Each DeepPavlov model can be easily made available for inference as a REST web service. The general method is:

python -m deeppavlov riseapi <config_path> [-d] [-p <port>] [--https] [--key <SSL key file path>] \
[--cert <SSL certificate file path>]
  • -d: downloads model specific data before starting the service.

  • -p <port>: sets the port to <port>. Overrides default value from deeppavlov/utils/settings/server_config.json.

  • --https: use https instead of http. Overrides default value from deeppavlov/utils/settings/server_config.json.

  • --key <SSL key file path>: path to SSL key file. Overrides default value from deeppavlov/utils/settings/server_config.json.

  • --cert <SSL certificate file path>: path to SSL certificate file. Overrides default value from deeppavlov/utils/settings/server_config.json.

The command will print the used host and port. Default web service properties (host, port, POST request arguments) can be modified via changing deeppavlov/utils/settings/server_config.json file.


Starting from the 1.0.0rc2 model response format in riseapi mode matches Chainer response format. To start model with the old format, give the COMPATIBILITY_MODE environment variable any non-empty value (e.g. COMPATIBILITY_MODE=true python -m deeppavlov riseapi ...). COMPATIBILITY_MODE will be removed in DeepPavlov 1.2.0.

API routes


Send POST request to <host>:<port>/model to infer model. See details at /docs.


Send POST request to <host>:<port>/probe to check if API is working. The server will send a response ["Test passed"] if it is working. Requests to /probe are not logged.


To get model argument and response names send GET request to <host>:<port>/api. Server will return dict with model input and output names.


To interact with the REST API via graphical interface open <host>:<port>/docs in a browser (Swagger UI).


Endpoint to monitor a running service using Prometheus. Metrics:

  • http_requests_count: Counter, tracks number of processed requests. Labels: endpoint, status_code.

  • http_requests_latency_seconds: Histogram, tracks responses latency (only with 200 status code). Labels: endpoint.

  • http_requests_in_progress: Gauge, tracks inprogress requests. Labels: endpoint.

Advanced configuration

By modifying deeppavlov/utils/settings/server_config.json you can change host, port, POST request arguments and other properties of the API service.

Properties from common_defaults section are used by default unless they are overridden by model-specific properties, provided in model_defaults section of the server_config.json. Model-specific properties are bound to the model by server_utils label in metadata section of the model config. Value of server_utils label from model config should match with properties key from model_defaults section of server_config.json.

For example, adding metadata/server_utils key to kbqa/kbqa_cq.json with value KBQA will initiate the search of KBQA tag at model_defaults section of server_config.json. Therefore, if this section is present, all parameters with non empty (i.e. not "", not [] etc.) values stored by this tag will overwrite the parameter values in common_defaults.

If model_args_names parameter of server_config.json is empty string, then model argument names are provided as list from chainer/in section of the model config file, where arguments order corresponds to model API. When inferencing model via REST api, JSON payload keys should match model arguments names from chainer/in section. If model_args_names parameter of server_config.json is list, its values are used as model argument names instead of the list from model config’s chainer/in section. Here are POST request payload examples for some of the library models:


POST request JSON payload example

One argument models

NER model

{“x”:[“Elon Musk launched his cherry Tesla roadster to the Mars orbit”]}

Intent classification model

{“x”:[“I would like to go to a restaurant with Asian cuisine this evening”]}

Automatic spelling correction model


Ranking model

{“x”:[“What is the average cost of life insurance services?”]}

Goal-oriented bot

{“x”:[“Hello, can you help me to find and book a restaurant this evening?”]}

Multiple arguments models

Question Answering model

{“context_raw”:[“After 1765, growing philosophical and political differences strained the relationship between Great Britain and its colonies.”],
 “question_raw”:[“What strained the relationship between Great Britain and its colonies?”]}

REST API Usage Example

To start server with squad_bert model run:

python -m deeppavlov riseapi squad_bert -id

To get response from this model on another terminal run:

curl -X POST -H 'Content-Type: application/json' -d '{
    "context_raw": [
        "All work and no play makes Jack a dull boy.",
        "I used to be an adventurer like you, then I took an arrow in the knee."
    "question_raw": [
        "What makes Jack a dull boy?",
        "Who I used to be?"