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A look inside queries

Overview​


In this unit, we have looked at various ways to construct a vector search.

We started by learning how to use Get and Aggregate vector search functions, adding search operators such as nearVector, nearObject and nearText, before wrapping up with various filters such as where, limit and offset.

Now let's stop to take a look at how these searches are actually performed, starting with the vector search process.

The vector search process​

Vector search, behind the scenes​

As the name suggests, vector search relies on vectors to perform its operations. When you perform a vector search in Weaviate, regardless of whether nearVector, nearObject or nearText is used, the input is converted to a vector.

The input vector is then compared to the stored vectors in Weaviate to return the most relevant objects.

For queries using the nearVector operator, Weaviate simply takes the provided vector and performs the vector search.

For queries such as nearObject and nearText where the vector is not directly provided, Weaviate obtains the vector using a suitable method.

nearObject​

If the nearObject operator is used, Weaviate retrieves the associated vector for the object, which becomes the input vector.

Let's confirm this by performing a vector search using the nearObject operator, and replicating it with an equivalent nearVector operator.

Here is the nearObject query:

response = client.query.get(
    "JeopardyQuestion",
    ["question", "answer"]
).with_limit(2).with_near_object(
    {"id": "d53fd7ea-35c1-5f8d-a35a-e53511db1a2a"}
).with_additional(
    ["distance", "id"]
).do()

print(json.dumps(response, indent=2))
See the JSON response
{
  "data": {
    "Get": {
      "JeopardyQuestion": [
        {
          "_additional": {
            "distance": 3.5762787e-07,
            "id": "d53fd7ea-35c1-5f8d-a35a-e53511db1a2a"
          },
          "answer": "meerkats",
          "question": "Group of mammals seen <a href=\"http://www.j-archive.com/media/1998-06-01_J_28.jpg\" target=\"_blank\">here</a>:  [like Timon in <i>The Lion King</i>]"
        },
        {
          "_additional": {
            "distance": 0.12663543,
            "id": "9eaf38fe-e7f0-5da3-b703-6b44c49faf7d"
          },
          "answer": "hyena",
          "question": "It's the amused African mammal heard <a href=\"http://www.j-archive.com/media/2006-12-14_J_10.mp3\">here</a>"
        }
      ]
    }
  }
}

Now, try running this nearVector query and compare the results:

To run the nearVector query, you need to provide the full vector. You can view and copy the full vector below.


# Shortened for clarity

meerkat_vector = [-0.024790961,  -0.01860295, ..., -0.008665809,-0.02698336]

This is the query.

response = client.query.get(
    "JeopardyQuestion",
    ["question", "answer"]
).with_limit(2).with_near_vector(
    {"vector": meerkat_vector}
).with_additional(
    ["distance", "id"]
).do()

print(json.dumps(response, indent=2))
See the full `meerkat_vector`

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Explain this query

If you run the second (nearVector) query, you will see that it returns the same results as the nearObject query. The distances are also identical. This is because the vector of object in the nearObject query is identical to the vector you specify in the nearVector query.

nearText​

If the nearText operator is used, Weaviate converts the input text to a vector to use as the input. The specific method would depend on the vectorizer applicable to the relevant class. Depending on the setting, Weaviate may use the text2vec-openai module or the text2vec-transformers module to do this.

Vectorizer setting

We will cover how to set the vectoriser in the next section.

response = client.query.get(
    "JeopardyQuestion",
    ["question", "answer"]
).with_limit(2).with_near_text(
    {"concepts": ["popular 90s comedy"]}
).with_additional(
    ["distance", "id"]
).do()

print(json.dumps(response, indent=2))
See the JSON response
{
  "data": {
    "Get": {
      "JeopardyQuestion": [
        {
          "_additional": {
            "distance": 0.1800943,
            "id": "92710bd6-de3c-5220-a60a-d386b2748e28"
          },
          "answer": "Two Guys and a Girl",
          "question": "In 1999 an ABC sitcom dropped \"a Pizza Place\" from its name, which changed to this"
        },
        {
          "_additional": {
            "distance": 0.18657643,
            "id": "7e7a6572-02bd-529f-8943-38ccd4a2a90b"
          },
          "answer": "Beavis & Butthead of the Class",
          "question": "2 MTV cartoon teens join Howard Heh-Heh-Hesseman's honors program on '80's TV"
        }
      ]
    }
  }
}

Now, try running this nearVector query and compare the results:

response = client.query.get(
    "JeopardyQuestion",
    ["question", "answer"]
).with_limit(2).with_near_vector(
    {"vector": popular_90s_comedy_vector}
).with_additional(
    ["distance", "id"]
).do()

print(json.dumps(response, indent=2))
See the full `popular_90s_comedy_vector`

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Explain this query

When you run the second (nearVector) query, once again the query returns the same objects as the nearText query as well as distances.

In this case, Weaviate vectorizes the input text using the text2vec-openai module, which relies on the OpenAI inference API to convert the input to a vector. The vectorization process is deterministic, meaning that the same input text will always result in the same vector. For the nearVector query, we generated the vector directly using the OpenAI API and used it as the input vector, leading to the same results.

Filtering​

Filters can be applied during the vector search process to further refine the results.

Under the hood, Weaviate implements "pre-filtering". This means that Weaviate applies the filter on the entire database to end up with an "allow list", from which vector search results are returned.

This, combined with efficient filtering methods, allows Weaviate to easily return the right number of results even under challenging conditions, such as when restrictive filters are applied to a large database.

Conceptual diagram (filtering + vector search)​

The conceptual diagram below shows how the vector search process works. When an input is provided containing filters and search operators, Weaviate first performs a pre-filtering step to obtain an "allow list" of objects.

The input is used to determine an input vector, which may involve an additional step, such as retrieving the object's vector with nearObject or vectorizing the input text with nearText. The input vector is then compared to perform a vector search, returning the most relevant objects from the allow list.

Conceptual diagram of how search works

Review​

Review exercise​

  • Replicate a nearObject query with a nearVector query.
  • Replicate a nearText query with a nearVector query.

Key takeaways​

  • nearObject operator retrieves the associated vector for an object, while nearText converts input text to a vector based on a specified vectorizer.
  • You have seen how to create equivalent nearVector queries to nearObject or nearText queries.
  • Weaviate uses "pre-filtering", meaning that filters are applied on the entire database prior to performing vector search.

Questions and feedback​

If you have any questions or feedback, let us know in the user forum.