Text Embeddings Inference

A blazing fast inference solution for text embeddings models.

Benchmark for BAAI/bge-base-en-v1.5 on an Nvidia A10 with a sequence length of 512 tokens:

Table of contents

• Get Started
  • Supported Models
  • Docker
  • Docker Images
  • API Documentation
  • Using a private or gated model
  • Using Re-rankers models
  • Using Sequence Classification models
  • Using SPLADE pooling
  • Distributed Tracing
  • gRPC
• Local Install
• Docker Build
  • Apple M1/M2 Arm
• Examples

Text Embeddings Inference (TEI) is a toolkit for deploying and serving open source text embeddings and sequence classification models. TEI enables high-performance extraction for the most popular models, including FlagEmbedding, Ember, GTE and E5. TEI implements many features such as:

• No model graph compilation step
• Metal support for local execution on Macs
• 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)

Get Started

Supported Models

Text Embeddings

You can use any JinaBERT model with Alibi or absolute positions or any BERT, CamemBERT, RoBERTa, or XLM-RoBERTa model with absolute positions in text-embeddings-inference.

Support for other model types will be added in the future.

Examples of supported models:

You can explore the list of best performing text embeddings models here.

Sequence Classification and Re-Ranking

text-embeddings-inference v0.4.0 added support for Bert, CamemBERT, RoBERTa and XLM-RoBERTa Sequence Classification models.

Example of supported sequence classification models:

Docker

model=BAAI/bge-large-en-v1.5
revision=refs/pr/5
volume=$PWD/data # share a volume with the Docker container to avoid downloading weights every run

docker run --gpus all -p 8080:80 -v $volume:/data --pull always ghcr.io/huggingface/text-embeddings-inference:1.2 --model-id $model --revision $revision

And then you can make requests like

curl 127.0.0.1:8080/embed \
    -X POST \
    -d '{"inputs":"What is Deep Learning?"}' \
    -H 'Content-Type: application/json'

Note: To use GPUs, you need to install the NVIDIA Container Toolkit. NVIDIA drivers on your machine need to be compatible with CUDA version 12.2 or higher.

To see all options to serve your models:

text-embeddings-router --help

Usage: text-embeddings-router [OPTIONS]

Options:
      --model-id <MODEL_ID>
          The name of the model to load. Can be a MODEL_ID as listed on <https://hf.co/models> like `thenlper/gte-base`.
          Or it can be a local directory containing the necessary files as saved by `save_pretrained(...)` methods of
          transformers

          [env: MODEL_ID=]
          [default: thenlper/gte-base]

      --revision <REVISION>
          The actual revision of the model if you're referring to a model on the hub. You can use a specific commit id
          or a branch like `refs/pr/2`

          [env: REVISION=]

      --tokenization-workers <TOKENIZATION_WORKERS>
          Optionally control the number of tokenizer workers used for payload tokenization, validation and truncation.
          Default to the number of CPU cores on the machine

          [env: TOKENIZATION_WORKERS=]

      --dtype <DTYPE>
          The dtype to be forced upon the model

          [env: DTYPE=]
          [possible values: float16, float32]

      --pooling <POOLING>
          Optionally control the pooling method for embedding models.

          If `pooling` is not set, the pooling configuration will be parsed from the model `1_Pooling/config.json` configuration.

          If `pooling` is set, it will override the model pooling configuration

          [env: POOLING=]

          Possible values:
          - cls:    Select the CLS token as embedding
          - mean:   Apply Mean pooling to the model embeddings
          - splade: Apply SPLADE (Sparse Lexical and Expansion) to the model embeddings. This option is only available if the loaded model is a `ForMaskedLM` Transformer model

      --max-concurrent-requests <MAX_CONCURRENT_REQUESTS>
          The maximum amount of concurrent requests for this particular deployment.
          Having a low limit will refuse clients requests instead of having them wait for too long and is usually good
          to handle backpressure correctly

          [env: MAX_CONCURRENT_REQUESTS=]
          [default: 512]

      --max-batch-tokens <MAX_BATCH_TOKENS>
          **IMPORTANT** This is one critical control to allow maximum usage of the available hardware.

          This represents the total amount of potential tokens within a batch.

          For `max_batch_tokens=1000`, you could fit `10` queries of `total_tokens=100` or a single query of `1000` tokens.

          Overall this number should be the largest possible until the model is compute bound. Since the actual memory
          overhead depends on the model implementation, text-embeddings-inference cannot infer this number automatically.

