Merge pull request #3604 from opensearch-project/main

Push DeepSeek blog updates to prod

Author: kolchfa-aws

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

@@ -10,14 +10,16 @@ date: 2025-01-29
 categories:
   - technical-posts
 meta_keywords: OpenSearch DeepSeek integration, LLM integration, RAG, AI search, machine learning, natural language processing, open-source LLM
-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.
+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**: 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.
 
@@ -253,24 +255,24 @@ The response contains the model output:
   ...
   "ext": {
     "retrieval_augmented_generation": {
-      "answer": "The population of the New York City metro area in 2022 was 18,867,000.",
+      "answer": "The population of the New York City metro area in 2023 was 18,867,000.",
       "message_id": "p3CvcI0BfUsSoeNTj9iH"
     }
   }
 }
 ```
 ## Tutorials
 
-The following tutorials guide you through integrating RAG in OpenSearch with the [DeepSeek chat model](https://api-docs.deepseek.com/api/create-chat-completion) and [DeepSeek R1 model](https://huggingface.co/deepseek-ai/DeepSeek-R1):
+The following tutorials guide you through integrating RAG in OpenSearch with the [DeepSeek chat model](https://api-docs.deepseek.com/api/create-chat-completion) and [DeepSeek-R1 model](https://huggingface.co/deepseek-ai/DeepSeek-R1):
 
 - [OpenSearch + DeepSeek Chat Service API](https://github.com/opensearch-project/ml-commons/blob/main/docs/tutorials/aws/RAG_with_DeepSeek_Chat_model.md)
-- [OpenSearch + DeepSeek R1 on Amazon Bedrock](https://github.com/opensearch-project/ml-commons/blob/main/docs/tutorials/aws/RAG_with_DeepSeek_R1_model_on_Bedrock.md)
-- [OpenSearch + DeepSeek R1 on Amazon SageMaker](https://github.com/opensearch-project/ml-commons/blob/main/docs/tutorials/aws/RAG_with_DeepSeek_R1_model_on_Sagemaker.md)
+- [OpenSearch + DeepSeek-R1 on Amazon Bedrock](https://github.com/opensearch-project/ml-commons/blob/main/docs/tutorials/aws/RAG_with_DeepSeek_R1_model_on_Bedrock.md)
+- [OpenSearch + DeepSeek-R1 on Amazon SageMaker](https://github.com/opensearch-project/ml-commons/blob/main/docs/tutorials/aws/RAG_with_DeepSeek_R1_model_on_Sagemaker.md)
 
 ## Wrapping up
 
-By integrating DeepSeek R1, OpenSearch continues its mission to democratize AI-powered search and analytics—offering developers **more choice, greater flexibility, and lower costs**.
+By integrating DeepSeek-R1, OpenSearch continues its mission to democratize AI-powered search and analytics—offering developers **more choice, greater flexibility, and lower costs**.
 
-**Try DeepSeek R1 now!**
+**Try DeepSeek-R1 now!**
 
 As always, we welcome your feedback, and we'd love to hear from you on the [OpenSearch forum](https://forum.opensearch.org/).