Gerar incorporações automaticamente usando a Vector Search do MongoDB

Esse recurso está em visualização privada. O recurso e a documentação correspondente podem mudar a qualquer momento durante o período de Pré-visualização. Portanto, não use esse recurso em produção. Para saber mais, consulte Recursos de visualização. Para ativar esse recurso para seu cluster M10 ou superior em seu ambiente de desenvolvimento ou teste, entre em contato com sua equipe de conta. Não há cobranças pelo uso desse recurso durante o período de Pré-visualização. No entanto, as cobranças padrão para clusters do Atlas ainda se aplicam. Para saber mais, consulte a página de preços do MongoDB.

Você pode configurar o MongoDB pesquisa vetorial para gerar e gerenciar automaticamente incorporações vetoriais para os dados de texto em seu cluster. Você pode criar um índice de pesquisa semântica de IA de um clique em seu cluster M10 ou superior Atlas e usar modelos de incorporação do Voyage IA, simplificando a indexação, a atualização e a consulta com vetores.

When you enable Automated Embedding, MongoDB Vector Search automatically generates embeddings using the specified embedding model at index-time for the specified text field in your Atlas collection and at query-time for your text string in your query against the field indexed for automated embeddings.

Considerações

Importante

You can use MongoDB Vector Search Automated Embedding on any M10 or higher cluster on any cloud provider. However, the service that handles the inference process for generating vector embeddings runs on Google Cloud. This means that your data is sent to Google Cloud for embedding generation and retrieval, regardless of your cluster's cloud provider. We provide enterprise grade security and therefore, your data is only stored in your cluster.

Os modelos de incorporação são executados em uma plataforma de inferência compartilhada e multilocatária. Portanto, durante o período de visualização, você deve usar conjuntos de dados com menos de 100k documento e executar queries somente para a avaliação do recurso e não para o teste de carga. Entre em contato com sua equipe de conta se você tiver um caso de uso com limites mais altos.

Embora não haja limites de taxa rígidos para sua carga de trabalho, existem limites globais. Se suas queries retornarem um erro de limite de taxa (erro 409), execute um backoff e tente novamente no código do seu aplicação . Isso permite que seu aplicação lide normalmente com os limites de taxa e garanta a funcionalidade contínua.

Pré-requisitos

To enable vector search using Automated Embedding, you must have the following:

M10 ou cluster superior
Uma coleção com um campo de texto que você deseja indexar para incorporações automatizadas.
Um dos seguintes clientes:
- IU do Atlas para criação de índices
- mongosh para criar índices e executar query
- Driver de nó 6.6.0 ou superior para criar índices e executar queries
- Driver Python 4.7 ou superior para criar índices e executar query
Use o seguinte menu suspenso para definir o cliente para os procedimentos nesta página.
Selecione a linguagem

Índice de Vector Search do MongoDB para incorporação automatizada

The following sections describe the MongoDB Vector Search index syntax and fields for enabling automatic generation of embeddings for text fields and walk you through the steps for configuring your index for Automated Embedding.

Acesso necessário

Você precisa ter a função Project Data Access Admin ou superior para criar e gerenciar índices do MongoDB Vector Search.

Sintaxe do Índice

A seguir está a sintaxe para ativar a geração automática de incorporações:

1 {
2   "fields": [
3     {
4       "type": "text",
5       "path": "<field-name>",
6       "model": "voyage-3-large | voyage-3.5 | voyage-3.5-lite"
7     }
8   ]
9 }

Campos do índice

Os seguintes campos são obrigatórios na definição do índice:

Campo	Tipo	Descrição
`type`	string	The type of the field. For Automated Embedding, this must be `text`.
`path`	string	The name of the field in the collection that you want to index for Automated Embedding.
`model`	string	O modelo de incorporação de IA Voyage a ser usado para gerar as incorporações para o índice. Você pode especificar um dos seguintes modelos: `voyage-3-large` - Recuperação da mais alta qualidade em idiomas e domínios. `voyage-3.5` - Modelo balanceado para uso multilíngue e precisão de recuperação de uso geral. `voyage-3.5-lite` - Modelo leve e mais rápido, otimizado para latência e custo mais baixo. Depois de criar o índice, se você alterar o modelo de incorporação posteriormente, o MongoDB Vector Search gerará novas incorporações para o conjunto de dados. Enquanto o MongoDB Vector Search gera as incorporações, você pode continuar a consultar usando as incorporações antigas. Quando as incorporações antigas são substituídas por incorporações do novo modelo de incorporação, o MongoDB Vector Search remove as incorporações antigas.

Considerações

The index fields for Automated Embedding are mutually exclusive with the following vector type index fields:

numDimensions
similarity
quantization

Se a sua coleção já tiver incorporações, você deve usar os campos de tipo vector para indexar as incorporações. Para aprender mais sobre como indexar campos com incorporações, consulte Como indexar campos para a pesquisa vetorial.

Você pode criar um índice com os tipos text e vector se quiser indexar um campo de texto para gerar automaticamente incorporações e também indexar um campo com suas próprias incorporações. O MongoDB Vector Search gerará automaticamente incorporações para consultas somente com o campo indexado como o tipo text . Você deve especificar incorporações na query para pesquisar o campo indexado como o tipo vector.

Você também pode indexar campos para pré-filtrar seus dados usando o tipo MongoDB Vector Search filter. A pré-filtragem reduz o número de documentos nos quais comparações de similaridade devem ser executadas, o que pode diminuir a latência da query e aumentar a precisão dos resultados da pesquisa. Para saber mais sobre como pré-filtrar seus dados, consulte Sobre o tipo filter.

Criar um índice para incorporação automatizada

The following procedure walks through the steps for enabling automated embeddings in your MongoDB Vector Search index. If you loaded the sample_mflix.movies dataset, the example in the procedure demonstrates how to enable Automated Embedding for the fullplot field in the collection.

No Atlas, vá Clusters para a página do seu projeto.

Se ainda não tiver sido exibido, selecione a organização que contém seu projeto no menu Organizations na barra de navegação.
Se ainda não estiver exibido, selecione o projeto desejado no menu Projects na barra de navegação.
Na barra lateral, clique em Clusters sob o título Database.

A página Clusters é exibida.

No Atlas, vá Search & Vector Search para a página para seu cluster.

Você pode acessar a página de pesquisa do MongoDB a partir da opção Search & Vector Search ou do Data Explorer.

Se ainda não tiver sido exibido, selecione a organização que contém seu projeto no menu Organizations na barra de navegação.
Se ainda não estiver exibido, selecione seu projeto no menu Projects na barra de navegação.
Na barra lateral, clique em Search & Vector Search sob o título Database.
Se você não tiver clusters, clique em Create cluster para criar um. Para saber mais, consulte Criar um cluster.
Se o seu projeto tiver vários clusters, selecione o cluster que deseja usar no menu suspenso Select cluster e clique em Go to Atlas Search.
A página Pesquisa & Pesquisa Vetorial é exibida.

Se ainda não tiver sido exibido, selecione a organização que contém seu projeto no menu Organizations na barra de navegação.
Se ainda não estiver exibido, selecione seu projeto no menu Projects na barra de navegação.
Na barra lateral, clique em Data Explorer sob o título Database.
Expanda o banco de dados e selecione a coleção.
Clique na guia Indexes da coleção.
A página Atlas Search é exibida.

Inicie a configuração do seu índice.

Faça as seguintes seleções na página e clique em Next.

