تشغيل أي مهارة في Manus بنقرة واحدة

ابدأ الآن

$pwd:

create-and-query-vector-indexes

Name: Create And Query Vector Indexes
Author: FalkorDB

// Use HNSW vector indexes for Approximate Nearest Neighbor (ANN) search with embeddings

تشغيل في Manus

$ git log --oneline --stat

stars:٢١

forks:٢

updated:٤ فبراير ٢٠٢٦ في ٢١:٤١

SKILL.md

readonly

name	Create and query vector indexes
description	Use HNSW vector indexes for Approximate Nearest Neighbor (ANN) search with embeddings

Create and query vector indexes

Use HNSW vector indexes for Approximate Nearest Neighbor (ANN) search.

Usage

Create vector indexes with specific dimension and similarity configurations, then query them using db.idx.vector.queryNodes.

Example

redis-cli GRAPH.QUERY social "CREATE VECTOR INDEX FOR (p:Product) ON (p.embedding)
OPTIONS {dimension: 768, similarityFunction: 'cosine', M: 32, efConstruction: 200}"

redis-cli GRAPH.QUERY social "CALL db.idx.vector.queryNodes('Product', 'embedding', 5, vecf32([0.1, 0.2, 0.3]))
YIELD node, score RETURN node.name, score"

Notes

Vector indexes use HNSW (Hierarchical Navigable Small World) algorithm
Configure dimension to match your embedding size
Similarity functions include 'cosine', 'euclidean', etc.
M and efConstruction parameters tune index performance and accuracy
Use vecf32() to pass vector values in queries
Returns nodes with their similarity scores

related-skills.json

نفس المستودع

falkordb-skills.md

from "FalkorDB/skills"

Practical FalkorDB guidance — Cypher queries, UDF management, Docker operations, and data ingestion. Use when writing or reviewing FalkorDB queries, setting up FalkorDB containers, working with user-defined functions, or migrating data from other sources.

2026-05-2021

migrate-neo4j-to-falkordb.md

from "FalkorDB/skills"

Extract data from Neo4j to CSV and load it into FalkorDB

2026-05-1721

migrate-neptune-to-falkordb.md

from "FalkorDB/skills"

Convert AWS Neptune Export CSVs and load them into FalkorDB

2026-05-1721

migrate-sql-to-falkordb.md

from "FalkorDB/skills"

Migrate and continuously sync data from SQL systems into FalkorDB

2026-05-1721

use-bulk-loader-from-csv.md

from "FalkorDB/skills"

Build FalkorDB databases from CSV inputs using the falkordb-bulk-loader utility

2026-05-1721

apply-falkordb-cypher-limitations-correctly.md

from "FalkorDB/skills"

Account for FalkorDB Cypher limitations like non-indexed not-equal filters when designing queries

2026-02-0421

package.json

"author": "FalkorDB"

"repository": "FalkorDB/skills"

فتح مستودع GitHub عرض مستودعات المنشئ

$ install --global

$ download --local

تشغيل في Manus

$ useful --forSOC

مصممو قواعد البياناتمهن الحاسوب والرياضيات15-1243L4

name	Create and query vector indexes
description	Use HNSW vector indexes for Approximate Nearest Neighbor (ANN) search with embeddings

Create and query vector indexes

Use HNSW vector indexes for Approximate Nearest Neighbor (ANN) search.

Usage

Create vector indexes with specific dimension and similarity configurations, then query them using db.idx.vector.queryNodes.

Example

redis-cli GRAPH.QUERY social "CREATE VECTOR INDEX FOR (p:Product) ON (p.embedding)
OPTIONS {dimension: 768, similarityFunction: 'cosine', M: 32, efConstruction: 200}"

redis-cli GRAPH.QUERY social "CALL db.idx.vector.queryNodes('Product', 'embedding', 5, vecf32([0.1, 0.2, 0.3]))
YIELD node, score RETURN node.name, score"

Notes

Vector indexes use HNSW (Hierarchical Navigable Small World) algorithm
Configure dimension to match your embedding size
Similarity functions include 'cosine', 'euclidean', etc.
M and efConstruction parameters tune index performance and accuracy
Use vecf32() to pass vector values in queries
Returns nodes with their similarity scores

create-and-query-vector-indexes

Create and query vector indexes

Usage

Example

Notes

المزيد من هذا المستودع

المزيد من هذا المستودع

Create and query vector indexes

Usage

Example

Notes