surrealdb-vector

Q: How do I install surrealdb-vector?

Run npx skills add https://github.com/surrealdb/agent-skills --skill surrealdb-vector in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/surrealdb-vector/ (personal) or .claude/skills/surrealdb-vector/ (project).

by surrealdbAI & Agents

Vector search with SurrealDB using HNSW indexes, KNN queries, and similarity scoring. Use when creating vector indexes, querying vectors with KNN distance operators, building semantic search or RAG pipelines, tuning HNSW parameters (EFC, M, M0, distance function, type), or implementing recommendation systems with SurrealDB. Triggers: HNSW, vector, embedding, KNN, cosine, euclidean, semantic search, RAG, vector::distance.

Installs: 537
Last 24h: —
Trending rank: —
Security: Not audited

Install

Works with every agent the skills CLI supports.

npx skills add https://github.com/surrealdb/agent-skills --skill surrealdb-vector

01
Install
Run the command in your project. The CLI asks which agents to install for.
02
Describe the task
Ask your agent for something this skill covers. It loads surrealdb-vector automatically when relevant.
03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.

SKILL.md

The instructions your agent reads when the skill is triggered.

SurrealDB Vector Search

HNSW Index

Create a basic HNSW index:

DEFINE INDEX hnsw_idx ON pts FIELDS point HNSW DIMENSION 4;

With specific distance function and type:

DEFINE INDEX hnsw_idx ON pts FIELDS point HNSW DIMENSION 4 DIST EUCLIDEAN TYPE F64;

Available types: F64, F32, I64, I32, I16.

Full Table Example

DEFINE TABLE OVERWRITE document SCHEMALESS;
DEFINE FIELD OVERWRITE embedding ON document TYPE array<float>;
DEFINE INDEX OVERWRITE hnsw_idx_document ON document
    FIELDS embedding
    HNSW DIMENSION 384
    DIST COSINE
    TYPE F32
    EFC 150 M 12 M0 24;

HNSW Parameters

Parameter	Description
DIMENSION	Vector dimensionality (must match your embeddings)
DIST	Distance function: `COSINE`, `EUCLIDEAN`, etc.
TYPE	Numeric type: `F64`, `F32`, `I64`, `I32`, `I16`
EFC	Construction search effort (higher = better index)
M	Max connections per node
M0	Max connections at layer 0

Querying Vectors

The <|K, EF|> operator performs KNN search. K is the number of results, EF is the search effort (higher = more accurate, slower).

Recommended effort values:

40 — default, good accuracy
17 — fast but may miss some results

Basic KNN Query

SELECT
    *,
    vector::distance::knn() AS dist
FROM document
WHERE embedding <|10, 40|> $vector;

vector::distance::knn() uses the distance function defined by the index.

Scored Results with Threshold

SELECT *, score
FROM (
    SELECT *, (1 - vector::distance::knn()) AS score
    FROM document
    WHERE embedding <|20, 40|> $vector
)
WHERE score >= $threshold
ORDER BY score DESC;

FAQ

What does the surrealdb-vector skill do?

How do I install surrealdb-vector?

Run npx skills add https://github.com/surrealdb/agent-skills --skill surrealdb-vector in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/surrealdb-vector/ (personal) or .claude/skills/surrealdb-vector/ (project).

Which AI agents can use surrealdb-vector?

Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.

Is surrealdb-vector safe to install?

No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.

Who maintains surrealdb-vector?

It is published by surrealdb in surrealdb/agent-skills. It has 537 recorded installs.

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