嵌入模型（Embedding）

export API_KEY="YOUR_API_KEY"

import os
import requests

api_key = os.environ["API_KEY"]
api_url = "https://api.siflow.cn/model-api/embeddings"

response = requests.post(
    api_url,
    headers={
        "Authorization": f"Bearer {api_key}",
        "Content-Type": "application/json",
    },
    json={
        "model": "Qwen/Qwen3-Embedding-4B",
        "input": [
            "Machine learning is a subset of artificial intelligence",
            "Deep learning uses neural networks with multiple layers",
            "Natural language processing enables computers to understand text",
        ],
    },
)

data = response.json()
for i, item in enumerate(data["data"]):
    print(f"Embedding {i}: {len(item['embedding'])} dimensions")

curl -X POST https://api.siflow.cn/model-api/embeddings \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $API_KEY" \
  -d '{
    "model": "Qwen/Qwen3-Embedding-4B",
    "input": [
      "Machine learning is a subset of artificial intelligence",
      "Deep learning uses neural networks with multiple layers"
    ]
  }'

import os
import requests
import numpy as np

api_key = os.environ["API_KEY"]
api_url = "https://api.siflow.cn/model-api/embeddings"


def embed(texts):
    resp = requests.post(
        api_url,
        headers={
            "Authorization": f"Bearer {api_key}",
            "Content-Type": "application/json",
        },
        json={"model": "BAAI/bge-m3", "input": texts},
    )
    return [np.array(item["embedding"]) for item in resp.json()["data"]]


def cosine(a, b):
    return float(a @ b / (np.linalg.norm(a) * np.linalg.norm(b)))


query, *docs = embed([
    "How do I reset my password?",
    "Steps to recover a forgotten account password.",
    "Our refund policy for annual plans.",
])

for i, doc in enumerate(docs):
    print(f"doc {i}: {cosine(query, doc):.3f}")