How to Use OpenAI Vector Embedding and Store Large Vectors in Apache Hudi for Cost-Effective Data Storage with MiniO and Empowering AI Applications

In today's data-driven world, efficiently managing and utilizing vector embeddings from AI models is crucial for powering downstream applications like recommendation systems, semantic search, and more. Apache Hudi provides a robust solution for storing large datasets with cost-effective storage options, making it an ideal choice for managing vector embeddings alongside metadata. This blog will guide you through leveraging Apache Hudi to store and query vector embeddings, focusing on incremental updates and cost-efficient storage strategies using MinIO as an object store.

Keyword : MinIO Apache Hudi

Why Apache Hudi for Vector Embeddings?

Apache Hudi is a powerful data lake technology that offers:

• Cost-Effective Storage: Utilizes efficient storage formats and strategies, such as Copy-on-Write (COW), enabling significant cost savings when storing large datasets.
• Incremental Data Ingestion: Supports incremental updates and efficient querying of changed data, perfect for managing vector embeddings updated over time.
• Metadata Management: Facilitates metadata management, essential for tracking vector metadata alongside embeddings for versioning and lineage.
• Integration Flexibility: Seamless integration with various downstream applications like Elasticsearch, Pinecone, or PostgreSQL for AI model inference.

Setting Up the Environment

To begin, set up your Spark session with Apache Hudi and MinIO as the storage backend:

Spin up Stack docker-compose up --build -d

Create Spark Session

Generating and Storing Vector Embeddings

Next, define a function to fetch vector embeddings using OpenAI's API and store them in an Apache Hudi table:

Create Hudi tables

Lets convert some sample Mock text into vectors and serialize them as string and store in hudi data lakes

Insert Embedding into Hudi Datalakes

Incremental Querying and Downstream Applications

Apache Hudi enables efficient incremental querying of vector embeddings, essential for powering downstream AI applications:

Output

To insert this data into down stream such as Pinecone | Elastic Search | postgres (pgvector)

Follow these links

Learn How to Ingest Data from Hudi Incrementally (hudi_table_changes) into Postgres Using Spark

Link : https://soumilshah1995.blogspot.com/2024/06/learn-how-to-ingest-data-from-hudi.html

Power your Down Stream Elastic Search Stack From Apache Hudi Transaction Datalake with CDC|DeepDive

Video: https://www.youtube.com/watch?v=rr2V5xhgPeM

Github Code: https://github.com/soumilshah1995/Power-your-Down-Stream-Elastic-Search-Stack-From-Apache-Hudi-Transaction-Datalake-with-CDC

Develop Full Text Search (Semantics Search) with Postgres (PGVector) and Python Hands on Lab

Blog : https://soumilshah1995.blogspot.com/2024/05/develop-full-text-search-semantics.html

Enhancing Your Hudi Tables

To enhance your Apache Hudi tables, consider adding additional columns such as tags, created_at, or other relevant metadata. These additions can significantly enhance the filtering and retrieval capabilities of your data lakes, ensuring that only relevant vector embeddings are loaded into your target systems.

Example Use Cases

1. Temporal Filtering: Store embeddings for the last 30 days and query incrementally to load only recent data into your AI model inference pipeline.
2. Metadata-Driven Queries: Use tags or categories to selectively load embeddings relevant to specific domains or applications, improving query efficiency.
3. Versioning and Lineage: Track changes in embeddings over time using Apache Hudi's metadata capabilities, ensuring reproducibility and traceability in AI model development.

Code Links : https://soumilshah1995.blogspot.com/2024/07/how-to-use-openai-vector-embedding-and.html

Conclusion

In this blog, we explored how Apache Hudi can efficiently store and manage vector embeddings, leveraging cost-effective storage options like MinIO. By utilizing Hudi's incremental querying capabilities, you can power downstream AI applications with fresh and relevant data while optimizing storage costs. This setup ensures scalable and performant management of vector embeddings, supporting a wide range of use cases from recommendation engines to semantic search systems.

References

https://www.onehouse.ai/blog/managing-ai-vector-embeddings-with-onehouse