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openapi: 3.0.0
info:
title: API
version: 1.0.0
servers:
- url: https://example.com
paths:
/foo:
post:
requestBody:
directive @cacheControl(
maxAge: Int
scope: CacheControlScope
) on FIELD_DEFINITION | OBJECT | INTERFACE
enum CacheControlScope {
PUBLIC
PRIVATE
}
type Continent {
directive @auth(
requires: String,
) on FIELD_DEFINITION
type Query {
getPerson(id: ID!): Person! @auth(requires: "policy-id")
allPersons(last: Int): [Person!]!
allPosts(last: Int): [Post!]!
search(text: String): [SearchResult]
searchID(text: String): [PlainID]
<?xml version="1.0" encoding="UTF-8"?>
<Response>
<Record maxLength="120" timeout="15" transcribe="true" />
</Response>
# -*- coding: utf-8 -*-
"""
Simple example using LSTM recurrent neural network to classify IMDB
sentiment dataset.
References:
- Long Short Term Memory, Sepp Hochreiter & Jurgen Schmidhuber, Neural
Computation 9(8): 1735-1780, 1997.
- Andrew L. Maas, Raymond E. Daly, Peter T. Pham, Dan Huang, Andrew Y. Ng,
and Christopher Potts. (2011). Learning Word Vectors for Sentiment
{
"type": "chat",
"id": "MX0A0JAZ0N",
"visitor_name": "Greg",
"visitor_id": "S1386170914.aecb35ee1b",
"visitor_ip": "4.49.114.135",
"agents": [{
"display_name": "Szymon",
"email": "s.golyski@livechatinc.com"
}],
{
"type": "chat",
"id": "MX0A0JAZ0N",
"visitor_name": "Greg",
"visitor_id": "S1386170914.aecb35ee1b",
"visitor_ip": "4.49.114.135",
"agents": [{
"display_name": "Szymon",
"email": "s.golyski@livechatinc.com"
}],
{
"size":"10",
"query":{
"query_string":{
"default_operator":"and",
"query":"test",
"phrase_slop":1,
"analyze_wildcard":true
}
},
{
"size": 0,
"query": {
"filtered": {
"query": {
"match_all": {}
},
"filter": {
"and": [
{
@morus12
morus12 / es_adjust_interval_bug.sh
Created November 4, 2013 12:30
Timezone adjust
curl -XPUT 'http://localhost:9200/twitter/tweet/1' -d '{
"user" : "kimchy",
"post_date" : "2013-10-05T14:12:12Z",
"message" : "trying out Elastic Search 1"
}'
curl -XPUT 'http://localhost:9200/twitter/tweet/2' -d '{
"user" : "kimchy",
"post_date" : "2013-10-06T14:12:12Z",