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https://github.com/patterns-ai-core/weaviate-ruby
Ruby wrapper for the Weaviate vector search database API
https://github.com/patterns-ai-core/weaviate-ruby
api-client embedding-vectors machine-learning ml ruby rubyml vector-search vector-search-engine weaviate
Last synced: 4 days ago
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Ruby wrapper for the Weaviate vector search database API
- Host: GitHub
- URL: https://github.com/patterns-ai-core/weaviate-ruby
- Owner: patterns-ai-core
- License: mit
- Created: 2023-03-25T05:09:30.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-03-06T16:12:24.000Z (8 months ago)
- Last Synced: 2024-05-22T03:09:21.995Z (6 months ago)
- Topics: api-client, embedding-vectors, machine-learning, ml, ruby, rubyml, vector-search, vector-search-engine, weaviate
- Language: Ruby
- Homepage: https://rubydoc.info/gems/weaviate-ruby
- Size: 146 KB
- Stars: 42
- Watchers: 8
- Forks: 14
- Open Issues: 4
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- License: LICENSE.txt
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README
# Weaviate
+
Ruby wrapper for the Weaviate.io 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/weaviate-ruby)
[](http://rubydoc.info/gems/weaviate-ruby)
[](https://github.com/andreibondarev/weaviate-ruby/blob/main/LICENSE.txt)
[](https://discord.gg/WDARp7J2n8)
[](https://twitter.com/rushing_andrei)
## Installation
Install the gem and add to the application's Gemfile by executing:
$ bundle add weaviate-ruby
If bundler is not being used to manage dependencies, install the gem by executing:
$ gem install weaviate-ruby
## Usage
### Instantiating API client
```ruby
require 'weaviate'
client = Weaviate::Client.new(
url: 'https://some-endpoint.weaviate.network', # Replace with your endpoint
api_key: '', # Weaviate API key
model_service: :openai, # Service that will be used to generate vectors. Possible values: :openai, :azure_openai, :cohere, :huggingface, :google_palm
model_service_api_key: 'xxxxxxx' # Either OpenAI, Azure OpenAI, Cohere, Hugging Face or Google PaLM api key
)
```
### Using the Schema endpoints
```ruby
# Creating a new data object class in the schema
client.schema.create(
class_name: 'Question',
description: 'Information from a Jeopardy! question',
properties: [
{
"dataType": ["text"],
"description": "The question",
"name": "question"
}, {
"dataType": ["text"],
"description": "The answer",
"name": "answer"
}, {
"dataType": ["text"],
"description": "The category",
"name": "category"
}
],
# Possible values: 'text2vec-cohere', 'text2vec-ollama', 'text2vec-openai', 'text2vec-huggingface', 'text2vec-transformers', 'text2vec-contextionary', 'img2vec-neural', 'multi2vec-clip', 'ref2vec-centroid'
vectorizer: "text2vec-openai"
)
# Get a single class from the schema
client.schema.get(class_name: 'Question')
# Get the schema
client.schema.list()
# Update settings of an existing schema class.
# Does not support modifying existing properties.
client.schema.update(
class_name: 'Question',
description: 'Information from a Wheel of Fortune question'
)
# Adding a new property
client.schema.add_property(
class_name: 'Question',
property: {
"dataType": ["boolean"],
"name": "homepage"
}
)
# Inspect the shards of a class
client.schema.shards(class_name: 'Question')
# Remove a class (and all data in the instances) from the schema.
client.schema.delete(class_name: 'Question')
# Creating a new data object class in the schema while configuring the vectorizer on the schema and on individual properties (Ollama example)
client.schema.create(
class_name: 'Question',
description: 'Information from a Jeopardy! question',
properties: [
{
"dataType": ["text"],
"description": "The question",
"name": "question"
# By default all properties are included in the vector
}, {
"dataType": ["text"],
"description": "The answer",
"name": "answer",
"moduleConfig": {
"text2vec-ollama": {
"skip": false,
"vectorizePropertyName": true,
},
},
}, {
"dataType": ["text"],
"description": "The category",
"name": "category",
"indexFilterable": true,
"indexSearchable": false,
"moduleConfig": {
"text2vec-ollama": {
"skip": true, # Don't include in the vector
},
},
}
],
vectorizer: "text2vec-ollama",
module_config: {
"text2vec-ollama": {
apiEndpoint: "http://localhost:11434",
model: "mxbai-embed-large",
},
},
)
```
### Using the Objects endpoint
```ruby
# Create a new data object.
output = client.objects.create(
class_name: 'Question',
properties: {
answer: '42',
question: 'What is the meaning of life?',
category: 'philosophy'
}
)
uuid = output["id"]
