base on A blazing fast inference solution for text embeddings models <div align="center"> # Text Embeddings Inference <a href="https://github.com/huggingface/text-embeddings-inference"> <img alt="GitHub Repo stars" src="https://img.shields.io/github/stars/huggingface/text-embeddings-inference?style=social"> </a> <a href="https://huggingface.github.io/text-embeddings-inference"> <img alt="Swagger API documentation" src="https://img.shields.io/badge/API-Swagger-informational"> </a> A blazing fast inference solution for text embeddings models. Benchmark for [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) on an Nvidia A10 with a sequence length of 512 tokens: <p> <img src="assets/bs1-lat.png" width="400" /> <img src="assets/bs1-tp.png" width="400" /> </p> <p> <img src="assets/bs32-lat.png" width="400" /> <img src="assets/bs32-tp.png" width="400" /> </p> </div> ## Table of contents - [Get Started](#get-started) - [Supported Models](#supported-models) - [Docker](#docker) - [Docker Images](#docker-images) - [API Documentation](#api-documentation) - [Using a private or gated model](#using-a-private-or-gated-model) - [Air gapped deployment](#air-gapped-deployment) - [Using Re-rankers models](#using-re-rankers-models) - [Using Sequence Classification models](#using-sequence-classification-models) - [Using SPLADE pooling](#using-splade-pooling) - [Distributed Tracing](#distributed-tracing) - [gRPC](#grpc) - [Local Install](#local-install) - [Docker Build](#docker-build) - [Apple M1/M2 Arm](#apple-m1m2-arm64-architectures) - [Examples](#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](https://github.com/HazyResearch/flash-attention), [Candle](https://github.com/huggingface/candle) and [cuBLASLt](https://docs.nvidia.com/cuda/cublas/#using-the-cublaslt-api) * [Safetensors](https://github.com/huggingface/safetensors) weight loading * Production ready (distributed tracing with Open Telemetry, Prometheus metrics) ## Get Started ### Supported Models #### Text Embeddings Text Embeddings Inference currently supports Nomic, BERT, CamemBERT, XLM-RoBERTa models with absolute positions, JinaBERT model with Alibi positions and Mistral, Alibaba GTE, Qwen2 models with Rope positions, and MPNet. Below are some examples of the currently supported models: | MTEB Rank | Model Size | Model Type | Model ID | |-----------|---------------------|-------------|--------------------------------------------------------------------------------------------------| | 1 | 7B (Very Expensive) | Mistral | [Salesforce/SFR-Embedding-2_R](https://hf.co/Salesforce/SFR-Embedding-2_R) | | 2 | 7B (Very Expensive) | Qwen2 | [Alibaba-NLP/gte-Qwen2-7B-instruct](https://hf.co/Alibaba-NLP/gte-Qwen2-7B-instruct) | | 9 | 1.5B (Expensive) | Qwen2 | [Alibaba-NLP/gte-Qwen2-1.5B-instruct](https://hf.co/Alibaba-NLP/gte-Qwen2-1.5B-instruct) | | 15 | 0.4B | Alibaba GTE | [Alibaba-NLP/gte-large-en-v1.5](https://hf.co/Alibaba-NLP/gte-large-en-v1.5) | | 20 | 0.3B | Bert | [WhereIsAI/UAE-Large-V1](https://hf.co/WhereIsAI/UAE-Large-V1) | | 24 | 0.5B | XLM-RoBERTa | [intfloat/multilingual-e5-large-instruct](https://hf.co/intfloat/multilingual-e5-large-instruct) | | N/A | 0.1B | NomicBert | [nomic-ai/nomic-embed-text-v1](https://hf.co/nomic-ai/nomic-embed-text-v1) | | N/A | 0.1B | NomicBert | [nomic-ai/nomic-embed-text-v1.5](https://hf.co/nomic-ai/nomic-embed-text-v1.5) | | N/A | 0.1B | JinaBERT | [jinaai/jina-embeddings-v2-base-en](https://hf.co/jinaai/jina-embeddings-v2-base-en) | | N/A | 0.1B | JinaBERT | [jinaai/jina-embeddings-v2-base-code](https://hf.co/jinaai/jina-embeddings-v2-base-code) | | N/A | 0.1B | MPNet | [sentence-transformers/all-mpnet-base-v2](https://hf.co/sentence-transformers/all-mpnet-base-v2) | To explore the list of best performing text embeddings models, visit the [Massive Text Embedding Benchmark (MTEB) Leaderboard](https://huggingface.co/spaces/mteb/leaderboard). #### Sequence Classification and Re-Ranking Text Embeddings Inference currently supports CamemBERT, and XLM-RoBERTa Sequence Classification models with absolute positions. Below are