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Vector DBFreemiumOpen Source
DEEPLAKE
Database for AI — multimodal datalake with vector search
Apache-2.0
ABOUT
AI teams working on multimodal applications often end up juggling multiple storage systems — one for raw data (images, audio, video), another for embeddings, and a third for metadata. This fragmentation creates synchronization headaches, inefficient data pipelines, and slow iteration cycles. Deep Lake solves this by providing a single data runtime that stores all data types alongside their vector embeddings in a unified format, with built-in streaming, version control, and query capabilities that work directly with PyTorch, TensorFlow, and LangChain.
INSTALL
pip install deeplakeINTEGRATION GUIDE
1. Build a multimodal RAG system that retrieves relevant images, audio clips, and text documents in a single query
2. Store and version-control training datasets with automatic embedding computation and update tracking
3. Stream large-scale image or video datasets directly into model training pipelines without downloading the entire dataset
4. Power agentic AI workflows: store conversation histories, tool outputs, and vector embeddings in a unified runtime accessible to autonomous agents
5. Create a real-time data pipeline that ingests, embeds, and indexes multimodal data for low-latency retrieval
TAGS
vector-databasemultimodaldata-lakeembeddingsdeep-learningdata-pipeline