khive-pack-knowledge

Knowledge verb pack for khive: a corpus of atoms (documented techniques, one per concept) grouped into domains, retrieved by TF-IDF search with optional embedding rerank, and composed into markdown briefings under a token budget.

Features

TF-IDF corpus search (knowledge.search) over atom name/tags/content, with score bands (>=0.46 reliable, 0.42-0.46 mixed, <0.42 mostly off-target), query decomposition, and RRF fusion against an ANN pass when an embedder is configured
Section-level records (knowledge.edit) — a closed 10-value section_type enum (overview, core_model, formalism, failure_modes, ... other) per atom, each independently disputable and adjudicable (ADR-051)
Budget-constrained fold (knowledge.fold) — knapsack selection of caller-scored candidates against a token/size budget
Domain suggestion + compose (knowledge.suggest, knowledge.compose) — find relevant domains for a query, then assemble a reranked markdown briefing from their member atoms
Concept sugar over the KG (knowledge.learn, knowledge.cite, knowledge.topic) — register a concept entity and link it to its introducing document/person without hand-rolling create/link calls
Section feedback (knowledge.feedback) — per-section useful/not_useful/ wrong signals update posterior weights, optionally forwarded to a configured brain profile (ADR-032)

Usage

This crate is not called directly as a Rust library — it registers KnowledgePack with the runtime's inventory-based pack registry and dispatches its 19 verbs through the MCP request DSL (or kkernel exec). A caller issues:

request(ops="knowledge.search(query=\"block-max wand posting list pruning\", limit=10)")

The same DSL runs from the shell without an MCP client via kkernel exec:

kkernel exec 'knowledge.search(query="block-max wand posting list pruning", limit=10)'

or, to build a fresh corpus and immediately reference it:

request(ops="[knowledge.upsert_atoms(atoms=[{\"slug\":\"bm25-wand\",\"name\":\"BM25 WAND\",\"content\":\"...\"}]), knowledge.compose(atom_ids=[\"bm25-wand\"], query=\"keyword search pruning strategies\")]")

Programmatic embedding is exposed via a small Rust API for the kkernel reindex binary, independent of the MCP surface:

use khive_pack_knowledge::{reindex_knowledge, KnowledgeReindexOptions};

let opts = KnowledgeReindexOptions {
    atoms: true,
    sections: true,
    drop_existing: false,
    rebuild_ann: true,
    batch_size: None,
};
let report = reindex_knowledge(&runtime, &token, opts, None, None).await?;

Verbs

Verb	What it does
`knowledge.upsert_atoms` / `knowledge.upsert_domains`	Bulk insert or update atoms / domains
`knowledge.get` / `knowledge.list` / `knowledge.delete_atoms` / `knowledge.stats`	Corpus CRUD and aggregate counts
`knowledge.index`	Backfill embeddings + FTS for atoms/domains
`knowledge.search` / `knowledge.suggest` / `knowledge.compose`	TF-IDF search, domain suggestion, briefing assembly
`knowledge.fold`	Knapsack selection of scored candidates against a budget
`knowledge.edit`	Upsert one atom's sections without wiping the rest
`knowledge.import`	Ingest atlas-format markdown as atoms with parsed sections
`knowledge.challenge` / `knowledge.adjudicate`	Dispute and resolve a section's content
`knowledge.learn` / `knowledge.cite` / `knowledge.topic`	Register/link/browse `concept` entities
`knowledge.feedback`	Apply per-section signals to posterior weights

All 19 verbs are Visibility::Verb (exposed on the agent-facing MCP surface).

Where this sits

khive-pack-knowledge sits in the pack tier, above khive-runtime / khive-storage / khive-score / khive-fusion / khive-vamana / khive-fold, alongside sibling packs such as khive-pack-kg (a hard REQUIRES dependency for the underlying concept/document entity substrate) and khive-pack-brain (feedback target). It is one of the eight packs loaded by default in khive-mcp. Governing ADRs: ADR-017 (Pack Standard), ADR-048 (Knowledge Section Profiles), ADR-051 (Section-level Embeddings and Hybrid Compose Scoring).

License