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Implemente o MongoDB Search e o Vector Search

Use o MongoDB Search e o Vector Search

Se você instalou o recurso MongoDBSearch com sucesso, poderá criar índices do MongoDB Search e Vector Search como mdb-user e executar queries do MongoDB Search e Vector Search usando os índices. Esta página descreve como carregar dados de amostra, criar índices de MongoDB Search e Vector Search para os dados e executar queries de MongoDB Search e Vector Search nos dados.

Pré-requisitos

Para usar o MongoDB Search e o Vector Search, você deve ter o seguinte:

  • Implementação bem-sucedida de MongoDB e MongoDBSearch.

    Para saber mais, consulte:

    • Instalar e usar a pesquisa com o MongoDB Enterprise Edition

    • Instalar e usar o MongoDB Search e o Vector Search com edição externa do MongoDB Enterprise

  • Usuário com permissões para criar queries do MongoDB Search e Vector Search e executar queries do MongoDB Search e Vector Search .

    Para saber mais, consulte Configurar um usuário do Cloud Manager ou do Ops Manager para MongoDB Search e Vector Search.

  • A ferramenta de linha de comando do Kubernetes, kubectl, configurada para se comunicar com seu cluster.

  • Bash v5.1 ou superior para executar os comandos neste tutorial.

Procedimento

1

Defina as variáveis de ambiente.

Para definir as variáveis de ambiente para uso nas etapas subsequentes deste procedimento, copie o seguinte, defina os valores para as variáveis de ambiente e carregue as variáveis de ambiente:

1# The env vars here are all commented out because this snippets module
2# is reusable across different MongoDB deployments and all the necessary variables
3# should be already defined there.
4
5# set it to the context name of the k8s cluster
6#export K8S_CTX="<local cluster context>"
7# the following namespace will be used to deploy mongodb tools pod
8#export MDB_NS="mongodb"
9
10# regular user performing restore and search queries on sample mflix database
11# the user should be able to restore database using mongorestore
12#export MDB_USER_PASSWORD="mdb-user-password-CHANGE-ME"
13
14# name of MongoDB or MongoDBCommunity resource in case it's deployed in the same cluster
15# user only for the connection string in MDB_CONNECTION_STRING env var below
16#export MDB_RESOURCE_NAME="mdbc-rs"
17
18# default connection string if MongoDB database is deployed using the operator
19#export MDB_CONNECTION_STRING="mongodb://mdb-user:${MDB_USER_PASSWORD}@${MDB_RESOURCE_NAME}-0.${MDB_RESOURCE_NAME}-svc.${MDB_NS}.svc.cluster.local:27017/?replicaSet=${MDB_RESOURCE_NAME}"
2

Implemente um pod MongoDB FERRAMENTAS.

Implemente um pod denominado mongodb-tools-pod para executar o cliente MongoDB FERRAMENTAS como mongosh e mongorestore a partir do cluster Kubernetes. A execução de um pod dentro do cluster simplifica a conectividade com o mongodb sem precisar expor o banco de dados externamente.

O comando a seguir implementa um pod chamado mongodb-tools-pod que executa uma imagem do servidor do MongoDB Community . O pod é mantido em execução com um comando sleep infinity para permitir que você use kubectl exec para executar o cliente MongoDB FERRAMENTAS.

1kubectl apply -n "${MDB_NS}" --context "${K8S_CTX}" -f - <<EOF
2apiVersion: v1
3kind: Pod
4metadata:
5  name: mongodb-tools-pod
6  labels:
7    app: mongodb-tools
8spec:
9  containers:
10  - name: mongodb-tools
11    image: mongodb/mongodb-community-server:${MDB_VERSION}-ubi8
12    command: ["/bin/bash", "-c"]
13    args: ["sleep infinity"]
14  restartPolicy: Never
15EOF
16
17echo "Waiting for the mongodb-tools to be ready..."
18kubectl --context "${K8S_CTX}" -n "${MDB_NS}" wait \
19  --for=condition=Ready pod/mongodb-tools-pod --timeout=60s
3

Importe os dados de amostra.

