Simplify stream processing for generative AI applications with Confluent Cloud for Apache Flink® and Elasticsearch®

We’re thrilled to be partnered with Confluent today as it announces the general availability of the industry’s only cloud-native, serverless Apache Flink® service. Available directly within Confluent’s data streaming platform alongside a cloud-native service for Apache Kafka®, the new Flink offering is now ready for use on AWS, Azure, and Google Cloud. 

Directly integrated with Elasticsearch®, Confluent provides a simple solution for accessing and processing data streams from across the entire business to build a real-time, contextual, and trustworthy knowledge base to fuel AI applications. Elasticsearch can cost-effectively and securely leverage these data streams to link your proprietary data with large language models (LLMs) for application output that’s up-to-date, accurate, relevant, and business specific. Use the power of Elasticsearch and its vector database with your own transformer models or integrate with generative AI to build new customer and employee experiences.

Developers are tasked with building search and generative AI applications that are relevant, performant, and cost-effective. Quite simply, you need to be able to retrieve data from all your proprietary data sources to build RAG to deliver the best, most pertinent results. Elasticsearch fulfills this need, minimizing LLM usage costs, improving token utilization, and providing private enterprise domain-specific knowledge.

Real-time data management in generative AI-powered search use cases encompass various stages — from preprocessing data through ingestion, filtering, and aggregation, either before augmentation or upon retrieval to inform models and adapt results promptly. This process also includes the implementation of dynamic feedback loops to maintain states, such as conversation histories, and incorporate user reactions into models. Here’s where Confluent’s Flink offering will tremendously help streamline the process. Flink supports stream-stream and stream-database joins, allowing for the enrichment of data streams with information from knowledge bases, documents, policies, or regulatory standards.This enables contextual validation of responses against a wealth of information.

Some key use cases where Elastic has been focusing its generative AI efforts include:

Natural language processing (NLP): Elastic has been leveraging AI to enhance its NLP capabilities, making it easier for users to perform complex searches using natural language queries. This means users can ask questions or make requests in plain English (or other supported languages) without needing to use complex query syntax. 

Enhanced observability: In the context of observability (monitoring and managing application and system performance), Elastic's AI features can automate anomaly detection and provide predictive analytics. This helps IT and DevOps teams preemptively address issues before they impact performance or user experience.

Personalized search experiences: By leveraging AI, Elastic can offer more personalized search experiences for users. This includes understanding user intent more accurately, providing more relevant search results, and even customizing search result rankings based on user behavior and preferences.

Automated code and content generation: For developers and administrators, generative AI features within Elastic like the Elastic AI Assistant (for both Observability and Security use cases) can suggest code snippets, configuration settings, and dashboards or even automate routine tasks based on the context of the work and the data within the user’s Elastic environment by allowing them to leverage human centered language queries. 

To speed up adoption, Elastic introduced the Elastic Learned Sparse EncodeR (ELSER) in mid 2023, a sparse vector based project that aims to enhance Elasticsearch by incorporating advanced machine learning and AI capabilities. ELSER involves aspects such as semantic search, improving search results by understanding the context and the meaning behind the search queries, not just matching keywords. This leads to more relevant and precise search outcomes and AI-driven data analysis, utilizing AI to provide deeper insights into data patterns, trends, and anomalies to help users make informed decisions based on their data.

The release and timing of any features or functionality described in this post remain at Elastic's sole discretion. Any features or functionality not currently available may not be delivered on time or at all.

In this blog post, we may have used third party generative AI tools, which are owned and operated by their respective owners. Elastic does not have any control over the third party tools and we have no responsibility or liability for their content, operation or use, nor for any loss or damage that may arise from your use of such tools. Please exercise caution when using AI tools with personal, sensitive or confidential information. Any data you submit may be used for AI training or other purposes. There is no guarantee that information you provide will be kept secure or confidential. You should familiarize yourself with the privacy practices and terms of use of any generative AI tools prior to use.

Elastic, Elasticsearch and associated marks are trademarks, logos or registered trademarks of Elasticsearch N.V. in the United States and other countries. All other company and product names are trademarks, logos or registered trademarks of their respective owners.