swirl-search VS qdrant

Understanding an Open Source Search Platform: Swirl

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Swirl is an open-source Python project. Contributing to Swirl can help you gain production-level knowledge of Python and improve your skills.

Note: This is not how you write a search engine. There's a lot more stuff that goes into it. If you want to know more, check this GitHub Repository:github.com/swirlai/swirl-search

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Swirl is an open-source search engine which is built using Python and Django. Things which makes Swirl more special is that individual developers and organizations can use Swirl without paying single penny and even customize the search results by connecting to Database (E.g. SQL, NoSQL), Public Data Services (E.g. Google) and Enterprise Sources (E.g. Jira). GitHub Link: https://github.com/swirlai/swirl-search

Hello Devs, The team at Swirl has created this amazing guide which contains all the relevant information for anyone who wants to extend Swirl by adding SearchProviders, Connectors, and Processors.

Swirl is an open-source search platform software that simultaneously searches multiple content sources and returns AI-ranked results. You can also use Generative AI Models to get answers based on your data. It’s written in Python.

Great. Now that we have the embeddings, we need to store them in a vector database. We will be using Qdrant for this purpose. Qdrant is an open-source vector database that allows you to store and query high-dimensional vectors. The easiest way to get started with the Qdrant database is using the docker.

I took Qdrant for this project. The reason was that Qdrant stands for high-performance vector search, the best choice against use cases like finding similar function calls based on semantic similarity. Qdrant is not only powerful but also scalable to support a variety of advanced search features that are greatly useful to nuanced caching mechanisms like ours.

I'm currently looking to implement locally, using QDrant [1] for instance.

I'm just playing around, but it makes sense to have a runnable example for our users at work too :) [2].

[1]. https://qdrant.tech/

Also compare with qdrant's Rust implementation; they tout their performance. https://github.com/qdrant/qdrant/tree/master/lib/segment/src...

There are much better known examples, such as https://qdrant.tech/ and https://github.com/lancedb/lancedb

For more information, see our release notes. Qdrant is an open source project. We welcome your contributions; raise issues, or contribute via pull requests!

Initialize the Qdrant Client with in-memory storage. The collection name will be “imagebind_data” and we will be using cosine distance.

Qdrant is an open-source vector search engine optimized for performance and flexibility. It supports both exact and approximate nearest neighbor search, providing a balance between accuracy and speed for various AI and ML applications.