https://github.com/marinac-dev/qdrant
Elixir client for Qdrant vector search engine
https://github.com/marinac-dev/qdrant
elixir elixir-client qdrant vector-database vector-search-engine
Last synced: about 2 months ago
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Elixir client for Qdrant vector search engine
- Host: GitHub
- URL: https://github.com/marinac-dev/qdrant
- Owner: marinac-dev
- License: mit
- Created: 2023-03-25T11:58:04.000Z (over 2 years ago)
- Default Branch: master
- Last Pushed: 2023-05-19T14:22:20.000Z (over 2 years ago)
- Last Synced: 2024-03-14T22:50:22.941Z (over 1 year ago)
- Topics: elixir, elixir-client, qdrant, vector-database, vector-search-engine
- Language: Elixir
- Homepage:
- Size: 68.4 KB
- Stars: 16
- Watchers: 1
- Forks: 4
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- License: LICENSE
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README
# Qdrant Elixir Client
## ⚠️ 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.
[](https://hex.pm/packages/qdrant) [](https://hex.pm/packages/qdrant) [](https://hex.pm/packages/qdrant)
## Installation
It's [available in Hex](https://hexdocs.pm/qdrant/readme.html), the package can be installed
by adding `qdrant` to your list of dependencies in `mix.exs`:
```elixir
def deps do
[
{:qdrant, "~> 0.8.0"}
# Or use the latest version from GitHub | Recommended during development phase
{:qdrant, git: "git@github.com:marinac-dev/qdrant.git"},
]
end
```
## Config
```elixir
config :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")
```
## Usage
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.
```elixir
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_points(collection_name, %{vector: vector3, limit: 3})
```
## Contributing
- Fork the repository
- Create a branch for your changes
- Make your changes
- Run `mix format` to format your code
## Change Log
Generate change log with `git-chglog -o CHANGELOG.md`
## License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details