Importe o banco de dados do sample_mflix , que contém as seguintes collections:

Nome da collection
Descrição

comments

Contém comentários associados a filmes específicos.

embedded_movies

Contém detalhes sobre filmes nos gêneros Western, Action e Fantasy da coleção movies com os seguintes campos adicionais:

  • plot_embedding_voyage_3_large - Contém incorporações 2048d criadas a partir do campo plot usando o modelo de incorporação voyage-3-large da Voyage AI e convertidas em binData para armazenamento e recuperação eficientes.

  • plot_embedding - Contém incorporações 1536d criadas a partir do campo plot usando o modelo de incorporação text-embedding-ada-002 da OpenAI e convertidas em binData para armazenamento e recuperação eficientes.

movies

Contém informações sobre o filme, incluindo ano de lançamento, diretor e avaliações.

sessions

campo de metadados . Contém os JSON web tokens dos usuários.

theaters

Contém localizações de salas de cinema.

users

Contém informações do usuário.

O seguinte comando baixa o conjunto de dados de amostra e utiliza o mongorestore para carregar os dados no banco de dados do sample_mflix em seu deployment do MongoDB , conectando como o mdb-user.

1kubectl exec -n "${MDB_NS}" --context "${K8S_CTX}" \
2  mongodb-tools-pod -- /bin/bash -eu -c "$(cat <<EOF
3echo "Downloading sample database archive..."
4curl https://atlas-education.s3.amazonaws.com/sample_mflix.archive \
5  -o /tmp/sample_mflix.archive
6echo "Restoring sample database"
7mongorestore \
8  --archive=/tmp/sample_mflix.archive \
9  --verbose=1 \
10  --drop \
11  --nsInclude 'sample_mflix.*' \
12  --uri="${MDB_CONNECTION_STRING}"
13EOF
14)"
4

Crie um índice.

Você pode criar uma pesquisa MongoDB ou um índice de Vector Search . Você deve criar um índice do MongoDB Search para executar queries $search e $searchMeta e um índice do Vector Search para executar queries $vectorSearch.

  • O MongoDB Search fornece definições de índice flexíveis que suportam mapeamentos de campo dinâmicos e estáticos, vários analisadores (padrão, específicos de idioma, personalizado) e recursos como sinônimos e pesquisa facetada. Para saber mais, consulte Definir seu índice.

  • O Vector Search fornece uma definição de índice simples que suporta a consulta de incorporações vetoriais em seus dados com base na similaridade semântica usando o algoritmo de pesquisa ANN e ENN. Para saber mais, consulte Definir índice de Vector Search.

  1. Copie, cole e execute o seguinte comando no seu terminal para criar um índice do MongoDB Search ou do Vector Search .

    O comando utiliza kubectl exec para executar mongosh no mongodb-tools-pod. Ele se conecta ao método sample_mflix db.<collection>.createSearchIndex() para criar um índice.

    O seguinte comando cria um índice de Pesquisa MongoDB denominado default com mapeamentos dinâmicos na coleção movies. O mapeamento dinâmico indexa automaticamente todos os tipos de campo indexáveis dinamicamente.

    1kubectl exec --context "${K8S_CTX}" -n "${MDB_NS}" mongodb-tools-pod -- \
    2  mongosh --quiet \
    3    "${MDB_CONNECTION_STRING}" \
    4    --eval "use sample_mflix" \
    5    --eval 'db.movies.createSearchIndex("default", { mappings: { dynamic: true } });'

    O seguinte comando cria um índice do Vector Search denominado vector_index no campo plot_embedding_voyage_3_large com 2048 número de dimensões na coleção embedded_movies para executar consultas ANN.