some examples of the currently supported models: | Task | Model Type | Model ID | |--------------------|-------------|-----------------------------------------------------------------------------------------------------------------| | Re-Ranking | XLM-RoBERTa | [BAAI/bge-reranker-large](https://huggingface.co/BAAI/bge-reranker-large) | | Re-Ranking | XLM-RoBERTa | [BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base) | | Re-Ranking | GTE | [Alibaba-NLP/gte-multilingual-reranker-base](https://huggingface.co/Alibaba-NLP/gte-multilingual-reranker-base) | | Sentiment Analysis | RoBERTa | [SamLowe/roberta-base-go_emotions](https://huggingface.co/SamLowe/roberta-base-go_emotions) | ### Docker ```shell model=BAAI/bge-large-en-v1.5 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:1.6 --model-id $model ``` And then you can make requests like ```bash 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](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html). NVIDIA drivers on your machine need to be compatible with CUDA version 12.2 or higher. To see all options to serve your models: ```shell 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 - last-token: Select the last token as embedding --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] --auto-truncate Automatically truncate inputs that are longer than the maximum supported size Unused for gRPC servers [env: AUTO_TRUNCATE=] --default-prompt-name <DEFAULT_PROMPT_NAME> The name of the prompt that should be used by default for encoding. If not set, no prompt will be applied. Must be a key in the `sentence-transformers` configuration `prompts` dictionary. For example if ``default_prompt_name`` is "query" and the ``prompts`` is {"query": "query: ", ...}, then the sentence "What is the capital of France?" will be encoded as "query: What is the capital of France?" because the prompt text will be prepended before any text to encode. The argument '--default-prompt-name <DEFAULT_PROMPT_NAME>' cannot be used with '--default-prompt <DEFAULT_PROMPT>` [env: DEFAULT_PROMPT_NAME=] --default-prompt <DEFAULT_PROMPT> The prompt that should be used by default for encoding. If not set, no prompt will be applied. For example if ``default_prompt`` is "query: " then the sentence "What is the capital of France?" will be encoded as "query: What is the capital of France?" because the prompt text will be prepended before any text to encode. The argument '--default-prompt <DEFAULT_PROMPT>' cannot be used with '--default-prompt-name <DEFAULT_PROMPT_NAME>` [env: DEFAULT_PROMPT=] --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=] --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=] --otlp-service-name <OTLP_SERVICE_NAME> The service name for opentelemetry. e.g. `text-embeddings-inference.server` [env: OTLP_SERVICE_NAME=] [default: text-embeddings-inference.server] --cors-allow-origin <CORS_ALLOW_ORIGIN> Unused for gRPC servers [env: CORS_ALLOW_ORIGIN=] ``` ### Docker Images Text Embeddings Inference ships with multiple Docker images that you can use to target a specific backend: | Architecture | Image | |-------------------------------------|-------------------------------------------------------------------------| | CPU | ghcr.io/huggingface/text-embeddings-inference:cpu-1.6 | | Volta | NOT SUPPORTED | | Turing (T4, RTX 2000 series, ...) | ghcr.io/huggingface/text-embeddings-inference:turing-1.6 (experimental) | | Ampere 80 (A100, A30) | ghcr.io/huggingface/text-embeddings-inference:1.6 | | Ampere 86 (A10, A40, ...) | ghcr.io/huggingface/text-embeddings-inference:86-1.6 | | Ada Lovelace (RTX 4000 series, ...) | ghcr.io/huggingface/text-embeddings-inference:89-1.6 | | Hopper (H100) | ghcr.io/huggingface/text-embeddings-inference:hopper-1.6 (experimental) | **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](https://huggingface.github.io/text-embeddings-inference). ### Using a private or gated model You have the option to utilize the `HF_API_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 `HF_API_TOKEN=<your cli READ token>` or with Docker: ```shell 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 HF_API_TOKEN=$token -p 8080:80 -v $volume:/data --pull always ghcr.io/huggingface/text-embeddings-inference:1.6 --model-id $model ``` ### Air gapped deployment To deploy Text Embeddings Inference in an air-gapped environment, first download the weights and then mount them inside the container using a volume. For example: ```shell # (Optional) create a `models` directory mkdir models cd models # Make sure you have git-lfs installed (https://git-lfs.com) git lfs install git clone https://huggingface.co/Alibaba-NLP/gte-base-en-v1.5 # Set the models directory as the volume path volume=$PWD # Mount the models directory inside the container with a volume and set the model ID docker run --gpus all -p 8080:80 -v $volume:/data --pull always ghcr.io/huggingface/text-embeddings-inference:1.6 --model-id /data/gte-base-en-v1.5 ``` ### Using Re-rankers models `text-embeddings-inference` v0.4.0 added support for CamemBERT, RoBERTa, XLM-RoBERTa, and GTE Sequence Classification models. Re-rankers models are Sequence Classification cross-encoders models with a single class that scores the similarity between a query and a text. See [this blogpost](https://blog.llamaindex.ai/boosting-rag-picking-the-best-embedding-reranker-models-42d079022e83) by the LlamaIndex team to understand how you can use re-rankers models in your RAG pipeline to improve downstream performance. ```shell model=BAAI/bge-reranker-large volume=$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:1.6 --model-id $model ``` And then you can rank the similarity between a query and a list of texts with: ```bash curl 127.0.0.1:8080/rerank \ -X POST \ -d '{"query": "What is Deep Learning?", "texts": ["Deep Learning is not...", "Deep learning is..."]}' \ -H 'Content-Type: application/json' ``` ### Using Sequence Classification models You can also use classic Sequence Classification models like `SamLowe/roberta-base-go_emotions`: ```shell model=SamLowe/roberta-base-go_emotions volume=$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:1.6 --model-id $model ``` Once you have deployed the model you can use the `predict` endpoint to get the emotions most associated with an input: ```bash curl 127.0.0.1:8080/predict \ -X POST \ -d '{"inputs":"I like you."}' \ -H 'Content-Type: application/json' ``` ### Using SPLADE pooling You can choose to activate SPLADE pooling for Bert and Distilbert MaskedLM architectures: ```shell model=naver/efficient-splade-VI-BT-large-query volume=$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:1.6 --model-id $model --pooling splade ``` Once you have deployed the model you can use the `/embed_sparse` endpoint to get the sparse embedding: ```bash curl 127.0.0.1:8080/embed_sparse \ -X POST \ -d '{"inputs":"I like you."}' \ -H 'Content-Type: application/json' ``` ### Distributed Tracing `text-embeddings-inference` is instrumented with distributed tracing using OpenTelemetry. You can use this feature by setting the address to an OTLP collector with the `--otlp-endpoint` argument. ### gRPC `text-embeddings-inference` offers a gRPC API as an alternative to the default HTTP API for high performance deployments. The API protobuf definition can be found [here](https://github.com/huggingface/text-embeddings-inference/blob/main/proto/tei.proto). You can use the gRPC API by adding the `-grpc` tag to any TEI Docker image. For example: ```shell model=BAAI/bge-large-en-v1.5 volume=$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:1.6-grpc --model-id $model ``` ```shell 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](https://rustup.rs/): ```shell curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh ``` Then run: ```shell # 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: ```shell model=BAAI/bge-large-en-v1.5 text-embeddings-router --model-id $model --port 8080 ``` **Note:** on some machines, you may also need the OpenSSL libraries and gcc. On Linux machines, run: ```shell sudo apt-get install libssl-dev gcc -y ``` ### Cuda GPUs with Cuda compute capabilities < 7.5 are