Vector Search
Vector search allows you to run queries on vector types in your MongoDB database. This page explains how to configure vector search in Hasura using native queries.
Prerequisites
Example
This uses the sample_mflix.embedded_movies collection to perform an approximate nearest neighbor search on a vector in
the plot_embeddings field.
Native Query configuration
In the native_queries folder of our connector configuration directory add a json file named movieSearch.json and
enter the following:
{
"name": "moviesSearch",
"representation": "function",
"inputCollection": "embedded_movies",
"description": "Movies approximate nearest neighbor search",
"arguments": {
"ninGenres": {
"type": {
"arrayOf": {
"scalar": "string"
}
}
},
"inGenres": {
"type": {
"arrayOf": {
"scalar": "string"
}
}
},
"gteYear": {
"type": {
"scalar": "int"
}
},
"lteYear": {
"type": {
"scalar": "int"
}
},
"numCandidates": {
"type": {
"scalar": "int"
}
},
"limit": {
"type": {
"scalar": "int"
}
}
},
"resultDocumentType": "MoviesSearch",
"objectTypes": {
"MoviesSearch": {
"fields": {
"__value": {
"type": {
"arrayOf": {
"object": "MoviesSearchOutput"
}
}
}
}
},
"MoviesSearchOutput": {
"fields": {
"title": {
"type": {
"scalar": "string"
}
},
"plot": {
"type": {
"scalar": "string"
}
},
"genres": {
"type": {
"arrayOf": {
"scalar": "string"
}
}
},
"year": {
"type": {
"scalar": "int"
}
},
"score": {
"type": {
"scalar": "double"
}
}
}
}
},
"pipeline": [
{
"$vectorSearch": {
"index": "vector-search-tutorial",
"path": "plot_embedding",
"filter": {
"$and": [
{
"genres": {
"$nin": "{{ ninGenres }}",
"$in": "{{ inGenres }}"
}
},
{
"year": {
"$gte": "{{ gteYear }}",
"$lte": "{{ lteYear }}"
}
}
]
},
"queryVector": [
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0.011693806, 0.0071483953, -0.010156869, -0.013799738, -0.00034703725, -0.010706242, -0.02289056,
0.0039339066, -0.0015835363, -0.014532236, 0.012445925, -0.00009779583, 0.0053335004, 0.0055329753,
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0.017344505, 0.006667693, -0.005461034, 0.0066742334, 0.01998673, 0.024591003, -0.007717389, 0.0096598165,
0.03225607, 0.018626377, -0.020248337, 0.0017740185, 0.012589809, 0.0014927916, -0.040235065, 0.01713522,
0.016206518, 0.017776156, 0.024734886, 0.0040516295, -0.009627116, 0.002001289, -0.010496957, -0.0121058365,
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0.0058240127, 0.013943622, 0.001675916, 0.01347273, 0.023335291, 0.008129419, 0.0047187256, 0.032099105,
0.0007701039, 0.0068344674, 0.0004672127, -0.00610851, 0.026396086, -0.010738943, 0.024591003, 0.008220982,
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-0.034558207, -0.029064473, -0.03058179, -0.019450437, 0.01062122, -0.014179068, -0.010012985, 0.007874353,
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-0.00937859, -0.035264544, 0.0027959184, 0.012982218, -0.012609429, 0.0065270797, 0.010712783
],
"numCandidates": "{{ numCandidates }}",
"limit": "{{ limit }}"
}
},
{
"$project": {
"_id": 0,
"title": 1,
"genres": 1,
"plot": 1,
"year": 1,
"score": {
"$meta": "vectorSearchScore"
}
}
},
{
"$group": {
"_id": null,
"__value": {
"$addToSet": {
"title": "$title",
"genres": "$genres",
"plot": "$plot",
"year": "$year",
"score": "$score"
}
}
}
},
{
"$project": {
"_id": 0,
"__value": 1
}
}
]
}
Usage
Now you can use the flexibility of Hasura's GraphQL API to search your vectors in many ways.
Search movies
query MyQuery {
moviesSearch(
ninGenres: ["Drama", "Western", "Crime"]
inGenres: ["Action", "Adventure", "Family"]
gteYear: 1960
lteYear: 2000
numCandidates: 200
limit: 2
) {
genres
plot
score
year
title
}
}