Table of documentation contents

Contextionary Guide

Weaviate has a contextionary endpoint which you can call to see how it indexed words and concepts.

Index

Basics

  • You can use this functionality to see how Weaviate functions under the hood.
  • All contextionary functionality is consolidated inside Weaviate itself, which means that in practice you will probably not need this end-point often.
  • The more training data is available to create the contextionary the better, and the latest versions are trained on the following corpus-sizes;
    • English (8.7G en-corpus.txt)
    • German (3.8G de-corpus.txt)
    • Dutch (1.1G nl-corpus.txt)

Find concepts

To find a concept you can run the following request.

$ curl http://localhost:8080/v1/c11y/words/{CamelCasedWords}

Examples;

$ curl http://localhost:8080/v1/c11y/words/apple

or

$ curl http://localhost:8080/v1/c11y/words/appleIphone

which results in (depending on the current C11y) something like;

{
    "concatenatedWord": {
        "concatenatedNearestNeighbors": [
            {
                "distance": 4.885435,
                "word": "apple"
            },
            {
                "distance": 5.6968684,
                "word": "ios"
            },
            {
                "distance": 6.4164195,
                "word": "app"
            },
            {
                "distance": 6.4438953,
                "word": "apps"
            },
            {
                "distance": 6.5316076,
                "word": "android"
            },
            {
                "distance": 7.006211,
                "word": "phones"
            },
            {
                "distance": 7.109171,
                "word": "utilizing"
            },
            {
                "distance": 7.294036,
                "word": "latest"
            },
            {
                "distance": 7.3058743,
                "word": "update"
            },
            {
                "distance": 7.31079,
                "word": "functionality"
            },
            {
                "distance": 7.3121843,
                "word": "downloaded"
            },
            {
                "distance": 7.3809056,
                "word": "newer"
            }
        ],
        "concatenatedVector": [
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        "concatenatedWord": "appleIphone",
        "singleWords": [
            "apple",
            "iphone"
        ]
    },
    "individualWords": [
        {
            "inC11y": true,
            "info": {
                "nearestNeighbors": [
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                        "word": "apple"
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                        "word": "itunes"
                    },
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                        "word": "facebook"
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                        "word": "entirety"
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            "word": "iphone"
        }
    ]
}

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