- Embeddings: EmbeddingProvider SPI, OpenAI-compatible /embeddings
(batching, Azure path), assistant_model.model_kind CHAT/EMBEDDING and
embedding_dimensions; chat selection ignores embedding models.
- VectorStore SPI: in-memory store, and the new qbit-assistant-rag-pgvector
module (one schema per instance, HNSW cosine index, parameterized SQL).
The index holds NO record content: table, key, field, chunk, hash and
vector only.
- assistant_rag_source (Knowledge sources) with Index now / Clear index
record actions that read through the UI API as the clicking admin,
skip unchanged chunks and drop removed records.
- search_knowledge tool: vector search, then every hit re-read through the
UI API with the asking user's session (403/404 hits dropped), AI field
exposure and masking applied, /app citations; PHI rule for embeddings;
classification raised by results.
- pgvector container test (pgvector/pgvector:pg16) passes with Docker.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
james.maesandClaude Opus 5.5
c487c87051