From c2c16b60b9ddb4feef4212d9416bbabf49777531 Mon Sep 17 00:00:00 2001 From: Brendan Clement Date: Mon, 5 Oct 2026 13:25:35 -0700 Subject: [PATCH] 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 | 2 +- docs/web-tests/py/test_multimodal.py | 2 +- docs/web/snippets/integrations.mdx | 2 +- docs/web/snippets/multimodal.mdx | 2 +- 4 files changed, 4 insertions(+), 4 deletions(-) diff --git a/docs/web-tests/py/test_integrations.py b/docs/web-tests/py/test_integrations.py index 20c317072..ff07dfae0 100644 --- a/docs/web-tests/py/test_integrations.py +++ b/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) diff --git a/docs/web-tests/py/test_multimodal.py b/docs/web-tests/py/test_multimodal.py index a2656bd01..cb4a647c3 100644 --- a/docs/web-tests/py/test_multimodal.py +++ b/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()), diff --git a/docs/web/snippets/integrations.mdx b/docs/web/snippets/integrations.mdx index 67c48ad88..22bbde338 100644 --- a/docs/web/snippets/integrations.mdx +++ b/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"; diff --git a/docs/web/snippets/multimodal.mdx b/docs/web/snippets/multimodal.mdx index 22c3617a1..f41af6629 100644 --- a/docs/web/snippets/multimodal.mdx +++ b/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";