Chat & RAG

import { Tabs, TabItem } from ‘@astrojs/starlight/components’;

Kiomon answers questions in natural language, grounded strictly in your own saved documents and memories. There are two chat surfaces in the dashboard — and several programmatic ones.

The two surfaces

Surface	Model	Tools	Streaming
RAG Q&A (/api/rag/ask)	Answer engine, focused prompt	No	Text streaming + sources
Workspace Agent (/api/agent/chat)	Answer engine + tool use	get_workspace_stats, list_recent_documents, search_knowledge_base, save_memory	SSE events: tool progress → deltas → sources → saved conversation

RAG Q&A

The RAG path is a purpose-built grounded QA loop:

1. Retrieve — keyword retrieval with snippet extraction (passages, never full bodies)
2. Stage — follow-ups are answered first from documents already cited in the conversation (scoped retrieval), then fall back to a full-corpus scan
3. Assemble — up to 4 passages, numbered [1]–[4], formatted as grounded context
4. Answer — the answer is generated only from saved documents; outside knowledge is not used
5. Cite — every answer carries numbered inline citations linked back to the source documents

Workspace Agent

The agent path runs a tool loop:

• get_workspace_stats — corpus/usage overview before answering
• list_recent_documents — what’s new in the workspace
• search_knowledge_base — hybrid retrieval on demand
• save_memory — persist durable learnings as review-inbox drafts right from chat

Stream shape (SSE):

data: {"type":"tool","tool":{"name":"search_knowledge_base","status":"call","detail":{...}}}
data: {"type":"delta","text":"Here's what I found…"}
data: {"type":"sources","sources":[{...citation...}]}
data: {"type":"saved","conversation":{"id":"…","title":"…","updatedAt":"…"}}
data: {"type":"error","message":"…"}

The dashboard’s chat renders tool progress chips, streaming text, and clickable citation chips.

Conversations

Every chat is a persisted conversation (stored server-side, so reopening the app on another device restores it):

• List / get / rename / delete via the REST API (/api/rag/conversations)
• The rolling history keeps the last 6 Q&A pairs verbatim; older turns are condensed into an incremental summary refreshed in the background after each answer — so long conversations stay coherent without blowing the context window
• Follow-up questions are staged to already-cited documents first (see above)

Asking good questions

• Be explicit about scope: “What did I save about Stripe checkout?” beats “Tell me about Stripe.”
• Chain follow-ups — the agent remembers the conversation and prefers cited docs.
• Ask for summaries — “Summarize the key API patterns in the docs I saved this week.”
• Ask it to save — end a research session with “Save the key takeaways as memories for approval.” They’ll land as drafts in your Review Inbox.

Programmatic access

Endpoint	Purpose
POST /api/rag/ask	Grounded RAG answer (streaming). The single writer for conversations.
GET /api/rag/sources	List your recent sources/citations
GET/PATCH/DELETE /api/rag/conversations	Conversation CRUD
POST /api/agent/chat	Tool-loop agent chat (SSE)
POST /v1/chat/completions	OpenAI-compatible grounded chat (see OpenAI-Compatible API)

Rate limits & plans

Free/Pro daily caps apply per surface (e.g. agent turns, RAG asks). The /v1/chat/completions path rate-limits per key/plan too. Upgrading raises the caps significantly — see Plans & Quotas.

Grounding & safety

The answer engine is always constrained to your library: answers are generated only from saved documents, with citations. A client-supplied system message can flavor the style but can never override the “answer from your saved documents only” rule.