docs: fix two typos the spell check found

"explictly" and "Additionaly", in snippet sources and their generated
snippets. integrations.mdx is updated in place: regenerating it whole
would also drop PyRerankingWatsonxUsage, which has no source.

docs/web-tests/py/test_integrations.py

@@ -1163,7 +1163,7 @@ def test_frameworks_langchain_examples() -> None:
     # --8<-- [start:frameworks_langchain_add_texts]
     vector_store.add_texts(texts=["test_123"], metadatas=[{"source": "wiki"}])
 
-    # Additionaly, to explore the table you can load it into a df or save it in a csv file:
+    # Additionally, to explore the table you can load it into a df or save it in a csv file:
 
     tbl = vector_store.get_table()
     print("tbl:", tbl)

docs/web-tests/py/test_multimodal.py

@@ -54,7 +54,7 @@ def test_multimodal_ingestion(db_path_factory):
     # --8<-- [end:create_dummy_data]
 
     # --8<-- [start:define_schema]
-    # Define schema explictly to ensure image_blob is treated as binary
+    # Define schema explicitly to ensure image_blob is treated as binary
     schema = pa.schema([
         pa.field("id", pa.int32()),
         pa.field("filename", pa.string()),

docs/web/snippets/integrations.mdx

@@ -54,7 +54,7 @@ export const PyFrameworksAgnoSetup = "import os\nimport re\n\nfrom agno.agent im
 
 export const PyFrameworksLangchainAddImages = "image_uris = [\"./assets/image-1.png\", \"./assets/image-2.png\"]\nvector_store.add_images(uris=image_uris)\n# here image_uris are local fs paths to the images.\n";
 
-export const PyFrameworksLangchainAddTexts = "vector_store.add_texts(texts=[\"test_123\"], metadatas=[{\"source\": \"wiki\"}])\n\n# Additionaly, to explore the table you can load it into a df or save it in a csv file:\n\ntbl = vector_store.get_table()\nprint(\"tbl:\", tbl)\npd_df = tbl.to_pandas()\npd_df.to_csv(\"docsearch.csv\", index=False)\n\n# you can also create a new vector store object using an older connection object:\nvector_store = LanceDB(connection=tbl, embedding=embeddings)\n";
+export const PyFrameworksLangchainAddTexts = "vector_store.add_texts(texts=[\"test_123\"], metadatas=[{\"source\": \"wiki\"}])\n\n# Additionally, to explore the table you can load it into a df or save it in a csv file:\n\ntbl = vector_store.get_table()\nprint(\"tbl:\", tbl)\npd_df = tbl.to_pandas()\npd_df.to_csv(\"docsearch.csv\", index=False)\n\n# you can also create a new vector store object using an older connection object:\nvector_store = LanceDB(connection=tbl, embedding=embeddings)\n";
 
 export const PyFrameworksLangchainCreateIndex = "# for creating vector index\nvector_store.create_index(vector_col=\"vector\", metric=\"cosine\")\n\n# for creating scalar index(for non-vector columns)\nvector_store.create_index(col_name=\"text\")\n";
 

docs/web/snippets/multimodal.mdx

@@ -8,7 +8,7 @@ export const PyBlobApiToPandas = "# Default: blob columns come back lazily\ndf_l
 
 export const PyCreateDummyData = "# Create some dummy images\ndef create_dummy_image(color):\n    img = Image.new('RGB', (100, 100), color=color)\n    buf = io.BytesIO()\n    img.save(buf, format='PNG')\n    return buf.getvalue()\n\n# Create dataset with metadata, vectors, and image blobs\ndata = [\n    {\n        \"id\": 1,\n        \"filename\": \"red_square.png\",\n        \"vector\": np.random.rand(128).astype(np.float32),\n        \"image_blob\": create_dummy_image('red'),\n        \"label\": \"red\"\n    },\n    {\n        \"id\": 2,\n        \"filename\": \"blue_square.png\",\n        \"vector\": np.random.rand(128).astype(np.float32),\n        \"image_blob\": create_dummy_image('blue'),\n        \"label\": \"blue\"\n    }\n]\n";
 
-export const PyDefineSchema = "# Define schema explictly to ensure image_blob is treated as binary\nschema = pa.schema([\n    pa.field(\"id\", pa.int32()),\n    pa.field(\"filename\", pa.string()),\n    pa.field(\"vector\", pa.list_(pa.float32(), 128)),\n    pa.field(\"image_blob\", pa.binary()), # Important: Use pa.binary() for blobs\n    pa.field(\"label\", pa.string())\n])\n";
+export const PyDefineSchema = "# Define schema explicitly to ensure image_blob is treated as binary\nschema = pa.schema([\n    pa.field(\"id\", pa.int32()),\n    pa.field(\"filename\", pa.string()),\n    pa.field(\"vector\", pa.list_(pa.float32(), 128)),\n    pa.field(\"image_blob\", pa.binary()), # Important: Use pa.binary() for blobs\n    pa.field(\"label\", pa.string())\n])\n";
 
 export const PyIngestData = "tbl = db.create_table(\"images\", data=data, schema=schema, mode=\"overwrite\")\n";