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https://github.com/patterns-ai-core/qdrant-ruby

Ruby wrapper for the Qdrant vector search database API
https://github.com/patterns-ai-core/qdrant-ruby

api-client embedding-vectors machine-learning ml qdrant ruby rubyml vector-search vector-search-engine

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Ruby wrapper for the Qdrant vector search database API

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README 

        
# Qdrant





    Qdrant logo

      +  

    Ruby logo




Ruby wrapper for the Qdrant vector search database API.



Part of the [Langchain.rb](https://github.com/andreibondarev/langchainrb) stack.



![Tests status](https://github.com/andreibondarev/qdrant-ruby/actions/workflows/ci.yml/badge.svg)

[![Gem Version](https://badge.fury.io/rb/qdrant-ruby.svg)](https://badge.fury.io/rb/qdrant-ruby)

[![Docs](http://img.shields.io/badge/yard-docs-blue.svg)](http://rubydoc.info/gems/qdrant-ruby)

[![License](https://img.shields.io/badge/license-MIT-green.svg)](https://github.com/andreibondarev/qdrant-ruby/blob/main/LICENSE.txt)

[![](https://dcbadge.vercel.app/api/server/WDARp7J2n8?compact=true&style=flat)](https://discord.gg/WDARp7J2n8)



## 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



```ruby

require 'qdrant'



client = Qdrant::Client.new(

  url: ENV["QDRANT_URL"],

  api_key: ENV["QDRANT_API_KEY"]

)

```



### Collections



```ruby

# 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

```ruby

# 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

```ruby

# 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

```ruby

# 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

```ruby

# 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](https://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.