# Embeddings API Reference

OpenAI-compatible embeddings API with multiple backend support.

## Base URL

```
{BASE_URL}
```

- **Local development:** `http://localhost:54100`
- **Docker (internal):** `http://didiAI-embeddings-api:14100`
- **Production:** Use your configured hostname

## Authentication

Authentication is **optional**. If `EMB_API_TOKENS` is set, requests require a Bearer token:

```
Authorization: Bearer <token>
```

Health endpoints (`/health`, `/ready`) are always public.

## Endpoints

### Create Embeddings

Generate embeddings for the given input texts.

**Endpoint:** `POST /v1/embeddings`

**Request Headers:**
| Header | Required | Description |
|--------|----------|-------------|
| `Content-Type` | Yes | Must be `application/json` |
| `Authorization` | If auth enabled | `Bearer <token>` |

**Request Body:**
```json
{
  "input": "text to embed",
  "model": "BAAI/bge-m3",
  "encoding_format": "float",
  "dimensions": null,
  "backend": null
}
```

| Field | Type | Required | Description |
|-------|------|----------|-------------|
| `input` | string or string[] | Yes | Text(s) to embed |
| `model` | string | Yes | Model identifier |
| `encoding_format` | string | No | `"float"` (default) or `"base64"` |
| `dimensions` | integer | No | Desired embedding dimensions (if supported) |
| `backend` | string | No | Override default backend: `"vllm"` or `"llamacpp"` |

**Response:**
```json
{
  "object": "list",
  "data": [
    {
      "object": "embedding",
      "index": 0,
      "embedding": [0.0023, -0.0142, 0.0083, ...]
    }
  ],
  "model": "BAAI/bge-m3",
  "usage": {
    "prompt_tokens": 5,
    "total_tokens": 5
  },
  "backend": "vllm"
}
```

**Example:**
```bash
curl -X POST http://localhost:54100/v1/embeddings \
  -H "Content-Type: application/json" \
  -d '{
    "input": ["Hello world", "How are you?"],
    "model": "BAAI/bge-m3"
  }'
```

**Multiple texts:**
```bash
curl -X POST http://localhost:54100/v1/embeddings \
  -H "Content-Type: application/json" \
  -d '{
    "input": [
      "First document to embed",
      "Second document to embed",
      "Third document to embed"
    ],
    "model": "BAAI/bge-m3"
  }'
```

**Base64 encoding:**
```bash
curl -X POST http://localhost:54100/v1/embeddings \
  -H "Content-Type: application/json" \
  -d '{
    "input": "Hello world",
    "model": "BAAI/bge-m3",
    "encoding_format": "base64"
  }'
```

---

### List Models

List available embedding models.

**Endpoint:** `GET /v1/models`

**Query Parameters:**
| Parameter | Type | Required | Description |
|-----------|------|----------|-------------|
| `backend` | string | No | Filter by backend: `"vllm"` or `"llamacpp"` |

**Response:**
```json
{
  "object": "list",
  "data": [
    {
      "id": "BAAI/bge-m3",
      "backend": "vllm",
      "loaded": true,
      "dimensions": null,
      "max_input_tokens": null
    }
  ]
}
```

**Example:**
```bash
# List all models
curl http://localhost:54100/v1/models

# List models from specific backend
curl "http://localhost:54100/v1/models?backend=vllm"
```

---

### List Backends

List available backends.

**Endpoint:** `GET /v1/backends`

**Response:**
```json
{
  "backends": ["vllm", "llamacpp"]
}
```

**Example:**
```bash
curl http://localhost:54100/v1/backends
```

---

### Health Check

Detailed health status including per-backend health.

**Endpoint:** `GET /health`

**Response:**
```json
{
  "status": "healthy",
  "backends": [
    {
      "name": "vllm",
      "healthy": true,
      "message": null
    }
  ]
}
```

Status values:
- `"healthy"` - All backends are healthy
- `"degraded"` - Some backends are unhealthy
- `"unhealthy"` - All backends are unhealthy

**Example:**
```bash
curl http://localhost:54100/health
```

---

### Readiness Probe

Simple readiness check for Kubernetes.

**Endpoint:** `GET /ready`

**Response:**
```json
{
  "ready": true
}
```

**Example:**
```bash
curl http://localhost:54100/ready
```

---

## Error Responses

All errors follow this format:

```json
{
  "detail": "Error message describing what went wrong"
}
```

### HTTP Status Codes

| Code | Meaning |
|------|---------|
| 200 | Success |
| 400 | Bad request (invalid parameters, backend not enabled) |
| 401 | Authentication required or failed |
| 429 | Rate limit exceeded |
| 503 | Service unavailable (backend connection failed) |
| 504 | Gateway timeout (backend request timed out) |

### Rate Limit Response

```json
{
  "detail": "Too many requests",
  "retry_after": 1.5
}
```

Headers include: `Retry-After: 2`

### Authentication Error

```json
{
  "detail": {
    "error": "Authentication required",
    "message": "Missing Authorization header"
  }
}
```

---

## Request Headers

| Header | Required | Description |
|--------|----------|-------------|
| `Content-Type` | Yes (POST) | Must be `application/json` |
| `Authorization` | If auth enabled | `Bearer <token>` |
| `X-Request-ID` | No | Request tracking ID (generated if not provided) |

Response always includes `X-Request-ID` header for tracking.

---

## SDK Examples

### Python (httpx)

```python
import httpx

async def embed_texts(texts: list[str]) -> list[list[float]]:
    async with httpx.AsyncClient() as client:
        response = await client.post(
            "http://localhost:54100/v1/embeddings",
            json={
                "input": texts,
                "model": "BAAI/bge-m3",
            },
            headers={"Authorization": "Bearer your-token"},
        )
        response.raise_for_status()
        data = response.json()
        return [item["embedding"] for item in data["data"]]
```

### Python (openai SDK)

```python
from openai import OpenAI

client = OpenAI(
    base_url="http://localhost:54100/v1",
    api_key="your-token",  # or "not-needed" if auth disabled
)

response = client.embeddings.create(
    input=["Hello world"],
    model="BAAI/bge-m3",
)

embedding = response.data[0].embedding
print(f"Embedding dimensions: {len(embedding)}")
```

### curl

```bash
# Simple embedding
curl -X POST http://localhost:54100/v1/embeddings \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer your-token" \
  -d '{"input": "Hello world", "model": "BAAI/bge-m3"}'

# With specific backend
curl -X POST http://localhost:54100/v1/embeddings \
  -H "Content-Type: application/json" \
  -d '{"input": "Hello world", "model": "BAAI/bge-m3", "backend": "vllm"}'
```