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GraphQL - Explore{}

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Explore{} query structure and syntax

The Explore{} function is always defined based on the following principle:

{
Explore (
limit: <Int>, # The maximum amount of objects to return
nearText: { # Either this or 'nearVector' is required
concepts: [<String>]!, # Required - An array of search items. If the text2vec-contextionary is the vectorization module, the concepts should be present in the Contextionary.
certainty: <Float>, # Minimal level of certainty, computed by normalized distance
moveTo: { # Optional - Giving directions to the search
concepts: [<String>]!, # List of search items
force: <Float>! # The force to apply for a particular movement. Must be between 0 (no movement) and 1 (largest possible movement).
},
moveAwayFrom: { # Optional - Giving directions to the search
concepts: [<String>]!, # List of search items
force: <Float>! # The force to apply for a particular movement. Must be between 0 (no movement) and 1 (largest possible movement).
}
},
nearVector: { # Either this or 'nearText' is required
vector: [<Float>]!, # Required - An array of search items, which length should match the vector space
certainty: <Float> # Minimal level of certainty, computed by normalized distance
}
) {
beacon
certainty # certainty value based on a normalized distance calculation
className
}
}

An example query:

{
Explore (
nearVector: {
vector: [-0.36840257,0.13973749,-0.28994447,-0.18607682,0.20019795,0.15541431,-0.42353877,0.30262852,0.2724561,0.07069917,0.4877447,0.038771532,0.64523,-0.15907241,-0.3413626,-0.026682584,-0.63310874,-0.33411884,0.082939014,0.30305764,0.045918174,-0.21439327,-0.5005205,0.6210859,-0.2729049,-0.51221114,0.09680918,0.094923325,-0.15688285,-0.07325482,0.6588305,0.0523736,-0.14173415,-0.27428055,0.25526586,0.057506185,-0.3103442,0.028601522,0.124522656,0.66984487,0.12160647,-0.5090515,-0.540393,-0.39546522,-0.2201204,0.34625968,-0.21068871,0.21132985,0.048714135,0.09043683,0.3176081,-0.056684002,-0.12117501,-0.6591976,-0.26731065,0.42615625,0.33333477,-0.3240578,-0.18771006,0.2328068,-0.17239179,-0.33583146,-0.6556605,-0.10608161,-0.5135395,-0.25123677,-0.23004892,0.7036331,0.04456794,0.41253626,0.27872285,-0.28226635,0.11927197,-0.4677766,0.4343466,-0.17538455,0.10621233,0.95815116,0.23587844,-0.006406698,-0.10512518,-1.1125883,-0.37921682,0.040789194,0.676718,0.3369762,0.040712647,0.580487,0.20063736,-0.021220192,-0.09071747,-0.0023735985,0.30007777,-0.039925132,0.4035474,-0.2518212,-0.17846306,0.12371392,-0.0703354,-0.3752431,-0.652917,0.5952828,1.3426708,-0.08167235,-0.38515738,0.058423538,-0.08100355,-0.192886,0.3745164,-0.23291737,0.33326542,-0.6019264,-0.42822492,-0.6524583,-0.15210791,-0.5073593,0.022548754,-0.058033653,-0.47369233,-0.30890635,0.6338296,0.0017854869,0.1954949,0.99348027,-0.26558784,-0.058124136,1.149388,0.02915948,0.013422121,0.25484946,-0.030017598,-0.23879935,0.053123385,-0.36463016,-0.0024245526,0.1202083,-0.45966506,-0.34140104,-0.08484162,-0.03537422,-0.2817959,0.25044164,-0.5060605,0.1252808,-0.032539487,0.110069446,-0.20679846,-0.46421885,-0.4141739,0.26994973,-0.070687145,0.16862138,-0.20162229,0.22199251,-0.2771402,0.23653336,0.16585203,-0.08286354,-0.15343396,0.23893964,-0.7453282,-0.16549355,-0.1947069,0.46136436,0.22064126,0.28654936,-0.038697664,0.037633028,-0.80988157,0.5094175,-0.0920082,0.25405347,-0.64169943,0.43366328,-0.2999211,-0.4090591,0.11957859,0.00803617,-0.0433745,0.12818244,0.28464508,-0.31760025,0.16558012,-0.33553946,-0.3943465,0.59569097,-0.6524206,0.3683173,-0.60456693,0.2046492,0.46010277,0.24695799,0.2946015,0.11376746,-0.027988048,0.03749422,-0.16577742,0.23407385,-0.0231737,-0.023245076,0.08752677,0.2299883,0.35467404,0.046193745,-0.39828986,0.21079691,0.38396686,-0.0018698421,0.16047359,-0.057517264,-0.203534,0.23438136,-0.84250915,0.22371331,0.0058325706,0.30733636,0.19518353,-0.108008966,0.6509316,0.070131645,-0.24023099,0.28779706,0.2326336,0.07004021,-0.45955566,0.20426086,-0.37472793,-0.049604423,0.4537271,0.6133582,-1.0527759,-0.5472505,0.15193434,0.5296606,-0.11560251,0.07279209,0.40557706,0.2505283,0.24490519,0.017602902,-0.004647707,0.16608049,0.12576887,0.118216865,0.4403996,0.39552462,-0.22196701,-0.061155193,0.03693534,-0.4022908,0.3842317,-0.0831345,0.01930883,0.3446575,-0.2167439,-0.23994556,-0.09370326,-0.3671856,0.044011243,0.017895095,-0.019855855,-0.16416992,0.17858285,0.31287143,0.38368022,-0.006513525,0.45780763,-0.23027879,0.108570844,-0.4449492,-0.035763215,0.03818417,0.040017277,-0.17022872,-0.2622464,0.65610534,0.16720143,0.2515769,-0.23535803,0.62484455,0.16771325,-0.62404263,0.19176348,-0.72786695,0.18485649,-0.30914405,-0.3230534,-0.24064465,0.28841522,0.39792386,0.15618932,0.03928854,0.18277727,-0.101632096,0.1868196,-0.33366352,0.086561844,0.48557812,-0.6198209,-0.07978742]
}
) {
beacon
certainty // only supported if distance==cosine
distance // always supported
className
}
}

