embed
GitHub调用 ZeroGPU API 将文本转换为384维向量，用于语义搜索、RAG检索、聚类或相似度比较。支持 all-minilm-l6-v2 和 bge-small-en-v1.5 模型，返回标准 JSON 格式嵌入结果。
Trigger Scenarios
Install
npx skills add zerogpu/zerogpu-router --skill embed -g -y
SKILL.md
Frontmatter
{
"name": "embed",
"metadata": {
"openclaw": {
"install": [
{
"bins": [
"zerogpu"
],
"kind": "node",
"package": "zerogpu-cli"
}
],
"requires": {
"bins": [
"zerogpu"
]
}
}
},
"description": "Turn text into a 384-dimensional embedding vector for semantic search, RAG retrieval, clustering, deduplication, or similarity comparison. Use when the user asks to embed text, build or query a vector index, or compare passages by meaning rather than by keyword.",
"allowed-tools": "Bash(zerogpu embeddings *)",
"argument-hint": "<text> [-m <model>]"
}
Sends your input to ZeroGPU's hosted API for inference — this is not local processing. Don't pass secrets, credentials, or regulated data you aren't cleared to share with a third party. See the plugin README's "Data & privacy" section.
Embed the text. Run this with the exec tool, pasting the text into the heredoc verbatim — no escaping or quoting required (the quoted heredoc handles every shell metacharacter, newline, quote, and paren safely):
zerogpu embeddings -m all-minilm-l6-v2 <<'ZGPU_END_OF_INPUT'
<the text to embed, verbatim>
ZGPU_END_OF_INPUT
Defaults to all-minilm-l6-v2 (22.7M parameters, 512-token window), the general-purpose choice for semantic similarity over short chunks. Use -m bge-small-en-v1.5 instead (33M parameters, 512-token window) when the user asks for it, for English retrieval, or when retrieval quality is the bottleneck. Both cost $0.004 per 1M input tokens and bill nothing on output, and both return 384-dimensional vectors, so they are interchangeable in an existing index.
Output is OpenAI's embeddings envelope as JSON: data[].embedding holds the vector, data[].index maps it back to its input, and usage reports input tokens. Do not print the raw vector back to the user — it is 384 floats and unreadable. Say what was embedded, which model produced it, and how many dimensions came back. Print or write the numbers only if the user explicitly asks for the vector or is piping it somewhere.
These models are served only by the Embeddings API, so this skill calls them there rather than through Chat Completions. Inputs longer than the model's window are truncated, so chunk long documents and embed the chunks.
Savings note: only if the command output literally contains a line starting with 💰 ZeroGPU savings, append that exact line, unchanged, as the last line of your reply. If no such line is present, say nothing about savings and do not mention or suggest the cost-savings skill — this note is intentionally occasional, not shown every time.
Version History
-
4e1b070
Current 2026-09-22 08:08
v5.0.0：价格从$0.50降至$0.004；all-minilm-l6-v2上下文窗口从256增至512；bge-small-en-v1.5参数量微调；更新为使用 zerogpu-cli 3.8.0 的通用端点命令。
- 87b63e9 2026-08-27 16:49