not supported (V100, Titan V, GTX 1000 series, ...). Make sure you have Cuda and the nvidia drivers installed. NVIDIA drivers on your device need to be compatible with CUDA version 12.2 or higher. You also need to add the nvidia binaries to your path: ```shell export PATH=$PATH:/usr/local/cuda/bin ``` Then run: ```shell # 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: ```shell model=BAAI/bge-large-en-v1.5 text-embeddings-router --model-id $model --port 8080 ``` ## Docker build You can build the CPU container with: ```shell docker build . ``` To build the Cuda containers, you need to know the compute cap of the GPU you will be using at runtime. Then you can build the container with: ```shell # Example for Turing (T4, RTX 2000 series, ...) runtime_compute_cap=75 # Example for A100 runtime_compute_cap=80 # Example for A10 runtime_compute_cap=86 # Example for Ada Lovelace (RTX 4000 series, ...) runtime_compute_cap=89 # Example for H100 runtime_compute_cap=90 docker build . -f Dockerfile-cuda --build-arg CUDA_COMPUTE_CAP=$runtime_compute_cap ``` ### Apple M1/M2 arm64 architectures #### DISCLAIMER As explained here [MPS-Ready, ARM64 Docker Image](https://github.com/pytorch/pytorch/issues/81224), 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 --platform=linux/arm64 ``` ## Examples - [Set up an Inference Endpoint with TEI](https://huggingface.co/learn/cookbook/automatic_embedding_tei_inference_endpoints) - [RAG containers with TEI](https://github.com/plaggy/rag-containers) ", Assign "at most 3 tags" to the expected json: {"id":"3586","tags":[]} "only from the tags list I provide: [{"id":77,"name":"3d"},{"id":89,"name":"agent"},{"id":17,"name":"ai"},{"id":54,"name":"algorithm"},{"id":24,"name":"api"},{"id":44,"name":"authentication"},{"id":3,"name":"aws"},{"id":27,"name":"backend"},{"id":60,"name":"benchmark"},{"id":72,"name":"best-practices"},{"id":39,"name":"bitcoin"},{"id":37,"name":"blockchain"},{"id":1,"name":"blog"},{"id":45,"name":"bundler"},{"id":58,"name":"cache"},{"id":21,"name":"chat"},{"id":49,"name":"cicd"},{"id":4,"name":"cli"},{"id":64,"name":"cloud-native"},{"id":48,"name":"cms"},{"id":61,"name":"compiler"},{"id":68,"name":"containerization"},{"id":92,"name":"crm"},{"id":34,"name":"data"},{"id":47,"name":"database"},{"id":8,"name":"declarative-gui "},{"id":9,"name":"deploy-tool"},{"id":53,"name":"desktop-app"},{"id":6,"name":"dev-exp-lib"},{"id":59,"name":"dev-tool"},{"id":13,"name":"ecommerce"},{"id":26,"name":"editor"},{"id":66,"name":"emulator"},{"id":62,"name":"filesystem"},{"id":80,"name":"finance"},{"id":15,"name":"firmware"},{"id":73,"name":"for-fun"},{"id":2,"name":"framework"},{"id":11,"name":"frontend"},{"id":22,"name":"game"},{"id":81,"name":"game-engine "},{"id":23,"name":"graphql"},{"id":84,"name":"gui"},{"id":91,"name":"http"},{"id":5,"name":"http-client"},{"id":51,"name":"iac"},{"id":30,"name":"ide"},{"id":78,"name":"iot"},{"id":40,"name":"json"},{"id":83,"name":"julian"},{"id":38,"name":"k8s"},{"id":31,"name":"language"},{"id":10,"name":"learning-resource"},{"id":33,"name":"lib"},{"id":41,"name":"linter"},{"id":28,"name":"lms"},{"id":16,"name":"logging"},{"id":76,"name":"low-code"},{"id":90,"name":"message-queue"},{"id":42,"name":"mobile-app"},{"id":18,"name":"monitoring"},{"id":36,"name":"networking"},{"id":7,"name":"node-version"},{"id":55,"name":"nosql"},{"id":57,"name":"observability"},{"id":46,"name":"orm"},{"id":52,"name":"os"},{"id":14,"name":"parser"},{"id":74,"name":"react"},{"id":82,"name":"real-time"},{"id":56,"name":"robot"},{"id":65,"name":"runtime"},{"id":32,"name":"sdk"},{"id":71,"name":"search"},{"id":63,"name":"secrets"},{"id":25,"name":"security"},{"id":85,"name":"server"},{"id":86,"name":"serverless"},{"id":70,"name":"storage"},{"id":75,"name":"system-design"},{"id":79,"name":"terminal"},{"id":29,"name":"testing"},{"id":12,"name":"ui"},{"id":50,"name":"ux"},{"id":88,"name":"video"},{"id":20,"name":"web-app"},{"id":35,"name":"web-server"},{"id":43,"name":"webassembly"},{"id":69,"name":"workflow"},{"id":87,"name":"yaml"}]" returns me the "expected json"