        {"vector": [3.1, 4.1], "text": "Frodo was a happy puppy"},
+        {"vector": [5.9, 26.5], "text": "There are several kittens playing"},
+    ]
+    table = db.create_table(table_name, data=data)
+    table.create_fts_index("text")
+    # --8<-- [end:create_fts_index]
+
+def test_search():
+    # --8<-- [start:vector_search]
+    import lancedb
+
+    # connect to LanceDB
+    db = lancedb.connect(
+        uri="db://your-project-slug",
+        api_key="your-api-key",
+        region="us-east-1"
+    )
+
+    table_name = "myTable"
+    table = db.open_table(table_name)
+    query = [0.4, 1.4]
+    result = (
+        table.search(query)
+        .where("price > 10.0", prefilter=True)
+        .select(["item", "vector"])
+        .limit(2)
+        .to_pandas()
+    )
+    # --8<-- [end:vector_search]
+    # --8<-- [start:full_text_search]
+    import lancedb
+    
+    # connect to LanceDB
+    db = lancedb.connect(
+    uri="db://your-project-slug",
+    api_key="your-api-key",
+    region="us-east-1"
+    )
+    table_name = "myTable"
+    table = db.create_table(
+        table_name,
+        data=[
+            {"vector": [3.1, 4.1], "text": "Frodo was a happy puppy"},
+            {"vector": [5.9, 26.5], "text": "There are several kittens playing"},
+        ],
+    )
+
+    table.create_fts_index("text")
+    table.search("puppy", query_type="fts").limit(10).select(["text"]).to_list()
+    # --8<-- [end:full_text_search]
+    # --8<-- [start:hybrid_search]
+    import os
+
+    import lancedb
+    import openai
+    from lancedb.embeddings import get_registry
+    from lancedb.pydantic import LanceModel, Vector
+    from lancedb.rerankers import RRFReranker
+
+    # connect to LanceDB
+    db = lancedb.connect(
+    uri="db://your-project-slug",
+    api_key="your-api-key",
+    region="us-east-1"
+    )
+
+    # Configuring the environment variable OPENAI_API_KEY
+    if "OPENAI_API_KEY" not in os.environ:
+        # OR set the key here as a variable
+        openai.api_key = "sk-..."
+    embeddings = get_registry().get("openai").create()
+
+    class Documents(LanceModel):
+        text: str = embeddings.SourceField()
+        vector: Vector(embeddings.ndims()) = embeddings.VectorField()
+
+    table_name = "myTable"
+    table = db.create_table(table_name, schema=Documents)
+    data = [
+        {"text": "rebel spaceships striking from a hidden base"},
+        {"text": "have won their first victory against the evil Galactic Empire"},
+        {"text": "during the battle rebel spies managed to steal secret plans"},
+        {"text": "to the Empire's ultimate weapon the Death Star"},
+    ]
+    table.add(data=data)
+    table.create_index("L2", "vector")
+    table.create_fts_index("text")
+
+    # you can use table.list_indices() to make sure indices have been created
+    reranker = RRFReranker()
+    results = (
+        table.search(
+            "flower moon",
+            query_type="hybrid",
+            vector_column_name="vector",
+            fts_columns="text",
+        )
+        .rerank(reranker)
+        .limit(10)
+        .to_pandas()
+    )
+    # --8<-- [end:hybrid_search]
+
+def test_filtering():
+    # --8<-- [start:filtering]
+    import lancedb
+
+    # connect to LanceDB
+    db = lancedb.connect(
+    uri="db://your-project-slug",
+    api_key="your-api-key",
+    region="us-east-1"
+    )
+    table_name = "myTable"
+    table = db.open_table(table_name)
+    result = (
+        table.search([100, 102])
+        .where("(item IN ('foo', 'bar')) AND (price > 10.0)")
+        .to_arrow()
+    )
+    # --8<-- [end:filtering]
+    # --8<-- [start:sql_filtering]
+    table.search([100, 102]) \
+        .where("(item IN ('foo', 'bar')) AND (price > 10.0)") \
+        .to_arrow()
+    # --8<-- [end:sql_filtering]
+    
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