Como indexar campos para pesquisa vetorial

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{
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    "database": "sample_mflix",
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    "collectionName": "embedded_movies",
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    "type": "vectorSearch",
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    "name": "vector_index",
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    "fields": [ 
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      {
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        "type": "vector",
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        "path": "plot_embedding",
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        "numDimensions": 1536,
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        "similarity": "euclidean"
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      }
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    ]
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}

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{
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    "database": "sample_mflix",
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    "collectionName": "embedded_movies",
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    "type": "vectorSearch",
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    "name": "vector_index",
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    "fields":[ 
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      {
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        "type": "vector",
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        "path": "plot_embedding",
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        "numDimensions": 1536,
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        "similarity": "euclidean"
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      },
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      {
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        "type": "filter",
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        "path": "genres"
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      },
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      {
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        "type": "filter",
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        "path": "year"
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      }
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    ]
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}

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{
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  "fields": [{
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    "type": "vector",
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    "path": "plot_embedding",
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    "numDimensions": 1536,
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    "similarity": "euclidean"
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  }]
8
}

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{
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  "fields": [{
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    "type": "vector",
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    "path": "plot_embedding",
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    "numDimensions": 1536,
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    "similarity": "euclidean"
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  },
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  {
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    "type": "filter",
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    "path": "genres"
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  },
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  {
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    "type": "filter",
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    "path": "year"
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  }]
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}

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db.embedded_movies.createSearchIndex(
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  "vector_index", 
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  "vectorSearch", 
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  {
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    "fields": [
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      {
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        "type": "vector",
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        "path": "plot_embedding",
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        "numDimensions": 1536,
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        "similarity": "euclidean"
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      }
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    ]
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  }
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);

1
db.embedded_movies.createSearchIndex(
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  "vector_index", 
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  "vectorSearch", 
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  {
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    "fields": [
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      {
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        "type": "vector",
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        "path": "plot_embedding",
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        "numDimensions": 1536,
10
        "similarity": "euclidean"
11
      },
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      {
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        "type": "filter",
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        "path": "genres"
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      },
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      {
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        "type": "filter",
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        "path": "year"
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      }
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    ]
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  }
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);

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package main
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3
import (
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	"context"
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	"fmt"
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	"log"
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	"time"
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	"go.mongodb.org/mongo-driver/bson"
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	"go.mongodb.org/mongo-driver/mongo"
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	"go.mongodb.org/mongo-driver/mongo/options"
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)
13
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func main() {
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	ctx := context.Background()
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	// Replace the placeholder with your Atlas connection string
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	const uri = "<connectionString>"
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	// Connect to your Atlas cluster
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	clientOptions := options.Client().ApplyURI(uri)
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	client, err := mongo.Connect(ctx, clientOptions)
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	if err != nil {
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		log.Fatalf("failed to connect to the server: %v", err)
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	}
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	defer func() { _ = client.Disconnect(ctx) }()
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	// Set the namespace
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	coll := client.Database("sample_mflix").Collection("embedded_movies")
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	// Define the index details
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	type vectorDefinitionField struct {
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		Type          string `bson:"type"`
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		Path          string `bson:"path"`
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		NumDimensions int    `bson:"numDimensions"`
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		Similarity    string `bson:"similarity"`
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	}
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	type vectorDefinition struct {
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		Fields []vectorDefinitionField `bson:"fields"`
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	}
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	indexName := "vector_index"
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	opts := options.SearchIndexes().SetName(indexName).SetType("vectorSearch")
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	indexModel := mongo.SearchIndexModel{
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		Definition: vectorDefinition{
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			Fields: []vectorDefinitionField{{
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				Type:          "vector",
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				Path:          "plot_embedding",
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				NumDimensions: 1536,
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				Similarity:    "euclidean"}},
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		},
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		Options: opts,
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	}
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	// Create the index
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	searchIndexName, err := coll.SearchIndexes().CreateOne(ctx, indexModel)
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	if err != nil {
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		log.Fatalf("failed to create the search index: %v", err)
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	}
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	log.Println("New search index named " + searchIndexName + " is building.")
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	// Await the creation of the index.
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	log.Println("Polling to check if the index is ready. This may take up to a minute.")
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	searchIndexes := coll.SearchIndexes()
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	var doc bson.Raw
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	for doc == nil {
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		cursor, err := searchIndexes.List(ctx, options.SearchIndexes().SetName(searchIndexName))
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		if err != nil {
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			fmt.Errorf("failed to list search indexes: %w", err)
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		}
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		if !cursor.Next(ctx) {
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			break
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		}
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		name := cursor.Current.Lookup("name").StringValue()
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		queryable := cursor.Current.Lookup("queryable").Boolean()
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		if name == searchIndexName && queryable {
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			doc = cursor.Current
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		} else {
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			time.Sleep(5 * time.Second)
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		}
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	}
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	log.Println(searchIndexName + " is ready for querying.")
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}

