docs: regenerate the vector-index snippets the page imports

Porting the upstream changes replaced snippets/indexing.mdx with a hand
copy from lancedb/docs, which dropped six exports the vector-index page
still imports (`mint validate` warns on each) and added
PyQuantizationCustomParams, which had no source here.

The quantization test region is ported from lancedb/docs and the module
is regenerated, so every export comes from a test again. The regenerated
PyQuantizationCustomParams matches the copy byte for byte. Also fixes
"convetion" and "entires" on the page.

docs/web-tests/py/test_indexing.py

@@ -336,6 +336,25 @@ def test_vector_index_binary(tmp_db):
     assert not df.empty
 
 
+def test_quantization_custom_params(tmp_db):
+    table = tmp_db.create_table(
+        "quantization-custom-params",
+        _make_vector_rows(256, 64),
+        mode="overwrite",
+    )
+
+    # --8<-- [start:quantization_custom_params]
+    table.create_index(
+        index_type="IVF_RQ",
+        num_bits=2,
+        max_iterations=100,
+        sample_rate=512,
+    )
+    # --8<-- [end:quantization_custom_params]
+
+    assert table.list_indices()
+
+
 def test_vector_index_check_status(tmp_db):
     table = tmp_db.create_table(
         "vector_index_check_status",

docs/web/indexing/vector-index.mdx

@@ -322,7 +322,7 @@ These fields are populated for local and embedded tables. On LanceDB Enterprise
 </Note>
 #### Custom Index Names
 
-The `{column}_idx` suffix is the default naming convetion. 
+The `{column}_idx` suffix is the default naming convention. 
 Pass `name=...` to `create_index()` to override it. Once set, the custom name will be reflected in `list_indices()`, `index_stats(name)`, and `wait_for_index([name])`.
 
 <CodeGroup>
@@ -388,7 +388,7 @@ See this [blog post](https://lancedb.com/blog/feature-rabitq-quantization/) for
 Scalar quantization quantizes each entry of a vector independently, by simply replacing it with the closest of a pre-defined set of values. 
 In practice, it often uses $8$ bits per dimension of a vector, and supports very fast encoding and decoding.
 
-For example, suppose we know all vector entires across our dataset lie in the range $[-128 \times 10^5, 127 \times 10^5]$. 
+For example, suppose we know all vector entries across our dataset lie in the range $[-128 \times 10^5, 127 \times 10^5]$. 
 In this case, we could quantize a given value $v$ as an $8$-bit representation of the integer $j$, where $j \times 10^5$ is the closest value to $v$
 among all integral multiples $\{j \times 10^5: -128 \leq j \leq 127 \}$.
 

docs/web/snippets/indexing.mdx

@@ -40,14 +40,24 @@ export const PyScalarIndexWait = "index_name = \"label_idx\"\ntable.wait_for_ind
 
 export const PyVectorIndexAsyncConfig = "import lancedb\nimport numpy as np\nfrom lancedb.index import IvfPq\n\nasync def main():\n    data = [\n        {\"id\": i, \"vector\": np.random.random(8).astype(np.float32).tolist()}\n        for i in range(512)\n    ]\n\n    db = await lancedb.connect_async(\"ex_lancedb\")\n    table = await db.create_table(\n        \"vector_index_async\", data=data, mode=\"overwrite\"\n    )\n\n    await table.create_index(\n        \"vector\",\n        config=IvfPq(\n            distance_type=\"cosine\",\n            num_partitions=16,\n            num_sub_vectors=4,\n        ),\n    )\n    return await table.list_indices()\n";
 
+export const PyVectorIndexBinaryAddData = "table.add(data)\n";
+
+export const PyVectorIndexBinaryBuildIndex = "table.create_index(\n    metric=\"hamming\",\n    vector_column_name=\"vector\",\n    index_type=\"IVF_FLAT\",\n)\n";
+
+export const PyVectorIndexBinarySchema = "table = tmp_db.create_table(table_name, schema=schema, mode=\"overwrite\")\n";
+
+export const PyVectorIndexBinarySearch = "query = np.random.randint(0, 2, size=ndim)\nquery = np.packbits(query)\ndf = table.search(query).metric(\"hamming\").limit(10).to_pandas()\ndf.vector = df.vector.apply(np.unpackbits)\n";
+
 export const PyVectorIndexBuildHnsw = "table.create_index(index_type=\"IVF_HNSW_SQ\")\n";
 
-export const PyVectorIndexBuildIvf = "table_name = \"vector-index-build-ivf\"\ntable = db.open_table(table_name)\ntable.create_index(\n    metric=\"cosine\",\n    vector_column_name=\"keywords_embeddings\",\n    index_type=\"IVF_PQ\",\n)\n";
+export const PyVectorIndexBuildIvf = "table_name = \"vector-index-build-ivf\"\ntable = db.open_table(table_name)\ntable.create_index(\n    metric=\"cosine\",\n    vector_column_name=\"keywords_embeddings\",\n)\n";
 
 export const PyVectorIndexBypassRecall = "query = np.random.random(128)\nk = 10\n\n# Ground truth: flat (exhaustive) scan, ignoring the ANN index.\ntruth = set(table.search(query).bypass_vector_index().limit(k).to_pandas()[\"id\"])\n\n# ANN results with the current nprobes setting.\nann = set(table.search(query).nprobes(20).limit(k).to_pandas()[\"id\"])\n\nrecall_at_k = len(truth & ann) / k\n";
 
 export const PyVectorIndexCheckStatus = "index_name = \"keywords_embeddings_idx\"\ntable.wait_for_index([index_name])\nprint(table.index_stats(index_name))\n";
 
+export const PyVectorIndexConfigureIvf = "table.create_index(metric=\"l2\", num_partitions=16, num_sub_vectors=4)\n";
+
 export const PyVectorIndexCustomName = "# Override the default `{column}_idx` convention by passing `name=...`.\ntable.create_index(\n    metric=\"cosine\",\n    vector_column_name=\"keywords_embeddings\",\n    name=\"my_custom_index\",\n)\ntable.wait_for_index([\"my_custom_index\"])\nprint(table.index_stats(\"my_custom_index\"))\n";
 
 export const PyVectorIndexDistanceRange = "# Only return results whose distance falls within [0.0, 0.5).\n# Useful for near-duplicate detection or thresholded similarity search.\n(\n    table.search(np.random.random(128))\n    .distance_range(lower_bound=0.0, upper_bound=0.5)\n    .limit(10)\n    .to_pandas()\n)\n";
@@ -60,3 +70,5 @@ export const PyVectorIndexQueryHnsw = "tbl = table\ntbl.search(np.random.random(
 
 export const PyVectorIndexQueryIvf = "tbl = table\ntbl.search(np.random.random((1536))).limit(2).nprobes(20).refine_factor(\n    10\n).to_pandas()\n";
 
+export const PyVectorIndexSetup = "table_name = \"vector-index-tbl\"\ntable = db.open_table(table_name)\n";
+