Documentation Index
Fetch the complete documentation index at: /llms.txt
Use this file to discover all available pages before exploring further.
Venice에서 벡터 임베딩을 생성하여 /embeddings 엔드포인트로 시맨틱 검색, RAG 검색, 클러스터링 및 추천에 활용하세요.
/embeddings로 전송한 다음, 반환된 벡터를 데이터베이스 또는 벡터 인덱스에 저장하세요.
import os
from openai import OpenAI
client = OpenAI(
api_key=os.environ["VENICE_API_KEY"],
base_url="https://api.venice.ai/api/v1",
)
response = client.embeddings.create(
model="text-embedding-bge-m3",
input="Privacy-first AI infrastructure for semantic search",
)
vector = response.data[0].embedding
print(len(vector), vector[:5])
import OpenAI from "openai";
const client = new OpenAI({
apiKey: process.env.VENICE_API_KEY,
baseURL: "https://api.venice.ai/api/v1",
});
const response = await client.embeddings.create({
model: "text-embedding-bge-m3",
input: "Privacy-first AI infrastructure for semantic search",
});
const vector = response.data[0].embedding;
console.log(vector.length, vector.slice(0, 5));
curl https://api.venice.ai/api/v1/embeddings \
-H "Authorization: Bearer $VENICE_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "text-embedding-bge-m3",
"input": "Privacy-first AI infrastructure for semantic search",
"encoding_format": "float"
}'
{
"model": "text-embedding-bge-m3",
"input": [
"Venice supports private chat completions.",
"Embeddings help retrieve relevant documents.",
"Vector search powers RAG applications."
]
}