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package main
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3
import (
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	"context"
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	"fmt"
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	"log"
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	"time"
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	"go.mongodb.org/mongo-driver/bson"
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	"go.mongodb.org/mongo-driver/mongo"
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	"go.mongodb.org/mongo-driver/mongo/options"
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)
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func main() {
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	ctx := context.Background()
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	// Replace the placeholder with your Atlas connection string
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	const uri = "<connectionString>"
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	// Connect to your Atlas cluster
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	clientOptions := options.Client().ApplyURI(uri)
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	client, err := mongo.Connect(ctx, clientOptions)
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	if err != nil {
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		log.Fatalf("failed to connect to the server: %v", err)
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	}
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	defer func() { _ = client.Disconnect(ctx) }()
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	// Set the namespace
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	coll := client.Database("sample_mflix").Collection("embedded_movies")
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	indexName := "vector_index"
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	opts := options.SearchIndexes().SetName(indexName).SetType("vectorSearch")
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	type vectorDefinitionField struct {
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		Type          string `bson:"type"`
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		Path          string `bson:"path"`
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		NumDimensions int    `bson:"numDimensions"`
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		Similarity    string `bson:"similarity"`
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	}
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	type filterField struct {
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		Type string `bson:"type"`
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		Path string `bson:"path"`
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	}
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	type indexDefinition struct {
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		Fields []vectorDefinitionField `bson:"fields"`
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	}
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	vectorDefinition := vectorDefinitionField{
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		Type:          "vector",
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		Path:          "plot_embedding",
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		NumDimensions: 1536,
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		Similarity:    "euclidean"}
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	genreFilterDefinition := filterField{"filter", "genres"}
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	yearFilterDefinition := filterField{"filter", "year"}
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	indexModel := mongo.SearchIndexModel{
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		Definition: bson.D{{"fields", [3]interface{}{
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			vectorDefinition,
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			genreFilterDefinition,
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			yearFilterDefinition}}},
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		Options: opts,
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	}
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	// Create the index
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	searchIndexName, err := coll.SearchIndexes().CreateOne(ctx, indexModel)
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	if err != nil {
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		log.Fatalf("failed to create the search index: %v", err)
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	}
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	log.Println("New search index named " + searchIndexName + " is building.")
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	// Await the creation of the index.
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	log.Println("Polling to check if the index is ready. This may take up to a minute.")
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	searchIndexes := coll.SearchIndexes()
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	var doc bson.Raw
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	for doc == nil {
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		cursor, err := searchIndexes.List(ctx, options.SearchIndexes().SetName(searchIndexName))
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		if err != nil {
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			fmt.Errorf("failed to list search indexes: %w", err)
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		}
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		if !cursor.Next(ctx) {
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			break
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		}
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		name := cursor.Current.Lookup("name").StringValue()
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		queryable := cursor.Current.Lookup("queryable").Boolean()
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		if name == searchIndexName && queryable {
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			doc = cursor.Current
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		} else {
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			time.Sleep(5 * time.Second)
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		}
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	}
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	log.Println(searchIndexName + " is ready for querying.")
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}

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const { MongoClient } = require("mongodb");
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// connect to your Atlas deployment
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const uri =  "<connectionString>";
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const client = new MongoClient(uri);
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async function run() {
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   try {
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     const database = client.db("sample_mflix");
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     const collection = database.collection("embedded_movies");
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     // define your Atlas Vector Search index
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     const index = {
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         name: "vector_index",
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         type: "vectorSearch",
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         definition: {
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           "fields": [
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             {
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               "type": "vector",
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               "numDimensions": 1536,
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               "path": "plot_embedding",
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               "similarity": "euclidean"
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             }
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           ]
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         }
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     }
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     // run the helper method
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     const result = await collection.createSearchIndex(index);
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     console.log(`New search index named ${result} is building.`);
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     // wait for the index to be ready to query
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     console.log("Polling to check if the index is ready. This may take up to a minute.")
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     let isQueryable = false;
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     while (!isQueryable) {
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       const cursor = collection.listSearchIndexes();
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       for await (const index of cursor) {
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         if (index.name === result) {
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           if (index.queryable) {
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             console.log(`${result} is ready for querying.`);
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             isQueryable = true;
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           } else {
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             await new Promise(resolve => setTimeout(resolve, 5000));
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           }
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         }
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       }
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     }
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   } finally {
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     await client.close();
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   }
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}
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run().catch(console.dir);