🟢 Try out this GraphQL example in the Weaviate Console.

The result might look like this:

{
"data": {
"Explore": [
{
"beacon": "weaviate://localhost/7e9b9ffe-e645-302d-9d94-517670623b35",
"certainty": 0.975523,
"className": "Publication"
}
]
},
"errors": null
}

CamelCase interpretation

Weaviate's vectorization module text2vec-contextionary splits words based on CamelCase. For example, if a user wants to explore for the iPhone (the Apple device) they should use iphone rather than iPhone because the latter will be interpreted as [i, phone].

Explore filter arguments

Concepts

Strings written in the Concepts array are your fuzzy search terms. An array of concepts is required to set in the Explore query, and all words in this array should be present in the Contextionary.

There are three ways to define the concepts array argument in the Explore filter.

  • ["New York Times"] = one vector position is determined based on the occurrences of the words
  • ["New", "York", "Times"] = all concepts have a similar weight.
  • ["New York", "Times"] = a combination of the two above.

A practical example would be: concepts: ["beatles", "John Lennon"]

Distance

You can set a maximum allowed distance, which will be used to determine which data results to return. The interpretation of the value of the distance field depends on the distance metric used.

If the distance metric is cosine you can also use certainty instead of distance. Certainty normalizes the distance in a range of 0..1, where 0 represents a perfect opposite (cosine distance of 2) and 1 represents vectors with an identical angle (cosine distance of 0). Certainty is not available on non-cosine distance metrics.

Moving

Because pagination is not possible in multidimensional storage, you can improve your results with additional explore functions which can move away from semantic concepts or towards semantic concepts. E.g., if you look for the concept 'New York Times' but don't want to find the city New York, you can use the moveAwayFrom{} function by using the words 'New York'. This is also a way to exclude concepts and to deal with negations (not operators in similar query languages). Concepts in the moveAwayFrom{} filter are not per definition excluded from the result, but the resulting concepts are further away from the concepts in this filter.

Additional filters

Explore{} functions can be extended with search filters (both semantic filters as traditional filters). Because the filters work on multiple core functions (like Aggregate{}) there is a specific documentation page dedicated to filters.

More Resources

If you can't find the answer to your question here, please look at the:

  1. Frequently Asked Questions. Or,
  2. Knowledge base of old issues. Or,
  3. For questions: Stackoverflow. Or,
  4. For issues: Github. Or,
  5. Ask your question in the Slack channel: Slack.