Search Type	Selecione o tipo de índice Vector Search.
Index Name and Data Source	Especifique as seguintes informações: Index Name: <INDEX-NAME> Database and Collection: <DATABASE-NAME> <COLLECTION-NAME>
Configuration Method	Selecione JSON Editor.

Exemplo

Por exemplo, insira movies_automated_embeddings para o nome do índice e selecione sample_mflix e movies no menu suspenso Database and Collection.

Defina seu índice.

No editor JSON, insira a seguinte definição de índice:

1 {
2   "fields": [
3     {
4       "type": "text",
5       "path": "<FIELD-NAME>",
6       "model": "voyage-3-large | voyage-3.5 | voyage-3.5-lite"
7     }
8   ]
9 }

Exemplo

Por exemplo, para criar um índice que permita incorporações automatizadas usando o modelo voyage-3-large para o campo fullplot na coleção, insira o seguinte:

1 {
2   "fields": [
3     {
4       "type": "text",
5       "path": "fullplot",
6       "model": "voyage-3-large"
7     }
8   ]
9 }

Crie seu índice.

Clique em Next.
Revise sua configuração de índice e clique em Create Vector Search Index.

Observação

A criação do índice pode levar algum tempo para ser concluída. Quando o índice está sendo criado, a coluna Status na IU do Atlas exibe Pending. Quando a criação do índice for concluída, a coluna Status na IU do Atlas exibirá Ready.

Conecte ao Atlas cluster utilizando `mongosh` o.

Para saber mais, consulte Conectar-se a um cluster via mongosh.

Alterne para o banco de dados que contém a collection para a qual você deseja criar o índice.

Para alternar, execute o seguinte comando:

use <DATABASE-NAME>

Aqui, <DATABASE-NAME> é o nome do banco de dados que você deseja usar.

Exemplo

Por exemplo, para alternar para o banco de dados sample_mflix , que contém a coleção movies, execute o seguinte comando:

use sample_mflix

Execute o `db.collection.createSearchIndex()` método.

O método db.collection.createSearchIndex() tem a seguinte sintaxe:

1 db.<COLLECTION-NAME>.createSearchIndex(
2   "<INDEX-NAME>",
3   "vectorSearch", 
4   {
5     "fields": [
6       {
7         "type": "text",
8         "path": "<FIELD-NAME>",
9         "model": "voyage-3-large | voyage-3.5 | voyage-3.5-lite"
10       },
11       {
12         "type": "filter",
13         "path": "<FIELD-NAME>"
14       },
15       ...
16     ]
17   }
18 );

Exemplo

Por exemplo, para criar um índice que habilite incorporações automatizadas usando o modelo voyage-3-large para o campo fullplot na coleção, execute o seguinte comando em seu terminal:

1 db.movies.createSearchIndex(
2   "movies_automated_embeddings",
3   "vectorSearch",
4   {
5     "fields": [
6       {
7         "type": "text",
8         "path": "fullplot",
9         "model": "voyage-3-large"
10       }
11     ]
12   }
13 );

Inicialize seu projeto Node.js.

Execute os seguintes comandos em seu terminal para criar um novo diretório chamado auto-embeddings-project e inicializar seu projeto:

mkdir auto-embeddings-project
cd auto-embeddings-project
npm init -y

Crie o `.js` arquivo.

touch <file-name>.js

Exemplo

Por exemplo, crie um arquivo chamado create_index.js.

touch create_index.js

Defina o índice no `.js` arquivo.

1 const { MongoClient } = require("mongodb");
2 
3 // connect to your Atlas deployment
4 const uri =  "<CONNECTION-STRING>";
5 const client = new MongoClient(uri);
6 
7 async function run() {
8     try {
9       const database = client.db("<DATABASE-NAME>");
10       const collection = database.collection("<COLLECTION-NAME>");
11 
12       // define your MongoDB Vector Search index
13       const index = {
14         "name": "<INDEX-NAME>",
15         "type": "vectorSearch",
16         "definition": {
17           "fields": [
18             {
19               "type": "vector",
20               "path": "<FIELD-NAME>",
21               "model": "voyage-3-large | voyage-3.5 | voyage-3.5-lite"
22             },
23           ]
24         }
25       }
26       // run the helper method
27       await collection.createSearchIndex("<INDEX-NAME>", index);
28     } finally {
29       await client.close();
30     }
31 }
32 run().catch(console.dir);

Exemplo

Por exemplo, para criar um índice que habilite incorporações automatizadas usando o modelo voyage-3-large para o campo fullplot no namespace sample_mflix.movies, copie e cole o seguinte no arquivo create_index.js:

1 const { MongoClient } = require("mongodb");
2 
3 // connect to your Atlas deployment
4 const uri =  "<CONNECTION-STRING>";
5 const client = new MongoClient(uri);
6 
7 async function run() {
8   try {
9     const database = client.db("sample_mflix");
10     const collection = database.collection("movies");
11 
12     // define your MongoDB Vector Search index
13     const index = {
14       "name": "movies_automated_embeddings",
15       "type": "vectorSearch",
16       "definition": {
17       "fields": [
18         {
19           "type": "text",
20           "path": "fullplot",
21           "model": "voyage-3-large"
22         }
23       ]
24     }
25   }
26   // run the helper method
27   await collection.createSearchIndex(index);
28   } finally {
29     await client.close();
30   }
31 }
32 run().catch(console.dir);

Substitua os seguintes valores e salve o arquivo.

`<CONNECTION-STRING>`	A string de conexão do seu Atlas cluster.
`<DATABASE-NAME>`	O nome do banco de dados que contém a collection para a qual você deseja criar o índice.
`<COLLECTION-NAME>`	O nome da coleção para a qual você deseja criar o índice.
`<FIELD-NAME>`	The name of the field in the collection that you want to index for Automated Embedding.
`<INDEX-NAME>`	O nome do índice que você deseja criar.

Execute o seguinte comando para criar o índice.

node <file-name>.js

Exemplo

Por exemplo, para criar o índice definido no arquivo create_index.js, execute o seguinte comando no terminal:

node create_index.js

Crie um `.py` arquivo.

touch <file-name>.py

Exemplo

Por exemplo, crie um arquivo chamado create_index.py.

touch create_index.py

Defina o índice no `.py` arquivo.

1 from pymongo import MongoClient
2 from pymongo.operations import SearchIndexModel
3 
4 client = pymongo.MongoClient("<CONNECTION-STRING>")
5 db = client["<DATABASE-NAME>"]
6 collection = db["<COLLECTION-NAME>"]
7 
8 search_index_model = SearchIndexModel(
9   definition={
10     "fields": [
11       {
12         "type": "text",
13         "path": "<FIELD-NAME>",
14         "model": "voyage-3-large | voyage-3.5 | voyage-3.5-lite" 
15       }
16     ]
17   },
18   name="<INDEX-NAME>",
19   type="vectorSearch",
20 )
21 result = collection.create_search_index(model=search_index_model)
22 print(result)

Exemplo

1 from pymongo import MongoClient
2 from pymongo.operations import SearchIndexModel
3 
4 client = MongoClient("<CONNECTION-STRING>")
5 db = client["sample_mflix"]
6 collection = db["movies"]
7 
8 search_index_model = SearchIndexModel(
9   definition={
10     "fields": [
11       {
12         "type": "text",
13         "path": "fullplot",
14         "model": "voyage-3-large"
15       }
16     ]
17   },
18     name="movies_automated_embeddings",
19     type="vectorSearch",
20 )
21 result = collection.create_search_index(model=search_index_model)
22 print(result)

Substitua os seguintes valores e salve o arquivo.

