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docs: clean up prose flagged by the shared Vale rules (#4596)

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The existing docs have 254 findings from our shared Vale rules.
Contributors would otherwise inherit these when editing the same lines.

Clean up the prose across 80 pages: correct product names and a repeated
word, simplify a few phrases, and replace dashes with punctuation that
fits each sentence. The pinned Vale scan now reports zero findings, with
no suppressions.
This commit is contained in:
Brendan Clement
2026-10-19 13:45:55 -07:00
committed by GitHub
parent 1ad97b148b
commit 198812fd53
80 changed files with 252 additions and 252 deletions
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docs/web/integrations/embedding/huggingface.mdx
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@@ -5,7 +5,7 @@ sidebarTitle: Hugging Face
import { PyEmbeddingHuggingfaceUsage } from '/snippets/integrations.mdx';
We offer support for all Hugging Face models (which can be loaded via [transformers](https://huggingface.co/docs/transformers/en/index) library). The default model is `colbert-ir/colbertv2.0` which also has its own special callout - `registry.get("colbert")`. Some Hugging Face models might require custom models defined on the HuggingFace Hub in their own modeling files. You may enable this by setting `trust_remote_code=True`. This option should only be set to True for repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine. Pin `revision` to a commit hash, and note that tables using such a model need it approved with `get_registry().allow_remote_code(...)` when opened in a new process. See [Trusting remote code and custom endpoints](/embedding#trusting-remote-code-and-custom-endpoints).
We offer support for all Hugging Face models (which can be loaded via [transformers](https://huggingface.co/docs/transformers/en/index) library). The default model is `colbert-ir/colbertv2.0` which also has its own special callout - `registry.get("colbert")`. Some Hugging Face models might require custom models defined on the Hugging Face Hub in their own modeling files. You may enable this by setting `trust_remote_code=True`. This option should only be set to True for repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine. Pin `revision` to a commit hash, and note that tables using such a model need it approved with `get_registry().allow_remote_code(...)` when opened in a new process. See [Trusting remote code and custom endpoints](/embedding#trusting-remote-code-and-custom-endpoints).
Example usage:
docs/web/integrations/embedding/sentence-transformers.mdx
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@@ -20,7 +20,7 @@ Setting `trust_remote_code=True` allows the model to execute arbitrary code from
| `name` | `str` | `all-MiniLM-L6-v2` | The name of the model |
| `device` | `str` | `cpu` | The device to run the model on (can be `cpu` or `gpu`) |
| `normalize` | `bool` | `True` | Whether to normalize the input text before feeding it to the model |
| `trust_remote_code` | `bool` | `False` | Whether to trust and execute remote code from the model's Huggingface repository |
| `trust_remote_code` | `bool` | `False` | Whether to trust and execute remote code from the model's Hugging Face repository |
| `revision` | `str` | `None` | The model revision to load, such as a commit hash |
@@ -166,5 +166,5 @@ Here is an example that uses the BAAI embedding model from the Hugging Face Hub
</CodeBlock>
</CodeGroup>
</Note>
Visit sentence-transformers [HuggingFace HUB](https://huggingface.co/sentence-transformers) page for more information on the available models.
Visit the sentence-transformers [Hugging Face Hub](https://huggingface.co/sentence-transformers) page for more information on the available models.
docs/web/integrations/embedding/voyageai.mdx
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@@ -1,6 +1,6 @@
---
title: VoyageAI
sidebarTitle: VoyageAI
title: Voyage AI
sidebarTitle: Voyage AI
---
import { PyEmbeddingVoyageaiUsage, PyEmbeddingVoyageaiMultimodal } from '/snippets/integrations.mdx';
@@ -9,7 +9,7 @@ Voyage AI provides cutting-edge embedding and rerankers.
Using voyageai API requires voyageai package, which can be installed using `pip install voyageai`. Voyage AI embeddings are used to generate embeddings for text data. The embeddings can be used for various tasks like semantic search, clustering, and classification.
You also need to set the `VOYAGE_API_KEY` environment variable to use the VoyageAI API.
You also need to set the `VOYAGE_API_KEY` environment variable to use the Voyage AI API.
```shell
export VOYAGE_API_KEY="your-api-key"
@@ -32,7 +32,7 @@ Supported models are:
- voyage-multimodal-3.5 (multimodal - supports text, images, and video)
<Info>
**Multimodal Model:** `voyage-multimodal-3.5` supports text, images, and video inputs. It outputs 1024-dimensional embeddings by default, configurable via the `output_dimension` parameter (256, 512, 1024, 2048). See the [VoyageAI multimodal embeddings documentation](https://docs.voyageai.com/docs/multimodal-embeddings) for more details.
**Multimodal Model:** `voyage-multimodal-3.5` supports text, images, and video inputs. It outputs 1024-dimensional embeddings by default, configurable via the `output_dimension` parameter (256, 512, 1024, 2048). See the [Voyage AI multimodal embeddings documentation](https://docs.voyageai.com/docs/multimodal-embeddings) for more details.
</Info>
Supported parameters (to be passed in `create` method) are: