Step 1: copy and paste these type definitions into your index.js file:

Ron Itelman

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Nov 4, 2018

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//JSON-AI type definitions
const typeDefs = gql`
  type Query {
    agents: [Agent]
    concepts: [Concept]
    documents: [Document]
    heroes: [Hero]
    stories: [Story]
  }
  
  type Agent {
    id: String!
    description: String!
    request: AgentRequest
  }type AgentRequest {
    timestamp: String
    url: String
    method: String
    query: String
    data: String
  }type Concept {
    id: String!
    contexts: [ConceptContext]
  }type ConceptContext {
    id: String!
  }type Document {
    id: String!
    url: String
    knowledge: [Knowledge]
  }type Element {
    id: String!
    label: String
    sentences: [Sentence]
  }type Epoch {
    id: String!
    documents: [Document]
    heroes: [Hero]
    agents: [Agent]
  }type Hero {
    id: String!
    firstName: String
    lastName: String
  }type Knowledge {
    id: String!
    concepts: [Concept]
    elements: [Element]
  }type Sentence {
    id: String!
    text: String!
  }type Story {
    id: String!
    epochs: [Epoch]
  }`;

Step 2: Update your resolvers

const resolvers = {
  Query: {
    agents: () => agents,
    concepts: () => concepts,
    documents: () => documents,
    heroes: () => heroes,
    stories: () => stories,
  }
};

Step 3: Declare your GraphQL Object Values (Dummy Data)

let agents = [
  {
    id: "Apollo",
    description: "GraphQL Server",
    request: {
      timestamp: "Nov 3 2018",
      url:"localhost:4000",
      method:"GraphQL",
      query:"/api",
      data: "JSON goes here"
    }
  },
  {
    id: "Synthesizer",
    description: "JSON-AI Server",
    request: {
      timestamp: "Nov 3 2018",
      url:"localhost:8001",
      method:"GraphQL",
      query:"/api",
      data: "JSON goes here"
    }
  }
];let concepts = [
  {
    id: "Board",
    contexts: [
      { id:"Charter School Board" },
      { id: "Corporate Board" }
    ]
  }
];let documents = [
  {
    id:"Machine Teaching",
    url: "https://medium.com/@ron_69694/machine-teaching-knowledge-snippets-98fcc01a4f79",
    knowledge: [
      {
        id:"our first snippet",
        concepts: [
          { id: "JSON-AI" },
          { id: "Machine Teaching Concepts" },
          { id: "Machine Learning Research" }
        ],
        elements: [
          {
            id:"paragraph #",
            label: "Definition 2.1",
            sentences:[ 
              { 
                id: "1", 
                text: "Definition 2.1 (Machine learning research) Machine Learning research aims at making the learner better by improving ML algorithms."
              }, 
              { 
                id: "2", 
                text: "This field covers, for instance, any new variations or breakthroughs in deep learning, unsupervised learning, recurrent networks, convex optimization, and so on."
              }, 
              { 
                id: "3", 
                text: "Conversely, we see version control, concept decomposition, semantic data exploration, expressiveness of the teaching language, interpretability of the model, and productivity as having more in common with programming and human-computer interaction than with machine learning."
              }, 
              { 
                id: "4", 
                text: "These \“machine teaching\” concepts, however, are extraordinarily important to any practitioners of machine learning."
              }, 
              { 
                id: "4", 
                text: "These \“machine teaching\” concepts, however, are extraordinarily important to any practitioners of machine learning."
              },                                                 
            ]
          }
        ]
      }
    ]
  }
]let heroes = [
  {
    id: "1242",
    firstName: "Bilbo",
    lastName: "Baggins"
  }
]let stories = [
  {
    id: "_origin",
    epochs: [
      {
        id:"epoch 1",
        heroes: heroes,
        documents: documents,
        agents: agents
      }
    ]
  }
]