-
Notifications
You must be signed in to change notification settings - Fork 123
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
- Loading branch information
1 parent
0aa1f00
commit bc98a9f
Showing
1 changed file
with
80 additions
and
0 deletions.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Original file line number | Diff line number | Diff line change |
---|---|---|
@@ -0,0 +1,80 @@ | ||
# Baseten: Infinity Embedding Server Truss | ||
|
||
This is a [Truss](https://truss.baseten.co/) to deploy [infinity embedding server](https://github.com/michaelfeil/infinity), a high-throughput, low-latency REST API server for serving vector embeddings. | ||
|
||
## Deployment | ||
|
||
Before deployment: | ||
|
||
1. Make sure you have a [Baseten account](https://app.baseten.co/signup) and [API key](https://app.baseten.co/settings/account/api_keys). | ||
2. Install the latest version of Truss: `pip install --upgrade truss` | ||
3. [Required for gated/private models] Retrieve your Hugging Face token from the [settings](https://huggingface.co/settings/tokens). Set your Hugging Face token as a Baseten secret [here](https://app.baseten.co/settings/secrets) with the key `hf_access_key`. | ||
|
||
First, clone this repository: | ||
|
||
```sh | ||
git clone https://github.com/basetenlabs/truss-examples.git | ||
cd custom-server/infinity-embedding-server | ||
``` | ||
|
||
With `infinity-embedding-server` as your working directory, you can deploy the model with the following command, paste your Baseten API key if prompted. | ||
|
||
```sh | ||
truss push --publish --trusted | ||
``` | ||
|
||
## Call your model | ||
|
||
### curl | ||
|
||
```bash | ||
curl -X POST https://model-xxx.api.baseten.co/development/predict \ | ||
-H "Authorization: Api-Key YOUR_API_KEY" \ | ||
-d '{"input": "text string"}' | ||
``` | ||
|
||
### request python library | ||
|
||
```python | ||
import requests | ||
|
||
resp = requests.post( | ||
"https://model-xxx.api.baseten.co/development/predict", | ||
headers={"Authorization": "Api-Key YOUR_API_KEY"}, | ||
json={"input": "text string"}, | ||
) | ||
|
||
print(resp.json()) | ||
``` | ||
|
||
### openai python SDK | ||
|
||
```python | ||
import os | ||
from openai import OpenAI | ||
|
||
client = OpenAI( | ||
api_key=os.environ["YOUR_API_KEY"], | ||
base_url="https://bridge.baseten.co/v1/direct" | ||
) | ||
|
||
model_id = "xxx" | ||
deployment_id = "xxx" | ||
|
||
response = client.embeddings.create( | ||
input="text string", | ||
model="BAAI/bge-small-en-v1.5", | ||
extra_body={ | ||
"baseten": { | ||
"model_id": model_id, | ||
"deployment_id": deployment_id | ||
} | ||
} | ||
) | ||
|
||
print(response.data[0].embedding) | ||
``` | ||
|
||
## Support | ||
|
||
If you have any questions or need assistance, please open an issue in this repository or contact our support team. |