`<CONNECTION-STRING>`	A string de conexão do seu Atlas cluster.
`<database-name>`	O nome do banco de dados que contém a collection para a qual você deseja criar o índice.
`<collection-name>`	O nome da coleção para a qual você deseja criar o índice.
`<field-name>`	The name of the field in the collection that you want to index for Automated Embedding.
`<index-name>`	O nome do índice que você deseja criar.

Execute o seguinte comando para criar o índice.

python <file-name>.py

Exemplo

Por exemplo, para criar o índice definido no arquivo create_index.py, execute o seguinte comando no terminal:

python create_index.py

Query de Vector Search do MongoDB com incorporação automatizada

After you create an index with Automated Embedding, you can run text queries against the indexed field. MongoDB Vector Search automatically generates embeddings for the text string in your query using the same embedding model specified in the index. It uses the embeddings to search the index for documents that are semantically similar to the specified query text.

The following sections describe the $vectorSearch pipeline syntax and fields for automatically generating embeddings for your query text against the field indexed for Automated Embedding and demonstrate how to run semantic search queries against the fields indexed for Automated Embedding.

Sintaxe de consulta

The following syntax demonstrates how to run a query against a field indexed for Automated Embedding:

1 {
2   "$vectorSearch": {
3     "index": "<index-name>",
4     "limit": <number-of-results>,
5     "numCandidates": <number-of-candidates>,
6     "path": "<field-to-search>",
7     "query": "<query-string>"
8   }
9 }

Campos de query

Os campos a seguir são obrigatórios para uma query do MongoDB Vector Search usando incorporações automatizadas:

Campo	Tipo	necessidade	Descrição
`exact`	booleano	Condicional	Este campo é obrigatório se `numCandidates` for omitido. Mutuamente exclusivo com `numCandidates`. Sinalizador que especifica se a pesquisa ENN ou ANN deve ser executada. O valor pode ser um dos seguintes: `false` - para executar a pesquisa ANN `true` - para executar a pesquisa ENN Se omitido, o padrão é `false`.
`index`	string	Obrigatório	Nome do índice do MongoDB Vector Search a ser usado. A Vector Search do MongoDB não retorna resultados se você digitar incorretamente o nome do índice ou se o índice especificado ainda não existir no cluster.
`limit`	número	Obrigatório	Número (somente do tipo `int`) de documentos para retornar nos resultados. Este valor não pode exceder o valor de `numCandidates` se você especificar `numCandidates`.
`numCandidates`	número	Condicional	Este campo é obrigatório se `exact` for `false` ou omitido. Mutuamente exclusivo com `exact`. Número de vizinhos mais próximos a serem utilizados durante a pesquisa. O valor deve ser menor ou igual a (`<=`) `10000`. Você não pode especificar um número menor que o número de documentos a serem retornados (`limit`).
`path`	string	Obrigatório	Campo do tipo vetor indexado para pesquisa.
`query`	string	Obrigatório	Texto para o qual deverão ser geradas automaticamente as incorporações e realizar a pesquisa semântica.

Considerações

Você pode executar uma query ANN ou ENN no campo indexado. Para saber mais, veja ANN pesquisa e ENN pesquisa.

You can't specify vector embeddings in your query against fields indexed for Automated Embedding. Instead, you must run a natural language query against the field. When you run a natural language query against the field indexed for Automated Embedding, MongoDB Vector Search automatically generates the embeddings for the query text using the same embedding model as the indexed field. It then uses the generated embeddings to perform a semantic search against the indexed field.

Opcionalmente, você pode especificar filter campos em sua query para pré-filtrar os documentos nos quais o MongoDB pesquisa vetorial executa a pesquisa semântica. Para saber mais, consulte Pré-filtragem da pesquisa vetorial do MongoDB.

Opcionalmente, você também pode recuperar a pontuação dos documentos nos resultados. Para saber mais, consulte Pontuação de pesquisa vetorial do MongoDB.

Executar uma query usando a incorporação automática

Você não pode executar queries do MongoDB Vector Search na UI do Atlas . Utilize o mongosh ou um driver suportado para executar queries.

Conecte ao Atlas cluster utilizando `mongosh` o.

Para saber mais, consulte Conectar-se a um cluster via mongosh.

Alterne para o banco de dados que contém a collection para a qual você deseja executar a query.

use <database-name>

Exemplo

Por exemplo, para alternar para o banco de dados sample_mflix, execute o seguinte comando no seu terminal:

use sample_mflix

Execute o `db.collection.aggregate()` método com o `$vectorSearch` estágio.

O método db.collection.aggregate() tem a seguinte sintaxe:

1 db.<COLLECTION-NAME>.aggregate([
2   {
3     "$vectorSearch": {
4       "index": "<INDEX-NAME>", 
5       "path": "<FIELD-NAME>", 
6       "query": "<QUERY-TEXT>", 
7       "numCandidates": <NUMBER-OF-CANDIDATES-TO-CONSIDER>, 
8       "limit": <NUMBER-OF-DOCUMENTS-TO-RETURN>
9     }
10   }
11 ])

Exemplo

Por exemplo, para executar uma consulta no campo fullplot na coleção movies para uma pesquisa semântica de filmes semanticamente semelhantes a young heroes caught in epic struggles between light and darkness, copie, cole e execute a seguinte consulta.

1 db.movies.aggregate([
2   {
3     "$vectorSearch": {
4       "index": "movies_automated_embeddings", 
5       "path": "fullplot", 
6       "query": "young heroes caught in epic struggles between light and darkness", 
7       "numCandidates": 1000, 
8       "limit": 10
9     }
10   },
11   {
12     "$project": {
13       "_id": 0, 
14       "title": 1, 
15       "fullplot": 1, 
16       "score": {"$meta": "vectorSearchScore"}
17     }
18   }
19 ])

