Awesome
Qdrant
<p> <img alt='Qdrant logo' src='https://qdrant.tech/images/logo_with_text.svg' height='50' /> + <img alt='Ruby logo' src='https://user-images.githubusercontent.com/541665/230231593-43861278-4550-421d-a543-fd3553aac4f6.png' height='40' /> </p>Ruby wrapper for the Qdrant vector search database API.
Part of the Langchain.rb stack.
Installation
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
Usage
Instantiating API client
require 'qdrant'
client = Qdrant::Client.new(
url: "your-qdrant-url",
api_key: "your-qdrant-api-key"
)
Collections
# 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
)
Points
# 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"
)
Snapshots
# 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"
)
Cluster
# 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"
)
Service
# 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"
)
Development
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.
Contributing
Bug reports and pull requests are welcome on GitHub at https://github.com/andreibondarev/qdrant.
License
qdrant-ruby is licensed under the Apache License, Version 2.0. View a copy of the License file.