global (workspace-wide) or ontology (tied to a single ontology via a scope_id).
This note scope model (
global | ontology + scope_id) is specific to the Context Layer. A note scoped to ontology with a given scope_id is only retrieved when the agent operates on that same ontology. The scope_id is the ontology ID — the same identifier you pass to the AI Agent chat endpoint.The platform also ships an onboarding interview wizard that populates context notes interactively. Those endpoints (
GET /knowledge/interview/template, POST /knowledge/interview) are UI-internal and not documented here.Note fields
Every context note has these fields:Activation
activation controls how a note enters the agent’s context:
Context notes
List notes
Return active context notes for the given scope, optionally filtered by kind or activation.Create a note
Write a single context note into the knowledge base.Get a note
Retrieve a single context note by ID.Update a note
Partially update a context note in place (the note keeps its ID). Only fields present in the request body are modified. Omittingscope/scope_id leaves them unchanged; passing them explicitly (even as null) overwrites them.
Delete a note
Permanently delete a context note.Search
Semantic search
Run a vector similarity search over context notes. Returns notes ranked by semantic relevance to the query string, each annotated with a similarity score.Ingestion
Ingest a file as context
Parse and chunk an already-uploaded file into context notes. The file must first be uploaded viaPOST /data/files/upload, which returns a disk/<name> path to pass here.
The ingestion pipeline extracts text, splits it into chunks, embeds each chunk, and persists them as document_chunk notes tied back to a shared document_id. If the embedding service is unavailable the file is still stored as a single un-vectorized note.
Auto-seed from data sources
Profile connected data sources and automatically write observation notes into the Context Layer. The platform inspects schemas, samples, and statistics for each source and uses the LLM to generate structured observations. This endpoint returns a streaming NDJSON response. Each line is a JSON object with atype field describing the event.
Project document
Render project document
Return a combined document view for a project: an LLM-generated natural-language summary alongside the full executable Vadalog program assembled from all concepts. The summary is cached and is automatically invalidated when concepts or context notes change. Passrefresh=true to force regeneration regardless of the cache state.

