NanoDB is a CUDA-optimized multimodal vector database supporting text and image vectors via CLIP embeddings for similarity search. Features multi-modal indexing in shared embedding space for text-to-image queries. Use cases include CV+text search and edge multimedia recommendations; GPU-accelerated alternative to text-only pgvector for vision-language tasks.

Python framework for AI Engineers building high-performance search and recommendation applications that combine structured and unstructured data through vector compute.

Multi-modal tensor DB for vectors/images/texts/videos with hybrid embedding + metadata/tensor search. Supports multimodal RAG datasets with versioning. Data lake scale vs pure vector stores like Qdrant.

Graph-vector DB for multimodal data (images/videos/docs/embeddings) with hybrid vector similarity + graph traversal + metadata/keyword filtering. Enables complex multimodal RAG queries. Combines FAISS vectors with graphs unlike pure vector DBs like Qdrant.

Open-source database specializing in unstructured and multimodal data for AI/ML applications. Handles images, videos, and other data with decent vector operations, high recall for multimodal integration, and tight compatibility with PyTorch and TensorFlow.

Multi-model database with vector search, graph queries, full-text keyword search, and real-time subscriptions for hybrid vector+keyword+graph retrieval. Ideal for multimodal RAG in full-stack apps. More versatile than pure vector DBs like Qdrant with embedded/multi-model support.