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Scenario: Hybrid Search
- 1. Run CrateDB
- 2. Choose Scenario
- 3. Get Ready
- 4. Import Data
- 5. Explore Queries
- 6. More Queries
- 7. Connect
- 8. Next Steps
B. Vector Similarity Search
CrateDB also handles vectors natively, using the FLOAT_VECTOR data type. Vectors are numerical representations of data that capture semantic meaning, allowing similarity comparisons between items such as text, images, or audio.
You can store, retrieve and search data represented as vector embeddings. These embeddings capture the semantic meaning of the original data and can be seamlessly integrated into your existing datasets, making CrateDB a powerful foundation for AI and machine learning systems.
For our Chicago community areas data, we've already created vector representations of the area's description text for you, using OpenAI's text-embedding-3-large model. For more information about embeddings with this model, check out the OpenAI documentation.
Let's begin by running a simple SELECT query that returns the vector data for a couple of community areas:
SELECT name, details['description_vec'] as description_vec FROM demo.community_areas WHERE areanumber in (75, 76)
Now we can explore vector similarity using a k-nearest neighbor (kNN) search. CrateDB enables us to do this in SQL, using the KNN_MATCH function. This finds the k data points that are most similar to a given query data point.
KNN_MATCH takes 3 parameters:
-
A search vector: the name of a column in the database containing vectors to search.
-
A query vector: the vector we want to find the nearest neighbors in the search vector column for. This will be our query text, in vector form.
-
k: the number of most similar vectors to the given query vector
Below, we've created a vector representation of the text "Which neighborhoods are associated with European immigrants?", again using the OpenAI text-embeddings-3-large model. Using this as the query vector, we can find out which community area descriptions contain information about this topic.
In vector search, _score reflects similarity between vectors. Higher values indicate closer semantic similarity to the query.
/* For demonstration purposes, the following query includes a precomputed vector. In real-world applications, this would typically be generated dynamically using an embedding model. */ WITH vector_data AS ( SELECT [0.029187094420194626, 0.0059433975256979465, -0.0307051669806242, -0.0024525464978069067, -0.04534167796373367, -0.05244510993361473, -0.015452832914888859, -0.056541044265031815, 0.01020402554422617, 0.003014663001522422, 0.0018224031664431095, -0.013977725058794022, -0.02854262851178646, -0.020035693421959877, 0.027282342314720154, -0.03150716796517372, -0.029530808329582214, -0.021066837012767792, 0.00476545887067914, -0.008012845180928707, -0.012760402634739876, 0.011800866574048996, -0.04989589378237724, -0.026179591193795204, 0.04978132247924805, 0.007783702574670315, -0.00924448948353529, 0.0061045135371387005, -0.04287838935852051, 0.02184019610285759, 0.011679134331643581, 0.005105593241751194, 0.03147852420806885, -0.043136175721883774, -0.028943629935383797, -0.02682405710220337, 0.02131030149757862, 0.035803597420454025, 0.010075132362544537, 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-0.01336190290749073, 0.009946240112185478, -0.001996050588786602, -0.0029573773499578238, -0.03697795793414116, -0.029444878920912743, -0.02308616042137146, -0.013884635642170906, 0.014837011694908142, 0.004826324991881847, -0.0005303408252075315, 0.009466471150517464, -0.04617232084274292, -0.013684135861694813, 0.004622244741767645, 0.03414231166243553, -0.019362585619091988, 0.023000231012701988, 0.030246879905462265, 0.03190816566348076, 0.022814054042100906, -0.0016684477450326085, 0.011056151241064072, 0.01429995708167553, 0.023730624467134476, -0.012960902415215969, 0.002450756262987852, 0.006383781787008047, -0.029187094420194626, -0.03176495432853699, -0.007454309146851301, -0.0008507830207236111, -0.004153217654675245, -0.008506935089826584, 0.001492114970460534, -0.009502274915575981, -0.0009908645879477262, -0.025105483829975128, 0.0029090424068272114, -0.009043988771736622, -0.024790411815047264, -0.020565588027238846, 0.023258017376065254, -0.02529166266322136, 0.05387725681066513, -0.0067991032265126705, 0.008363720960915089, -0.019749265164136887, -0.01125665195286274, -0.0033995516132563353, 0.0051485574804246426, -0.005080530885607004, 0.0025384752079844475, 0.01891862228512764, -0.008184703066945076, -0.005789441987872124, 0.019462836906313896, -0.026036377996206284, -0.009824507869780064, -0.020250516012310982, 0.013354741968214512, -0.03394181281328201, 0.006036486942321062, 0.008471131324768066, -0.005327575374394655, -0.005016084294766188, -0.006000683177262545, 0.00680268369615078, 0.025549449026584625, -0.027797915041446686, -0.0030719488859176636, -0.01571061834692955, 0.03099159523844719, 0.00838520284742117, -0.009831667877733707, -0.0015977355651557446, 0.011528759263455868, -0.002046175766736269, 0.010196864604949951, 0.01156456209719181, -0.01173641998320818, -0.006351558491587639, -0.0015699878567829728, 0.0026423055678606033, -0.0012235880130901933, 0.017615370452404022, -0.02145351655781269, 0.0074328272603452206, 0.04345124587416649, 0.007260969839990139, 0.01646965555846691, 0.000619402271695435, 0.03156445175409317, -0.013562403619289398, 0.03391316905617714, -0.02020755037665367, -0.010461811907589436, 0.011979884468019009, 0.0010391994146630168, -0.020852016285061836, 0.0054886918514966965, 0.046200964599847794] AS input_vector ) SELECT name, _score, details['description'] AS description FROM demo.community_areas WHERE KNN_MATCH(details['description_vec'], (SELECT input_vector FROM vector_data), 2) ORDER BY _score DESC LIMIT 5;
As vector similarity search is expressed in SQL, we can of course combine it with other types of search. Imagine you're moving to Chicago and have been trying to choose which area to live in. You really like the Hyde Park area and wonder which other areas might be like it.
