Hybrid search

Overview

This page shows you how to perform hybrid searches.

Weaviate client APIs are transitioning from the "class" model to a "collection" model. The new Python Client (v4) is the first client to use the new model. Expect to see differences in terminology, descriptions, and output formats during the transition period.

Basic hybrid search

To use hybrid search, you must provide a search string.

This example uses default settings to search. It matches objects in multiple ways.

• If the object contains the keyword food anywhere
• If the object's vector is similar to the vector for food

Hybrid search ranks the results using a combination of the bm25 search ranking and the vector search ranking. The query returns the top three results.

Python (v4)

jeopardy = client.collections.get("JeopardyQuestion")
response = jeopardy.query.hybrid(
    query="food",
    limit=3
)

for o in response.objects:
    print(json.dumps(o.properties, indent=2))

Python (v3)

response = (
    client.query
    .get("JeopardyQuestion", ["question", "answer"])
    .with_hybrid(
        query="food"
    )
    .with_limit(3)
    .do()
)

print(json.dumps(response, indent=2))

JavaScript/TypeScript

let result;

result = await client.graphql
  .get()
  .withClassName('JeopardyQuestion')
  .withHybrid({
    query: 'food',
  })
  .withLimit(3)
  .withFields('question answer')
  .do();

console.log(JSON.stringify(result, null, 2));

GraphQL

{
  Get {
    JeopardyQuestion(
      limit: 3
      hybrid: {
        query: "food"
      }
    ) {
      question
      answer
    }
  }
}

Example response

It should produce a response like the one below:

{
  "data": {
    "Get": {
      "JeopardyQuestion": [
        {
          "answer": "a closer grocer",
          "question": "A nearer food merchant"
        },
        {
          "answer": "Famine",
          "question": "From the Latin for \"hunger\", it's a period when food is extremely scarce"
        },
        {
          "answer": "Tofu",
          "question": "A popular health food, this soybean curd is used to make a variety of dishes & an ice cream substitute"
        }
      ]
    }
  }
}

Explain the search results

To understand why particular objects are returned, use the object sub-properties to explain the results.

To retrieve the sub-properties with one of the legacy clients, use the _additional property to specify score and explainScore. The new Python client returns the information as metadata.

Python (v4)

import weaviate.classes as wvc

jeopardy = client.collections.get("JeopardyQuestion")
response = jeopardy.query.hybrid(
    query="food",
    alpha=0.5,
    return_metadata=wvc.MetadataQuery(score=True),
    limit=3
)

for o in response.objects:
    print(json.dumps(o.properties, indent=2))
    print(o.metadata.explain_score, o.metadata.score)

Python (v3)

response = (
    client.query
    .get("JeopardyQuestion", ["question", "answer"])
    .with_hybrid(
        query="food",
    )
    .with_additional(["score", "explainScore"])
    .with_limit(3)
    .do()
)

print(json.dumps(response, indent=2))

JavaScript/TypeScript

let result;

result = await client.graphql
  .get()
  .withClassName('JeopardyQuestion')
  .withHybrid({
    query: 'food',
  })
  .withFields('question answer _additional { score explainScore }')
  .withLimit(3)
  .do();

console.log(JSON.stringify(result, null, 2));

GraphQL

{
  Get {
    JeopardyQuestion(
      limit: 3
      hybrid: {
        query: "food"
      }
    ) {
      question
      answer
      _additional {
        score
        explainScore
      }
    }
  }
}

Example response

It should produce a response like the one below:

{
  "data": {
    "Get": {
      "JeopardyQuestion": [
        {
          "_additional": {
            "explainScore": "(bm25)\n(hybrid) Document df958a90-c3ad-5fde-9122-cd777c22da6c contributed 0.003968253968253968 to the score\n(hybrid) Document df958a90-c3ad-5fde-9122-cd777c22da6c contributed 0.012295081967213115 to the score",
            "score": "0.016263336"
          },
          "answer": "a closer grocer",
          "question": "A nearer food merchant"
        },
        {
          "_additional": {
            "explainScore": "(vector) [0.0223698 -0.02752683 -0.0061537363 0.0023812135 -0.00036100898 -0.0078375945 -0.018505432 -0.037500713 -0.0042215516 -0.012620432]...  \n(hybrid) Document ec776112-e651-519d-afd1-b48e6237bbcb contributed 0.012096774193548387 to the score",
            "score": "0.012096774"
          },
          "answer": "Famine",
          "question": "From the Latin for \"hunger\", it's a period when food is extremely scarce"
        },
        {
          "_additional": {
            "explainScore": "(vector) [0.0223698 -0.02752683 -0.0061537363 0.0023812135 -0.00036100898 -0.0078375945 -0.018505432 -0.037500713 -0.0042215516 -0.012620432]...  \n(hybrid) Document 98807640-cd16-507d-86a1-801902d784de contributed 0.011904761904761904 to the score",
            "score": "0.011904762"
          },
          "answer": "Tofu",
          "question": "A popular health food, this soybean curd is used to make a variety of dishes & an ice cream substitute"
        }
      ]
    }
  }
}

Limit the results

You can limit the number of results returned by a hybrid search,

• to a fixed number, using the limit: <N> operator
• to the first N "drops" in score, using the autocut operator

autocut can be combined with limit: N, which would limit autocut's input to the first N objects.

Limiting results with limit

Use the limit argument to specify the maximum number of results that should be returned:

Python (v4)

jeopardy = client.collections.get("JeopardyQuestion")
response = jeopardy.query.hybrid(
   query="food",
   limit=3
)

for o in response.objects:
    print(json.dumps(o.properties, indent=2))

Python (v3)

response = (
    client.query
    .get('JeopardyQuestion', ['question', 'answer'])
    .with_hybrid(
      query='safety'
    )
    .with_additional('score')
    .with_limit(3)
    .do()
)

print(json.dumps(response, indent=2))

JavaScript/TypeScript

let result;

result = await client.graphql
  .get()
  .withClassName('JeopardyQuestion')
  .withHybrid({
    query: 'safety',
  })
  .withFields('question answer _additional { score }')
  .withLimit(3)
  .do();

console.log(JSON.stringify(result, null, 2));

GraphQL

{
  Get {
    JeopardyQuestion(
      hybrid: {
        query: "safety"
      }
      limit: 3
    ) {
      question
      answer
      _additional {
        score
      }
    }
  }
}

Limiting results with autocut and auto_limit

Weaviate can also limit results based on discontinuities in the result set. In the legacy client, this filter is called autocut. The filter is called auto_limit in the new client.

The filter looks for discontinuities, or jumps, in the result score. In your query, you specify how many jumps there should be. The query stops returning results after the specified number of jumps.

hybrid search combines a vector search and a keyword (BM25F) search. The scores are different for each type of search so they cannot be compared directly. This means the cut points the filter chooses may not be intuitive.

Autocut can be used as follows:

Python (v4)

jeopardy = client.collections.get("JeopardyQuestion")
response = jeopardy.query.hybrid(
   query="food",
   auto_limit=1
)

for o in response.objects:
    print(json.dumps(o.properties, indent=2))

Python (v3)

response = (
    client.query
    .get('JeopardyQuestion', ['question', 'answer'])
    .with_hybrid(
      query='safety'
    )
    .with_additional('score')
    .with_autocut(1)
    .do()
)

print(json.dumps(response, indent=2))

JavaScript/TypeScript

let result;

result = await client.graphql
  .get()
  .withClassName('JeopardyQuestion')
  .withHybrid({
    query: 'safety',
  })
  .withFields('question answer _additional { score }')
  .withAutocut(1)
  .do();

console.log(JSON.stringify(result, null, 2));

GraphQL

{
  Get {
    JeopardyQuestion(
      hybrid: {
        query: "safety"
      }
      autocut: 1
    ) {
      question
      answer
      _additional {
        score
      }
    }
  }
}

Example response

It should produce a response like the one below:

{
  "data": {
    "Get": {
      "JeopardyQuestion": [
        {
          "answer": "Guards",
          "question": "Life, Security, Shin",
          "_additional": {
            "score": "0.75"
          },
        },
        # ... trimmed for brevity
      ]
    }
  }
}

Weight the keyword and vector results

You can use the alpha argument to add weight to the keyword or vector search results.

• An alpha of 1 is a pure vector search.
• An alpha of 0 is a pure keyword search.

In the legacy clients, the default value for alpha is 0.75. The new client uses a default value of 0.5.

The following example uses an alpha of 0.25 to increase the importance of the keyword search results.

Python (v4)

client.collections.get("JeopardyQuestion")
response = jeopardy.query.hybrid(
    query="food", 
    alpha=0.25,
    limit=3
)

for o in response.objects:
    print(json.dumps(o.properties, indent=2))

Python (v3)

response = (
    client.query
    .get("JeopardyQuestion", ["question", "answer"])
    .with_hybrid(
        query="food",
        alpha=0.25
    )
    .with_limit(3)
    .do()
)

print(json.dumps(response, indent=2))

JavaScript/TypeScript

let result;

result = await client.graphql
  .get()
  .withClassName('JeopardyQuestion')
  .withHybrid({
    query: 'food',
    alpha: 0.25,
  })
  .withLimit(3)
  .withFields('question answer')
  .do();

console.log(JSON.stringify(result, null, 2));

GraphQL

{
  Get {
    JeopardyQuestion(
      limit: 3
      hybrid: {
        query: "food"
        alpha: 0.25
      }
    ) {
      question
      answer
    }
  }
}

Example response

It should produce a response like the one below:

{
  "data": {
    "Get": {
      "JeopardyQuestion": [
        {
          "answer": "a closer grocer",
          "question": "A nearer food merchant"
        },
        {
          "answer": "food stores (supermarkets)",
          "question": "This type of retail store sells more shampoo & makeup than any other"
        },
        {
          "answer": "cake",
          "question": "Devil's food & angel food are types of this dessert"
        }
      ]
    }
  }
}

Combining and ranking results

Added in v1.20

BM25 and vector search results can be combined and ranked in different ways.

Ranked fusion

The rankedFusion algorithm is Weaviate's original hybrid fusion algorithm.

In this algorithm, each object is scored according to its position in the results for that search (vector or keyword). The top-ranked objects in each search get the highest scores. Scores decrease going from top to least ranked. The total score is calculated by adding the rank-based scores from the vector and keyword searches.

Relative score fusion

New in Weaviate version 1.20.

In relativeScoreFusion the vector search and keyword search scores are scaled between 0 and 1. The highest raw score becomes 1 in the scaled scores. The lowest value is assigned 0. The remaining values are ranked between 0 and 1. The total score is a scaled sum of the normalized vector similarity and normalized BM25 scores.

For a discussion of fusion methods, see this blog post

The default fusion method is rankedFusion. The following examples specify relativeScoreFusion.

Python (v4)

import weaviate.classes as wvc

jeopardy = client.collections.get("JeopardyQuestion")
response = jeopardy.query.hybrid(
  query="food",
  fusion_type=wvc.HybridFusion.RELATIVE_SCORE,
  limit=3
)
 
#print(response)
for o in response.objects:
    print(json.dumps(o.properties, indent=2))

Python (v3)

from weaviate.gql.get import HybridFusion

response = (
    client.query
    .get("JeopardyQuestion", ["question", "answer"])
    .with_hybrid(
        query="food",
        fusion_type=HybridFusion.RELATIVE_SCORE
    )
    .with_limit(3)
    .do()
)

print(json.dumps(response, indent=2))

JavaScript/TypeScript

import { FusionType } from 'weaviate-ts-client';

let result;

result = await client.graphql
  .get()
  .withClassName('JeopardyQuestion')
  .withHybrid({
    query: 'food',
    alpha: 0.25,
    fusionType: FusionType.rankedFusion
  })
  .withLimit(3)
  .withFields('question answer')
  .do();

console.log(JSON.stringify(result, null, 2));

GraphQL

{
  Get {
    JeopardyQuestion(
      limit: 3
      hybrid: {
        query: "food"
        fusionType: relativeScoreFusion
      }
    ) {
      question
      answer
    }
  }
}

Example response

It should produce a response like the one below:

{
  "data": {
    "Get": {
      "JeopardyQuestion": [
        {
          "answer": "a closer grocer",
          "question": "A nearer food merchant"
        },
        {
          "answer": "food stores (supermarkets)",
          "question": "This type of retail store sells more shampoo & makeup than any other"
        },
        {
          "answer": "cake",
          "question": "Devil's food & angel food are types of this dessert"
        }
      ]
    }
  }
}

Selected properties only

Starting in v1.19.0, you can specify the object properties for the bm25 portion of the search.

The next example performs a bm25 search for the keyword food in the question property only. It combines the results of the keyword search with the vector search results for food.

Why does this not affect the vector search?

This is not possible as doing so will require the entire database to be re-vectorized and re-indexed with the new vectorization options.

Python (v4)

jeopardy = client.collections.get("JeopardyQuestion")
response = jeopardy.query.hybrid(
    query="food",
    query_properties=["question"],
    alpha=0.25,
    limit=3
)

for o in response.objects:
    print(json.dumps(o.properties, indent=2))

Python (v3)

response = (
    client.query
    .get("JeopardyQuestion", ["question", "answer"])
    .with_hybrid(
        query="food",
        properties=["question"],
        alpha=0.25
    )
    .with_limit(3)
    .do()
)

print(json.dumps(response, indent=2))

JavaScript/TypeScript

let result;

result = await client.graphql
  .get()
  .withClassName('JeopardyQuestion')
  .withHybrid({
    query: 'food',
    properties: ['question'],
    alpha: 0.25,
  })
  .withLimit(3)
  .withFields('question answer')
  .do();

console.log(JSON.stringify(result, null, 2));

GraphQL

{
  Get {
    JeopardyQuestion(
      limit: 3
      hybrid: {
        query: "food"
        properties: ["question"]
        alpha: 0.25
      }
    ) {
      question
      answer
    }
  }
}

Example response

It should produce a response like the one below:

{
  "data": {
    "Get": {
      "JeopardyQuestion": [
        {
          "answer": "a closer grocer",
          "question": "A nearer food merchant"
        },
        {
          "answer": "cake",
          "question": "Devil's food & angel food are types of this dessert"
        },
        {
          "answer": "honey",
          "question": "The primary source of this food is the Apis mellifera"
        }
      ]
    }
  }
}

Weight (boost) searched properties

You can specify weighting of object properties in how they affect the BM25F component of hybrid searches.

This example searches for objects containing the keyword food. The BM25 search is done in the question property and the answer property, with the question property's weighting boosted by 2, and returns the top 3.

Python (v4)

jeopardy = client.collections.get("JeopardyQuestion")
response = jeopardy.query.hybrid(
    query="food",
    query_properties=["question^2", "answer"],
    alpha=0.25,
    limit=3
)

for o in response.objects:
    print(json.dumps(o.properties, indent=2))

Python (v3)

response = (
    client.query
    .get("JeopardyQuestion", ["question", "answer"])
    .with_hybrid(
        query="food",
        properties=["question^2", "answer"],
        alpha=0.25
    )
    .with_limit(3)
    .do()
)

print(json.dumps(response, indent=2))

JavaScript/TypeScript

result = await client.graphql
  .get()
  .withClassName('JeopardyQuestion')
  .withHybrid({
    query: 'food',
    properties: ['question^2', 'answer'],
    alpha: 0.25,
  })
  .withLimit(3)
  .withFields('question answer')
  .do();

console.log(JSON.stringify(result, null, 2));

GraphQL

{
  Get {
    JeopardyQuestion(
      limit: 3
      hybrid: {
        query: "food"
        properties: ["question^2", "answer"]
        alpha: 0.25
      }
    ) {
      question
      answer
    }
  }
}

Example response

It should produce a response like the one below:

{
  "data": {
    "Get": {
      "JeopardyQuestion": [
        {
          "answer": "a closer grocer",
          "question": "A nearer food merchant"
        },
        {
          "answer": "cake",
          "question": "Devil's food & angel food are types of this dessert"
        },
        {
          "answer": "food stores (supermarkets)",
          "question": "This type of retail store sells more shampoo & makeup than any other"
        }
      ]
    }
  }
}

With a custom vector

You can provide your own vector input to the hybrid query. In this scenario, Weaviate will use the query string for the bm25 search and the input vector for the vector search.

This example supplies the vector for "italian food", while using "food" as the query text. Note how the results are skewed towards Italian food.

Python (v4)

query_vector = [0.013085687533020973, -0.00777443777769804, 0.005439540836960077, -0.021052561700344086, -0.02164270170032978, -0.006985447835177183, -0.018974246457219124, -0.025260508060455322, -0.0013630924513563514, -0.03597281128168106, 0.027993107214570045, 0.01635710895061493, -0.02948128432035446, 0.009159981273114681, -0.0026780758053064346, 0.001033544773235917, 0.04051431640982628, 0.001919554895721376, 0.024952609091997147, -0.01960287243127823, 0.0001997531799133867, 0.031405650079250336, 0.021142365410923958, -0.007954045198857784, -0.0008338918560184538, -0.0040572043508291245, 7.381747127510607e-05, -0.019051222130656242, 0.004942412953823805, -0.01888444274663925, 0.028121398761868477, 0.004631306976079941, -0.031559597700834274, -0.003143130801618099, 0.01867917738854885, -0.024208521470427513, 0.0056351847015321255, -0.019333461299538612, -0.0012756941141560674, 0.01737060956656933, 0.020128866657614708, -0.007755194325000048, -0.0071329823695123196, -0.007761608809232712, -0.0074986121617257595, 0.00579554820433259, 0.002782312221825123, 0.01349621918052435, 0.003954571671783924, 0.003170392708852887, 0.017678506672382355, 0.008980373851954937, -0.027454284951090813, -0.004377932287752628, 0.013547535054385662, 0.028506271541118622, -0.011225467547774315, -0.003855146234855056, -8.654634439153597e-05, -0.021770991384983063, -0.004724318161606789, 0.037281379103660583, -0.0543954074382782, 0.01912819594144821, 0.009916898794472218, -0.007806510664522648, 0.0035921495873481035, 0.011757874861359596, -0.004980900324881077, -0.014381427317857742, 0.005952704697847366, 0.009839924052357674, -0.02256639674305916, 0.014561034739017487, 0.01888444274663925, 0.0006859562126919627, 0.0024984683841466904, 0.0033355674240738153, -0.007633317727595568, -0.015330781228840351, 0.02297692745923996, -0.02996879070997238, 0.017473241314291954, 0.01232235599309206, 0.019949259236454964, 0.009769363328814507, -0.038307707756757736, 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jeopardy = client.collections.get("JeopardyQuestion")
response = jeopardy.query.hybrid(
    query="food",
    query_properties=["question^2", "answer"],
    vector=query_vector,
    limit=3
)

for o in response.objects:
    print(json.dumps(o.properties, indent=2))

Python (v3)

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response = (
    client.query
    .get("JeopardyQuestion", ["question", "answer"])
    .with_hybrid(
        query="food",
        vector=vector
    )
    .with_limit(3)
    .do()
)

print(json.dumps(response, indent=2))

JavaScript/TypeScript

let result;

result = await client.graphql
  .get()
  .withClassName('JeopardyQuestion')
  .withHybrid({
    query: 'food',
    alpha: 0.25,
    vector: [0.013085687533020973, -0.00777443777769804, 0.005439540836960077, -0.021052561700344086, -0.02164270170032978, -0.006985447835177183, -0.018974246457219124, -0.025260508060455322, -0.0013630924513563514, -0.03597281128168106, 0.027993107214570045, 0.01635710895061493, -0.02948128432035446, 0.009159981273114681, -0.0026780758053064346, 0.001033544773235917, 0.04051431640982628, 0.001919554895721376, 0.024952609091997147, -0.01960287243127823, 0.0001997531799133867, 0.031405650079250336, 0.021142365410923958, -0.007954045198857784, -0.0008338918560184538, -0.0040572043508291245, 7.381747127510607e-05, -0.019051222130656242, 0.004942412953823805, -0.01888444274663925, 0.028121398761868477, 0.004631306976079941, -0.031559597700834274, -0.003143130801618099, 0.01867917738854885, -0.024208521470427513, 0.0056351847015321255, -0.019333461299538612, -0.0012756941141560674, 0.01737060956656933, 0.020128866657614708, -0.007755194325000048, -0.0071329823695123196, 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