القائمة
Zero-copy vector operations for efficient similarity search and embedding storage in agent memory systems. Use when working with zvec.
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npx skills add https://github.com/oyi77/1ai-skills --skill zvecيبقى الأمر في سطر واحد. مرّر أفقيًا لمراجعته كاملًا قبل النسخ.
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| name | zvec |
| description | Zero-copy vector operations for efficient similarity search and embedding storage in agent memory systems. Use when working with zvec. |
| domain | core |
| author | oyi77 |
| license | Apache-2.0 |
| subdomain | core-platform |
| tags | ["ai-agent","infrastructure","memory","self-improvement","zvec"] |
| persona | {"name":"Mikolov et al.","title":"The Word2Vec Pioneers - Masters of Vector Embeddings","expertise":["Word Embeddings","Vector Representations","Neural Networks","NLP"],"philosophy":"Words that appear in similar contexts have similar meanings.","credentials":["Created Word2Vec at Google","Published landmark embedding papers","Enabled modern NLP"],"principles":["Embed meaning","Capture semantic relationships","Train on large corpora","Visualize in 2D/3D"]} |
| version | 1.0.0 |
Alibaba's lightweight in-process vector database - "The SQLite of Vector Databases"
ZVec is an open-source, in-process vector database from Alibaba's Tongyi Lab. It's lightweight, blazing fast, and embeds directly into your application - no server needed. Built on Proxima (Alibaba's battle-tested vector search engine used in production across Taobao, Ele.me, and more).
| Rationalization | Reality |
|---|---|
| "I'll figure it out as I go" | A structured approach saves time and reduces errors. Follow the workflow in this skill rather than improvising. |
| "I already know this topic" | Familiarity breeds shortcuts. Use the checklist to verify you haven't missed critical steps. |
| "This doesn't apply to my situation" | The patterns here generalize across contexts. Adapt, don't skip — the underlying principles hold. |
| "One more tool will fix it" | Adding complexity rarely solves process gaps. Master the core workflow first. |
Trigger phrases:
Use this skill when you need:
pip install zvec and start searching# Python
pip install zvec
# Node.js
npm install @zvec/zvec
import zvec
# Define collection schema
schema = zvec.CollectionSchema(
name="example",
vectors=zvec.VectorSchema("embedding", zvec.DataType.VECTOR_FP32, 384)
)
# Create collection
collection = zvec.create("my_vectors", schema)
# Add vectors
collection.add(
ids=["doc1", "doc2"],
vectors=[[0.1] * 384, [0.2] * 384],
payloads=[{"text": "AI is great"}, {"text": "Vectors are useful"}]
)
# Search
results = collection.search(
query_vector=[0.1] * 384,
top_k=10,
filter={"text": {"$contains": "AI"}}
)
const zvec = require('@zvec/zvec');
const collection = await zvec.create('documents', { dimension: 384 });
await collection.add({ id: '1', vector: embedding, payload: { text: 'Hello' } });
const results = await collection.search({ vector: queryEmbedding, topK: 5 });
| Type | Use Case | Latency | Accuracy |
|---|---|---|---|
| Flat | Small datasets, exact results | Low | 100% |
| HNSW | Balanced speed/accuracy | Medium | ~95% |
| IVF | Large datasets | Fast | ~90% |
ZVec integrates perfectly with:
faceless-youtube - Video content embeddingai-research-agent - Document similarity searchcontent-generator - Content deduplication| Feature | ZVec | RuVector |
|---|---|---|
| Speed | ⚡⚡⚡⚡⚡ | ⚡⚡⚡ |
| Self-Learning | ❌ | ✅ GNN |
| Local LLM | ❌ | ✅ |
| Graph Queries | ❌ | ✅ Cypher |
| Complexity | Simple | Advanced |
| Size | Tiny | Full-featured |
Choose ZVec for: Simple, fast, lightweight vector storage Choose RuVector for: Self-learning memory, graph queries, local LLMs
SKILL.md - This file| Rationalization | Reality |
|---|---|
| "I'll do this later" | Explain why this excuse is wrong for this skill |
| "This is simple, skip steps" | Even simple tasks benefit from process |
After completing this skill, confirm: