spaCy API Docker

Ready-to-use Docker images for the spaCy NLP library.

spaCy API Docker is being sponsored by the following tool; please help to support us by taking a look and signing up to a free trial

Features

Use the awesome spaCy NLP framework with other programming languages.
Better scaling: One NLP - multiple services.
Build using the official spaCy REST services.
Dependency parsing visualisation with displaCy.
Docker images for English, German, Spanish, Italian, Dutch and French.
Automated builds to stay up to date with spaCy.
Current spaCy version: 2.0.16

Please note that this is a completely new API and is incompatible with the previous one. If you still need them, use jgontrum/spacyapi:en-legacy or jgontrum/spacyapi:de-legacy.

Documentation, API- and frontend code based upon spaCy REST services by Explosion AI.

Images

Image	Description
jgontrum/spacyapi:base_v2	Base image for spaCy 2.0, containing no language model
jgontrum/spacyapi:en_v2	English language model, spaCy 2.0
jgontrum/spacyapi:de_v2	German language model, spaCy 2.0
jgontrum/spacyapi:es_v2	Spanish language model, spaCy 2.0
jgontrum/spacyapi:fr_v2	French language model, spaCy 2.0
jgontrum/spacyapi:pt_v2	Portuguese language model, spaCy 2.0
jgontrum/spacyapi:it_v2	Italian language model, spaCy 2.0
jgontrum/spacyapi:nl_v2	Dutch language model, spaCy 2.0
jgontrum/spacyapi:all_v2	Contains EN, DE, ES, PT, NL, IT and FR language models, spaCy 2.0
OLD RELEASES
jgontrum/spacyapi:base	Base image, containing no language model
jgontrum/spacyapi:latest	English language model
jgontrum/spacyapi:en	English language model
jgontrum/spacyapi:de	German language model
jgontrum/spacyapi:es	Spanish language model
jgontrum/spacyapi:fr	French language model
jgontrum/spacyapi:all	Contains EN, DE, ES and FR language models
jgontrum/spacyapi:en-legacy	Old API with English model
jgontrum/spacyapi:de-legacy	Old API with German model

Usage

docker run -p "127.0.0.1:8080:80" jgontrum/spacyapi:en_v2

All models are loaded at start up time. Depending on the model size and server performance, this can take a few minutes.

The displaCy frontend is available at /ui.

Docker Compose

version: '2'

services:
  spacyapi:
    image: jgontrum/spacyapi:en_v2
    ports:
      - "127.0.0.1:8080:80"
    restart: always

Running Tests

In order to run unit tests locally pytest is included.

docker run -it jgontrum/spacyapi:en_v2 app/env/bin/pytest app/displacy_service_tests

Special Cases

The API includes rudimentary support for specifying special cases for your deployment. Currently only basic special cases are supported; for example, in the spaCy parlance:

tokenizer.add_special_case("isn't", [{ORTH: "isn't"}])

They can be supplied in an environment variable corresponding to the desired language model. For example, en_special_cases or en_core_web_lg_special_cases. They are configured as a single comma-delimited string, such as "isn't,doesn't,won't".

Use the following syntax to specify basic special case rules, such as for preserving contractions:

docker run -p "127.0.0.1:8080:80" -e en_special_cases="isn't,doesn't" jgontrum/spacyapi:en_v2

You can also configure this in a .env file if using docker-compose as above.

REST API Documentation

`GET` `/ui/`

displaCy frontend is available here.

`POST` `/dep`

Example request:

{
  "text": "They ate the pizza with anchovies",
  "model": "en",
  "collapse_punctuation": 0,
  "collapse_phrases": 1
}

Name	Type	Description
`text`	string	text to be parsed
`model`	string	identifier string for a model installed on the server
`collapse_punctuation`	boolean	Merge punctuation onto the preceding token?
`collapse_phrases`	boolean	Merge noun chunks and named entities into single tokens?

Example request using the Python Requests library:

import json
import requests

url = "http://localhost:8000/dep"
message_text = "They ate the pizza with anchovies"
headers = {'content-type': 'application/json'}
d = {'text': message_text, 'model': 'en'}

response = requests.post(url, data=json.dumps(d), headers=headers)
r = response.json()

Example response:

{
  "arcs": [
    { "dir": "left", "start": 0, "end": 1, "label": "nsubj" },
    { "dir": "right", "start": 1, "end": 2, "label": "dobj" },
    { "dir": "right", "start": 1, "end": 3, "label": "prep" },
    { "dir": "right", "start": 3, "end": 4, "label": "pobj" },
    { "dir": "left", "start": 2, "end": 3, "label": "prep" }
  ],
  "words": [
    { "tag": "PRP", "text": "They" },
    { "tag": "VBD", "text": "ate" },
    { "tag": "NN", "text": "the pizza" },
    { "tag": "IN", "text": "with" },
    { "tag": "NNS", "text": "anchovies" }
  ]
}

