Knowledge Hub & RAG
Turn private documents and structured data into grounded, searchable AI context.
What this replaces
General-purpose models cannot reliably answer from private organizational material, and keyword search misses semantic relationships.
What your team gains
A private retrieval layer combining structured relational data, vector similarity search, full-text search and chat UI knowledge workflows.
What is inside the blueprint
How it fits the platform
Documents/data -> extraction/chunking -> local embedding runtime/embedding endpoint -> relational database/vector store + full-text indexes -> retrieval -> chat UI/agents.
Relational database, Vector store, Local embedding runtime, Chat UI
- AICORTEX Core PlatformVector and full-text retrieval live in Core's relational data tier.
From prerequisites to operation
- Source documents/data
- Chunking/metadata strategy
- Embedding endpoint
- Storage sizing
- Create knowledge schema and vector/full-text indexes
- Deploy embedding endpoint
- Configure ingestion pipeline
- Connect the chat UI knowledge backend
- Test retrieval and citations
- Chunk size/overlap
- Embedding model/dimensions
- Metadata taxonomy
- Top-k and score thresholds
- Hybrid weighting/reranking
- Document access scope
- Source freshness
- Re-indexing
- Retrieval quality evaluation
- Embedding model migration
- Database backups
What this unlocks with other layers
Institutional Memory
The AI can retrieve documents, recall prior work and trace relationships and provenance.
Research-to-Knowledge
Discovery becomes a retained, structured research corpus instead of disappearing after one chat.