⚠️ This library is under active development and is subject to change. Please use the latest version from GitHub ⚠️
An Elixir client for the Qdrant vector similarity search engine. This library provides a convenient way to interact with the Qdrant API, offering functionality to create collections, insert vectors, search, delete data, and more.
It's available in Hex, the package can be installed
by adding qdrant to your list of dependencies in mix.exs:
def deps do
[
{:qdrant, "~> 0.8.0"}
# Or use the latest version from GitHub | Recommended during development phase
{:qdrant, git: "[email protected]:marinac-dev/qdrant.git"},
]
endconfig :qdrant,
port: 6333,
interface: "rest", # gRPC not yet supported
database_url: System.get_env("QDRANT_DATABASE_URL"),
# If you are using cloud version of Qdrant, add API key
api_key: System.get_env("QDRANT_API_KEY")The Qdrant Elixir Client provides a simple interface for interacting with the Qdrant API. For example, you can create a new collection, insert vectors, search, and delete data using the provided functions.
collection_name = "my-collection"
# Create a new collection
# The vectors are 1536-dimensional (because of OpenAi embedding) and use the Cosine distance metric
Qdrant.create_collection(collection_name, %{vectors: %{size: 1536, distance: "Cosine"}})
# Create embeddings for some text
vector1 = OpenAi.embed_text("Hello world")
vector2 = OpenAi.embed_text("This is OpenAI")
# Now we can insert the vectors with batch
Qdrant.upsert_points(collection_name, %{batch: %{ids: [1,2], vectors: [vector1, vector2]}})
# Or one by one
Qdrant.upsert_point(collection_name, %{points: [%{id: 1, vector: vector1}, %{id: 2, vector: vector2}]})
# Search for similar vectors
vector3 = OpenAi.embed_text("Hello world!")
Qdrant.search(collection_name, %{vector: vector3, limit: 3})- Fork the repository
- Create a branch for your changes
- Make your changes
- Run
mix formatto format your code
Generate change log with git-chglog -o CHANGELOG.md
This project is licensed under the MIT License - see the LICENSE file for details
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