          [env: MAX_BATCH_TOKENS=]
          [default: 16384]

      --max-batch-requests <MAX_BATCH_REQUESTS>
          Optionally control the maximum number of individual requests in a batch

          [env: MAX_BATCH_REQUESTS=]

      --max-client-batch-size <MAX_CLIENT_BATCH_SIZE>
          Control the maximum number of inputs that a client can send in a single request

          [env: MAX_CLIENT_BATCH_SIZE=]
          [default: 32]

      --hf-api-token <HF_API_TOKEN>
          Your HuggingFace hub token

          [env: HF_API_TOKEN=]

      --hostname <HOSTNAME>
          The IP address to listen on

          [env: HOSTNAME=]
          [default: 0.0.0.0]

  -p, --port <PORT>
          The port to listen on

          [env: PORT=]
          [default: 3000]

      --uds-path <UDS_PATH>
          The name of the unix socket some text-embeddings-inference backends will use as they communicate internally
          with gRPC

          [env: UDS_PATH=]
          [default: /tmp/text-embeddings-inference-server]

      --huggingface-hub-cache <HUGGINGFACE_HUB_CACHE>
          The location of the huggingface hub cache. Used to override the location if you want to provide a mounted disk for instance

          [env: HUGGINGFACE_HUB_CACHE=/data]

      --payload-limit <PAYLOAD_LIMIT>
          Payload size limit in bytes

          Default is 2MB

          [env: PAYLOAD_LIMIT=]
          [default: 2000000]

      --api-key <API_KEY>
          Set an api key for request authorization.

          By default the server responds to every request. With an api key set, the requests must have the Authorization header set with the api key as Bearer token.

          [env: API_KEY=]

      --json-output
          Outputs the logs in JSON format (useful for telemetry)

          [env: JSON_OUTPUT=]

      --otlp-endpoint <OTLP_ENDPOINT>
          The grpc endpoint for opentelemetry. Telemetry is sent to this endpoint as OTLP over gRPC. e.g. `http://localhost:4317`

          [env: OTLP_ENDPOINT=]

      --cors-allow-origin <CORS_ALLOW_ORIGIN>
          [env: CORS_ALLOW_ORIGIN=]

Docker Images

Text Embeddings Inference ships with multiple Docker images that you can use to target a specific backend:

Warning: Flash Attention is turned off by default for the Turing image as it suffers from precision issues. You can turn Flash Attention v1 ON by using the USE_FLASH_ATTENTION=True environment variable.

API documentation

You can consult the OpenAPI documentation of the text-embeddings-inference REST API using the /docs route. The Swagger UI is also available at: https://huggingface.github.io/text-embeddings-inference.

Using a private or gated model

You have the option to utilize the HUGGING_FACE_HUB_TOKEN environment variable for configuring the token employed by text-embeddings-inference. This allows you to gain access to protected resources.

For example:

1. Go to https://huggingface.co/settings/tokens
2. Copy your cli READ token
3. Export HUGGING_FACE_HUB_TOKEN=<your cli READ token>

or with Docker:

model=<your private model>
volume=$PWD/data # share a volume with the Docker container to avoid downloading weights every run
token=<your cli READ token>

docker run --gpus all -e HUGGING_FACE_HUB_TOKEN=$token -p 8080:80 -v $volume:/data --pull always ghcr.io/huggingface/text-embeddings-inference:1.2 --model-id $model

Using Re-rankers models

text-embeddings-inference v0.4.0 added support for CamemBERT, RoBERTa and XLM-RoBERTa Sequence Classification models. Re-rankers models are Sequence Classification cross-encoders models with a single class that scores the similarity between a query and a text.

See this blogpost by the LlamaIndex team to understand how you can use re-rankers models in your RAG pipeline to improve downstream performance.

model=BAAI/bge-reranker-large
revision=refs/pr/4
volume=$PWD/data # share a volume with the Docker container to avoid downloading weights every run

docker run --gpus all -p 8080:80 -v $volume:/data --pull always ghcr.io/huggingface/text-embeddings-inference:1.2 --model-id $model --revision $revision

And then you can rank the similarity between a query and a list of texts with:

curl 127.0.0.1:8080/rerank \
    -X POST \
    -d '{"query":"What is Deep Learning?", "texts": ["Deep Learning is not...", "Deep learning is..."]}' \
    -H 'Content-Type: application/json'