    1kubectl exec --context "${K8S_CTX}" -n "${MDB_NS}" mongodb-tools-pod -- \
    2  mongosh --quiet "${MDB_CONNECTION_STRING}" \
    3    --eval "use sample_mflix" \
    4    --eval 'db.embedded_movies.createSearchIndex("vector_index", "vectorSearch",
    5    { "fields": [ {
    6      "type": "vector",
    7      "path": "plot_embedding_voyage_3_large",
    8      "numDimensions": 2048,
    9      "similarity":
    10      "dotProduct",
    11      "quantization": "scalar"
    12    } ] });'

  2. Aguarde a conclusão da criação do índice.

    A criação de índices é um processo assíncrono. Execute o script a seguir para aguardar 60 segundos para que a criação do índice seja concluída.

    1# Currently it's not possible to check the status of search indexes, we need to just wait
    2echo "Sleeping to wait for search indexes to be created"
    3sleep 60

    Como alternativa, execute o seguinte comando para recuperar a lista de índices da coleção.

    1kubectl exec --context "${K8S_CTX}" -n "${MDB_NS}" mongodb-tools-pod -- \
    2  mongosh --quiet "${MDB_CONNECTION_STRING}" \
    3    --eval "use sample_mflix" \
    4    --eval 'db.runCommand({"listSearchIndexes": "movies"});'
    1kubectl exec --context "${K8S_CTX}" -n "${MDB_NS}" mongodb-tools-pod -- \
    2  mongosh --quiet "${MDB_CONNECTION_STRING}" \
    3    --eval "use sample_mflix" \
    4    --eval 'db.runCommand({"listSearchIndexes": "embedded_movies"});'
5

Consulte os dados usando o índice.

Após a criação do índice ser bem-sucedida, você poderá executar queries do MongoDB Search ou Vector Search usando o índice. Você também pode combinar a pesquisa do MongoDB com a Vector Search. Para saber mais sobre como executar queries no MongoDB Search, consulte Definir sua query. Para saber mais sobre queries de Vector Search, consulte Executar queries de Vector Search.

O seguinte script define os critérios de consulta do pipeline de agregação e utiliza o comando kubectl exec para carregar o mongosh para executar a consulta.

A seguinte query pesquisa o campo plot para gráficos de filme que contêm o termo baseball. Exclui filmes nos gêneros comedy e romance .

1mdb_script=$(cat <<'EOF'
2use sample_mflix;
3db.movies.aggregate([
4  {
5    $search: {
6      "compound": {
7        "must": [ {
8          "text": {
9            "query": "baseball",
10            "path": "plot"
11          }
12        }],
13        "mustNot": [ {
14          "text": {
15            "query": ["Comedy", "Romance"],
16            "path": "genres"
17          }
18        } ]
19      },
20      "sort": {
21        "released": -1
22      }
23    }
24  },
25  {
26    $limit: 3
27  },
28  {
29    $project: {
30      "_id": 0,
31      "title": 1,
32      "plot": 1,
33      "genres": 1,
34      "released": 1
35    }
36  }
37]);
38EOF
39)
40
41kubectl exec --context "${K8S_CTX}" -n "${MDB_NS}" \
42  mongodb-tools-pod -- /bin/bash -eu -c "$(cat <<EOF
43echo '${mdb_script}' > /tmp/mdb_script.js
44mongosh --quiet "${MDB_CONNECTION_STRING}" < /tmp/mdb_script.js
45EOF
46)"
1mdbc-rs [primary] test> switched to db sample_mflix
2mdbc-rs [primary] sample_mflix> ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... [
3  {
4    plot: 'A sports agent stages an unconventional recruitment strategy to get talented Indian cricket players to play Major League Baseball.',
5    genres: [ 'Biography', 'Drama', 'Sport' ],
6    title: 'Million Dollar Arm',
7    released: ISODate('2014-05-16T00:00:00.000Z')
8  },
9  {
10    plot: 'A Taiwanese high school baseball team travels to Japan in 1931 to compete in a national tournament.',
11    genres: [ 'Biography', 'Drama', 'History' ],
12    title: 'Kano',
13    released: ISODate('2014-02-27T00:00:00.000Z')
14  },
15  {
16    plot: "12-year-old Josh is a mixed race boy and a promising baseball player. He is abused by his mother's boyfriend Byrd, and neglected by his mother Debbie. He forges his own path in life when ...",
17    genres: [ 'Drama' ],
18    title: 'Calloused Hands',
19    released: ISODate('2013-03-03T00:00:00.000Z')
20  }
21]
22mdbc-rs [primary] sample_mflix>

A consulta a seguir pesquisa o campo plot_embedding_voyage_3_large usando 2048 incorporações de vetor de dimensão para a string time travel, gerada usando o modelo voyage-3-large da Voyage AI.