1 [
2    {
3        title: 'Day Watch',
4        fullplot: 'Anton belongs to the Forces of the Light as well as his powerful girlfriend and apprentice, but his son is a powerful teenager from the Darkness and Anton protects him. When the balance between Light and Darkness is affected by the death of some evil vampires, Anton is framed and accused of the murders, and he chases an ancient chalk that has the power of changing the destiny of its owner.',
5        score: 0.5449697971343994
6    },
7    {
8        title: 'Dungeons & Dragons',
9        fullplot: "The Empire of Izmer has long been a divided land. The Mages - an elite group of magic-users - rule whilst the lowly commoners are powerless. Izmer's young Empress, Savina, wants equality and prosperity for all, but the evil Mage Profion is plotting to depose her and establish his own rule. The Empress possesses a scepter which controls Izmer's Golden Dragons. To challenge her rule, Profion must have the scepter, and tricks the Council of Mages into believing Savina is unfit to hold it. Knowing that Profion will bring death and destruction to Izmer, Savina must find the legendary Rod of Savrille, a mythical rod that has the power to control Red Dragons, a species even mightier than the Gold. Enter two thieves, Ridley and Snails, who unwittingly become instrumental in Savina's search for the Rod. Joined by a feisty Dwarf named Elwood, and helped by the Empress's expert tracker, the Elf Norda, the young heroes go in search of the Rod of Savrille. From the deadly maze of the Thieves Guild at Antius to an Elven Village, secret grotto and abandoned castles, Ridley and his band must outwit Profion's chief henchman Damodar at every turn while, back in Izmer, Profion prepares to do battle with the Empress. All depends on the Rod, but the outcome of the race to reach it first is far from certain, and Izmar's very survival hangs in the balance.",
10        score: 0.5414832830429077
11    },
12    {
13        title: 'Brave Story',
14        fullplot: 'A young boy attempts to change his destiny by entering a magic gateway to another world; but on his quest to find the Tower of Fortune and be granted any wish, he must conjure up all his bravery to battle demons, his friends, and ultimately himself.',
15        score: 0.5404887795448303
16    },
17    {
18        title: 'Justin and the Knights of Valour',
19        fullplot: 'Justin lives in a kingdom where bureaucrats rule and knights have been ousted. His dream is to be become one of the Knights of Valour, like his grandfather was, but his father Reginald, the chief counsel to the Queen, wants his son to follow in his footsteps and become a lawyer. After an inspiring visit to his beloved Grandmother and bidding farewell to his supposed lady-love Lara, Justin leaves home and embarks on a quest to become a knight. Along the way he meets the beautiful, feisty Talia, a quirky wizard called Melquiades, and the handsome Sir Clorex and is mentored by three monks; Blucher, Legantir and Braulio, who teach and test him in the ancient ways of the Knights of Valour. Whilst an unlikely candidate for knighthood, Justin must rise to the challenge quickly when banished former knight Sir Heraclio and his army, lead by Sota, return and threaten to destroy the Kingdom.',
20        score: 0.5374966859817505
21    },
22    {
23        title: 'Forest Warrior',
24        fullplot: 'John McKenna is a spiritual being who is able to transform into bear, wolf or eagle. He lives in the forests of Tanglewood and has dedicated his life to protect them. One day a gang of evil lumberjacks led by Travis Thorne arrive Tanglewood to chop the forest down. McKenna cannot let this happen, and together with his new friends - Lords of the Tanglewood, a band of children who love to play in the forest - he battles against Thorne and his evil gang.',
25        score: 0.5331881642341614
26    },
27    {
28        title: 'Forest Warrior',
29        fullplot: 'John McKenna is a spiritual being who is able to transform into bear, wolf or eagle. He lives in the forests of Tanglewood and has dedicated his life to protect them. One day a gang of evil lumberjacks led by Travis Thorne arrive Tanglewood to chop the forest down. McKenna cannot let this happen, and together with his new friends - Lords of the Tanglewood, a band of children who love to play in the forest - he battles against Thorne and his evil gang.',
30        score: 0.5331881642341614
31    },
32    {
33        title: 'Catatan (Harian) si Boy',
34        fullplot: "A circle of friends risking their Friendship, Trust, Love and Hope in search of a legend. A young and privileged teenager with a golden heart, beset with challenges and tribulations we face today with the goal to open many young people's mind with inspirations and hopes that drive them in achieving their dreams. To get out of their comfort zone and finish what they started.",
35        score: 0.5322973728179932
36    },
37    {
38        title: 'Bionicle: Mask of Light',
39        fullplot: "In a land of living machines, two young ones are chosen to seek the legendary Mask of Light to reveal the savior of all the lands from the dark forces of the Makuta. During the course of their adventure, they will call on the heroes of their people, the great Toa. These Toa, masters of nature's forces such as Fire, Wind, Earth & Water, try to protect the chosen ones as they seek their destiny.",
40        score: 0.5315042734146118
41    },
42    {
43        title: 'Fear No Evil',
44        fullplot: 'High school student turns out to be personification of Lucifer. Two arch angels in human form (as women) take him on.',
45        score: 0.5295513868331909
46    },
47    {
48        title: 'Tales of Vesperia: The First Strike',
49        fullplot: 'In a mythical kingdom, the mighty Imperial Knights harness a magical substance known as Aer to power their weapons and protect humanity from the monsters of the forest. But something strange is afoot. The Aer is somehow changing, causing the wilderness to waste away and stirring the woodland beasts to attack with greater frequency. As danger creeps steadily closer to civilization, two young recruits - Flynn, the rigid son of a fallen hero, and the rebellious and brash Yuri - must ride with their fellow Imperial Knights to distant ruins in hopes of uncovering the truth behind the transforming Aer. Some will not survive the thrilling journey. Some will be betrayed. If Flynn and Yuri cannot overcome their differences and learn to fight together, all will be lost for the people of the realm.',
50        score: 0.5276793241500854
51    }
52 ]

Crie um `.js` arquivo.

Exemplo

Por exemplo, crie um arquivo chamado automated-embeddings-query.js.

touch automated-embeddings-query.js

Defina a query no arquivo.

1 const { MongoClient } = require("mongodb");
2 
3 // connect to your Atlas cluster
4 const uri = "<CONNECTION-STRING>";
5 
6 const client = new MongoClient(uri);
7 
8 async function run() {
9     try {
10         await client.connect();
11 
12         // set namespace
13         const database = client.db("<DATABASE-NAME>");
14         const coll = database.collection("<COLLECTION-NAME>");
15 
16         // define pipeline
17         const agg = [
18             {
19               '$vectorSearch': {
20                 'index': '<INDEX-NAME>',
21                 'path': '<FIELD-NAME>',
22                 'query': "<QUERY-TEXT>",
23                 'numCandidates': <NUMBER-OF-CANDIDATES-TO-CONSIDER>,
24                 'limit': <NUMBER-OF-DOCUMENTS-TO-RETURN>
25               }
26             }
27           ];
28         // run pipeline
29         const result = coll.aggregate(agg);
30 
31         // print results
32         await result.forEach((doc) => console.dir(JSON.stringify(doc)));
33     } finally {
34         await client.close();
35     }
36 }
37 run().catch(console.dir);

Exemplo

Por exemplo, no arquivo automated-embeddings-query.js , copie e cole o seguinte código para definir uma query no campo fullplot na collection movies para uma pesquisa semântica de filmes semanticamente semelhantes a young heroes caught in epic struggles between light and darkness.

1 const { MongoClient } = require("mongodb");
2 
3 // connect to your Atlas cluster
4 const uri = "<CONNECTION-STRING>";
5 
6 const client = new MongoClient(uri);
7 
8 async function run() {
9     try {
10         await client.connect();
11 
12         // set namespace
13         const database = client.db("sample_mflix");
14         const coll = database.collection("movies");
15 
16         // define pipeline
17         const agg = [
18             {
19               '$vectorSearch': {
20                 'index': 'movies_automated_embeddings',
21                 'path': 'fullplot',
22                 'query': "young heroes caught in epic struggles between light and darkness",
23                 'numCandidates': 100,
24                 'limit': 10
25               }
26             }, {
27               '$project': {
28                 '_id': 0,
29                 'fullplot': 1,
30                 'title': 1,
31                 'score': {
32                   '$meta': 'vectorSearchScore'
33                 }
34               }
35             }
36           ];
37         // run pipeline
38         const result = coll.aggregate(agg);
39 
40         // print results
41         await result.forEach((doc) => console.dir(JSON.stringify(doc)));
42     } finally {
43         await client.close();
44     }
45 }
46 run().catch(console.dir);

Substitua o seguinte na query.