Let's run a knn_match query, using the vector representation of Hyde Park's description as the query vector:
SELECT name, _score FROM demo.community_areas WHERE KNN_MATCH(details['description_vec'], (SELECT details['description_vec'] FROM demo.community_areas WHERE name = 'HYDE PARK'), 2) ORDER BY _score DESC LIMIT 5;
Here, we get Hyde Park itself back, as it's a good match for its own vector :) Let's run a more specific query that does a couple of additional things:
Excludes Hyde Park from the results.
Requires community areas to be at least partially inside a geo polygon describing an area of the city that we want to be in, for proximity to work.
SELECT name, _score FROM demo.community_areas WHERE KNN_MATCH(details['description_vec'], (SELECT details['description_vec'] FROM demo.community_areas WHERE name = 'HYDE PARK'), 2) AND name <> 'HYDE PARK' AND INTERSECTS(boundaries, 'POLYGON ((-87.6260901267975 41.83558299924806, -87.62229326051806 41.723377049484725, -87.61754602677676 41.71558271165077, -87.59761054877902 41.71451963553886, -87.59049048769104 41.70566112778525, -87.5235581113496 41.722313603775035, -87.60900410022978 41.83983231675009, -87.6260901267975 41.83558299924806))') ORDER BY _score DESC LIMIT 5;
Our result set no longer contains Hyde Park, as we know we like that area. Notice also that Rogers Park is no longer in the result set, since it is outside of our provided geographical boundaries.
C. Hybrid Search
Hybrid Search (read our blog post) is a technique that enhances relevancy and accuracy by combining the results of two or more search algorithms, achieving better accuracy and relevancy than each algorithm would individually.
A common scenario is to combine semantic search (vector search) with lexical search (keyword search). Semantic search excels at understanding the context of a phrase. Lexical search is great at finding how many times a keyword or phrase appears in a document, taking into account the length and the average length of your documents.
As we've seen, CrateDB supports both full-text and vector similarity searches. We can perform a hybrid search by combining the results of each of these types of search, in a process known as re-ranking. In this example, both full-text and vector search are applied together, allowing us to combine keyword relevance with semantic similarity.
WITH vector_data AS ( SELECT [0.029187094420194626, 0.0059433975256979465, -0.0307051669806242, -0.0024525464978069067, -0.04534167796373367, -0.05244510993361473, -0.015452832914888859, -0.056541044265031815, 0.01020402554422617, 0.003014663001522422, 0.0018224031664431095, -0.013977725058794022, -0.02854262851178646, -0.020035693421959877, 0.027282342314720154, -0.03150716796517372, -0.029530808329582214, -0.021066837012767792, 0.00476545887067914, -0.008012845180928707, -0.012760402634739876, 0.011800866574048996, -0.04989589378237724, -0.026179591193795204, 0.04978132247924805, 0.007783702574670315, -0.00924448948353529, 0.0061045135371387005, -0.04287838935852051, 0.02184019610285759, 0.011679134331643581, 0.005105593241751194, 0.03147852420806885, -0.043136175721883774, -0.028943629935383797, -0.02682405710220337, 0.02131030149757862, 0.035803597420454025, 0.010075132362544537, 0.004901512525975704, -0.02006433717906475, 0.0023952608462423086, 0.033598098903894424, 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Here we see we can retrieve the scores for both types of search, which can then be normalized and re-ranked.