embed
GitHub调用 ZeroGPU API 将文本转换为 384 维向量，用于语义搜索、RAG 检索或聚类。支持多模型选择与截断处理，返回标准 JSON 格式嵌入结果。
触发场景
安装
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 embed *)",
"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. $ARGUMENTS is the raw text — pass it verbatim, no escaping or quoting required (the heredoc below handles every shell metacharacter, newline, quote, and paren safely):
ZGPU_TEXT=$(cat <<'ZGPU_END_OF_INPUT'
$ARGUMENTS
ZGPU_END_OF_INPUT
)
zerogpu embed "$ZGPU_TEXT"
Defaults to all-minilm-l6-v2 (22.7M parameters, 256-token window), the general-purpose choice for semantic similarity over short chunks. For English retrieval, for chunks longer than 256 tokens, or when retrieval quality is the bottleneck, append -m bge-small-en-v1.5 (33.4M parameters, 512-token window). Both cost $0.50 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 by the Embeddings API rather than the Responses API; the CLI routes them automatically. 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.
版本历史
- 87b63e9 当前 2026-08-27 16:49
