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Co-authored-by: Nathan Bower <[email protected]>
Signed-off-by: kolchfa-aws <[email protected]>

_posts/2025-01-28-OpenSearch-Now-Supports-DeepSeek-Chat-Models.md

@@ -13,13 +13,13 @@ meta_keywords: OpenSearch DeepSeek integration, LLM integration, RAG, AI search,
 meta_description: Explore how OpenSearch's integration with DeepSeek R1 LLM models enables cost-effective Retrieval-Augmented Generation (RAG) while maintaining high performance comparable to leading LLMs.
 ---
 
-We're excited to announce that OpenSearch now supports DeepSeek integration, providing powerful and cost-effective AI capabilities. DeepSeek R1 is a recently released open-source large language model (LLM) that delivers **similar benchmarking performance** to leading LLMs like OpenAI O1 ([report](https://github.com/deepseek-ai/DeepSeek-R1/blob/main/DeepSeek_R1.pdf)) at a significantly **lower cost** ([DeepSeek API pricing](https://api-docs.deepseek.com/quick_start/pricing)). Because DeepSeek R1 is open source, you can download and deploy it to your preferred infrastructure. This enables you to build more cost-effective and sustainable retrieval-augmented generation (RAG) solutions in OpenSearch's vector database.
+We're excited to announce that OpenSearch now supports DeepSeek integration, providing powerful and cost-effective AI capabilities. DeepSeek-R1 is a recently released open-source large language model (LLM) that delivers **similar benchmarking performance** to leading LLMs like OpenAI O1 ([report](https://github.com/deepseek-ai/DeepSeek-R1/blob/main/DeepSeek_R1.pdf)) at a significantly **lower cost** ([DeepSeek API pricing](https://api-docs.deepseek.com/quick_start/pricing)). Because DeepSeek-R1 is open source, you can download and deploy it to your preferred infrastructure. This enables you to build more cost-effective and sustainable retrieval-augmented generation (RAG) solutions in OpenSearch's vector database.
 
-_Note_: Since DeepSeek R1 is open source, you can host it on your preferred machine learning platform. For example, you can deploy it on AWS (see AWS documentation at [AWS News Blog](http://aws.amazon.com/blogs/aws/deepseek-r1-models-now-available-on-aws)). To connect to your hosted model, update the `endpoint` and `credentials` parameters in your configuration. For detailed deployment instructions, please refer to our guides at the end of the blog.
+**Note**: Because DeepSeek-R1 is open source, you can host it on AWS (see [DeepSeek-R1 models now available on AWS](http://aws.amazon.com/blogs/aws/deepseek-r1-models-now-available-on-aws)). To connect to your hosted model, update the `endpoint` and `credentials` parameters in your configuration. For detailed deployment instructions, please refer to the guides at the end of this blog post.
 
 OpenSearch gives you the flexibility to connect to any remote inference service, such as DeepSeek or OpenAI, using machine learning (ML) connectors. You can use [prebuilt connector blueprints](https://github.com/opensearch-project/ml-commons/tree/main/docs/remote_inference_blueprints) or customize connectors based on your requirements. For more information about connector blueprints, see [Blueprints](https://opensearch.org/docs/latest/ml-commons-plugin/remote-models/blueprints/).  
 
-We've added a new [connector blueprint](https://github.com/opensearch-project/ml-commons/blob/main/docs/remote_inference_blueprints/deepseek_connector_chat_blueprint.md) for the DeepSeek R1 model. This integration, combined with OpenSearch's built-in vector database capabilities, makes it easier and more cost effective to build [RAG applications](https://opensearch.org/docs/latest/search-plugins/conversational-search) in OpenSearch.  
+We've added a new [connector blueprint](https://github.com/opensearch-project/ml-commons/blob/main/docs/remote_inference_blueprints/deepseek_connector_chat_blueprint.md) for the DeepSeek-R1 model. This integration, combined with OpenSearch's built-in vector database capabilities, makes it easier and more cost effective to build [RAG applications](https://opensearch.org/docs/latest/search-plugins/conversational-search) in OpenSearch.  
 
 The following example shows you how to implement RAG with DeepSeek in OpenSearch's vector database. This example guides you through creating a connector for the [DeepSeek chat model](https://api-docs.deepseek.com/api/create-chat-completion) and setting up a [RAG pipeline](https://opensearch.org/docs/latest/search-plugins/search-pipelines/rag-processor/) in OpenSearch.