Neum AI

Neum AI RAG Framework (Open-Source) and Cloud Platform is an open-source, scalable retrieval-augmented generation (RAG) framework designed to build, test, and deploy data pipelines for real-time data embedding, indexing, and retrieval. It provides SDKs to compose data flows, built-in connectors to data sources, embedding models, and vector databases, and a production-ready cloud platform for scalable embedding generation and ingestion across billions of data points. The framework emphasizes iterative configuration, observability, governance, and self-improvement of context quality through feedback on retrieval results. It also offers real-time synchronization of embeddings, scheduling, and monitoring to keep vectors up-to-date and consistent across systems.

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Key Capabilities

• Open-source RAG framework optimized for large-scale, real-time data
• Configure RAG pipelines in seconds with built-in data loaders, chunkers, and embedding steps
• Built-in connectors for common data sources, embedding models, and vector databases
• Extend with your own connectors via the open-source framework
• Local testing and cloud deployment of pipelines (Neum AI cloud)
• Production-ready cloud platform with scalable architecture for embeddings and ingestion
• Real-time vector synchronization, scheduling, and observability
• Retrieval-informed governance and self-improvement through feedback loops
• Real-time retrieval evaluation and testing tools for pipeline configurations
• Real-time data embedding and indexing with Neum AI and Supabase integration
• Comprehensive blog content and tutorials to guide implementation (Part 1, Part 2, etc.)

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How It Works

• Build data pipelines by composing loaders, chunkers, and embedding steps using open-source SDKs
• Use built-in connectors or add custom connectors to source data and vector stores
• Run pipelines locally for development and deploy to Neum AI cloud for production
• Leverage real-time syncing to keep embeddings up-to-date in vector databases
• Use retrieval-informed features to optimize search quality and context relevance
• Monitor governance and retrieval actions to ensure compliance and traceability

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Core Concepts

• RAG-first framework: emphasizes retrieval-augmented generation as the core pattern
• Data transformations: loading, chunking, embedding as primary steps
• Connectors: ready-made integrations for common services; extensible via open-source framework
• Observability: metrics and dashboards around synchronization and retrieval quality
• Self-improvement: feedback loops to improve context quality over time
• Governance: tracking actions like searches and data movements

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Use Cases

• Real-time RAG pipelines for large-scale data environments
• Embedding generation and indexing for billions of data points
• Integrations with Supabase for real-time embedding synchronization
• Testing and evaluating different pipeline configurations to optimize retrieval

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How to Use Neum AI RAG Framework

1. Install and configure the open-source SDKs
2. Define data sources and connectors (customizable or use built-ins)
3. Build a pipeline with loaders, chunkers, and embedding models
4. Run locally for testing or deploy to Neum AI cloud for production
5. Enable real-time syncing to keep vector databases current
6. Use retrieval evaluation tools and governance features to monitor performance

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Pricing and Offerings

• Starter: Free with access to open-source connectors and tools; limited scale; Discord access
• Pro: $500/mo for unlimited scale on common cloud; includes pipeline scheduling and real-time syncing; early feature access; priority Discord support
• Enterprise: Quote-based; dedicated support on Discord, Slack, Email, WhatsApp; dedicated infrastructure; custom connectors

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Getting Started Resources

• Book Demo: Access latest blog posts and tutorials
• Blog posts cover topics like real-time embedding with Neum and Supabase, scalable RAG pipelines, and testing/benchmarking pipelines

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Safety and Governance

• Emphasizes governance by logging actions and data movements
• Encourages testing and iterative configuration with retrieval quality feedback to improve results over time

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FAQ Highlights

• What is AI Copywriting? (Not applicable; placeholder text in source)
• Which languages are supported? (Not explicit in source; placeholder text)
• Who can use AI Copywriting? (Not explicit in source; placeholder text)
• Pricing model details include Starter, Pro, and Enterprise tiers with features described above

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Core Features

• Open-source RAG framework optimized for large-scale data and real-time embedding/indexing
• Build, test, and deploy RAG pipelines locally or to Neum AI cloud
• SDKs to compose data flows with loaders, chunkers, and embedding steps
• Built-in connectors for data sources, embeddings, and vector databases; extendable with custom connectors
• Real-time embedding synchronization and vector database indexing
• Pipeline scheduling and observability for production-grade pipelines
• Retrieval-informed results with governance and self-improvement feedback loops
• Real-time testing and evaluation tools for pipeline configurations