Basic text vectorization (embedding) service. Converts natural language into high-dimensional dense vectors, providing core data support for downstream tasks such as semantic search, clustering analysis, and recommendation systems.
将输入文本转换为高维向量表示,用于语义相似度计算、聚类分析等下游任务。
from services.embedding_service.client import EmbeddingServiceClient
client = EmbeddingServiceClient()
# 单个文本向量化
vector = client.embed_query("人工智能") # -> list[float]
# 多个文本向量化
texts = ["机器学习", "深度学习", "自然语言处理"]
vectors = client.embed_documents(texts) # -> list[list[float]]
{
"object": "list",
"data": [
{
"object": "embedding",
"index": 0,
"embedding": [-0.031, -0.016, -0.007, ...]
}
],
"model": "Qwen/Qwen3-Embedding-0.6B"
}
Search for places (restaurants, cafes, etc.) via Google Places API proxy on localhost.
Interact with GitHub using the `gh` CLI. Use `gh issue`, `gh pr`, `gh run`, and `gh api` for issues, PRs, CI runs, and advanced queries.
Create or update AgentSkills. Use when designing, structuring, or packaging skills with scripts, references, and assets.
Start voice calls via the OpenClaw voice-call plugin.
Notion API for creating and managing pages, databases, and blocks.
Gemini CLI for one-shot Q&A, summaries, and generation.
Category:developer