knowledge-ingest

name	knowledge-ingest
description	Ingest URLs, documents, and text into the memory system as structured knowledge
category	knowledge
requires	{"tools":["url_ingest","remember"]}
contexts	["heartbeat","chat"]
bound_tools	["url_ingest","fast_ingest","slow_ingest","hybrid_ingest","remember","recall","search_documents","open_document","open_documents","load_documents","search_document_chunks","load_document_chunks","list_desk","git_ingest"]

Knowledge Base Ingestion

Transform external content -- web pages, documents, raw text -- into structured semantic memories that persist in the knowledge graph.

When to Use

• When the user shares a URL and says "learn this" or "remember this article"
• When a research workflow finds valuable sources that should be retained long-term
• When the user pastes raw text (notes, transcripts, outlines) to be ingested
• During heartbeats when a goal involves building knowledge on a specific topic
• When importing reference material for a project or domain

Step-by-Step Methodology

1. Assess the source: Before ingesting, determine what kind of content it is (article, documentation, transcript, raw notes). This guides how aggressively to summarize.
2. Fetch and parse: For URLs, use url_ingest which handles fetching, HTML-to-text conversion, and chunking. For files or raw knowledge sources, use the fast/slow/hybrid ingestion tools as appropriate.
3. Check for duplicates: Use recall with the URL or a key phrase from the content to see if it has already been ingested. Avoid storing the same source twice.
4. Chunk intelligently: Long content is automatically chunked by the ingestion pipeline. Each chunk becomes a separate semantic memory linked by source metadata, and the raw source artifact remains searchable with search_documents and retrievable with open_document or open_documents. Use load_documents when a large source should be deliberately placed on the RecMem desk for later exact search. Trust the pipeline's chunking; do not manually split content unless it is clearly failing.
5. Add context: When storing via remember, include metadata about the source: URL, author, date published, and why it was ingested (which goal or topic it serves).
6. Verify ingestion: After ingestion completes, run a quick recall on a key concept from the content to confirm distilled knowledge is retrievable. For exact wording or large specifications, use search_documents, open_document, or load_documents instead of expecting recall to carry the whole source. For passage-level verification, search_document_chunks should find key phrases with page/section locators; list_desk shows what is already loaded before you load more.
7. Connect to goals: If the ingested content relates to an active goal, note the connection so future heartbeats can leverage it.

Quality Guidelines

• Prefer ingesting authoritative, primary sources over summaries or aggregators.
• Do not ingest entire websites. Be selective -- ingest the specific pages that contain the needed information.
• When ingesting long documents, let the chunking pipeline do its job. Each chunk retains a reference to the parent source.
• Always record the source URL or origin. Memories without provenance are harder to evaluate and update later.
• Respect rate limits and robots.txt when fetching URLs. If a fetch fails, note the failure and move on rather than retrying aggressively.
• For sensitive or private content (internal docs, personal notes), ensure the user understands that ingested content persists in the local database.