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WeKnora explained: document answers, agents, Wiki and personal memory
Understand four different knowledge behaviors and why a self-hosted RAG application is not automatically private or correct.
Browse all 9 articles in this open-source series. Nine chapters from first document ingestion to event-driven RAG, candidate-pool recall, deployment, cost and sandbox boundaries.
Featured starting point
Understand four different knowledge behaviors and why a self-hosted RAG application is not automatically private or correct.
Suggested learning path
01 → 09
Understand four different knowledge behaviors and why a self-hosted RAG application is not automatically private or correct.
Use a small synthetic handbook and an unanswerable question to test the path from upload to source-supported answers.
Separate standard Compose, Lite and development mode; understand cached images, migration risk and why full profiles are optional.
Follow the documented plugin pipeline without confusing internal stage completion, SSE delivery and source-supported answer quality.
Read scoped vector ranking, candidate growth and reciprocal rank fusion through a concrete low-importance memory regression.
Plan measurements for model calls, queues, semantic recall and vector backfill without converting source constants into speed guarantees.
Distinguish knowledge access from personalization and skill selection from execution isolation, network policy and credential exposure.
Select the smallest knowledge workflow that answers the task, and choose Lite or team deployment by operating requirements rather than feature count.
Design a read-only learning project that links source versions, recall behavior and claim-level acceptance without importing private conversations.