`<CONNECTION-STRING>`	string de conexão do cluster.
`<DATABASE-NAME>`	Nome do banco de dados que contém a coleção.
`<COLLECTION-NAME>`	Nome da collection que contém o campo indexado.
`<INDEX-NAME>`	Nome do índice.
`<FIELD-NAME>`	Nome do campo indexado.
`<QUERY-TEXT>`	String de texto para gerar incorporações e usar na pesquisa semântica.
`<NUMBER-OF-CANDIDATES-TO-CONSIDER>`	Número de vizinhos mais próximos a serem considerados.
`<NUMBER-OF-DOCUMENTS-TO-RETURN>`	Número de documentos a retornar nos resultados.

Execute a consulta.

node <FILE-NAME>.js

Aqui <FILE-NAME> está o nome do arquivo .js que você criou.

Exemplo

Por exemplo, para executar a query de exemplo no arquivo automated-embeddings-query.js, execute o seguinte comando:

node automated-embeddings-query.js

'{"title":"Day Watch","fullplot":"Anton belongs to the Forces of the Light as well as his powerful girlfriend and apprentice, but his son is a powerful teenager from the Darkness and Anton protects him. When the balance between Light and Darkness is affected by the death of some evil vampires, Anton is framed and accused of the murders, and he chases an ancient chalk that has the power of changing the destiny of its owner.","score":0.5449697971343994}'
`{"title":"Dungeons & Dragons","fullplot":"The Empire of Izmer has long been a divided land. The Mages - an elite group of magic-users - rule whilst the lowly commoners are powerless. Izmer's young Empress, Savina, wants equality and prosperity for all, but the evil Mage Profion is plotting to depose her and establish his own rule. The Empress possesses a scepter which controls Izmer's Golden Dragons. To challenge her rule, Profion must have the scepter, and tricks the Council of Mages into believing Savina is unfit to hold it. Knowing that Profion will bring death and destruction to Izmer, Savina must find the legendary Rod of Savrille, a mythical rod that has the power to control Red Dragons, a species even mightier than the Gold. Enter two thieves, Ridley and Snails, who unwittingly become instrumental in Savina's search for the Rod. Joined by a feisty Dwarf named Elwood, and helped by the Empress's expert tracker, the Elf Norda, the young heroes go in search of the Rod of Savrille. From the deadly maze of the Thieves Guild at Antius to an Elven Village, secret grotto and abandoned castles, Ridley and his band must outwit Profion's chief henchman Damodar at every turn while, back in Izmer, Profion prepares to do battle with the Empress. All depends on the Rod, but the outcome of the race to reach it first is far from certain, and Izmar's very survival hangs in the balance.","score":0.5414832830429077}`
'{"title":"Brave Story","fullplot":"A young boy attempts to change his destiny by entering a magic gateway to another world; but on his quest to find the Tower of Fortune and be granted any wish, he must conjure up all his bravery to battle demons, his friends, and ultimately himself.","score":0.5404887795448303}'
'{"title":"Justin and the Knights of Valour","fullplot":"Justin lives in a kingdom where bureaucrats rule and knights have been ousted. His dream is to be become one of the Knights of Valour, like his grandfather was, but his father Reginald, the chief counsel to the Queen, wants his son to follow in his footsteps and become a lawyer. After an inspiring visit to his beloved Grandmother and bidding farewell to his supposed lady-love Lara, Justin leaves home and embarks on a quest to become a knight. Along the way he meets the beautiful, feisty Talia, a quirky wizard called Melquiades, and the handsome Sir Clorex and is mentored by three monks; Blucher, Legantir and Braulio, who teach and test him in the ancient ways of the Knights of Valour. Whilst an unlikely candidate for knighthood, Justin must rise to the challenge quickly when banished former knight Sir Heraclio and his army, lead by Sota, return and threaten to destroy the Kingdom.","score":0.5374966859817505}'
'{"title":"Forest Warrior","fullplot":"John McKenna is a spiritual being who is able to transform into bear, wolf or eagle. He lives in the forests of Tanglewood and has dedicated his life to protect them. One day a gang of evil lumberjacks led by Travis Thorne arrive Tanglewood to chop the forest down. McKenna cannot let this happen, and together with his new friends - Lords of the Tanglewood, a band of children who love to play in the forest - he battles against Thorne and his evil gang.","score":0.5331881642341614}'
'{"title":"Forest Warrior","fullplot":"John McKenna is a spiritual being who is able to transform into bear, wolf or eagle. He lives in the forests of Tanglewood and has dedicated his life to protect them. One day a gang of evil lumberjacks led by Travis Thorne arrive Tanglewood to chop the forest down. McKenna cannot let this happen, and together with his new friends - Lords of the Tanglewood, a band of children who love to play in the forest - he battles against Thorne and his evil gang.","score":0.5331881642341614}'
`{"title":"Catatan (Harian) si Boy","fullplot":"A circle of friends risking their Friendship, Trust, Love and Hope in search of a legend. A young and privileged teenager with a golden heart, beset with challenges and tribulations we face today with the goal to open many young people's mind with inspirations and hopes that drive them in achieving their dreams. To get out of their comfort zone and finish what they started.","score":0.5322973728179932}`
`{"title":"Bionicle: Mask of Light","fullplot":"In a land of living machines, two young ones are chosen to seek the legendary Mask of Light to reveal the savior of all the lands from the dark forces of the Makuta. During the course of their adventure, they will call on the heroes of their people, the great Toa. These Toa, masters of nature's forces such as Fire, Wind, Earth & Water, try to protect the chosen ones as they seek their destiny.","score":0.5315042734146118}`
'{"title":"Fear No Evil","fullplot":"High school student turns out to be personification of Lucifer. Two arch angels in human form (as women) take him on.","score":0.5295513868331909}'
'{"title":"Tales of Vesperia: The First Strike","fullplot":"In a mythical kingdom, the mighty Imperial Knights harness a magical substance known as Aer to power their weapons and protect humanity from the monsters of the forest. But something strange is afoot. The Aer is somehow changing, causing the wilderness to waste away and stirring the woodland beasts to attack with greater frequency. As danger creeps steadily closer to civilization, two young recruits - Flynn, the rigid son of a fallen hero, and the rebellious and brash Yuri - must ride with their fellow Imperial Knights to distant ruins in hopes of uncovering the truth behind the transforming Aer. Some will not survive the thrilling journey. Some will be betrayed. If Flynn and Yuri cannot overcome their differences and learn to fight together, all will be lost for the people of the realm.","score":0.5276793241500854}'

Crie um `.py` arquivo.

Exemplo

Por exemplo, crie um arquivo chamado automated-embeddings-query.js.

touch automated-embeddings-query.py

Defina a query no arquivo.

1 import pymongo
2 
3 # connect to your Atlas cluster
4 client = pymongo.MongoClient("<CONNECTION-STRING>")
5 
6 # define pipeline
7 pipeline = [
8   {
9     '$vectorSearch': {
10       'index': '<INDEX-NAME>', 
11       'path': '<FIELD-NAME>', 
12       'query': '<QUERY-TEXT>', 
13       'numCandidates': <NUMBER-OF-CANDIDATES-TO-CONSIDER>,
14       'limit': <NUMBER-OF-DOCUMENTS-TO-RETURN>
15     }
16   }
17 ]
18 
19 # run pipeline
20 result = client["<DATABASE-NAME>"]["<COLLECTION-NAME>"].aggregate(pipeline)
21 
22 # print results
23 for i in result:
24     print(i)
25

Exemplo

Por exemplo, no arquivo automated-embeddings-query.py , copie e cole o seguinte código para definir uma query no campo fullplot na collection movies para uma pesquisa semântica de filmes semanticamente semelhantes a young heroes caught in epic struggles between light and darkness.

