Update snippet to fix missing vector in search query

docs/snippets/basic_usage.mdx

@@ -26,7 +26,7 @@ export const PyBasicVectorSearch = "query_vector = [0.03, 0.85, 0.61, 0.90]\nres
 
 export const PyBasicVectorSearchQ1 = "# Who are the characters similar to  \"wizard\"?\nquery_vector_1 = [0.03, 0.85, 0.61, 0.90]\nr1 = (\n    table.search(query_vector_1)\n    .limit(5)\n    .select([\"name\", \"role\", \"description\"])\n    .to_polars()\n)\nprint(r1)\n";
 
-export const PyBasicVectorSearchQ2 = "# Who are the characters similar to \"wizard\" with high magic stats?\nquery_vector_2 = [0.03, 0.85, 0.61, 0.90]\nr2 = (\n    table.search()\n    .where(\"stats.magic > 3\")\n    .select([\"name\", \"role\", \"description\"])\n    .limit(5)\n    .to_polars()\n)\nprint(r2)\n";
+export const PyBasicVectorSearchQ2 = "# Who are the characters with high magic stats?\nquery_vector_2 = [0.03, 0.85, 0.61, 0.90]\nr2 = (\n    table.search(query_vector_2)\n    .where(\"stats.magic > 3\")\n    .select([\"name\", \"role\", \"description\"])\n    .limit(5)\n    .to_polars()\n)\nprint(r2)\n";
 
 export const PyBasicVectorSearchQ3 = "# Who are the strongest characters?\nr3 = (\n    table.search()\n    .where(\"stats.strength > 3\")\n    .select([\"name\", \"role\", \"description\"])\n    .limit(5)\n    .to_polars()\n)\nprint(r3)\n";
 

docs/snippets/quickstart.mdx

@@ -14,8 +14,6 @@ export const PyQuickstartVectorSearch1 = "# Let's search for vectors similar to
 
 export const PyQuickstartVectorSearch2 = "# Let's search for vectors similar to \"wizard\"\nquery_vector = [0.7, 0.3, 0.5]\n\nresults = table.search(query_vector).limit(2).to_polars()\nprint(results)\n";
 
-export const TsQuickstartOutputPandas = "result = await table.search(queryVector).limit(2).toArray();\n";
-
 export const TsQuickstartAddData = "const moreData = [\n  { id: \"7\", text: \"mage\", vector: [0.6, 0.3, 0.4] },\n  { id: \"8\", text: \"bard\", vector: [0.3, 0.8, 0.4] },\n];\n\n// Add data to table\nawait table.add(moreData);\n";
 
 export const TsQuickstartCreateTable = "const data = [\n  { id: \"1\", text: \"knight\", vector: [0.9, 0.4, 0.8] },\n  { id: \"2\", text: \"ranger\", vector: [0.8, 0.4, 0.7] },\n  { id: \"9\", text: \"priest\", vector: [0.6, 0.2, 0.6] },\n  { id: \"4\", text: \"rogue\", vector: [0.7, 0.4, 0.7] },\n];\nlet table = await db.createTable(\"adventurers\", data, { mode: \"overwrite\" });\n";
@@ -26,6 +24,8 @@ export const TsQuickstartOpenTable = "table = await db.openTable(\"adventurers\"
 
 export const TsQuickstartOutputArray = "result = await table.search(queryVector).limit(2).toArray();\nconsole.table(result);\n";
 
+export const TsQuickstartOutputPandas = "result = await table.search(queryVector).limit(2).toArray();\n";
+
 export const TsQuickstartVectorSearch1 = "// Let's search for vectors similar to \"warrior\"\nlet queryVector = [0.8, 0.3, 0.8];\n\nlet result = await table.search(queryVector).limit(2).toArray();\nconsole.table(result);\n";
 
 export const TsQuickstartVectorSearch2 = "// Let's search for vectors similar to \"wizard\"\nqueryVector = [0.7, 0.3, 0.5];\n\nconst results = await table.search(queryVector).limit(2).toArray();\nconsole.table(results);\n";