Using Sequence Classification models

You can also use classic Sequence Classification models like SamLowe/roberta-base-go_emotions:

model=SamLowe/roberta-base-go_emotions
volume=$PWD/data # share a volume with the Docker container to avoid downloading weights every run

docker run --gpus all -p 8080:80 -v $volume:/data --pull always ghcr.io/huggingface/text-embeddings-inference:1.2 --model-id $model

Once you have deployed the model you can use the predict endpoint to get the emotions most associated with an input:

curl 127.0.0.1:8080/predict \
    -X POST \
    -d '{"inputs":"I like you."}' \
    -H 'Content-Type: application/json'

Using SPLADE pooling

You can choose to activate SPLADE pooling for Bert and Distilbert MaskedLM architectures:

model=naver/efficient-splade-VI-BT-large-query
volume=$PWD/data # share a volume with the Docker container to avoid downloading weights every run

docker run --gpus all -p 8080:80 -v $volume:/data --pull always ghcr.io/huggingface/text-embeddings-inference:1.2 --model-id $model --pooling splade

Once you have deployed the model you can use the /embed_sparse endpoint to get the sparse embedding:

curl 127.0.0.1:8080/embed_sparse \
    -X POST \
    -d '{"inputs":"I like you."}' \
    -H 'Content-Type: application/json'

Distributed Tracing

text-embeddings-inference is instrumented with distributed tracing using OpenTelemetry. You can use this feature by setting the address to an OTLP collector with the --otlp-endpoint argument.

gRPC

text-embeddings-inference offers a gRPC API as an alternative to the default HTTP API for high performance deployments. The API protobuf definition can be found here.

You can use the gRPC API by adding the -grpc tag to any TEI Docker image. For example:

model=BAAI/bge-large-en-v1.5
revision=refs/pr/5
volume=$PWD/data # share a volume with the Docker container to avoid downloading weights every run

docker run --gpus all -p 8080:80 -v $volume:/data --pull always ghcr.io/huggingface/text-embeddings-inference:1.2-grpc --model-id $model --revision $revision

grpcurl -d '{"inputs": "What is Deep Learning"}' -plaintext 0.0.0.0:8080 tei.v1.Embed/Embed

Local install

CPU

You can also opt to install text-embeddings-inference locally.

First install Rust:

curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh

Then run:

# On x86
cargo install --path router -F mkl
# On M1 or M2
cargo install --path router -F metal

You can now launch Text Embeddings Inference on CPU with:

model=BAAI/bge-large-en-v1.5
revision=refs/pr/5

text-embeddings-router --model-id $model --revision $revision --port 8080

Note: on some machines, you may also need the OpenSSL libraries and gcc. On Linux machines, run:

sudo apt-get install libssl-dev gcc -y

Cuda

GPUs with Cuda compute capabilities < 7.5 are not supported (V100, Titan V, GTX 1000 series, ...).

Make sure you have Cuda and the nvidia drivers installed. NVIDIA drivers on your device need to be compatible with CUDA version 12.2 or higher. You also need to add the nvidia binaries to your path:

export PATH=$PATH:/usr/local/cuda/bin

Then run:

# This can take a while as we need to compile a lot of cuda kernels

# On Turing GPUs (T4, RTX 2000 series ... )
cargo install --path router -F candle-cuda-turing -F http --no-default-features

# On Ampere and Hopper
cargo install --path router -F candle-cuda -F http --no-default-features

You can now launch Text Embeddings Inference on GPU with:

model=BAAI/bge-large-en-v1.5
revision=refs/pr/5

text-embeddings-router --model-id $model --revision $revision --port 8080

Docker build

You can build the CPU container with:

docker build .

To build the Cuda containers, you need to know the compute cap of the GPU you will be using at runtime.

Then you can build the container with:

# Example for Turing (T4, RTX 2000 series, ...)
runtime_compute_cap=75

# Example for A100
runtime_compute_cap=80

# Example for A10
runtime_compute_cap=86

# Example for Ada Lovelace (RTX 4000 series, ...)
runtime_compute_cap=89

# Example for H100
runtime_compute_cap=90

docker build . -f Dockerfile-cuda --build-arg CUDA_COMPUTE_CAP=$runtime_compute_cap

Apple M1/M2 arm64 architectures

DISCLAIMER

As explained here MPS-Ready, ARM64 Docker Image, Metal / MPS is not supported via Docker. As such inference will be CPU bound and most likely pretty slow when using this docker image on an M1/M2 ARM CPU.

docker build . -f Dockerfile-arm64 --platform=linux/arm64

Examples

• Set up an Inference Endpoint with TEI
• RAG containers with TEI