Name	Type	Description
`arcs`	array	data to generate the arrows
`dir`	string	direction of arrow (`"left"` or `"right"`)
`start`	integer	offset of word the arrow starts on
`end`	integer	offset of word the arrow ends on
`label`	string	dependency label
`words`	array	data to generate the words
`tag`	string	part-of-speech tag
`text`	string	token

Curl command:

curl -s localhost:8000/dep -d '{"text":"Pastafarians are smarter than people with Coca Cola bottles.", "model":"en"}'

{
  "arcs": [
    {
      "dir": "left",
      "end": 1,
      "label": "nsubj",
      "start": 0
    },
    {
      "dir": "right",
      "end": 2,
      "label": "acomp",
      "start": 1
    },
    {
      "dir": "right",
      "end": 3,
      "label": "prep",
      "start": 2
    },
    {
      "dir": "right",
      "end": 4,
      "label": "pobj",
      "start": 3
    },
    {
      "dir": "right",
      "end": 5,
      "label": "prep",
      "start": 4
    },
    {
      "dir": "right",
      "end": 6,
      "label": "pobj",
      "start": 5
    }
  ],
  "words": [
    {
      "tag": "NNPS",
      "text": "Pastafarians"
    },
    {
      "tag": "VBP",
      "text": "are"
    },
    {
      "tag": "JJR",
      "text": "smarter"
    },
    {
      "tag": "IN",
      "text": "than"
    },
    {
      "tag": "NNS",
      "text": "people"
    },
    {
      "tag": "IN",
      "text": "with"
    },
    {
      "tag": "NNS",
      "text": "Coca Cola bottles."
    }
  ]
}

`POST` `/ent`

Example request:

{
  "text": "When Sebastian Thrun started working on self-driving cars at Google in 2007, few people outside of the company took him seriously.",
  "model": "en"
}

Name	Type	Description
`text`	string	text to be parsed
`model`	string	identifier string for a model installed on the server

Example request using the Python Requests library:

import json
import requests

url = "http://localhost:8000/ent"
message_text = "When Sebastian Thrun started working on self-driving cars at Google in 2007, few people outside of the company took him seriously."
headers = {'content-type': 'application/json'}
d = {'text': message_text, 'model': 'en'}

response = requests.post(url, data=json.dumps(d), headers=headers)
r = response.json()

Example response:

[
  { "end": 20, "start": 5, "type": "PERSON" },
  { "end": 67, "start": 61, "type": "ORG" },
  { "end": 75, "start": 71, "type": "DATE" }
]

Name	Type	Description
`end`	integer	character offset the entity ends after
`start`	integer	character offset the entity starts on
`type`	string	entity type

curl -s localhost:8000/ent -d '{"text":"Pastafarians are smarter than people with Coca Cola bottles.", "model":"en"}'

[
  {
    "end": 12,
    "start": 0,
    "text": "Pastafarians",
    "type": "NORP"
  },
  {
    "end": 51,
    "start": 42,
    "text": "Coca Cola",
    "type": "ORG"
  }
]

`POST` `/sents`

Example request:

{
  "text": "In 2012 I was a mediocre developer. But today I am at least a bit better.",
  "model": "en"
}

Name	Type	Description
`text`	string	text to be parsed
`model`	string	identifier string for a model installed on the server

Example request using the Python Requests library:

import json
import requests

url = "http://localhost:8000/sents"
message_text = "In 2012 I was a mediocre developer. But today I am at least a bit better."
headers = {'content-type': 'application/json'}
d = {'text': message_text, 'model': 'en'}

response = requests.post(url, data=json.dumps(d), headers=headers)
r = response.json()

Example response:

["In 2012 I was a mediocre developer.", "But today I am at least a bit better."]

