sod VS qdrant

PixLab (https://pixlab.io) & FACEIO (https://faceio.net) | Full-or-part-time | Remote | Computer Vision / Full stack Engineers |

PixLab, a leading provider of Machine Vision, Face Recognition & Media Processing APIs is looking for:

* Embedded C & Computer Vision engineer(s) to work on the SOD (https://sod.pixlab.io), embedded computer vision library.

* Senior Python engineer with proficiency in PyTorch to work on FACEIO (https://faceio.net), our facial authentication web framework for web sites & apps.

* C++ developer with ML expertise to work on the port of Tiny-Dream (https://pixlab.io/tiny-dream), our embedded Stable Diffusion C++ library from ncnn to ggml.

* React/Vue JS Web developer(s) with expertise in fabric.js to work on a brand new, web based photo editing software backed by generative AI.

Reach out to Vincent via contact AT pixlab.io with your resume if interested.

Shameless plug: take a look to our embedded computer vision library SOD: https://sod.pixlab.io.

Khalid Elborai elaborates on their experiences integrating the SOD library into a Raku module.

Qdrant is one of the most popular open-source vector databases for AI and semantic search applications. Whether you're building RAG applications, recommendation systems, or semantic search engines, Qdrant provides high-performance vector similarity search with advanced filtering capabilities.

Qdrant is a high performance vector database. We use it to store and query the embeddings.

Weaviate (weaviate.io) / Qdrant (qdrant.tech):

Qdrant — open-source and super developer-friendly

Qdrant — Rust-powered, fast, with filtering & hybrid search.

The only thing left to do then was to build something that could showcase the power of code ingestion within a vector database, and it immediately clicked in my mind: "Why don't I ingest my entire codebase of solved Go exercises from Exercism?" That's how I created Code-RAGent, your friendly coding assistant based on your personal codebases and grounded in web search. It is built on top of GPT-4.1, powered by OpenAI, LinkUp, LlamaIndex, Qdrant, FastAPI and Streamlit. The building of this project was aimed at providing a reproducible and adaptable agent, that people can therefore customize based on their needs, and it was composed of three phases:

Qdrant is an easy-to-set-up, highly performing and scalable vector database, that offers numerous functionalities (among which hybrid search and metadata filtering).

In cases where a company possesses a strong technological foundation and faces a substantial workload demanding advanced vector search capabilities, its ideal solution lies in adopting a specialized vector database. Prominent options in this domain include Chroma (having raised $20 million), Zilliz (having raised $113 million), Pinecone (having raised $138 million), Qdrant (having raised $9.8 million), Weaviate (having raised $67.7 million), LanceDB (YC W22), Vespa, Marqo, and others. Many of these players have secured significant funding in recent years and are well-positioned to capture notable market share. These vector databases offer efficient storage, indexing, and similarity search functionalities for vectors. They often incorporate specific optimizations tailored for vector data, such as similarity search based on inverted indexes and efficient vector computations. As a result, they cater to the requirements of companies operating in areas like recommendation systems, image search, and natural language processing.

/filters:no_upscale()/news/2025/04/microsoft-dotnet-ai-template-p2/en/resources/1use-aspire-orchestration-1745167526397.png) A notable addition in Preview 2 is the support for .NET Aspire, enhancing the development toolkit with advanced AI capabilities. The Qdrant vector database can be utilized alongside .NET Aspire to create scalable applications. The template continues to utilize the Retrieval Augmented Generation (RAG) pattern for chat applications while adding new features tailored for .NET Aspire.

@Anush008 from qdrant made their first contribution in #182. We are super excited for the upcoming official integration from our friends at qdrant!