Context layers

ContextOutput exposes the four sources of context independently so the UI can show provenance and the model can be told which is which.

Layers

1. System prompt — always first, always authoritative. Mandatory; TokenBudgetedContextBuilder raises if it does not fit.
2. Profile — derived from the user's prior messages. Optional; if it would push the prompt over budget, it is dropped silently.
3. Session memory — compressed summary of older turns. Pulled from the vector store under the synthetic doc_id session_{chat_id}.
4. RAG documents — top-k chunks from the documents the user has attached to the chat.

When to enable what

Scenario	RAG	Session	Profile
One-shot Q&A on a document	✅	❌	❌
Long-running chat with one document	✅	✅	✅
Continuing a conversation (no new context)	❌	✅	✅
Multi-tenant user-specific assistant	✅	✅	✅

Field-by-field reference

ContextInput fields:

• query (required) — the user's current question.
• system_prompt — anchored to the top of the final prompt.
• doc_ids — list of integer document IDs to search over.
• chat_id — when set, session memory is queried under session_{chat_id}.
• embedding_model — passed to LLM.embed.
• top_k_rag, top_k_session — retrieval depth per layer.
• include_rag, include_session, include_profile — toggles.

ContextOutput fields:

• system_prompt — the final assembled string.
• rag_blocks, session_blocks — the actual chunks that went in.
• profile_used — whether the profile was included.
• budget_report — only set by TokenBudgetedContextBuilder.