1mdb_script=$(cat <<'EOF'
2use sample_mflix;
3db.embedded_movies.aggregate([
4  {
5    "$vectorSearch": {
6      "index": "vector_index",
7      "path": "plot_embedding_voyage_3_large",
8      "queryVector": 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9      "numCandidates": 150,
10      "limit": 10,
11      "quantization": "scalar"
12    }
13  },
14  {
15    "$project": {
16      "_id": 0,
17      "plot": 1,
18      "title": 1,
19      "score": { $meta: "vectorSearchScore" }
20    }
21  }
22]);
23EOF
24)
25
26kubectl exec --context "${K8S_CTX}" -n "${MDB_NS}" mongodb-tools-pod -- /bin/bash -eu -c "$(cat <<EOF
27echo '${mdb_script}' > /tmp/mdb_script.js
28mongosh --quiet "${MDB_CONNECTION_STRING}" < /tmp/mdb_script.js
29EOF
30)"
1mdbc-rs [primary] test> switched to db sample_mflix
2mdbc-rs [primary] sample_mflix> ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... [
3  {
4    plot: 'At the age of 21, Tim discovers he can travel in time and change what happens and has happened in his own life. His decision to make his world a better place by getting a girlfriend turns out not to be as easy as you might think.',
5    title: 'About Time',
6    score: 0.7704131603240967
7  },
8  {
9    plot: 'A psychiatrist makes multiple trips through time to save a woman that was murdered by her brutal husband.',
10    title: 'Retroactive',
11    score: 0.7597770690917969
12  },
13  {
14    plot: 'An officer for a security agency that regulates time travel, must fend for his life against a shady politician who has a tie to his past.',
15    title: 'Timecop',
16    score: 0.7574796676635742
17  },
18  {
19    plot: 'A time-travel experiment in which a robot probe is sent from the year 2073 to the year 1973 goes terribly wrong thrusting one of the project scientists, a man named Nicholas Sinclair into a...',
20    title: 'A.P.E.X.',
21    score: 0.7573235034942627
22  },
23  {
24    plot: 'After visiting 2015, Marty McFly must repeat his visit to 1955 to prevent disastrous changes to 1985... without interfering with his first trip.',
25    title: 'Back to the Future Part II',
26    score: 0.751945972442627
27  },
28  {
29    plot: 'A reporter, learning of time travelers visiting 20th century disasters, tries to change the history they know by averting upcoming disasters.',
30    title: 'Thrill Seekers',
31    score: 0.7503504753112793
32  },
33  {
34    plot: 'Hoping to alter the events of the past, a 19th century inventor instead travels 800,000 years into the future, where he finds humankind divided into two warring races.',
35    title: 'The Time Machine',
36    score: 0.750007152557373
37  },
38  {
39    plot: 'Lyle, a motorcycle champion is traveling the Mexican desert, when he find himself in the action radius of a time machine. So he find himself one century back in the past between rapists, ...',
40    title: 'Timerider: The Adventure of Lyle Swann',
41    score: 0.7499568462371826
42  },
43  {
44    plot: 'A romantic drama about a Chicago librarian with a gene that causes him to involuntarily time travel, and the complications it creates for his marriage.',
45    title: "The Time Traveler's Wife",
46    score: 0.7492842674255371
47  },
48  {
49    plot: 'A modern aircraft carrier is thrown back in time to 1941 near Hawaii, just hours before the Japanese attack on Pearl Harbor.',
50    title: 'The Final Countdown',
51    score: 0.7472751140594482
52  }
53]
54mdbc-rs [primary] sample_mflix>

Recursos

Para saber mais sobre como criar índices e executar consultas de pesquisa, consulte:

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