Ruby wrapper for the Qdrant vector search database API.
Part of the Langchain.rb stack.
Install the gem and add to the application's Gemfile by executing:
$ bundle add qdrant-ruby
If bundler is not being used to manage dependencies, install the gem by executing:
$ gem install qdrant-ruby
require 'qdrant'
client = Qdrant::Client.new(
url: ENV["QDRANT_URL"],
api_key: ENV["QDRANT_API_KEY"]
)# Get list name of all existing collections
client.collections.list
# Get detailed information about specified existing collection
client.collections.get(collection_name: "string")
# Create new collection with given parameters
client.collections.create(
collection_name: "string", # required
vectors: {}, # required
shard_number: nil,
replication_factor: nil,
write_consistency_factor: nil,
on_disk_payload: nil,
hnsw_config: nil,
wal_config: nil,
optimizers_config: nil,
init_from: nil,
quantization_config: nil
)
# Update parameters of the existing collection
client.collections.update(
collection_name: "string", # required
optimizers_config: nil,
params: nil
)
# Drop collection and all associated data
client.collections.delete(collection_name: "string")
# Get list of all aliases (for a collection)
client.collections.aliases(
collection_name: "string" # optional
)
# Update aliases of the collections
client.collections.update_aliases(
actions: [{
# `create_alias:`, `delete_alias` and/or `rename_alias` is required
create_alias: {
collection_name: "string", # required
alias_name: "string" # required
}
}]
)
# Create index for field in collection
client.collections.create_index(
collection_name: "string", # required
field_name: "string", # required
field_schema: "string",
wait: "boolean",
ordering: "ordering"
)
# Delete field index for collection
client.collections.delete_index(
collection_name: "string", # required
field_name: "string", # required
wait: "boolean",
ordering: "ordering"
)
# Get cluster information for a collection
client.collections.cluster_info(
collection_name: "test_collection" # required
)
# Update collection cluster setup
client.collections.update_cluster(
collection_name: "string", # required
move_shard: { # required
shard_id: "int",
to_peer_id: "int",
from_peer_id: "int"
},
timeout: "int"
)
# Create new snapshot for a collection
client.collections.create_snapshot(
collection_name: "string", # required
)
# Get list of snapshots for a collection
client.collections.list_snapshots(
collection_name: "string", # required
)
# Delete snapshot for a collection
client.collections.delete_snapshot(
collection_name: "string", # required
snapshot_name: "string" # required
)
# Recover local collection data from a snapshot. This will overwrite any data, stored on this node, for the collection. If collection does not exist - it will be created.
client.collections.restore_snapshot(
collection_name: "string", # required
filepath: "string", # required
wait: "boolean",
priority: "string"
)
# Download specified snapshot from a collection as a file
client.collections.download_snapshot(
collection_name: "string", # required
snapshot_name: "string", # required
filepath: "/dir/filename.snapshot" #require
)# Retrieve full information of single point by id
client.points.get(
collection_name: "string", # required
id: "int/string", # required
consistency: "int"
)
# Retrieve full information of points by ids
client.points.get_all(
collection_name: "string", # required
ids: "[int]", # required
with_payload: "boolean"
with_vector: "boolean"
)
# Lists all data objects in reverse order of creation. The data will be returned as an array of objects.
client.points.list(
collection_name: "string", # required
ids: "[int/string]", # required
with_payload: nil,
with_vector: nil,
consistency: nil
)