1 import pymongo
2 
3 # connect to your Atlas cluster
4 client = pymongo.MongoClient("<CONNECTION-STRING>")
5 
6 # define pipeline
7 pipeline = [
8   {
9     '$vectorSearch': {
10       'index': 'movies_automated_embeddings',
11       'path': 'fullplot',
12       'query': "young heroes caught in epic struggles between light and darkness",
13       'numCandidates': 100,
14       'limit': 10
15     },
16   },
17   {
18     '$project': {
19       '_id': 0,
20       'title': 1,
21       'fullplot': 1,
22       'score': {'$meta': 'vectorSearchScore'}
23     }
24   }
25 ]
26 
27 # run pipeline
28 result = client["sample_mflix"]["movies"].aggregate(pipeline)
29 
30 # print results
31 for i in result:
32     print(i)
33

Substitua o seguinte na query.

`<CONNECTION-STRING>`	string de conexão do cluster.
`<DATABASE-NAME>`	Nome do banco de dados que contém a coleção.
`<COLLECTION-NAME>`	Nome da collection que contém o campo indexado.
`<INDEX-NAME>`	Nome do índice.
`<FIELD-NAME>`	Nome do campo indexado.
`<QUERY-TEXT>`	String de texto para gerar incorporações e usar na pesquisa semântica.
`<NUMBER-OF-CANDIDATES-TO-CONSIDER>`	Número de vizinhos mais próximos a serem considerados.
`<NUMBER-OF-DOCUMENTS-TO-RETURN>`	Número de documentos a retornar nos resultados.

Execute a consulta.

python <FILE-NAME>.py

Aqui <FILE-NAME> está o nome do arquivo .py que você criou.

Exemplo

Por exemplo, para executar a query de exemplo no arquivo automated-embeddings-query.py, execute o seguinte comando:

python automated-embeddings-query.js

{'title': 'Day Watch', 'fullplot': 'Anton belongs to the Forces of the Light as well as his powerful girlfriend and apprentice, but his son is a powerful teenager from the Darkness and Anton protects him. When the balance between Light and Darkness is affected by the death of some evil vampires, Anton is framed and accused of the murders, and he chases an ancient chalk that has the power of changing the destiny of its owner.', 'score': 0.5449697971343994}
{'title': 'Dungeons & Dragons', 'fullplot': "The Empire of Izmer has long been a divided land. The Mages - an elite group of magic-users - rule whilst the lowly commoners are powerless. Izmer's young Empress, Savina, wants equality and prosperity for all, but the evil Mage Profion is plotting to depose her and establish his own rule. The Empress possesses a scepter which controls Izmer's Golden Dragons. To challenge her rule, Profion must have the scepter, and tricks the Council of Mages into believing Savina is unfit to hold it. Knowing that Profion will bring death and destruction to Izmer, Savina must find the legendary Rod of Savrille, a mythical rod that has the power to control Red Dragons, a species even mightier than the Gold. Enter two thieves, Ridley and Snails, who unwittingly become instrumental in Savina's search for the Rod. Joined by a feisty Dwarf named Elwood, and helped by the Empress's expert tracker, the Elf Norda, the young heroes go in search of the Rod of Savrille. From the deadly maze of the Thieves Guild at Antius to an Elven Village, secret grotto and abandoned castles, Ridley and his band must outwit Profion's chief henchman Damodar at every turn while, back in Izmer, Profion prepares to do battle with the Empress. All depends on the Rod, but the outcome of the race to reach it first is far from certain, and Izmar's very survival hangs in the balance.", 'score': 0.5414832830429077}
{'title': 'Brave Story', 'fullplot': 'A young boy attempts to change his destiny by entering a magic gateway to another world; but on his quest to find the Tower of Fortune and be granted any wish, he must conjure up all his bravery to battle demons, his friends, and ultimately himself.', 'score': 0.5404887795448303}
{'title': 'Justin and the Knights of Valour', 'fullplot': 'Justin lives in a kingdom where bureaucrats rule and knights have been ousted. His dream is to be become one of the Knights of Valour, like his grandfather was, but his father Reginald, the chief counsel to the Queen, wants his son to follow in his footsteps and become a lawyer. After an inspiring visit to his beloved Grandmother and bidding farewell to his supposed lady-love Lara, Justin leaves home and embarks on a quest to become a knight. Along the way he meets the beautiful, feisty Talia, a quirky wizard called Melquiades, and the handsome Sir Clorex and is mentored by three monks; Blucher, Legantir and Braulio, who teach and test him in the ancient ways of the Knights of Valour. Whilst an unlikely candidate for knighthood, Justin must rise to the challenge quickly when banished former knight Sir Heraclio and his army, lead by Sota, return and threaten to destroy the Kingdom.', 'score': 0.5374966859817505}
{'title': 'Forest Warrior', 'fullplot': 'John McKenna is a spiritual being who is able to transform into bear, wolf or eagle. He lives in the forests of Tanglewood and has dedicated his life to protect them. One day a gang of evil lumberjacks led by Travis Thorne arrive Tanglewood to chop the forest down. McKenna cannot let this happen, and together with his new friends - Lords of the Tanglewood, a band of children who love to play in the forest - he battles against Thorne and his evil gang.', 'score': 0.5331881642341614}
{'title': 'Forest Warrior', 'fullplot': 'John McKenna is a spiritual being who is able to transform into bear, wolf or eagle. He lives in the forests of Tanglewood and has dedicated his life to protect them. One day a gang of evil lumberjacks led by Travis Thorne arrive Tanglewood to chop the forest down. McKenna cannot let this happen, and together with his new friends - Lords of the Tanglewood, a band of children who love to play in the forest - he battles against Thorne and his evil gang.', 'score': 0.5331881642341614}
{'title': 'Catatan (Harian) si Boy', 'fullplot': "A circle of friends risking their Friendship, Trust, Love and Hope in search of a legend. A young and privileged teenager with a golden heart, beset with challenges and tribulations we face today with the goal to open many young people's mind with inspirations and hopes that drive them in achieving their dreams. To get out of their comfort zone and finish what they started.", 'score': 0.5322973728179932}
{'title': 'Bionicle: Mask of Light', 'fullplot': "In a land of living machines, two young ones are chosen to seek the legendary Mask of Light to reveal the savior of all the lands from the dark forces of the Makuta. During the course of their adventure, they will call on the heroes of their people, the great Toa. These Toa, masters of nature's forces such as Fire, Wind, Earth & Water, try to protect the chosen ones as they seek their destiny.", 'score': 0.5315042734146118}
{'title': 'Fear No Evil', 'fullplot': 'High school student turns out to be personification of Lucifer. Two arch angels in human form (as women) take him on.', 'score': 0.5295513868331909}
{'title': 'Tales of Vesperia: The First Strike', 'fullplot': 'In a mythical kingdom, the mighty Imperial Knights harness a magical substance known as Aer to power their weapons and protect humanity from the monsters of the forest. But something strange is afoot. The Aer is somehow changing, causing the wilderness to waste away and stirring the woodland beasts to attack with greater frequency. As danger creeps steadily closer to civilization, two young recruits - Flynn, the rigid son of a fallen hero, and the rebellious and brash Yuri - must ride with their fellow Imperial Knights to distant ruins in hopes of uncovering the truth behind the transforming Aer. Some will not survive the thrilling journey. Some will be betrayed. If Flynn and Yuri cannot overcome their differences and learn to fight together, all will be lost for the people of the realm.', 'score': 0.5276793241500854}