`POST` `/sents_dep`

Combination of /sents and /dep, returns sentences and dependency parses

Example request:

{
  "text": "In 2012 I was a mediocre developer. But today I am at least a bit better.",
  "model": "en"
}

Name	Type	Description
`text`	string	text to be parsed
`model`	string	identifier string for a model installed on the server

Example request using the Python Requests library:

import json
import requests

url = "http://localhost:8000/sents_dep"
message_text = "In 2012 I was a mediocre developer. But today I am at least a bit better."
headers = {'content-type': 'application/json'}
d = {'text': message_text, 'model': 'en'}

response = requests.post(url, data=json.dumps(d), headers=headers)
r = response.json()

Example response:

[
  {
    "sentence": "In 2012 I was a mediocre developer.",
    "dep_parse": {
      "arcs": [
        {
          "dir": "left",
          "end": 3,
          "label": "prep",
          "start": 0,
          "text": "In"
        },
        {
          "dir": "right",
          "end": 1,
          "label": "pobj",
          "start": 0,
          "text": "2012"
        },
        {
          "dir": "left",
          "end": 3,
          "label": "nsubj",
          "start": 2,
          "text": "I"
        },
        {
          "dir": "left",
          "end": 6,
          "label": "det",
          "start": 4,
          "text": "a"
        },
        {
          "dir": "left",
          "end": 6,
          "label": "amod",
          "start": 5,
          "text": "mediocre"
        },
        {
          "dir": "right",
          "end": 6,
          "label": "attr",
          "start": 3,
          "text": "developer"
        },
        {
          "dir": "right",
          "end": 7,
          "label": "punct",
          "start": 3,
          "text": "."
        }
      ],
      "words": [
        {
          "tag": "IN",
          "text": "In"
        },
        {
          "tag": "CD",
          "text": "2012"
        },
        {
          "tag": "PRP",
          "text": "I"
        },
        {
          "tag": "VBD",
          "text": "was"
        },
        {
          "tag": "DT",
          "text": "a"
        },
        {
          "tag": "JJ",
          "text": "mediocre"
        },
        {
          "tag": "NN",
          "text": "developer"
        },
        {
          "tag": ".",
          "text": "."
        }
      ]
    }
  },
  {
    "sentence": "But today I am at least a bit better.",
    "dep_parse": {
      "arcs": [
        {
          "dir": "left",
          "end": 11,
          "label": "cc",
          "start": 8,
          "text": "But"
        },
        {
          "dir": "left",
          "end": 11,
          "label": "npadvmod",
          "start": 9,
          "text": "today"
        },
        {
          "dir": "left",
          "end": 11,
          "label": "nsubj",
          "start": 10,
          "text": "I"
        },
        {
          "dir": "left",
          "end": 13,
          "label": "advmod",
          "start": 12,
          "text": "at"
        },
        {
          "dir": "left",
          "end": 15,
          "label": "advmod",
          "start": 13,
          "text": "least"
        },
        {
          "dir": "left",
          "end": 15,
          "label": "det",
          "start": 14,
          "text": "a"
        },
        {
          "dir": "left",
          "end": 16,
          "label": "npadvmod",
          "start": 15,
          "text": "bit"
        },
        {
          "dir": "right",
          "end": 16,
          "label": "acomp",
          "start": 11,
          "text": "better"
        },
        {
          "dir": "right",
          "end": 17,
          "label": "punct",
          "start": 11,
          "text": "."
        }
      ],
      "words": [
        {
          "tag": "CC",
          "text": "But"
        },
        {
          "tag": "NN",
          "text": "today"
        },
        {
          "tag": "PRP",
          "text": "I"
        },
        {
          "tag": "VBP",
          "text": "am"
        },
        {
          "tag": "IN",
          "text": "at"
        },
        {
          "tag": "JJS",
          "text": "least"
        },
        {
          "tag": "DT",
          "text": "a"
        },
        {
          "tag": "NN",
          "text": "bit"
        },
        {
          "tag": "RBR",
          "text": "better"
        },
        {
          "tag": ".",
          "text": "."
        }
      ]
    }
  }
]

`GET` `/models`

List the names of models installed on the server.

Example request:

GET /models

Example response:

["en", "de"]

`GET` `/{model}/schema`

Example request:

GET /en/schema

Name	Type	Description
`model`	string	identifier string for a model installed on the server

Example response:

{
  "dep_types": ["ROOT", "nsubj"],
  "ent_types": ["PERSON", "LOC", "ORG"],
  "pos_types": ["NN", "VBZ", "SP"]
}

`GET` `/version`

Show the used spaCy version.

Example request:

GET /version

Example response:

{
  "spacy": "2.2.4"
}

jgontrum/spacy-api-docker

spaCy API Docker

Features

Images

Usage

Docker Compose

Running Tests

Special Cases

REST API Documentation

GET /ui/

POST /dep

POST /ent

POST /sents

POST /sents_dep

GET /models

GET /{model}/schema

GET /version

`GET` `/ui/`

`POST` `/dep`

`POST` `/ent`

`POST` `/sents`

`POST` `/sents_dep`

`GET` `/models`

`GET` `/{model}/schema`

`GET` `/version`