# Get a single data object.
client.points.upsert(
collection_name: "string", # required
batch: {}, # required
wait: "boolean",
ordering: "string"
)
# Delete points
client.points.delete(
collection_name: "string", # required
points: "[int/string]", # either `points:` or `filter:` required
filter: {},
wait: "boolean",
ordering: "string"
)
# Set payload values for points
client.points.set_payload(
collection_name: "string", # required
payload: { # required
"property name" => "value"
},
points: "[int/string]", # `points:` or `filter:` are required
filter: {},
wait: "boolean",
ordering: "string"
)
# Replace full payload of points with new one
client.points.overwrite_payload(
collection_name: "string", # required
payload: {}, # required
wait: "boolean",
ordering: "string",
points: "[int/string]",
filter: {}
)
# Delete specified key payload for points
client.points.clear_payload_keys(
collection_name: "string", # required
keys: "[string]", # required
points: "[int/string]",
filter: {},
wait: "boolean",
ordering: "string"
)
# Delete specified key payload for points
client.points.clear_payload(
collection_name: "string", # required
points: "[int/string]", # required
wait: "boolean",
ordering: "string"
)
# Scroll request - paginate over all points which matches given filtering condition
client.points.scroll(
collection_name: "string", # required
limit: "int",
filter: {},
offset: "string",
with_payload: "boolean",
with_vector: "boolean",
consistency: "int/string"
)
# Retrieve closest points based on vector similarity and given filtering conditions
client.points.search(
collection_name: "string", # required
limit: "int", # required
vector: "[int]", # required
filter: {},
params: {},
offset: "int",
with_payload: "boolean",
with_vector: "boolean",
score_threshold: "float"
)
# Retrieve by batch the closest points based on vector similarity and given filtering conditions
client.points.batch_search(
collection_name: "string", # required
searches: [{}], # required
consistency: "int/string"
)
# Look for the points which are closer to stored positive examples and at the same time further to negative examples.
client.points.recommend(
collection_name: "string", # required
positive: "[int/string]", # required; Arrray of point IDs
limit: "int", # required
negative: "[int/string]",
filter: {},
params: {},
offset: "int",
with_payload: "boolean",
with_vector: "boolean",
score_threshold: "float"
using: "string",
lookup_from: {},
)
# Look for the points which are closer to stored positive examples and at the same time further to negative examples.
client.points.batch_recommend(
collection_name: "string", # required
searches: [{}], # required
consistency: "string"
)
# Count points which matches given filtering condition
client.points.count(
collection_name: "string", # required
filter: {},
exact: "boolean"
)# Get list of snapshots of the whole storage
client.snapshots.list(
collection_name: "string" # optional
)
# Create new snapshot of the whole storage
client.snapshots.create(
collection_name: "string" # required
)
# Delete snapshot of the whole storage
client.snapshots.delete(
collection_name: "string", # required
snapshot_name: "string" # required
)
# Download specified snapshot of the whole storage as a file
client.snapshots.download(
collection_name: "string", # required
snapshot_name: "string" # required
filepath: "~/Downloads/backup.txt" # required
)
# Get the backup
client.backups.get(
backend: "filesystem",
id: "my-first-backup"
)
# Restore backup
client.backups.restore(
backend: "filesystem",
id: "my-first-backup"
)
# Check the backup restore status
client.backups.restore_status(
backend: "filesystem",
id: "my-first-backup"
)# Get information about the current state and composition of the cluster
client.cluster.info
# Tries to recover current peer Raft state.
client.cluster.recover
# Tries to remove peer from the cluster. Will return an error if peer has shards on it.
client.cluster.remove_peer(
peer_id: "int", # required
force: "boolean"
)# Collect telemetry data including app info, system info, collections info, cluster info, configs and statistics
client.telemetry(
anonymize: "boolean" # optional
)
# Collect metrics data including app info, collections info, cluster info and statistics
client.metrics(
anonymize: "boolean" # optional
)
# Get lock options. If write is locked, all write operations and collection creation are forbidden
client.locks
# Set lock options. If write is locked, all write operations and collection creation are forbidden. Returns previous lock options
client.set_lock(
write: "boolean" # required
error_message: "string"
)After checking out the repo, run bin/setup to install dependencies. Then, run rake spec to run the tests. You can also run bin/console for an interactive prompt that will allow you to experiment.
To install this gem onto your local machine, run bundle exec rake install. To release a new version, update the version number in version.rb, and then run bundle exec rake release, which will create a git tag for the version, push git commits and the created tag, and push the .gem file to rubygems.org.
Bug reports and pull requests are welcome on GitHub at https://github.com/andreibondarev/qdrant.
qdrant-ruby is licensed under the Apache License, Version 2.0. View a copy of the License file.