1	{
2	"fields": [
3	{
4	"type": "text",
5	"path": "<field-name>",
6	"model": "voyage-3-large \| voyage-3.5 \| voyage-3.5-lite"
7	}
8	]
9	}

1	{
2	"fields": [
3	{
4	"type": "text",
5	"path": "fullplot",
6	"model": "voyage-3-large"
7	}
8	]
9	}

1	db.<COLLECTION-NAME>.createSearchIndex(
2	"<INDEX-NAME>",
3	"vectorSearch",
4	{
5	"fields": [
6	{
7	"type": "text",
8	"path": "<FIELD-NAME>",
9	"model": "voyage-3-large \| voyage-3.5 \| voyage-3.5-lite"
10	},
11	{
12	"type": "filter",
13	"path": "<FIELD-NAME>"
14	},
15	...
16	]
17	}
18	);

1	db.movies.createSearchIndex(
2	"movies_automated_embeddings",
3	"vectorSearch",
4	{
5	"fields": [
6	{
7	"type": "text",
8	"path": "fullplot",
9	"model": "voyage-3-large"
10	}
11	]
12	}
13	);

1	const { MongoClient } = require("mongodb");
2
3	// connect to your Atlas deployment
4	const uri = "<CONNECTION-STRING>";
5	const client = new MongoClient(uri);
6
7	async function run() {
8	try {
9	const database = client.db("<DATABASE-NAME>");
10	const collection = database.collection("<COLLECTION-NAME>");
11
12	// define your MongoDB Vector Search index
13	const index = {
14	"name": "<INDEX-NAME>",
15	"type": "vectorSearch",
16	"definition": {
17	"fields": [
18	{
19	"type": "vector",
20	"path": "<FIELD-NAME>",
21	"model": "voyage-3-large \| voyage-3.5 \| voyage-3.5-lite"
22	},
23	]
24	}
25	}
26	// run the helper method
27	await collection.createSearchIndex("<INDEX-NAME>", index);
28	} finally {
29	await client.close();
30	}
31	}
32	run().catch(console.dir);

1	from pymongo import MongoClient
2	from pymongo.operations import SearchIndexModel
3
4	client = pymongo.MongoClient("<CONNECTION-STRING>")
5	db = client["<DATABASE-NAME>"]
6	collection = db["<COLLECTION-NAME>"]
7
8	search_index_model = SearchIndexModel(
9	definition={
10	"fields": [
11	{
12	"type": "text",
13	"path": "<FIELD-NAME>",
14	"model": "voyage-3-large \| voyage-3.5 \| voyage-3.5-lite"
15	}
16	]
17	},
18	name="<INDEX-NAME>",
19	type="vectorSearch",
20	)
21	result = collection.create_search_index(model=search_index_model)
22	print(result)

1	from pymongo import MongoClient
2	from pymongo.operations import SearchIndexModel
3
4	client = MongoClient("<CONNECTION-STRING>")
5	db = client["sample_mflix"]
6	collection = db["movies"]
7
8	search_index_model = SearchIndexModel(
9	definition={
10	"fields": [
11	{
12	"type": "text",
13	"path": "fullplot",
14	"model": "voyage-3-large"
15	}
16	]
17	},
18	name="movies_automated_embeddings",
19	type="vectorSearch",
20	)
21	result = collection.create_search_index(model=search_index_model)
22	print(result)

1	{
2	"$vectorSearch": {
3	"index": "<index-name>",
4	"limit": <number-of-results>,
5	"numCandidates": <number-of-candidates>,
6	"path": "<field-to-search>",
7	"query": "<query-string>"
8	}
9	}

1	db.<COLLECTION-NAME>.aggregate([
2	{
3	"$vectorSearch": {
4	"index": "<INDEX-NAME>",
5	"path": "<FIELD-NAME>",
6	"query": "<QUERY-TEXT>",
7	"numCandidates": <NUMBER-OF-CANDIDATES-TO-CONSIDER>,
8	"limit": <NUMBER-OF-DOCUMENTS-TO-RETURN>
9	}
10	}
11	])

1	db.movies.aggregate([
2	{
3	"$vectorSearch": {
4	"index": "movies_automated_embeddings",
5	"path": "fullplot",
6	"query": "young heroes caught in epic struggles between light and darkness",
7	"numCandidates": 1000,
8	"limit": 10
9	}
10	},
11	{
12	"$project": {
13	"_id": 0,
14	"title": 1,
15	"fullplot": 1,
16	"score": {"$meta": "vectorSearchScore"}
17	}
18	}
19	])

