# API Documentation

The Unified Embedding API provides an OpenAI-compatible interface for dense embeddings, sparse embeddings, and document reranking.

**Base URL**

```text
http://localhost:7860/api/v1
```

For Hugging Face Spaces:

```text
https://YOUR_SPACE.hf.space/api/v1
```

---

# Authentication

Authentication is not required by default.

When using the official OpenAI SDK, an API key is still required by the client but its value is ignored.

```python
client = OpenAI(
    base_url="https://YOUR_SPACE.hf.space/api/v1",
    api_key="not-required"
)
```

---

# Endpoints

| Method | Endpoint        | Description                     |
| ------ | --------------- | ------------------------------- |
| POST   | `/embeddings`   | Generate dense embeddings       |
| POST   | `/embed_sparse` | Generate sparse embeddings      |
| POST   | `/rerank`       | Rank documents based on a query |
| GET    | `/models`       | List available models           |
| GET    | `/health`       | Health check                    |

---

# Dense Embeddings

Generate dense vector embeddings.

## Request

**POST**

```text
/api/v1/embeddings
```

### Body

```json
{
  "input": [
    "Machine learning",
    "Artificial Intelligence"
  ],
  "model": "qwen3-0.6b"
}
```

| Field           | Type              | Required | Description                        |
| --------------- | ----------------- | -------- | ---------------------------------- |
| input           | string | string[] | Yes      | Text or list of texts              |
| model           | string            | Yes      | Configured embedding model         |
| encoding_format | string            | No       | Currently returns float embeddings |

---

## Response

```json
{
  "object": "list",
  "data": [
    {
      "object": "embedding",
      "index": 0,
      "embedding": [
        0.124,
        -0.554,
        0.873
      ]
    }
  ],
  "model": "qwen3-0.6b",
  "usage": {
    "prompt_tokens": 2,
    "total_tokens": 2
  }
}
```

---

## Python (OpenAI SDK)

```python
from openai import OpenAI

client = OpenAI(
    base_url="https://YOUR_SPACE.hf.space/api/v1",
    api_key="not-required"
)

response = client.embeddings.create(
    input="Large Language Models",
    model="qwen3-0.6b"
)

embedding = response.data[0].embedding
```

---

## cURL

```bash
curl -X POST \
http://localhost:7860/api/v1/embeddings \
-H "Content-Type: application/json" \
-d '{
    "input":"Large Language Models",
    "model":"qwen3-0.6b"
}'
```

---

# Sparse Embeddings

Generate sparse embeddings (SPLADE).

## Request

```text
POST /api/v1/embed_sparse
```

### Body

```json
{
  "input": [
    "Hybrid Search"
  ],
  "model": "splade"
}
```

---

## Response

```json
{
  "object":"list",
  "data":[
    {
      "index":0,
      "indices":[
        24,
        91,
        118
      ],
      "values":[
        0.87,
        0.41,
        0.29
      ]
    }
  ]
}
```

---

# Document Reranking

Ranks retrieved documents according to their relevance to a query.

## Request

```text
POST /api/v1/rerank
```

### Body

```json
{
  "query":"What is LangGraph?",
  "documents":[
    "LangGraph is an orchestration framework.",
    "React is a frontend library.",
    "FastAPI is a Python framework."
  ],
  "model":"bge-reranker-base",
  "top_k":2
}
```

| Field     | Type     | Required |
| --------- | -------- | -------- |
| query     | string   | Yes      |
| documents | string[] | Yes      |
| model     | string   | Yes      |
| top_k     | integer  | No       |

---

## Response

```json
{
  "results":[
    {
      "index":0,
      "score":0.992,
      "text":"LangGraph is an orchestration framework."
    },
    {
      "index":2,
      "score":0.843,
      "text":"FastAPI is a Python framework."
    }
  ]
}
```

---

# Available Models

Returns every configured model.

## Request

```text
GET /api/v1/models
```

## Response

```json
{
  "models":[
    {
      "id":"qwen3-0.6b",
      "type":"embeddings"
    },
    {
      "id":"splade",
      "type":"sparse-embeddings"
    },
    {
      "id":"bge-reranker-base",
      "type":"rerank"
    }
  ]
}
```

---

# Health Check

Check server status.

## Request

```text
GET /health
```

## Response

```json
{
  "status":"healthy"
}
```

---

# Error Responses

The API follows standard HTTP status codes.

| Status | Description           |
| ------ | --------------------- |
| 200    | Success               |
| 400    | Invalid request       |
| 404    | Resource not found    |
| 422    | Validation error      |
| 500    | Internal server error |

Example:

```json
{
  "detail":"Model 'unknown-model' is not configured."
}
```

---

# Model Configuration

Available models are defined in:

```text
config/models.yaml
```

Example:

```yaml
models:

  qwen3:
    name: Qwen/Qwen3-Embedding-0.6B
    type: embeddings

  splade:
    name: prithivida/Splade_PP_en_v1
    type: sparse-embeddings

  reranker:
    name: BAAI/bge-reranker-base
    type: rerank
```

Adding a new model only requires updating this configuration file. No application code changes are needed.

---

# OpenAI Compatibility

The `/embeddings` endpoint implements the OpenAI Embeddings API specification.

Migrating from OpenAI usually requires changing only the client configuration.

```python
client = OpenAI(
    base_url="https://YOUR_SPACE.hf.space/api/v1",
    api_key="not-required"
)
```

Your existing embedding requests can remain unchanged.