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https://github.com/patterns-ai-core/milvus
Ruby wrapper for the Milvus vector search database API
https://github.com/patterns-ai-core/milvus
api-client embedding-vectors machine-learning milvus ml ruby rubyml search-engine vector-search
Last synced: about 2 months ago
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Ruby wrapper for the Milvus vector search database API
- Host: GitHub
- URL: https://github.com/patterns-ai-core/milvus
- Owner: patterns-ai-core
- License: apache-2.0
- Created: 2023-04-20T17:52:05.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-07-28T21:41:22.000Z (about 2 months ago)
- Last Synced: 2024-07-29T17:41:53.196Z (about 2 months ago)
- Topics: api-client, embedding-vectors, machine-learning, milvus, ml, ruby, rubyml, search-engine, vector-search
- Language: Ruby
- Homepage: http://rubydoc.info/gems/milvus
- Size: 81.1 KB
- Stars: 20
- Watchers: 7
- Forks: 4
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- License: LICENSE
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README
# Milvus
Ruby wrapper for the Milvus vector search database API.
Part of the [Langchain.rb](https://github.com/andreibondarev/langchainrb) stack.
Available for paid consulting engagements! [Email me](mailto:[email protected]).
[](https://badge.fury.io/rb/milvus)
[](http://rubydoc.info/gems/milvus)
[](https://github.com/andreibondarev/milvus/blob/main/LICENSE.txt)
[](https://discord.gg/WDARp7J2n8)
[](https://twitter.com/rushing_andrei)
## API Docs
https://docs.zilliz.com/reference/restful/data-plane-v2
## Installation
Install the gem and add to the application's Gemfile by executing:
$ bundle add milvus
If bundler is not being used to manage dependencies, install the gem by executing:
$ gem install milvus
## Usage
### Instantiating API client
```ruby
require 'milvus'
client = Milvus::Client.new(
url: 'http://localhost:19530'
)
```
### Using the Collections endpoints
```ruby
# Check if the collection exists.
client.collections.has(collection_name: "example_collection")
```
```ruby
# Rename a collection.
client.collections.rename(collection_name: "example_collection", new_collection_name: "example_collection")
```
```ruby
# Get collection stats
client.collections.get_stats(collection_name: "example_collection")
```
```ruby
# Data types: https://github.com/patterns-ai-core/milvus/blob/main/lib/milvus/constants.rb
# Creating a new collection schema
client.collections.create(
collection_name: "example_collection",
auto_id: true,
fields: [
{
fieldName: "book_id",
isPrimary: true,
autoID: false,
dataType: "Int64"
},
{
fieldName: "content",
dataType: "VarChar",
elementTypeParams: {
max_length: "512"
}
},
{
fieldName: "vector",
dataType: "FloatVector",
elementTypeParams: {
dim: 1536
}
}
]
)
```
```ruby
# Descrbie the collection
client.collections.describe(collection_name: "example_collection")
```
```ruby
# Drop the collection
client.collections.drop(collection_name: "example_collection")
```
```ruby
# Load the collection to memory before a search or a query
client.collections.load(collection_name: "example_collection")
```
```ruby
# Load status of a specific collection.
client.collections.get_load_state(collection_name: "example_collection")
```
```ruby
# List all collections in the specified database.
client.collections.list
```
```ruby
# Release a collection from memory after a search or a query to reduce memory usage
client.collections.release(collection_name: "example_collection")
```
### Inserting Data
```ruby
client.entities.insert(
collection_name: "example_collection",
data: [
{ id: 1, content: "The quick brown fox jumps over the lazy dog", vector: ([0.1]*1536) },
{ id: 2, content: "Lorem ipsum dolor sit amet, consectetur adipiscing elit", vector: ([0.2]*1536) },
{ id: 3, content: "Sed do eiusmod tempor incididunt ut labore et dolore magna aliqua", vector: ([0.3]*1536) }
]
)
```
```ruby
# Delete the entities with the boolean expression you created
client.entities.delete(
collection_name: "example_collection",
expression: "book_id in [0,1]"
)
