Aiven for OpenSearch provides a unified engine to perform vector similarity and text search queries backing up Labelf AI’s customer support classification models and preventative AI.
Labelf
Supported integrations
Customer spotlight
By combining Aiven’s data platform with Sway Al’s modeling and insight capabilities, we can offer an end-to-end answer for customer data platform needs.
Sway.ai
Use cases
How Does it All Work Together
Reduce GenAI Hallucination with contextual information
Perform Retrieval Augmented Generation (RAG) directly from your operational data layer
- Store Generative AI embeddings alongside your operational data, without onboarding new technologies
- Enrich Gen AI context and avoid hallucination by performing semantic search at scale
- Retrieve real-time, tailored, and transactionally accurate results by mixing vector and standard queries through hybrid search
Examples:
Remove data silos with Aiven for Apache Kafka
Provide real-time and governed data to your AI
- Join data from external and internal sources while removing sensitive information to get a holistic view of your business
- Evolve your existing applications to use AI without changing or impacting legacy software
- Create your AI model using historical data, and keep the model current using real-time data
- Provide a layer of abstraction between data sources and the AI serving layer without changing the underlying data structure
Examples:
Accelerate scalable application development and maintenance with Aiven for AI
Write performant code and resolve CloudOps issues faster
- Accelerate writing performant code by providing developers with index and SQL rewrite suggestions
- Reduce the time to CloudOps incident resolution through real-time performance insights and optimization suggestions
- Optimize applications for performance and scalability before releasing them into production
- Free up your DBA resources to spend more time on high-value tasks
Check out the Aiven AI database optimizer to understand how to tune your PostgreSQL and MySQL databases.
Examples:
Resources
Check out the practical examples
Developer Center
Get an introduction to machine learning using Aiven services and the Hugging Face API to recommend movies based on Wikipedia synopses.
Developer Center
Use OpenSearch® as a vector database to generate responses to user queries using AI
Developer Center
Leveraging TensorFlow, PostgreSQL®, PGVector, and Next.js for vector search with this step-by-step video guide.
Developer Center
Learn how to train AI models and perform live scoring with a set of SQL statements and Aiven for ClickHouse®
Developer Center
Vector embeddings are key to ML, and here we describe how to use OpenCV, OpenAI CLIP and pgvector to generate vectors and use them to perform image recognition on a corpus of photos.
Developer Center
Learn the theory and the details of how to speed up PostgreSQL® pgvector queries using indexes IVFFlat, HNSW and traditional indexes
Get started with Aiven today
All inclusive pricing so you have no end of the month surprises.
No add-on maintenance, networking and storage fees.