1	[
2	{
3	title: 'Day Watch',
4	fullplot: 'Anton belongs to the Forces of the Light as well as his powerful girlfriend and apprentice, but his son is a powerful teenager from the Darkness and Anton protects him. When the balance between Light and Darkness is affected by the death of some evil vampires, Anton is framed and accused of the murders, and he chases an ancient chalk that has the power of changing the destiny of its owner.',
5	score: 0.5449697971343994
6	},
7	{
8	title: 'Dungeons & Dragons',
9	fullplot: "The Empire of Izmer has long been a divided land. The Mages - an elite group of magic-users - rule whilst the lowly commoners are powerless. Izmer's young Empress, Savina, wants equality and prosperity for all, but the evil Mage Profion is plotting to depose her and establish his own rule. The Empress possesses a scepter which controls Izmer's Golden Dragons. To challenge her rule, Profion must have the scepter, and tricks the Council of Mages into believing Savina is unfit to hold it. Knowing that Profion will bring death and destruction to Izmer, Savina must find the legendary Rod of Savrille, a mythical rod that has the power to control Red Dragons, a species even mightier than the Gold. Enter two thieves, Ridley and Snails, who unwittingly become instrumental in Savina's search for the Rod. Joined by a feisty Dwarf named Elwood, and helped by the Empress's expert tracker, the Elf Norda, the young heroes go in search of the Rod of Savrille. From the deadly maze of the Thieves Guild at Antius to an Elven Village, secret grotto and abandoned castles, Ridley and his band must outwit Profion's chief henchman Damodar at every turn while, back in Izmer, Profion prepares to do battle with the Empress. All depends on the Rod, but the outcome of the race to reach it first is far from certain, and Izmar's very survival hangs in the balance.",
10	score: 0.5414832830429077
11	},
12	{
13	title: 'Brave Story',
14	fullplot: 'A young boy attempts to change his destiny by entering a magic gateway to another world; but on his quest to find the Tower of Fortune and be granted any wish, he must conjure up all his bravery to battle demons, his friends, and ultimately himself.',
15	score: 0.5404887795448303
16	},
17	{
18	title: 'Justin and the Knights of Valour',
19	fullplot: 'Justin lives in a kingdom where bureaucrats rule and knights have been ousted. His dream is to be become one of the Knights of Valour, like his grandfather was, but his father Reginald, the chief counsel to the Queen, wants his son to follow in his footsteps and become a lawyer. After an inspiring visit to his beloved Grandmother and bidding farewell to his supposed lady-love Lara, Justin leaves home and embarks on a quest to become a knight. Along the way he meets the beautiful, feisty Talia, a quirky wizard called Melquiades, and the handsome Sir Clorex and is mentored by three monks; Blucher, Legantir and Braulio, who teach and test him in the ancient ways of the Knights of Valour. Whilst an unlikely candidate for knighthood, Justin must rise to the challenge quickly when banished former knight Sir Heraclio and his army, lead by Sota, return and threaten to destroy the Kingdom.',
20	score: 0.5374966859817505
21	},
22	{
23	title: 'Forest Warrior',
24	fullplot: 'John McKenna is a spiritual being who is able to transform into bear, wolf or eagle. He lives in the forests of Tanglewood and has dedicated his life to protect them. One day a gang of evil lumberjacks led by Travis Thorne arrive Tanglewood to chop the forest down. McKenna cannot let this happen, and together with his new friends - Lords of the Tanglewood, a band of children who love to play in the forest - he battles against Thorne and his evil gang.',
25	score: 0.5331881642341614
26	},
27	{
28	title: 'Forest Warrior',
29	fullplot: 'John McKenna is a spiritual being who is able to transform into bear, wolf or eagle. He lives in the forests of Tanglewood and has dedicated his life to protect them. One day a gang of evil lumberjacks led by Travis Thorne arrive Tanglewood to chop the forest down. McKenna cannot let this happen, and together with his new friends - Lords of the Tanglewood, a band of children who love to play in the forest - he battles against Thorne and his evil gang.',
30	score: 0.5331881642341614
31	},
32	{
33	title: 'Catatan (Harian) si Boy',
34	fullplot: "A circle of friends risking their Friendship, Trust, Love and Hope in search of a legend. A young and privileged teenager with a golden heart, beset with challenges and tribulations we face today with the goal to open many young people's mind with inspirations and hopes that drive them in achieving their dreams. To get out of their comfort zone and finish what they started.",
35	score: 0.5322973728179932
36	},
37	{
38	title: 'Bionicle: Mask of Light',
39	fullplot: "In a land of living machines, two young ones are chosen to seek the legendary Mask of Light to reveal the savior of all the lands from the dark forces of the Makuta. During the course of their adventure, they will call on the heroes of their people, the great Toa. These Toa, masters of nature's forces such as Fire, Wind, Earth & Water, try to protect the chosen ones as they seek their destiny.",
40	score: 0.5315042734146118
41	},
42	{
43	title: 'Fear No Evil',
44	fullplot: 'High school student turns out to be personification of Lucifer. Two arch angels in human form (as women) take him on.',
45	score: 0.5295513868331909
46	},
47	{
48	title: 'Tales of Vesperia: The First Strike',
49	fullplot: 'In a mythical kingdom, the mighty Imperial Knights harness a magical substance known as Aer to power their weapons and protect humanity from the monsters of the forest. But something strange is afoot. The Aer is somehow changing, causing the wilderness to waste away and stirring the woodland beasts to attack with greater frequency. As danger creeps steadily closer to civilization, two young recruits - Flynn, the rigid son of a fallen hero, and the rebellious and brash Yuri - must ride with their fellow Imperial Knights to distant ruins in hopes of uncovering the truth behind the transforming Aer. Some will not survive the thrilling journey. Some will be betrayed. If Flynn and Yuri cannot overcome their differences and learn to fight together, all will be lost for the people of the realm.',
50	score: 0.5276793241500854
51	}
52	]

1	const { MongoClient } = require("mongodb");
2
3	// connect to your Atlas cluster
4	const uri = "<CONNECTION-STRING>";
5
6	const client = new MongoClient(uri);
7
8	async function run() {
9	try {
10	await client.connect();
11
12	// set namespace
13	const database = client.db("<DATABASE-NAME>");
14	const coll = database.collection("<COLLECTION-NAME>");
15
16	// define pipeline
17	const agg = [
18	{
19	'$vectorSearch': {
20	'index': '<INDEX-NAME>',
21	'path': '<FIELD-NAME>',
22	'query': "<QUERY-TEXT>",
23	'numCandidates': <NUMBER-OF-CANDIDATES-TO-CONSIDER>,
24	'limit': <NUMBER-OF-DOCUMENTS-TO-RETURN>
25	}
26	}
27	];
28	// run pipeline
29	const result = coll.aggregate(agg);
30
31	// print results
32	await result.forEach((doc) => console.dir(JSON.stringify(doc)));
33	} finally {
34	await client.close();
35	}
36	}
37	run().catch(console.dir);

1	import pymongo
2
3	# connect to your Atlas cluster
4	client = pymongo.MongoClient("<CONNECTION-STRING>")
5
6	# define pipeline
7	pipeline = [
8	{
9	'$vectorSearch': {
10	'index': '<INDEX-NAME>',
11	'path': '<FIELD-NAME>',
12	'query': '<QUERY-TEXT>',
13	'numCandidates': <NUMBER-OF-CANDIDATES-TO-CONSIDER>,
14	'limit': <NUMBER-OF-DOCUMENTS-TO-RETURN>
15	}
16	}
17	]
18
19	# run pipeline
20	result = client["<DATABASE-NAME>"]["<COLLECTION-NAME>"].aggregate(pipeline)
21
22	# print results
23	for i in result:
24	print(i)
25

Considerações

Importante

Pré-requisitos

Índice de Vector Search do MongoDB para incorporação automatizada

Acesso necessário

Sintaxe do Índice

Campos do índice

Considerações

Criar um índice para incorporação automatizada

No Atlas, vá .leafygreen-ui-8n27nz{font-style:normal;font-weight:700;}Clusters para a página do seu projeto.

No Atlas, vá Search & Vector Search para a página para seu cluster.

Inicie a configuração do seu índice.

Exemplo

Defina seu índice.

Exemplo

Crie seu índice.

Observação

Conecte ao Atlas cluster utilizando mongosh o.

Alterne para o banco de dados que contém a collection para a qual você deseja criar o índice.

Exemplo

Execute o db.collection.createSearchIndex() método.

Exemplo

Inicialize seu projeto Node.js.

Crie o .js arquivo.

Exemplo

Defina o índice no .js arquivo.

Exemplo

Substitua os seguintes valores e salve o arquivo.

Execute o seguinte comando para criar o índice.

Exemplo

Crie um .py arquivo.

Exemplo

Defina o índice no .py arquivo.

Exemplo

Substitua os seguintes valores e salve o arquivo.

Execute o seguinte comando para criar o índice.

Exemplo

Query de Vector Search do MongoDB com incorporação automatizada

Sintaxe de consulta

Campos de query

Considerações

Executar uma query usando a incorporação automática

Conecte ao Atlas cluster utilizando mongosh o.

Alterne para o banco de dados que contém a collection para a qual você deseja executar a query.

Exemplo

Execute o db.collection.aggregate() método com o $vectorSearch estágio.

Exemplo

Crie um .js arquivo.

Exemplo

Defina a query no arquivo.

Exemplo

Substitua o seguinte na query.

Execute a consulta.

Exemplo

Crie um .py arquivo.

Exemplo

Defina a query no arquivo.

Exemplo

Substitua o seguinte na query.

Execute a consulta.

Exemplo

No Atlas, vá Clusters para a página do seu projeto.

Conecte ao Atlas cluster utilizando `mongosh` o.

Execute o `db.collection.createSearchIndex()` método.

Crie o `.js` arquivo.

Defina o índice no `.js` arquivo.

Crie um `.py` arquivo.

Defina o índice no `.py` arquivo.

Conecte ao Atlas cluster utilizando `mongosh` o.

Execute o `db.collection.aggregate()` método com o `$vectorSearch` estágio.

Crie um `.js` arquivo.

Crie um `.py` arquivo.