Indexes

Indexes can be added to collections to speed up document lookups. Every collection has a primary hash index on _key field by default. This index cannot be deleted or modified. Every edge collection has additional indexes on fields _from and _to. For more information on indexes, refer to ArangoDB manual.

Example:

from arango import ArangoClient

# Initialize the ArangoDB client.
client = ArangoClient()

# Connect to "test" database as root user.
db = client.db('test', username='root', password='passwd')

# Create a new collection named "cities".
cities = db.create_collection('cities')

# List the indexes in the collection.
cities.indexes()

# Add a new persistent index on document fields "continent" and "country".
persistent_index = {'type': 'persistent', 'fields': ['continent', 'country'], 'unique': True}
index = cities.add_index(persistent_index)

# Add new fulltext indexes on fields "continent" and "country".
index = cities.add_index({'type': 'fulltext', 'fields': ['continent']})
index = cities.add_index({'type': 'fulltext', 'fields': ['country']})

# Add a new persistent index on field 'population'.
persistent_index = {'type': 'persistent', 'fields': ['population'], 'sparse': False}
index = cities.add_index(persistent_index)

# Add a new geo-spatial index on field 'coordinates'.
geo_index = {'type': 'geo', 'fields': ['coordinates']}
index = cities.add_index(geo_index)

# Add a new persistent index on field 'currency'.
persistent_index = {'type': 'persistent', 'fields': ['currency'], 'sparse': True}
index = cities.add_index(persistent_index)

# Add a new TTL (time-to-live) index on field 'currency'.
ttl_index = {'type': 'ttl', 'fields': ['currency'], 'expireAfter': 200}
index = cities.add_index(ttl_index)

# Add MDI (multi-dimensional) index on field 'x' and 'y'.
mdi_index = {'type': 'mdi', 'fields': ['x', 'y'], 'fieldValueTypes': 'double'}
index = cities.add_index(mdi_index)

# Indexes may be added with a name that can be referred to in AQL queries.
persistent_index = {'type': 'persistent', 'fields': ['country'], 'unique': True, 'name': 'my_hash_index'}
index = cities.add_index(persistent_index)

# Delete the last index from the collection.
cities.delete_index(index['id'])

# Insert documents with vector embeddings.
cities.insert_many([
    {
        '_key': f'city{i}',
        'continent': f'continent{i}',
        'country': f'country{i}',
        'population': i,
        'coordinates': [float(i % 180), float(i % 90)],
        'x': float(i),
        'y': float(i),
        'embedding': [float(i), float(i % 7), float(i % 11), 1.0],
    }
    for i in range(100)
])

# Let ArangoDB determine the number of vector-index centroids.
vector_index = cities.add_index({
    'type': 'vector',
    'fields': ['embedding'],
    'name': 'vector_index',
    'params': {
        'metric': 'cosine',
        'dimension': 4,
    },
})

# Index creation may succeed even if there is insufficient training data.
if vector_index.get('trainingState') != 'ready':
    raise RuntimeError(
        vector_index.get('errorMessage', 'Vector index is not ready')
    )

Omitted or scaling-object nLists, numberOfDocsPerCentroid, factory placeholders such as IVF{},Flat, and successful-but-unusable creation behavior require ArangoDB 3.12.10 or later. A successful creation response means that the index exists, but callers should check trainingState before using it. If there is insufficient data to train the index, the state is "unusable" and errorMessage describes the failure. Invalid index definitions, such as an nLists value that disagrees with the number of centroids in the factory string, are rejected during creation with IndexCreateError and no index is created.

See StandardCollection for API specification.