> ## Documentation Index
> Fetch the complete documentation index at: https://docs.flatkey.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Embeddings — POST /v1/embeddings

> Hasilkan embedding vektor untuk teks menggunakan POST /v1/embeddings. Mendukung input batch dan beberapa model embedding untuk pencarian semantik, pengelompokan, dan RAG.

Endpoint `/v1/embeddings` menghasilkan embedding vektor untuk input teks. Embedding merepresentasikan makna semantik teks sebagai array float berdimensi tinggi, yang memungkinkan pencarian kemiripan, pengelompokan, dan retrieval-augmented generation (RAG). Endpoint ini kompatibel dengan OpenAI Embeddings API.

## Endpoint

```
POST https://router.flatkey.ai/v1/embeddings
```

## Model embedding yang didukung

| Model                    | Dimensi | Penyedia |
| ------------------------ | ------- | -------- |
| `gemini-embedding-001`   | 3072    | Google   |
| `text-embedding-3-small` | 1536    | OpenAI   |
| `text-embedding-3-large` | 3072    | OpenAI   |

Lihat [Direktori Model](https://flatkey.ai/models) untuk ketersediaan dan harga terkini.

## Permintaan

### Header

| Header          | Nilai                     |
| --------------- | ------------------------- |
| `Authorization` | `Bearer $FLATKEY_API_KEY` |
| `Content-Type`  | `application/json`        |

### Parameter body

<ParamField body="model" type="string" required>
  ID model embedding. Contoh: `"gemini-embedding-001"`, `"text-embedding-3-small"`.
</ParamField>

<ParamField body="input" type="string | array" required>
  Teks yang akan di-embed. Dapat berupa string tunggal atau array string untuk embedding batch.
</ParamField>

<ParamField body="encoding_format" type="string">
  `"float"` (default) mengembalikan array float. `"base64"` mengembalikan string yang dikodekan base64.
</ParamField>

<ParamField body="dimensions" type="integer">
  Jumlah dimensi untuk output embedding. Tidak didukung oleh semua model.
</ParamField>

## Contoh permintaan

<CodeGroup>
  ```python python theme={"dark"}
  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]}")
  ```

  ```python python batch theme={"dark"}
  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")
  ```

  ```bash curl theme={"dark"}
  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"
    }'
  ```
</CodeGroup>

## Respons

```json theme={"dark"}
{
  "object": "list",
  "data": [
    {
      "object": "embedding",
      "index": 0,
      "embedding": [0.0023064255, -0.009327292, ...]
    }
  ],
  "model": "gemini-embedding-001",
  "usage": {
    "prompt_tokens": 9,
    "total_tokens": 9
  }
}
```

### Kolom respons

<ResponseField name="data" type="array">
  Array objek embedding, satu per string input.

  <Expandable title="objek embedding">
    <ResponseField name="index" type="integer">Posisi dalam array input.</ResponseField>
    <ResponseField name="embedding" type="array">Array float dari vektor embedding.</ResponseField>
  </Expandable>
</ResponseField>

<ResponseField name="usage.prompt_tokens" type="integer">
  Jumlah token yang diproses.
</ResponseField>

## Contoh kemiripan kosinus

```python python theme={"dark"}
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
```