# Lists all data objects in reverse order of creation.
client.objects.list()
# Get a single data object.
client.objects.get(
class_name: "Question",
id: uuid
)
# Check if a data object exists.
client.objects.exists?(
class_name: "Question",
id: uuid
)
# Perform a partial update on an object based on its uuid.
client.objects.update(
class_name: "Question",
id: uuid,
properties: {
category: "simple-math"
}
)
# Replace an object based on its uuid.
client.objects.replace(
class_name: "Question",
id: uuid,
properties: {
question: "What does 6 times 7 equal to?",
category: "math",
answer: "42"
}
)
# Delete a single data object from Weaviate.
client.objects.delete(
class_name: "Question",
id: uuid
)
# Batch create objects
output = client.objects.batch_create(objects: [
{
class: "Question",
properties: {
answer: "42",
question: "What is the meaning of life?",
category: "philosophy"
}
}, {
class: "Question",
properties: {
answer: "42",
question: "What does 6 times 7 equal to?",
category: "math"
}
}
])
uuids = output.pluck("id")
# Batch delete objects
client.objects.batch_delete(
class_name: "Question",
where: {
valueStringArray: uuids,
operator: "ContainsAny",
path: ["id"]
}
)
```
### Querying
#### Get{}
```ruby
near_text = '{ concepts: ["biology"] }'
near_vector = '{ vector: [0.1, 0.2, ...] }'
sort_obj = '{ path: ["category"], order: desc }'
where_obj = '{ path: ["id"], operator: Equal, valueString: "..." }'
with_hybrid = '{ query: "Sweets", alpha: 0.5 }'
client.query.get(
class_name: 'Question',
fields: "question answer category _additional { answer { result hasAnswer property startPosition endPosition } }",
limit: "1",
offset: "1",
after: "id",
sort: sort_obj,
where: where_obj,
# To use this parameter you must have created your schema by setting the `vectorizer:` property to
# either 'text2vec-transformers', 'text2vec-contextionary', 'text2vec-openai', 'multi2vec-clip', 'text2vec-huggingface' or 'text2vec-cohere'
near_text: near_text,
# To use this parameter you must have created your schema by setting the `vectorizer:` property to 'multi2vec-clip' or 'img2vec-neural'
near_image: near_image,
near_vector: near_vector,
with_hybrid: with_hybrid,
bm25: bm25,
near_object: near_object,
ask: '{ question: "your-question?" }'
)
# Example queries:
client.query.get class_name: 'Question', where: '{ operator: Like, valueText: "SCIENCE", path: ["category"] }', fields: 'answer question category', limit: "2"
client.query.get class_name: 'Question', fields: 'answer question category _additional { id }', after: "3c5f7039-37f3-4244-b3e2-8f4a083e448d", limit: "1"
```
#### Aggs{}
```ruby
client.query.aggs(
class_name: "Question",
fields: 'meta { count }',
group_by: ["category"],
object_limit: "10",
near_text: "{ concepts: [\"knowledge\"] }"
)
```
#### Explore{}
```ruby
client.query.explore(
fields: 'className',
near_text: "{ concepts: [\"science\"] }",
limit: "1"
)
```
### Classification
```ruby
# Start a classification
client.classifications.create(
type: "zeroshot",
class_name: "Posts",
classify_properties: ["hasColor"],
based_on_properties: ["text"]
)
# Get the status, results and metadata of a previously created classification
client.classifications.get(
id: ""
)
```
### Backups
```ruby
# Create backup
client.backups.create(
backend: "filesystem",
id: "my-first-backup",
include: ["Question"]
)
# 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"
)
```
### Nodes
```ruby
client.nodes
```
### Health
```ruby
# Live determines whether the application is alive. It can be used for Kubernetes liveness probe.
client.live?
```
```ruby
# Live determines whether the application is ready to receive traffic. It can be used for Kubernetes readiness probe.
client.ready?
```
### Tenants
Any schema can be multi-tenant by passing in these options for to `schema.create()`.
```ruby
client.schema.create(
# Other keys...
multi_tenant: true,
auto_tenant_creation: true,
auto_tenant_activation: true
)
```
See [Weaviate Multi-tenancy operations](https://weaviate.io/developers/weaviate/manage-data/multi-tenancy). Note that the mix of snake case(used by Ruby) and lower camel case(used by Weaviate) is intentional as that hash is passed directly to Weaviate.
All data methods in this library support an optional `tenant` argument which must be passed if multi-tenancy is enabled on the related collection
## 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/weaviate.
## License
The gem is available as open source under the terms of the [MIT License](https://opensource.org/licenses/MIT).