Neum AI

Neum AI offers a powerful tool suite to simplify the configuration of Rapidly Annotated Guides (RAG) pipelines. Their open-source Software Development Kits (SDKs) enable users to compose data flows and integrate with various services such as data sources, embedding models, and vector databases. They also provide built-in connectors for popular services, allowing users to add their connectors using the open-source framework. Neum AI supports local testing and deployment to their production-ready cloud platform, offering features like scaling, real-time syncing, observability, intelligent retrieval, self-improvement, and governance. The cloud platform is optimized for large-scale and real-time data, enabling efficient embedding generation and ingestion for billions of data points.

⚡Top 5 Neum AI Features:

1. Open-source SDKs: Compose data flows using open-source SDKs.
2. RAG-first framework: Build performant, scalable, and reliable data pipelines focusing on key data transformations like loading, chunking, and embedding.
3. Built-in connectors: Choose from connectors for data sources, embedding models, and vector databases. Add your own connectors using the open-source framework.
4. Test and Deploy Pipelines: Run your data pipelines locally using open-source SDKs and directly deploy them to the Neum AI cloud.
5. Production-Ready Cloud Platform: Scalable distributed architecture optimized for embedding generation and ingestion for billions of data points.

⚡Top 5 Neum AI Use Cases:

1. Real-time data embedding and indexing: Create a real-time Retrieval Augmented Generation pipeline with Neum and Supabase for efficient data processing.
2. Scalable RAG pipelines: Leverage distributed architecture tools like Celery and Redis Queues to build solutions for handling large volumes of data.
3. Semantic Selectors: Choose what data to embed based on structured data and improve the quality of generated embeddings.
4. Retrieval Augmented Generation at scale: Develop a distributed system for synchronizing and ingesting billions of text vectors.
5. Efficient Vector Store Integration: Connect various data sources and vector databases seamlessly using Neum AI.