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Use DeepSeek with Amazon OpenSearch Service vector database and Amazon SageMaker
- DeepSeek-R1 model excels at complex reasoning tasks, when combined with Amazon OpenSearch Service it enables robust Retrieval Augmented Generation (RAG) applications in enterprises.
- This post shows you how to set up RAG using DeepSeek-R1 on Amazon SageMaker with an OpenSearch Service vector database as the knowledge base.
- The OpenSearch Service provides rich capabilities for RAG use cases, as well as vector embedding-powered semantic search.
- You create an OpenSearch connector and model that enable the retrieval_augmented_generation processor within OpenSearch to execute a user query, perform a search, and use DeepSeek to generate a text response.
- You will create a connector to SageMaker with Amazon Titan Text Embeddings V2 to create embeddings for a set of documents with population statistics.
- The post guides you through running a set of scripts to create the entire architecture and data flow including SageMaker endpoint, two IAM roles, OpenSearch connector and model prepared for RAG workflow.
- A RAG workflow essentially involves adding information to the prompt so that the LLM generating the response is more accurate, using search pipelines and OpenSearch retrieval_augmented_generation processor.
- With an OpenSearch based knowledge base of population statistics for two cities (NYC and Miami), Amazon DeepSeek model on SageMaker generates a response to a question on the population changes in New York City and Miami.
- Adapt the code from this post to create your own knowledge base and run your own RAG queries.
- The OpenSearch connector framework is a flexible way to access models hosted on other platforms, while DeepSeek’s reasoning capabilities offer a powerful and cost-effective AI model for building generative applications.
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