SyncLite
BUILD ANYTHING SYNC ANYWHERE
OPEN-SOURCE Real-Time LOW-Code SECURE Scalable Fault-Tolerant Extensible EXactly once semantics No VENDOR LOCK-IN
Bridging the Gap Between Gen AI Search/RAG Applications for Edge and Cloud
In an era of rapid data expansion, efficiently harnessing information is more critical than ever.
Gen AI-powered search and analytics, especially for tasks like product reviews and similarity comparisons, have transformed data interaction, often relying on vector embeddings processed through cloud-based services.
While effective, this traditional approach faces challenges such as high costs, latency, and real-time demands, particularly as data volumes grow.
To address these issues, building Gen AI search and Retrieval Augmented Generation (RAG) applications at the edge offers a decentralized, cost-effective solution.
By processing data locally on edge with fine-tuned, domain-specific models, this approach reduces latency, lowers costs, and enhances insights.
However, consolidating data and embeddings from thousands of edge applications into a centralized vector database remains a challenge. Real-time synchronization is crucial to ensure this valuable data is unified, accessible, and fully utilized for scalable and efficient global RAG applications!
SyncLite enables real-time synchronization of data and embeddings generated by numerous edge applications into a centralized vector database like PostgreSQL/PGVector. This seamless integration ensures that Global AI search and RAG Applications can readily leverage this consolidated data without the need for re-generating embeddings, thereby reducing costs and improving performance.
Let’s explore some architectural possibilities with SyncLite:
DuckDB + SyncLite + PostgreSQL/PGVector:
DuckDB, an in-process analytical embedded database, simplifies working with vector embeddings through its SQL API. It provides an ARRAY data type to store embeddings with vector search capability, all with an SQL interface.
SyncLite Logger offering a JDBC wrapper over DuckDB, efficiently enables seamless replication and consolidation of data and embeddings from thousands of DuckDB databases into centralized vector databases like PostgreSQL + PGVector.
SyncLite’s real-time synchronization ensures that AI-powered search and RAG applications can operate on the consolidated dataset received from all edge applications without repeated embedding generation.
FAISS + SQLite + SyncLite + PostgreSQL/PGVector:
FAISS, a library developed by Facebook AI, excels at performing similarity search and clustering of dense vectors, making it ideal for large-scale vector search tasks.
SQLite is a lightweight, embedded database commonly used for local storage in mobile, desktop, and edge applications.
When combined with SQLite’s efficient local storage capabilities, FAISS enables robust AI search and RAG applications.
SyncLite enhances this setup by facilitating real-time consolidation of data and embeddings across thousands of edge applications into a centralized PostgreSQL + PGVector database, enabling scalable global RAG applications with immediate access to the latest data.
The possibilities with SyncLite are countless. Whether you’re working with DuckDB, FAISS, SQLite, or any other embedded database, SyncLite offers a transformative approach to building Gen AI-powered search and RAG applications at the edge. By bridging the gap between edge applications and centralized databases, SyncLite empowers you to unlock the full potential of your data, enabling faster, more accurate, and cost-effective insights.