docs/snippets/search.mdx

@@ -8,10 +8,10 @@ export const PyBasicHybridSearch = "data = [\n    {\"text\": \"rebel spaceships
 
 export const PyBasicHybridSearchAsync = "uri = \"data/sample-lancedb\"\nasync_db = await lancedb.connect_async(uri)\ndata = [\n    {\"text\": \"rebel spaceships striking from a hidden base\"},\n    {\"text\": \"have won their first victory against the evil Galactic Empire\"},\n    {\"text\": \"during the battle rebel spies managed to steal secret plans\"},\n    {\"text\": \"to the Empire's ultimate weapon the Death Star\"},\n]\nasync_tbl = await async_db.create_table(\"documents_async\", schema=Documents)\n# ingest docs with auto-vectorization\nawait async_tbl.add(data)\n# Create a fts index before the hybrid search\nawait async_tbl.create_index(\"text\", config=FTS())\ntext_query = \"flower moon\"\n# hybrid search with default re-ranker\nawait (await async_tbl.search(\"flower moon\", query_type=\"hybrid\")).to_pandas()\n";
 
-export const PyClassDocuments = "class Documents(LanceModel):\n    vector: Vector(embeddings.ndims()) = embeddings.VectorField()\n    text: str = embeddings.SourceField()\n";
-
 export const PyClassDefinition = "class Metadata(BaseModel):\n    source: str\n    timestamp: datetime\n\n\nclass Document(BaseModel):\n    content: str\n    meta: Metadata\n\n\nclass LanceSchema(LanceModel):\n    id: str\n    vector: Vector(1536)\n    payload: Document\n";
 
+export const PyClassDocuments = "class Documents(LanceModel):\n    vector: Vector(embeddings.ndims()) = embeddings.VectorField()\n    text: str = embeddings.SourceField()\n";
+
 export const PyCreateTableAsyncWithNestedSchema = "# Let's add 100 sample rows to our dataset\ndata = [\n    LanceSchema(\n        id=f\"id{i}\",\n        vector=np.random.randn(1536),\n        payload=Document(\n            content=f\"document{i}\",\n            meta=Metadata(source=f\"source{i % 10}\", timestamp=datetime.now()),\n        ),\n    )\n    for i in range(100)\n]\n\nasync_tbl = await async_db.create_table(\"documents_async\", data=data)\n";
 
 export const PyCreateTableWithNestedSchema = "# Let's add 100 sample rows to our dataset\ndata = [\n    LanceSchema(\n        id=f\"id{i}\",\n        vector=np.random.randn(1536),\n        payload=Document(\n            content=f\"document{i}\",\n            meta=Metadata(source=f\"source{i % 10}\", timestamp=datetime.now()),\n        ),\n    )\n    for i in range(100)\n]\n\n# Synchronous client\ntbl = db.create_table(\"documents\", data=data)\n";

docs/tables/index.mdx

@@ -380,9 +380,9 @@ the desired columns).
 We have Merlin, The Lady of the Lake, and Morgan le Fay in the top results, who
 all have magical abilities.
 
-Next, let's try to answer a more complex question that involves filtering on a
-nested struct field. Filtering is done using the `where` method, where you can
-pass in SQL-like expressions.
+Next, let's try to answer a different question that involves vector search while
+filtering on a nested struct field. Filtering is done using the `where` method,
+into which you can pass SQL-like expressions.
 
 > Q2: _Who are the characters similar to "wizard" with high magic stats?_
 

tests/py/test_basic_usage.py

@@ -124,10 +124,10 @@ def test_basic_usage(db_path_factory):
     # --8<-- [end:basic_vector_search_q1]
 
     # --8<-- [start:basic_vector_search_q2]
-    # Who are the characters similar to "wizard" with high magic stats?
+    # Who are the characters with high magic stats?
     query_vector_2 = [0.03, 0.85, 0.61, 0.90]
     r2 = (
-        table.search()
+        table.search(query_vector_2)
         .where("stats.magic > 3")
         .select(["name", "role", "description"])
         .limit(5)