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Implementar búsqueda y búsqueda vectorial
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Implementar búsqueda y búsqueda vectorial

Usa MongoDB Search y búsqueda vectorial

Si lo instalaste MongoDBSearch recurso correctamente, puedes crear índices de MongoDB Search y índices de búsqueda de Vectores como el mdb-user y ejecutar consultas de MongoDB Search y consultas de búsqueda de Vectores utilizando los índices. Esta página describe cómo cargar datos de muestra, crear índices de Búsqueda de MongoDB y Búsqueda Vectorial para los datos y ejecutar consultas de Búsqueda de MongoDB y Búsqueda Vectorial sobre los datos.

Requisitos previos

Para utilizar MongoDB Search y la búsqueda vectorial, debes tener lo siguiente:

  • Implementación exitosa de MongoDB y MongoDBSearch.

    Para obtener más información, consulta:

    • Instalar y usar la búsqueda con MongoDB Enterprise Edition

    • Instalar y usar MongoDB Search y búsqueda vectorial con una instancia externa de MongoDB Enterprise Edition

  • Usuario con permisos para crear MongoDB Search y búsqueda vectorial y ejecutar consultas de MongoDB Search y búsqueda vectorial.

    Para obtener más información,consulte Configurar un usuario de Cloud Manager u Ops Manager para MongoDB Search y Vector Search.

  • Herramienta de línea de comandos de Kubernetes, kubectl, configurada para comunicarse con su clúster.

  • Bash v5.1 o superior para ejecutar los comandos en este tutorial.

Procedimiento

1

Establece las variables de entorno.

Para configurar las variables de entorno para su uso en los pasos posteriores de este procedimiento, copie lo siguiente, establezca los valores de las variables de entorno y luego cargue las variables de entorno:

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

implementar MongoDB 工具 pod。

Implemente un pod llamado mongodb-tools-pod para ejecutar herramientas cliente de MongoDB como mongosh y mongorestore desde el clúster de Kubernetes. Ejecutar un pod dentro del clúster simplifica la conectividad con MongoDB sin necesidad de exponer la base de datos externamente.

El siguiente comando implementa un pod llamado mongodb-tools-pod que ejecuta una imagen del Servidor de la Comunidad MongoDB. El pod se mantiene en ejecución con un comando sleep infinity para permitirle usar kubectl exec para ejecutar las herramientas cliente de MongoDB.

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

Importar los datos de muestra.

Importa la base de datos sample_mflix, que contiene las siguientes colecciones:

Nombre de colección
Descripción

comments

Contiene comentarios asociados con películas específicas.

embedded_movies

Contiene detalles sobre películas en los géneros Western, Action y Fantasy de la colección movies con los siguientes campos adicionales:

  • plot_embedding_voyage_3_large - Contiene embeddings de 2048d creados a partir del campo plot utilizando el modelo de embedding voyage-3-large de Voyage IA y convertidos a binData para un almacenamiento y recuperación eficientes.

  • plot_embedding - Contiene incrustaciones 1536d creadas a partir del campo plot utilizando el modelo de incrustación text-embedding-ada-002 de OpenAI y convertido a binData para un almacenamiento y recuperación eficiente.

movies

Contiene información sobre películas, incluidos el año de lanzamiento, el director y las revisiones.

sessions

Campo de metadatos. Contiene los JSON Web Tokens de los usuarios.

theaters

Contiene las ubicaciones de los cines.

users

Contiene información de usuario.

El siguiente comando descarga el conjunto de datos de muestra y utiliza mongorestore para cargar los datos en la base de datos sample_mflix en su implementación de MongoDB, conectándose como 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

Crear un índice.

Puede crear un índice de búsqueda MongoDB o un índice de búsqueda vectorial. Debe crear un índice de búsqueda MongoDB para $search $searchMeta ejecutar consultas y, y un índice de búsqueda vectorial para ejecutar $vectorSearch consultas.

  • MongoDB Search ofrece definiciones de índice flexibles que admiten asignaciones de campos dinámicas y estáticas, diversos analizadores (estándar, específicos del lenguaje y personalizados) y funciones como sinónimos y búsqueda por facetas. Para obtener más información, consulte Defina su índice.

  • Búsqueda Vectorial proporciona una definición de índice simple que soporta la consulta de incrustaciones vectoriales en tus datos basadas en la similitud semántica usando Algoritmo debúsqueda ANN y ENN. Para más información, consulte Definir índice de búsqueda vectorial.

  1. Copie, pegue y ejecute el siguiente comando en su terminal para crear un índice de búsqueda MongoDB o de búsqueda vectorial.

    El comando usa kubectl exec para ejecutar mongosh en mongodb-tools-pod. Se conecta al método sample_mflix db.<collection>.createSearchIndex() para crear un índice.

    El siguiente comando crea un índice de búsqueda de MongoDB llamado default con asignaciones dinámicas en la colección movies. La asignación dinámica indexa automáticamente todos los tipos de campos indexables dinámicamente.

    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 } });'

    El siguiente comando crea un índice de búsqueda vectorial llamado vector_index en el campo plot_embedding_voyage_3_large con 2048 dimensiones en la colección embedded_movies para ejecutar queries 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. Espere a que se complete la creación del índice.

    La creación de índices es un proceso asíncrono. Ejecuta el siguiente script para esperar 60 segundos hasta que se complete la creación del índice.

    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, ejecuta el siguiente comando para recuperar la lista de índices de la colección.

    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

Query los datos usando el índice.

Después de que la creación del índice sea exitosa, puede ejecutar búsquedas de MongoDB (MongoDB Search) o búsquedas vectoriales (Vector Search) utilizando el índice. También puedes combinar MongoDB Search con búsqueda vectorial. Para obtener más información sobre cómo ejecutar consultas de MongoDB Search, consulta Define tu consulta. Para obtener más información sobre las consultas de Vector Search, consulta Ejecuta consultas de Vector Search.

El siguiente script define los criterios de consulta de la canalización de agregación y utiliza el comando kubectl exec para cargar mongosh para ejecutar la consulta.

La siguiente query busca en el campo plot argumentos de películas que contengan el término baseball. Excluye películas en los géneros comedy y 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>

La siguiente query busca el campo plot_embedding_voyage_3_large utilizando incrustaciones vectoriales dimensionales 2048 para la string time travel, generada con el modelo voyage-3-large de Voyage IA.

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 obtener más información sobre cómo crear índices y ejecutar consultas de búsqueda, consulte:

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