Build multimodal retrieval context layers with Deep Lake

Was macht Build multimodal retrieval context layers with Deep Lake?

Store raw multimodal data, embeddings, and vector-search indexes in Deep Lake so agents can retrieve grounded context for RAG and analysis workflows.

Prerequisites

Deep Lake Python package, storage target, source documents or multimodal data, embedding/model provider, and a RAG or agent runtime such as LangChain, LlamaIndex, or custom Python

Installation

Use the upstream install or setup path that matches your environment:

• pip install deeplake

Basic usage or getting-started notes:

• Use Deep Lake as a vector store for LLM apps. Our integration combines the [Lang...

• Deep Lake users can access and visualize a variety of popular datasets through a free integration with Deep Lake's App. Universities can get up to 1TB of data storage and 100,000 monthly queries on the Tensor Database...

• Source: https://github.com/activeloopai/deeplake

• Extracted from upstream docs: https://raw.githubusercontent.com/activeloopai/deeplake/HEAD/README.md

Documentation

• https://docs.deeplake.ai/latest/guides/rag/

Source

• Agent Skill Exchange