```
```ruby
# Inserts new records into the database or updates existing ones.
client.entities.upsert()
```
```ruby
# Get specific entities by their IDs
client.entities.get()
```
### Indexes
```ruby
# Create an index
index_params = [
{
metricType: "L2",
fieldName: "vector",
indexName: "vector_idx",
indexConfig: {
index_type: "AUTOINDEX"
}
}
]
client.indexes.create(
collection_name: "example_collection",
index_params: index_params
)
```
```ruby
# Describe an index
client.indexes.describe(
collection_name: "example_collection",
index_name: "example_index"
)
```
```ruby
# List indexes
client.indexes.list(
collection_name: "example_collection"
)
```
```ruby
# Drop an index
client.indexes.drop(
collection_name: "example_collection",
index_name: "example_index"
)
```
### Search, Querying & Hybrid Search
```ruby
client.entities.search(
collection_name: "example_collection",
anns_field: "vectors",
data: [embedding],
filter: "id in [450847466900987454]"
)
```
```ruby
client.entities.query(
collection_name: "example_collection",
filter: "id in [450847466900987455, 450847466900987454]"
)
```
```ruby
client.entities.hybrid_search(
collection_name: "example_collection",
search: [{
filter: "id in [450847466900987455]",
data: [embedding],
annsField: "vectors",
limit: 10,
outputFields: ["content", "id"]
}],
rerank: {
"strategy": "rrf",
"params": {
"k": 10
}
},
limit: 10,
output_fields: ["content", "id"]
)
```
### Partitions
```ruby
# List partitions
client.partitions.list(
collection_name: "example_collection"
)
```
```ruby
# Create a partition
client.partitions.create(
collection_name: "example_collection",
partition_name: "example_partition"
)
```
```ruby
# Check if a partition exists
client.partitions.has(
collection_name: "example_collection",
partition_name: "example_partition"
)
```
```ruby
# Load partition data into memory
client.partitions.load(
collection_name: "example_collection",
partition_names: ["example_partition"]
)
```
```ruby
# Release partition data from memory
client.partitions.release(
collection_name: "example_collection",
partition_names: ["example_partition"]
)
```
```ruby
# Get statistics of a partition
client.partitions.get_stats(
collection_name: "example_collection",
partition_name: "example_partition"
)
```
```ruby
# Drop a partition
client.partitions.drop(
collection_name: "example_collection",
partition_name: "example_partition"
)
```
### Roles
```ruby
# List roles available on the server
client.roles.list
```
```ruby
# Describe the role
client.roles.describe(role_name: 'public')
```
### Users
```ruby
# Create new user
client.users.create(user_name: 'user_name', password: 'password')
```
```ruby
# List of roles assigned to the user
client.users.describe(user_name: 'user_name')
```
```ruby
# List all users in the specified database.
client.users.list
```
```ruby
# Drop existing user
client.users.drop(user_name: 'user_name')
```
```ruby
# Update password for the user
client.users.update_password(user_name: 'user_name', password: 'old_password', new_password: 'new_password')
```
```ruby
# Grant role to the user
client.users.grant_role(user_name: 'user_name', role_name: 'admin')
```
```ruby
# Revoke role from the user
client.users.revoke_role(user_name: 'user_name', role_name: 'admin')
```
### Aliases
```ruby
# Lists all existing collection aliases in the specified database
client.aliases.list
```
```ruby
# Describes the details of a specific alias
client.aliases.describe
```
```ruby
# Reassigns the alias of one collection to another.
client.aliases.alter
```
```ruby
# Drops a specified alias
client.aliases.drop
```
```ruby
# Creates an alias for an existing collection
client.aliases.create
```
## 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).
## Development with Docker
Run `docker compose run --rm ruby_app bash` and install required gems (`bundle install`). It will give you a fully working development environment with Milvus services and gem's code.
For example inside docker container run `bin/console` and inside the ruby console:
```ruby
client = Milvus::Client.new(url: ENV["MILVUS_URL"])
client.collections.list
```
## Contributing
Bug reports and pull requests are welcome on GitHub at https://github.com/patterns-ai-core/milvus.
## License
`milvus` is licensed under the Apache License, Version 2.0. View a copy of the License file.