Build Multimodal Retrieval Context Layers With Deep Lake

---
name: "Build multimodal retrieval context layers with Deep Lake"
slug: "build-multimodal-retrieval-context-layers-with-deep-lake"
description: "Store raw multimodal data, embeddings, and vector-search indexes in Deep Lake so agents can retrieve grounded context for RAG and analysis workflows."
github_stars: 9181
verification: "security_reviewed"
source: "https://github.com/activeloopai/deeplake"
author: "Activeloop"
publisher_type: "organization"
category: "Data Extraction & Transformation"
framework: "Multi-Framework"
tool_ecosystem:
  github_repo: "activeloopai/deeplake"
  github_stars: 9181
---

# 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](https://www.activeloop.ai/resources/ultimate-guide-to-lang-chain-deep-lake-build-chat-gpt-to-answer-questions-on-your-financial-data/). 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...
- <details>

- 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](https://agentskillexchange.com/skills/build-multimodal-retrieval-context-layers-with-deep-lake/)