Beyond generic AI: achieve contextual accuracy with HPE's Knowledge Bases
April 15, 2025The demand for AI applications that deliver accurate, contextually relevant insights from enterprise data is rapidly increasing. However, the challenge of integrating fragmented, often sensitive, data into generative models remains a significant hurdle.
To solve this, Hewlett Packard Enterprise (HPE) has introduced knowledge bases in HPE AI Essentials Software, the software foundation that makes HPE Private Cloud AI a comprehensive, user-friendly platform for enterprises seeking to deploy and scale AI solutions. This new feature provides a fully managed Retrieval Augmented Generation (RAG) experience, enabling secure and efficient connection between foundation models and internal data. This streamlined approach handles everything from vector database setup to sophisticated query handling, allowing data science professionals to create highly customized and accurate AI applications that are tailored to their specific business needs.
HPE AI Essentials simplifies the implementation of RAG by automating the critical steps involved with connecting Large Language Modules (LLM) to enterprise data. Users retain control over LLM selection and embedding model choice, while the platform manages the underlying infrastructure. HPE AI Essentials automatically handles the conversion of diverse data formats into vector embeddings and ensures efficient storage and retrieval through a managed vector database. This approach allows developers to focus on application logic, rather than the intricacies of RAG pipeline management.
The platform manages the creation, storage, maintenance, and updates of vector embeddings, the numerical representations of semantic textual data. This automation simplifies data synchronization, allowing users to efficiently update source data. HPE AI Essentials Software provides granular control over the RAG pipeline through configurable parameters for chunking, retrieval, and response generation. This enables data science professionals to tailor a model's processing and understanding to specific use cases, resulting in improved retrieval accuracy and response coherence.
Automated document chunking defaults to 512-word segments, optimized for question-answering tasks. Users can further customize chunk sizes and overlaps, with a recommended 0-20% overlap for accuracy gains, while being aware of the potential for reduced relevancy with excessive overlap.
HPE AI Essentials features a playground, a dedicated environment for interactive knowledge base exploration and management across multiple sessions. This tool enables iterative refinement of model behavior through customizable response parameters and prompt templates. Users can inject background data, define user-specific constraints, and implement detailed prompting strategies, providing the flexibility required for advanced AI development and optimization.
To support applications requiring sophisticated, data-driven workflows, knowledge bases can be accessed programmatically via dedicated endpoints within HPE AI Essentials. Secure authorization is achieved using long-lived authorization tokens, allowing for sustained interaction with these endpoints. The following code example provides a clear demonstration of endpoint usage.
Summary
Leverage HPE AI Essentials knowledge bases to streamline the development of data-driven AI applications. The platform automates key RAG pipeline components, including vector embedding management and data synchronization reducing operational overhead. Data scientists can focus on application logic and customization, utilizing programmable endpoints and the interactive playground for efficient development. For implementation details and API integration, refer to the technical resources listed below.