RAG / Retrieval

Embeddings quickstart

Single-text / batch vectorization in 3 lines.

python

from nexevo_ai import Nexevo
client = Nexevo()

# 单条文本 → 单个向量
resp = client.embeddings.create(
    model="text-embedding-3-large",
    input="Nexevo.ai 是一个 LLM 网关",
)
vec = resp["data"][0]["embedding"]
print(f"维度: {len(vec)}")
print(f"消耗 token: {resp['usage']['prompt_tokens']}")

# 批量 — 1 次调用 embed 多条(更高效)
batch = client.embeddings.create(
    model="bge-m3",
    input=["文档 1", "文档 2", "文档 3"],
)
for row in batch["data"]:
    print(f"index={row['index']}  dim={len(row['embedding'])}")

Get a free API key →Go to Playground and try it →