Documentation Index
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使用 POST /v1/embeddings 为文本生成向量嵌入。支持批量输入和多种嵌入模型，可用于语义搜索、聚类和 RAG。
/v1/embeddings 端点为文本输入生成向量嵌入。嵌入以高维浮点数组表示文本的语义，可用于相似度搜索、聚类和检索增强生成（RAG）。此端点兼容 OpenAI Embeddings API。
POST https://router.flatkey.ai/v1/embeddings
| 模型 | 维度 | 供应商 |
|---|---|---|
gemini-embedding-001 | 3072 | |
text-embedding-3-small | 1536 | OpenAI |
text-embedding-3-large | 3072 | OpenAI |
| 请求头 | 值 |
|---|---|
Authorization | Bearer $FLATKEY_API_KEY |
Content-Type | application/json |
"gemini-embedding-001"、"text-embedding-3-small"。"float"（默认）返回浮点数组；"base64" 返回 Base64 编码的字符串。import os
from openai import OpenAI
client = OpenAI(
api_key=os.environ["FLATKEY_API_KEY"],
base_url="https://router.flatkey.ai/v1",
)
# Single text
response = client.embeddings.create(
model="gemini-embedding-001",
input="The quick brown fox jumps over the lazy dog",
)
embedding = response.data[0].embedding
print(f"Embedding dimensions: {len(embedding)}")
print(f"First 5 values: {embedding[:5]}")
import os
from openai import OpenAI
client = OpenAI(
api_key=os.environ["FLATKEY_API_KEY"],
base_url="https://router.flatkey.ai/v1",
)
# Batch embedding
texts = [
"What is machine learning?",
"How does neural network training work?",
"Explain gradient descent",
]
response = client.embeddings.create(
model="gemini-embedding-001",
input=texts,
)
for i, item in enumerate(response.data):
print(f"{texts[i]}: {len(item.embedding)} dimensions")
curl https://router.flatkey.ai/v1/embeddings \
-H "Authorization: Bearer $FLATKEY_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "gemini-embedding-001",
"input": "The quick brown fox jumps over the lazy dog"
}'
{
"object": "list",
"data": [
{
"object": "embedding",
"index": 0,
"embedding": [0.0023064255, -0.009327292, ...]
}
],
"model": "gemini-embedding-001",
"usage": {
"prompt_tokens": 9,
"total_tokens": 9
}
}
import os
import numpy as np
from openai import OpenAI
client = OpenAI(
api_key=os.environ["FLATKEY_API_KEY"],
base_url="https://router.flatkey.ai/v1",
)
def embed(text):
return client.embeddings.create(
model="gemini-embedding-001",
input=text,
).data[0].embedding
def cosine_similarity(a, b):
a, b = np.array(a), np.array(b)
return np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b))
q = embed("capital city of France")
doc1 = embed("Paris is the capital and most populous city of France.")
doc2 = embed("The Eiffel Tower is a famous landmark.")
print(cosine_similarity(q, doc1)) # higher — more relevant
print(cosine_similarity(q, doc2)) # lower