Vectorsearch

Vectorsearch library is an abstraction layer on top of many popular vector search databases. It is a modern ORM that allows developers to easily chunk data, generate embeddings, store, search, query and retrieve data from vector search databases. Vectorsearch offers a straight-forward DSL and abstracts away overly complicated machine learning/data science-specific configurations and concepts

Installation

Install the gem and add to the application's Gemfile by executing:

$ bundle add vectorsearch

If bundler is not being used to manage dependencies, install the gem by executing:

$ gem install vectorsearch

Usage

require "vectorsearch"

List of currently supported vector search databases and features:

Database	Querying	Storage	Schema Management	Backups	Rails Integration	???
Weaviate	✅	WIP	WIP	WIP
Qdrant	✅	WIP	WIP	WIP
Milvus	✅	WIP	WIP	WIP
Pinecone	✅	WIP	WIP	WIP

Create an instance

Choose the LLM provider you'll be using (OpenAI or Cohere) and retrieve the API key.

Pick the vector search database you'll be using and instantiate the client:

client = Vectorsearch::Weaviate.new(
    url: ENV["WEAVIATE_URL"],
    api_key: ENV["WEAVIATE_API_KEY"],
    llm: :openai, # or :cohere
    llm_api_key: ENV["OPENAI_API_KEY"]
)

# You can instantiate any other supported vector search database:
client = Vectorsearch::Milvus.new(...)
client = Vectorsearch::Qdrant.new(...)
client = Vectorsearch::Pinecone.new(...)

# Creating the default schema
client.create_default_schema

# Store your documents in your vector search database
client.add_texts(
    texts: [
        "Begin by preheating your oven to 375°F (190°C). Prepare four boneless, skinless chicken breasts by cutting a pocket into the side of each breast, being careful not to cut all the way through. Season the chicken with salt and pepper to taste. In a large skillet, melt 2 tablespoons of unsalted butter over medium heat. Add 1 small diced onion and 2 minced garlic cloves, and cook until softened, about 3-4 minutes. Add 8 ounces of fresh spinach and cook until wilted, about 3 minutes. Remove the skillet from heat and let the mixture cool slightly.",
        "In a bowl, combine the spinach mixture with 4 ounces of softened cream cheese, 1/4 cup of grated Parmesan cheese, 1/4 cup of shredded mozzarella cheese, and 1/4 teaspoon of red pepper flakes. Mix until well combined. Stuff each chicken breast pocket with an equal amount of the spinach mixture. Seal the pocket with a toothpick if necessary. In the same skillet, heat 1 tablespoon of olive oil over medium-high heat. Add the stuffed chicken breasts and sear on each side for 3-4 minutes, or until golden brown."
    ]
)

# Retrieve similar documents based on the query string passed in
client.similarity_search(
    query:,
    k:       # number of results to be retrieved
)

# Retrieve similar documents based on the embedding passed in
client.similarity_search_by_vector(
    embedding:,
    k:       # number of results to be retrieved
)

# Q&A-style querying based on the question passed in
client.ask(
    question:
)

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/vectorsearch.

License

The gem is available as open source under the terms of the MIT License.