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9853396 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 | # Embedding API Reference
## Overview
AxonHub provides comprehensive support for text and multimodal embedding generation through OpenAI-compatible and Jina AI-specific APIs.
## Key Benefits
- **OpenAI Compatibility**: Use existing OpenAI SDKs without modification
- **Jina AI Support**: Native Jina embedding format support for specialized use cases
- **Multiple Input Types**: Support for single text, text arrays, token arrays, and multiple token arrays
- **Flexible Output Formats**: Choose between float arrays or base64-encoded embeddings
## Supported Endpoints
**Endpoints:**
- `POST /v1/embeddings` - OpenAI-compatible embedding API
- `POST /jina/v1/embeddings` - Jina AI-specific embedding API
## Request Format
```json
{
"input": "The text to embed",
"model": "text-embedding-3-small",
"encoding_format": "float",
"dimensions": 1536,
"user": "user-id"
}
```
**Parameters:**
| Parameter | Type | Required | Description |
|-----------|------|----------|-------------|
| `input` | string \| string[] \| number[] \| number[][] | ✅ | The text(s) to embed. Can be a single string, array of strings, token array, or multiple token arrays. |
| `model` | string | ✅ | The model to use for embedding generation. |
| `encoding_format` | string | ❌ | Format to return embeddings in. Either `float` or `base64`. Default: `float`. |
| `dimensions` | integer | ❌ | Number of dimensions for the output embeddings. |
| `user` | string | ❌ | Unique identifier for the end-user. |
**Jina-Specific Parameters:**
| Parameter | Type | Required | Description |
|-----------|------|----------|-------------|
| `task` | string | ❌ | Task type for Jina embeddings. Options: `text-matching`, `retrieval.query`, `retrieval.passage`, `separation`, `classification`, `none`. |
## Response Format
```json
{
"object": "list",
"data": [
{
"object": "embedding",
"embedding": [0.123, 0.456, ...],
"index": 0
}
],
"model": "text-embedding-3-small",
"usage": {
"prompt_tokens": 4,
"total_tokens": 4
}
}
```
## Examples
### OpenAI SDK (Python)
```python
import openai
client = openai.OpenAI(
api_key="your-axonhub-api-key",
base_url="http://localhost:8090/v1"
)
response = client.embeddings.create(
input="Hello, world!",
model="text-embedding-3-small"
)
print(response.data[0].embedding[:5]) # First 5 dimensions
```
### OpenAI SDK (Go)
```go
package main
import (
"context"
"fmt"
"log"
"github.com/openai/openai-go"
"github.com/openai/openai-go/option"
)
func main() {
client := openai.NewClient(
option.WithAPIKey("your-axonhub-api-key"),
option.WithBaseURL("http://localhost:8090/v1"),
)
embedding, err := client.Embeddings.New(context.TODO(), openai.EmbeddingNewParams{
Input: openai.Union[string](openai.String("Hello, world!")),
Model: openai.String("text-embedding-3-small"),
option.WithHeader("AH-Trace-Id", "trace-example-123"),
option.WithHeader("AH-Thread-Id", "thread-example-abc"),
})
if err != nil {
log.Fatal(err)
}
fmt.Printf("Embedding dimensions: %d\n", len(embedding.Data[0].Embedding))
fmt.Printf("First 5 values: %v\n", embedding.Data[0].Embedding[:5])
}
```
### Multiple Texts
```python
response = client.embeddings.create(
input=["Hello, world!", "How are you?"],
model="text-embedding-3-small"
)
for i, data in enumerate(response.data):
print(f"Text {i}: {data.embedding[:3]}...")
```
### Jina-Specific Task
```python
import requests
response = requests.post(
"http://localhost:8090/jina/v1/embeddings",
headers={
"Authorization": "Bearer your-axonhub-api-key",
"Content-Type": "application/json"
},
json={
"input": "What is machine learning?",
"model": "jina-embeddings-v2-base-en",
"task": "retrieval.query"
}
)
result = response.json()
print(result["data"][0]["embedding"][:5])
```
## Authentication
The Embedding API uses Bearer token authentication:
- **Header**: `Authorization: Bearer <your-api-key>`
The API keys are managed through AxonHub's API Key management system.
## Best Practices
1. **Use Tracing Headers**: Include `AH-Trace-Id` and `AH-Thread-Id` headers for better observability
2. **Batch Requests**: When embedding multiple texts, send them in a single request for better performance
3. **Choose Appropriate Dimensions**: Use the `dimensions` parameter to reduce embedding size if full dimensionality isn't needed
4. **Select Proper Encoding**: Use `base64` encoding if you need to transmit embeddings over the network to reduce payload size
5. **Jina Task Types**: When using Jina embeddings, select the appropriate `task` type for your use case to optimize retrieval quality
## Related Resources
- [OpenAI API](openai-api.md)
- [Rerank API](rerank-api.md)
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