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Explore

Vector spaces and Explore

The Explore function is disabled where multiple inference (e.g. text2vec-xxx) modules are enabled.

As a result, Explore is not available on Weaviate Cloud Services (WCS) as its clusters are pre-configured with multiple inference modules for AWS, Cohere, Google, OpenAI and so on.

Overview​

Use Explore to perform vector searches across multiple collections. Note that Explore is currently not available in the gRPC API.

Requirements​

For Explore:

  • The Weaviate instance to be configured with a maximum of one vectorizer (e.g. text2vec-transformers, text2vec-openai) module.
  • Each Explore query can only be a vector search using nearText or nearVector.

Explore queries​

Explore structure and syntax​

The Explore function has the following syntax:

{
  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:

import weaviate

client = weaviate.Client("http://localhost:8080")

explore_articles_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
    }
  }
"""

query_result = client.query.raw(explore_articles_query)
print(query_result)

The result might look like this:

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

Search operators​

The nearText and nearVector operators work in Explore as they do in other queries. See search operators for more information.

Filters​

Explore queries can be combined with filters. See filters for more information.

Pagination​

Pagination (i.e. limit with offset) is not possible in Explore queries.

Moving​

Because pagination is not possible in multidimensional storage, we recommend using moveTo and moveAwayFrom if further query refinement is sought. They work as they do in other queries. See search operators#nearText for more information.

Questions and feedback​

If you have any questions or feedback, please let us know on our forum. For example, you can: