Hybrid search

Overview

This page shows you how to perform hybrid searches using Weaviate.

The hybrid parameter uses both the bm25 (i.e. sparse vector) and vector (i.e. dense vector) search algorithms, and combines their outputs to produce results.

The results are determined by a weighted combination of the two search outputs.

Related pages

• API References: Vector search parameters # Hybrid

Prerequisites

If you haven't yet, we recommend going through the Quickstart tutorial first to get the most out of this section.

Basic hybrid search

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

The below example uses default settings, looking for:

• Objects containing the keyword food anywhere in the object, and
• Objects most similar to the vector of food.

It ranks the results using a combination of the bm25 and vector search rankings, and returns the top 3.

Python

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

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

JavaScript/TypeScript

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"
        }
      ]
    }
  }
}

Score / explainScore

The score and explainScore sub-properties aim to explain the outputs. They can be retrieved under the _additional property.

The below example adds the two properties to the list of retrieved properties.

Python

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

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"
        }
      ]
    }
  }
}

Weight keyword vs vector results

You can use the alpha argument to weight the keyword (bm25) or vector search results. An alpha of 1 is for a pure vector search and 0 is for a pure keyword search. The default is 0.75.

The following example uses an alpha of 0.25, favoring keyword search results.

Python

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

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"
        }
      ]
    }
  }
}

Selected properties only

You can specify the object properties for the bm25 portion of the search.

The below example performs a bm25 search for the keyword food in the question property only, and combines it with 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

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

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": "Risotto",
          "question": "From the Italian word for rice, it's a rice dish cooked with broth & often grated cheese"
        },
        {
          "answer": "arrabiata",
          "question": "Italian for \"angry\", it describes a pasta sauce spiced up with plenty of chiles"
        },
        {
          "answer": "Fettucine Alfredo",
          "question": "Ribbon-shaped noodles, sweet butter, cream, parmesan cheese & black pepper make up this pasta dish"
        }
      ]
    }
  }
}

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.

The below example supplies the vector for "italian food", while using "food" as the query text. Note how the results have now skewed towards Italian food.

Python

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, 0.0278134997934103, 0.012264625169336796, -0.007062422577291727, -0.013316611759364605, -0.00465055089443922, 0.013188320212066174, 0.008210627362132072, -0.023862136527895927, 0.006998276803642511, -0.005157300271093845, 0.036024127155542374, -0.0211167074739933, -0.0013109742430970073, -0.0014577071415260434, 0.00858267117291689, -0.004637721460312605, -0.0176656786352396, -0.0035793203860521317, -0.004618478007614613, -0.0015691599110141397, -0.018692007288336754, 0.016536716371774673, -0.012476304545998573, -0.03150828182697296, 0.029789183288812637, 0.01989794336259365, -0.03304777666926384, -0.025234850123524666, 0.0034093346912413836, 0.04154064506292343, 0.013188320212066174, 4.1249832065659575e-06, -0.015279464423656464, -0.01488176267594099, 0.016498230397701263, 0.03025102987885475, -0.015574533492326736, 0.033304356038570404, 0.014445573091506958, 0.018114697188138962, -0.011077932082116604, -0.020719004794955254, -0.008069506846368313, 0.0259789377450943, 0.013079272583127022, 0.014920249581336975, -0.005500479135662317, -0.025119388476014137, 0.017062710598111153, -0.0018938967259600759, -0.003059741575270891, -0.005179751198738813, -0.0017062709666788578, 0.02025715820491314, 0.039077453315258026, -0.01520248968154192, 0.012912494130432606, -0.018730493262410164, 0.022669028490781784, 0.014458402059972286, 0.01916668377816677, -0.013406415469944477, 0.0027758977375924587, 0.02151441015303135, -0.025080900639295578, 0.017999235540628433, -0.01716534234583378, 0.014445573091506958, 0.014868932776153088, -0.008326089009642601, 0.021540068089962006, -0.009820680133998394, 0.0033516038674861193, -0.006863571237772703, 0.0016012326814234257, 0.00046625779941678047, -0.014419914223253727, 0.009442221373319626, 0.021976256743073463, 0.01558736339211464, 0.005356151610612869, 0.013104931451380253, -0.0007625299622304738, -0.0013294160598888993, 0.024580566212534904, -0.011308856308460236, 0.03032800555229187, -0.006594160106033087, 0.023041073232889175, -0.0036466731689870358, 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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

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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  })
  .withLimit(3)
  .withFields('question answer')
  .do();

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

GraphQL

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

Example response

It should produce a response like the one below:

{
  "data": {
    "Get": {
      "JeopardyQuestion": [
        {
          "answer": "Risotto",
          "question": "From the Italian word for rice, it's a rice dish cooked with broth & often grated cheese"
        },
        {
          "answer": "arrabiata",
          "question": "Italian for \"angry\", it describes a pasta sauce spiced up with plenty of chiles"
        },
        {
          "answer": "Fettucine Alfredo",
          "question": "Ribbon-shaped noodles, sweet butter, cream, parmesan cheese & black pepper make up this pasta dish"
        }
      ]
    }
  }
}

Add a conditional (where) filter

You can add a conditional filter to any hybrid search query, which will filter the outputs but not impact the ranking.

The below example performs a hybrid search for food in any field from objects that have the round property of Double Jeopardy!, and returns the top 3.

Python

response = (
    client.query
    .get("JeopardyQuestion", ["question", "answer", "round"])
    .with_hybrid(
        query="food"
    )
    .with_where({
        "path": ["round"],
        "operator": "Equal",
        "valueText": "Double Jeopardy!"
    })
    .with_limit(3)
    .do()
)

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

JavaScript/TypeScript

result = await client.graphql
  .get()
  .withClassName('JeopardyQuestion')
  .withHybrid({
    query: 'food',
  })
  .withWhere({
    path: ['round'],
    operator: 'Equal',
    valueText: 'Double Jeopardy!',
  })
  .withLimit(3)
  .withFields('question answer round')
  .do();

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

GraphQL

{
  Get {
    JeopardyQuestion(
      limit: 3
      hybrid: {
        query: "food"
      }
      where: {
        path: ["round"]
        operator: Equal
        valueText: "Double Jeopardy!"
      }
    ) {
      question
      answer
      round
    }
  }
}

Example response

It should produce a response like the one below:

{
  "data": {
    "Get": {
      "JeopardyQuestion": [
        {
          "answer": "food stores (supermarkets)",
          "question": "This type of retail store sells more shampoo & makeup than any other",
          "round": "Double Jeopardy!"
        },
        {
          "answer": "Tofu",
          "question": "A popular health food, this soybean curd is used to make a variety of dishes & an ice cream substitute",
          "round": "Double Jeopardy!"
        },
        {
          "answer": "gastronomy",
          "question": "This word for the art & science of good eating goes back to Greek for \"belly\"",
          "round": "Double Jeopardy!"
        }
      ]
    }
  }
}

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 more involved discussion: Weaviate Community Forum. Or,
5. We also have a Slack channel.