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, mean]

      --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]

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

          [env: JSON_OUTPUT=]

      --otlp-endpoint <OTLP_ENDPOINT>
          [env: OTLP_ENDPOINT=]

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