Building AI Apps with
Elasticsearch Data: Vector Search
for Smarter Insights
Making Elasticsearch data AI-ready and accessible for smarter apps by seamlessly connecting Elasticsearch, Zilliz Cloud, and airbyte.
What is Elasticsearch and What's Its Data Like?
Elasticsearch is a distributed, RESTful search and analytics engine capable of addressing a growing number of use cases. It processes structured, semi-structured, and unstructured data, optimizing search and discovery operations across diverse datasets. Elasticsearch is widely used for log and event data analysis, real-time application monitoring, and providing sophisticated search capabilities within large volumes of data, enhancing information retrieval and data-driven decision-making.
Challenges for Building AI Apps with Elasticsearch Data
Unstructured Data
Much of Elasticsearch’s data is unstructured and multimodal. Standard database queries struggle with such text-heavy information, making it difficult to unlock its full potential.
Data Silos
Elasticsearch data often exists in isolation, making it challenging to integrate with other enterprise systems or data sources.
Scalability
Massive amounts of user interactions are logged daily, and processing and querying this data in real time requires robust infrastructure, especially when building AI-driven applications.
Personalization
AI apps require deep insights into customer behavior to offer personalized experiences. However, standard searches and SQL queries don’t adequately surface the relationships and similarities hidden in Elasticsearch data.
Fueling Elasticsearch AI Apps with Vector Search for Smarter Insights
Making Unstructured Data Searchable
Vector search enables AI to explore unstructured Elasticsearch data like text and images by comparing the meaning and context of each data point. This allows your AI apps to uncover actionable insights that were previously buried in complex records.
Going Beyond Keywords with Semantic Search
With vector similarity search, your AI apps are no longer limited to basic keyword matching. Vector search solutions like Zilliz Cloud perform deep, context-aware searches, identifying patterns and similarities across Elasticsearch data that traditional methods can’t reach.
Uncovering Hidden Relationships
Vector search finds subtle trends and connections within unstructured data. By identifying these hidden patterns, your AI apps generate more accurate predictions, smarter recommendations, and better overall results.
Scalability for Real-Time AI
Designed for speed and scale, vector search engines like Zilliz Cloud can process massive Elasticsearch datasets in real-time. Whether it’s handling billions of records or delivering instantaneous insights, vector search ensures your AI applications can perform at peak efficiency.
Connect Elasticsearch, Zilliz Cloud, and airbyte to Unlock Instant, AI-Ready Insights
The seamless integration of airbyte and Zilliz Cloud takes the complexity out of building AI-powered apps using unstructured data from Elasticsearch and any other sources. With just a few clicks, you can deploy fast, efficient, and scalable search solutions, empowering your AI applications to deliver smarter insights.

1.
Unstructured data from Elasticsearch flows to airbyte.
2.
airbyte pre-processes and transforms the data into vector embeddings using OpenAI embedding services.
3.
The Zilliz Cloud connector channels the processed vector data into the Zilliz Cloud vector database in real time, ensuring instant availability for AI-powered tasks.
4.
Zilliz Cloud performs vector similarity searches to find relevant information to user queries.
5.
LLMs leverage the provided contextual information to generate meaningful, context-driven insights.
Try This Integration for Free
Make your data AI-ready by connecting Elasticsearch, airbyte, and Zilliz Cloud.
Frequently Asked Questions
New to Zilliz Cloud integrations? You're not alone. Here are some answers to common questions about how Zilliz Cloud works with Elasticsearch and airbyte.
What is a vector database?
Why integrating Elasticsearch, airbyte, and Zilliz Cloud for your GenAI apps?
What types of Elasticsearch data can I store and search in Zilliz Cloud?
What is Zilliz Cloud?
What is Airbyte?