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Hugging Face Automatic Embedding Tei Inference Endpoints

Automatic Embedding Tei Inference Endpoints

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alph-notebooks/hf-cookbook / automatic_embedding_tei_inference_endpoints.ipynb

How to use Inference Endpoints to Embed Documents

Authored by: Derek Thomas

Goal

I have a dataset I want to embed for semantic search (or QA, or RAG), I want the easiest way to do embed this and put it in a new dataset.

Approach

I'm using a dataset from my favorite subreddit r/bestofredditorupdates. Because it has long entries, I will use the new jinaai/jina-embeddings-v2-base-en since it has an 8k context length. I will deploy this using Inference Endpoint to save time and money. To follow this tutorial, you will need to have already added a payment method. If you haven't, you can add one here in billing. To make it even easier, I'll make this fully API based.

To make this MUCH faster I will use the Text Embeddings Inference image. This has many benefits like:

  • No model graph compilation step
  • Small docker images and fast boot times. Get ready for true serverless!
  • Token based dynamic batching
  • Optimized transformers code for inference using Flash Attention, Candle and cuBLASLt
  • Safetensors weight loading
  • Production ready (distributed tracing with Open Telemetry, Prometheus metrics)

img

Requirements

[ ]

Imports

[3]

Config

DATASET_IN is where your text data is DATASET_OUT is where your embeddings will be stored

Note I used 5 for the MAX_WORKERS since jina-embeddings-v2 are quite memory hungry.

[4]

Inference Endpoints offers a number of GPUs that you can choose from. Check the documentation for GPU and alternative accelerators for information.

[!TIP] You may need to email us for access to some architectures.

ProviderInstance TypeInstance SizeHourly rateGPUsMemoryArchitecture
awsnvidia-a10gx1$1124GBNVIDIA A10G
awsnvidia-t4x1$0.5114GBNVIDIA T4
awsnvidia-t4x4$3456GBNVIDIA T4
gcpnvidia-l4x1$0.8124GBNVIDIA L4
gcpnvidia-l4x4$3.8496GBNVIDIA L4
awsnvidia-a100x1$4180GBNVIDIA A100
awsnvidia-a10gx4$5496GBNVIDIA A10G
awsnvidia-a100x2$82160GBNVIDIA A100
awsnvidia-a100x4$164320GBNVIDIA A100
awsnvidia-a100x8$328640GBNVIDIA A100
gcpnvidia-t4x1$0.5116GBNVIDIA T4
gcpnvidia-l4x1$1124GBNVIDIA L4
gcpnvidia-l4x4$5496GBNVIDIA L4
gcpnvidia-a100x1$6180 GBNVIDIA A100
gcpnvidia-a100x2$122160 GBNVIDIA A100
gcpnvidia-a100x4$244320 GBNVIDIA A100
gcpnvidia-a100x8$488640 GBNVIDIA A100
gcpnvidia-h100x1$12.5180 GBNVIDIA H100
gcpnvidia-h100x2$252160 GBNVIDIA H100
gcpnvidia-h100x4$504320 GBNVIDIA H100
gcpnvidia-h100x8$1008640 GBNVIDIA H100
awsinf2x1$0.75132GBAWS Inferentia2
awsinf2x12$1212384GBAWS Inferentia2
[4]
[5]
VBox(children=(HTML(value='<center> <img\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…

Some users might have payment registered in an organization. This allows you to connect to an organization (that you are a member of) with a payment method.

Leave it blank is you want to use your username.

[6]
What is your Hugging Face 🤗 username or organization? (with an added payment method) ········

Get Dataset

[7]
Downloading readme:   0%|          | 0.00/1.73k [00:00<?, ?B/s]
Dataset({
,    features: ['id', 'content', 'score', 'date_utc', 'title', 'flair', 'poster', 'permalink', 'new', 'updated'],
,    num_rows: 10042
,})
[8]
(100,
, {'id': '10004zw',
,  'content': '[removed]',
,  'score': 1,
,  'date_utc': Timestamp('2022-12-31 18:16:22'),
,  'title': 'To All BORU contributors, Thank you :)',
,  'flair': 'CONCLUDED',
,  'poster': 'IsItAcOnSeQuEnCe',
,  'permalink': '/r/BestofRedditorUpdates/comments/10004zw/to_all_boru_contributors_thank_you/',
,  'new': False,
,  'updated': False})

