nornicdb-managed-embeddings

nornicdb-managed-embeddings is a skill for Claude Code, Codex from orneryd/NornicDB. It costs 90 tokens per session (2,349 once invoked), scanned A, original, MIT.

A database feature that creates text embeddings—numeric representations of meaning—inside NornicDB while running Cypher queries. It can use Ollama, OpenAI, or local GGUF models and store the results with nodes.

In plain words
What is it for?
Use it to embed node text, control which properties are included, and keep embeddings in NornicDB's managed chunk storage. It supports both automatic writes and one-off embedding requests.
Why use it?
It removes the need to generate and manage embeddings in a separate application. The database can prepare text for similarity search as part of writes or queries.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to embed node text, control which properties are included, and…

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Install with agentmods
npx agentmods add skills/orneryd/nornicdb/managed-embeddings
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add orneryd/NornicDB --skill managed-embeddings
Clone the repo
git clone --depth 1 https://github.com/orneryd/NornicDB

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for nornicdb-managed-embeddings

README.md
[![agentmods](https://agentmods.dev/badge/skills/orneryd/nornicdb/managed-embeddings.svg)](https://agentmods.dev/skills/orneryd/nornicdb/managed-embeddings)
Your own site
<a href="https://agentmods.dev/skills/orneryd/nornicdb/managed-embeddings"><img src="https://agentmods.dev/badge/skills/orneryd/nornicdb/managed-embeddings.svg" alt="Measured on agentmods" height="20"></a>
Per session 90 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,349 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00090 $0.02349
Opus 5 $0.00045 $0.01175
Sonnet 5 $0.00018 $0.00470
Haiku 4.5 $0.00009 $0.00235

Measured yesterday against content hash 917d103874ef, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-06, from the pricing page.

Security

Grade A, and why

nornicdb-managed-embeddings scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

docs/skills/managed-embeddings.skill.md · 216 lines

How it starts

The opening of the file, as written. The whole thing — 216 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Managed Embeddings (Cypher API)

NornicDB can generate embeddings server-side and store them on nodes for you. From the consumer's point of view this is a Cypher feature: a WITH EMBEDDING clause on writes, and a db.index.vector.embed procedure for ad-hoc embedding.

Two storage slots, both readable

Every node has two embedding slots:

  1. ChunkEmbeddings — managed by NornicDB. [][]float32 — one entry per chunk; ChunkEmbeddings[0] is the primary embedding. Built by the embedding worker or by WITH EMBEDDING. Internal metadata (model, dimensions, chunk_count, embedded_at) is stored separately so it does not pollute Properties.
  2. NamedEmbeddings — client-managed. map[string][]float32, keyed by vector name (e.g. "default", "openai-small"). Set via db.create.setNodeVectorProperty or the Qdrant gRPC compatibility layer.

Vector index lookup order is NamedEmbeddings[indexProperty] → node.Properties[indexProperty] → ChunkEmbeddings. You usually want a vector index on a label whose nodes have managed embeddings, in which case nothing extra is needed.

Enabling managed embeddings

export NORNICDB_EMBEDDING_ENABLED=true
export NORNICDB_EMBEDDING_PROVIDER=ollama          # ollama | openai | local
export NORNICDB_EMBEDDING_MODEL=mxbai-embed-large
export NORNICDB_EMBEDDING_API_URL=http://localhost:11434
export NORNICDB_EMBEDDING_DIMENSIONS=1024

YAML form:

embedding:
  enabled: true
  provider: ollama          # ollama | openai | local
  model: mxbai-embed-large
  url: http://localhost:11434
  dimensions: 1024

The OpenAI provider also reads embedding.api_key (or NORNICDB_EMBEDDING_API_KEY). The local provider resolves the model file inside NORNICDB_MODELS_DIR.

Defaults shipped with NornicDB: provider=local, model=bge-m3, dimensions=1024.

Per-database configuration

For a named database, use canonical keys in the YAML databases: map or PUT /admin/databases/{name}/config: db.nornic.embedding.provider, db.nornic.embedding.model, db.nornic.embedding.api.url, db.nornic.embedding.api.key, and db.nornic.embedding.dimensions. Their NORNICDB_* forms remain supported global environment alternatives and alternate input names; canonical keys win collisions.

Read the full file on GitHub · 216 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. yesterday First seen · 216 lines · 90 tokens per session scan A 917d103874ef

Subscribe to this mod's changes

nornicdb-managed-embeddings is a skill published in the GitHub repository orneryd/NornicDB (858 stars, last pushed yesterday), licensed MIT. It adds 90 tokens to every session and 2,349 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-05.

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