Amazon Bedrock models: change aws-bedrock to just bedrock (#479)

Author: Paul-Cornell

api-reference/how-to/embedding.mdx

@@ -43,7 +43,7 @@ To use the Ingest CLI or Ingest Python library to generate embeddings, do the fo
 
 1. Choose an embedding provider that you want to use from among the following allowed providers, and note the provider's ID:
 
-   - The provider ID `aws-bedrock` for [Amazon Bedrock](https://aws.amazon.com/bedrock/). [Learn more](https://python.langchain.com/v0.2/docs/integrations/text_embedding/bedrock/).
+   - The provider ID `bedrock` for [Amazon Bedrock](https://aws.amazon.com/bedrock/). [Learn more](https://python.langchain.com/v0.2/docs/integrations/text_embedding/bedrock/).
    - `huggingface` for [Hugging Face](https://huggingface.co/). [Learn more](https://python.langchain.com/v0.2/docs/integrations/text_embedding/huggingfacehub/).
    - `mixedbread-ai` for [Mixedbread](https://www.mixedbread.ai/). [Learn more](https://www.mixedbread.ai/docs/embeddings/overview).
    - `octoai` for [Octo AI](https://octo.ai/). [Learn more](https://octo.ai/docs/text-gen-solution/using-unstructured-io-for-embedding-documents).
@@ -54,7 +54,7 @@ To use the Ingest CLI or Ingest Python library to generate embeddings, do the fo
 
 2. Run the following command to install the required Python package for the embedding provider:
 
-   - For `aws-bedrock`, run `pip install "unstructured-ingest[bedrock]"`.
+   - For `bedrock`, run `pip install "unstructured-ingest[bedrock]"`.
    - For `huggingface`, run `pip install "unstructured-ingest[embed-huggingface]"`.
    - For `mixedbread-ai`, run `pip install "unstructured-ingest[embed-mixedbreadai]"`.
    - For `octoai`, run `pip install "unstructured-ingest[embed-octoai]"`.
@@ -65,7 +65,7 @@ To use the Ingest CLI or Ingest Python library to generate embeddings, do the fo
 
 3. For the following embedding providers, you can choose the model that you want to use. If you do choose a model, note the model's name:
 
-   - `aws-bedrock`. [Choose a model](https://docs.aws.amazon.com/bedrock/latest/userguide/model-ids.html). No default model is provided. [Learn more about the supported models](https://docs.aws.amazon.com/bedrock/latest/userguide/models-supported.html).
+   - `bedrock`. [Choose a model](https://docs.aws.amazon.com/bedrock/latest/userguide/model-ids.html). No default model is provided. [Learn more about the supported models](https://docs.aws.amazon.com/bedrock/latest/userguide/models-supported.html).
    - `huggingface`. [Choose a model](https://huggingface.co/models?other=embeddings), or use the default model [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2).
    - `mixedbread-ai`. [Choose a model](https://www.mixedbread.ai/docs/embeddings/models), or use the default model [mixedbread-ai/mxbai-embed-large-v1](https://www.mixedbread.ai/docs/embeddings/mxbai-embed-large-v1).
    - `octoai`. [Choose a model](https://octo.ai/blog/supercharge-rag-performance-using-octoai-and-unstructured-embeddings/), or use the default model `thenlper/gte-large`.
@@ -76,7 +76,7 @@ To use the Ingest CLI or Ingest Python library to generate embeddings, do the fo
 
