RAG Knowledge

Knowledge Growth

Retrieval-Augmented Generation for document ingestion, embeddings, and intelligent knowledge retrieval across the distributed mesh.

Knowledge Ingestion

Ingest documents from multiple sources — markdown, text, PDF — with automatic chunking for optimal embedding generation.

Convert text chunks to vector embeddings using state-of-the-art models for semantic search and intelligent retrieval.

Automatically cluster related information into vertices for organized knowledge accumulation and efficient retrieval.

Keep knowledge consistent across all neurons with merge, deduplication, and integrity verification strategies.

Find relevant information using vector similarity search that understands meaning beyond keyword matching.

Combine dense vector search with sparse keyword search for optimal retrieval accuracy across different query types.

Generate AI responses with retrieved context for accurate, grounded answers backed by your knowledge base.

Knowledge distributed across neurons for scalable retrieval with automatic load balancing and fault tolerance.

Compute nodes that store and retrieve knowledge from the distributed knowledge base.

Confidence scoring and pattern recognition for continuous knowledge improvement.