aiera-inc/spacy-api-docker

spaCy REST API, wrapped in a Docker container.

spaCy API Docker

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

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.

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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

---

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,
    "type": "NORP"
  },
  {
    "end": 51,
    "start": 42,
    "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."]

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": "1.9.0"
}