Inference Endpoints

Create Inference Endpoint

We are going to use the API to create an Inference Endpoint. This should provide a few main benefits:

  • It's convenient (No clicking)
  • It's repeatable (We have the code to run it easily)
  • It's cheaper (No time spent waiting for it to load, and automatically shut it down)
[9]

There are a few design choices here:

  • As discussed before we are using jinaai/jina-embeddings-v2-base-en as our model.
    • For reproducibility we are pinning it to a specific revision.
  • If you are interested in more models, check out the supported list here.
    • Note that most embedding models are based on the BERT architecture.
  • MAX_BATCH_TOKENS is chosen based on our number of workers and the context window of our embedding model.
  • type="protected" utilized the security from Inference Endpoints detailed here.
  • I'm using 1x Nvidia A10 since jina-embeddings-v2 is memory hungry (remember the 8k context length).
  • You should consider further tuning MAX_BATCH_TOKENS and MAX_CONCURRENT_REQUESTS if you have high workloads

Wait until it's running

[10]
CPU times: user 48.1 ms, sys: 15.7 ms, total: 63.8 ms
Wall time: 52.6 s

InferenceEndpoint(name='boru-jina-embeddings-demo-ie', namespace='HF-test-lab', repository='jinaai/jina-embeddings-v2-base-en', status='running', url='https://k7l1xeok1jwnpbx5.us-east-1.aws.endpoints.huggingface.cloud')

When we use endpoint.client.post we get a bytes string back. This is a little tedious because we need to convert this to an np.array, but it's just a couple quick lines in python.

[12]
array([-0.05630935, -0.03560849,  0.02789049,  0.02792823, -0.02800371,
,       -0.01530391, -0.01863454, -0.0077982 ,  0.05374297,  0.03672185,
,       -0.06114018, -0.06880157, -0.0093503 , -0.03174005, -0.03206085,
,        0.0610647 ,  0.02243694,  0.03217408,  0.04181686,  0.00248854])

You may have inputs that exceed the context. In such scenarios, it's up to you to handle them. In my case, I'd like to truncate rather than have an error. Let's test that it works.

[13]
The length of the embedding_input is: 300000

array([-0.03088215, -0.0351537 ,  0.05749275,  0.00983467,  0.02108356,
,        0.04539965,  0.06107162, -0.02536954,  0.03887688,  0.01998681,
,       -0.05391388,  0.01529677, -0.1279156 ,  0.01653782, -0.01940958,
,        0.0367411 ,  0.0031748 ,  0.04716022, -0.00713609, -0.00155313])

Get Embeddings

Here I send a document, update it with the embedding, and return it. This happens in parallel with MAX_WORKERS.

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[15]
  0%|          | 0/100 [00:00<?, ?it/s]
Embeddings = 100 documents = 100
0 min 21.33 sec

Pause Inference Endpoint

Now that we have finished, let's pause the endpoint so we don't incur any extra charges, this will also allow us to analyze the cost.

[16]
Endpoint Status: paused

Push updated dataset to Hub

We now have our documents updated with the embeddings we wanted. First we need to convert it back to a Dataset format. I find it easiest to go from list of dicts -> pd.DataFrame -> Dataset

[17]

I'm uploading it to the user's account by default (as opposed to uploading to an organization) but feel free to push to wherever you want by setting the user in the repo_id or in the config by setting DATASET_OUT

[18]
Pushing dataset shards to the dataset hub:   0%|          | 0/1 [00:00<?, ?it/s]
Downloading metadata:   0%|          | 0.00/823 [00:00<?, ?B/s]
[19]
Dataset is at https://huggingface.co/datasets/derek-thomas/processed-subset-bestofredditorupdates

Analyze Usage

  1. Go to your dashboard_url printed below
  2. Click on the Usage & Cost tab
  3. See how much you have spent
[20]
https://ui.endpoints.huggingface.co/HF-test-lab/endpoints/boru-jina-embeddings-demo-ie
[21]
Hit enter to continue with the notebook 

''

We can see that it only took $0.04 to pay for this!

Delete Endpoint

Now that we are done, we don't need our endpoint anymore. We can delete our endpoint programmatically.

Cost

[22]
Endpoint deleted successfully