1
const { MongoClient } = require("mongodb");
2
3
// connect to your Atlas deployment
4
const uri =  "<connectionString>";
5
6
const client = new MongoClient(uri);
7
8
async function run() {
9
   try {
10
     const database = client.db("sample_mflix");
11
     const collection = database.collection("embedded_movies");
12
    
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     // define your Atlas Vector Search index
14
     const index = {
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         name: "vector_index",
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         type: "vectorSearch",
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         definition: {
18
           "fields": [
19
             {
20
               "type": "vector",
21
               "numDimensions": 1536,
22
               "path": "plot_embedding",
23
               "similarity": "euclidean"
24
             },
25
             {
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               "type": "filter",
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               "path": "genres"
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             },
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             {
30
               "type": "filter",
31
               "path": "year"
32
             }
33
           ]
34
         }
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     }
36
37
     // run the helper method
38
     const result = await collection.createSearchIndex(index);
39
     console.log(`New search index named ${result} is building.`);
40
41
     // wait for the index to be ready to query
42
     console.log("Polling to check if the index is ready. This may take up to a minute.")
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     let isQueryable = false;
44
     while (!isQueryable) {
45
       const cursor = collection.listSearchIndexes();
46
       for await (const index of cursor) {
47
         if (index.name === result) {
48
           if (index.queryable) {
49
             console.log(`${result} is ready for querying.`);
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             isQueryable = true;
51
           } else {
52
             await new Promise(resolve => setTimeout(resolve, 5000));
53
           }
54
         }
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       }
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     }
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   } finally {
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     await client.close();
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   }
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}
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run().catch(console.dir);

1
from pymongo.mongo_client import MongoClient
2
from pymongo.operations import SearchIndexModel
3
import time
4
5
# Connect to your Atlas deployment
6
uri = "<connectionString>"
7
client = MongoClient(uri)
8
9
# Access your database and collection
10
database = client["sample_mflix"]
11
collection = database["embedded_movies"]
12
13
# Create your index model, then create the search index
14
search_index_model = SearchIndexModel(
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  definition={
16
    "fields": [
17
      {
18
        "type": "vector",
19
        "path": "plot_embedding",
20
        "numDimensions": 1536,
21
        "similarity": "euclidean"
22
      }
23
    ]
24
  },
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  name="vector_index",
26
  type="vectorSearch",
27
)
28
29
result = collection.create_search_index(model=search_index_model)
30
print("New search index named " + result + " is building.")
31
# Wait for initial sync to complete
32
print("Polling to check if the index is ready. This may take up to a minute.")
33
predicate=None
34
if predicate is None:
35
  predicate = lambda index: index.get("queryable") is True
36
37
while True:
38
  indices = list(collection.list_search_indexes(name))
39
  if len(indices) and predicate(indices[0]):
40
    break
41
  time.sleep(5)
42
print(result + " is ready for querying.")
43
44
client.close()

1
from pymongo.mongo_client import MongoClient
2
from pymongo.operations import SearchIndexModel
3
import time
4
5
# Connect to your Atlas deployment
6
uri = "<connectionString>"
7
client = MongoClient(uri)
8
9
# Access your database and collection
10
database = client["sample_mflix"]
11
collection = database["embedded_movies"]
12
13
# Create your index model, then create the search index
14
search_index_model = SearchIndexModel(
15
  definition={
16
    "fields": [
17
      {
18
        "type": "vector",
19
        "path": "plot_embedding",
20
        "numDimensions": 1536,
21
        "similarity": "euclidean"
22
      },
23
      {
24
        "type": "filter",
25
        "path": "genres"
26
      },
27
      {
28
        "type": "filter",
29
        "path": "year"
30
      }
31
    ]
32
  },
33
  name="vector_index",
34
  type="vectorSearch",
35
)
36
37
result = collection.create_search_index(model=search_index_model)
38
print("New search index named " + result + " is building.")
39
# Wait for initial sync to complete
40
print("Polling to check if the index is ready. This may take up to a minute.")
41
predicate=None
42
if predicate is None:
43
  predicate = lambda index: index.get("queryable") is True
44
45
while True:
46
  indices = list(collection.list_search_indexes(result))
47
  if len(indices) and predicate(indices[0]):
48
    break
49
  time.sleep(5)
50
print(result + " is ready for querying.")
51
52
client.close()