 4. Note the special settings to connect to the provider:
 
-   - For `aws-bedrock`, you'll need an AWS access key value, the corresponding AWS secret access key value, and the corresponding AWS Region identifier. [Get an AWS access key and secret access key](https://docs.aws.amazon.com/IAM/latest/UserGuide/id_credentials_access-keys.html).
+   - For `bedrock`, you'll need an AWS access key value, the corresponding AWS secret access key value, and the corresponding AWS Region identifier. [Get an AWS access key and secret access key](https://docs.aws.amazon.com/IAM/latest/UserGuide/id_credentials_access-keys.html).
    - For `huggingface`, if you use a gated model (a model with special conditions that you must accept before you can use it, or a privately published model), you'll need an HF inference API key value, beginning with `hf_`. [Get an HF inference API key](https://huggingface.co/docs/api-inference/en/quicktour#get-your-api-token). To learn whether your model requires an HF inference API key, see your model provider's documentation. 
    - For `mixedbread-ai`, you'll need a Mixedbread API key value. [Get a Mixedbread API key](https://www.mixedbread.ai/dashboard?next=api-keys).
    - For `octoai`, you'll need an Octo AI API token value. [Get an Octo AI API token](https://octo.ai/docs/getting-started/how-to-create-octoai-access-token).
@@ -94,7 +94,7 @@ To use the Ingest CLI or Ingest Python library to generate embeddings, do the fo
         - Set the command's `--embedding-provider` to the provider's ID, for example `huggingface`.
         - Set `--embedding-model-name` to the model name, as applicable, for example `sentence-transformers/sentence-t5-xl`. Or omit this to use the default model, as applicable.
         - Set `--embedding-api-key` to the provider's required API key value or credentials JSON file path, as appropriate.
-        - For `aws-bedrock`:
+        - For `bedrock`:
 
           - Set `--embedding-aws-access-key-id` to the AWS access key value.
           - Set `--embedding-aws-secret-access-key` to the corresponding AWS secret access key value.
@@ -106,7 +106,7 @@ To use the Ingest CLI or Ingest Python library to generate embeddings, do the fo
         - Set the `embedding_provider` parameter to the provider's ID, for example `huggingface`.
         - Set `embedding_model_name` to the model name, as applicable, for example `sentence-transformers/sentence-t5-xl`. Or omit this to use the default model, as applicable.
         - Set `embedding_api_key` to the provider's required API key value or credentials JSON file path, as appropriate.
-        - For `aws-bedrock`:
+        - For `bedrock`:
 
           - Set `embedding_aws_access_key_id` to the AWS access key value.
           - Set `embedding_aws_secret_access_key` to the corresponding AWS secret access key value.

snippets/ingest-configuration-shared/embedding-configuration.mdx

@@ -10,7 +10,7 @@ A common embedding configuration is a critical component that allows for dynamic
 
 *   <Icon icon="v"/><Icon icon="1"/>&nbsp;&nbsp;`aws_secret_access_key`: The AWS secret access key to be used for AWS-based embedders, such as Amazon Bedrock.
 
-*   <Icon icon="v"/><Icon icon="2"/>&nbsp;&nbsp;`embedding_provider`: The embedding provider to use while doing embedding. Available values include `aws-bedrock`, `azure-openai`, `huggingface`, `mixedbread-ai`, `octoai`, `openai`, `togetherai`, `vertexai`, and `voyageai`.
+*   <Icon icon="v"/><Icon icon="2"/>&nbsp;&nbsp;`embedding_provider`: The embedding provider to use while doing embedding. Available values include `bedrock`, `azure-openai`, `huggingface`, `mixedbread-ai`, `octoai`, `openai`, `togetherai`, `vertexai`, and `voyageai`.
 
 *   <Icon icon="v"/><Icon icon="2"/>&nbsp;&nbsp;`embedding_api_key`: The API key to use, if one is required to generate the embeddings through an API service, such as OpenAI.
 
@@ -24,12 +24,12 @@ A common embedding configuration is a critical component that allows for dynamic
 
 *   <Icon icon="v"/><Icon icon="1"/>&nbsp;&nbsp;`model_name`: The specific model to use for the embedding provider, if necessary.
 
-*   <Icon icon="v"/><Icon icon="1"/>&nbsp;&nbsp;`provider`: The embedding provider to use while doing embedding. Available values include `aws-bedrock`, `azure-openai`, `huggingface`, `mixedbread-ai`, `octoai`, `openai`, `togetherai`, `vertexai`, and `voyageai`.
+*   <Icon icon="v"/><Icon icon="1"/>&nbsp;&nbsp;`provider`: The embedding provider to use while doing embedding. Available values include `bedrock`, `azure-openai`, `huggingface`, `mixedbread-ai`, `octoai`, `openai`, `togetherai`, `vertexai`, and `voyageai`.
 
 
 <Icon icon="v"/><Icon icon="1"/>&nbsp;&nbsp;The default `model_name` values unless otherwise specified are:
 
-* `aws-bedrock`: None
+* `bedrock`: None
 
 * `azure-openai`: `text-embedding-ada-002`, with 1536 dimensions