AI data retrieval systems today face 3 challenges: limited modalities, lack of accuracy, and high costs at scale. Deep Lake 4.0 fixes this by enabling true multi-modality, enhancing accuracy, and reducing query costs by 2x with index-on-the-lake technology.
Index-on-the-Lake: Under-Second Queries Directly from Object Storage
Our innovative index-on-the-lake technology empowers sub-second queries directly from object storage like S3, utilizing lightweight compute and minimal memory. Experience up to 10x more cost efficiency compared to in-memory databases and 2x faster performance than other object storage-based alternatives—all without the need for additional disk-based caches.
You can simultaneously have fast streaming columnar access for directly training deep learning models and execute sub-second indexed queries for retrieval-augmented generation.
A Brief Recap: Unveiling the Key Issues in AI Retrieval Systems
Since open-sourcing Deep Lake in 2019 and learning from millions of downloads and thousands of projects built on top of Deep Lake, we’ve identified fundamental issues in AI retrieval systems that demand attention:
1. Lack of True Multi-Modality
Multi-modality isn’t just about storing vectorized versions of data. Our collaborations with industry leaders like Matterport (a CoStar subsidiary) and Bayer have revealed the untapped potential of raw data enriched with metadata alongside embeddings. Whether it’s scientific research on MRI in healthcare or manipulating 3D scans in real estate, leveraging multiple modalities leads to higher ROI.
2. Inaccuracy at Scale
Achieving accuracy in AI-generated insights is challenging, especially in sectors like legal and healthcare where accuracy is paramount. The issue magnifies with scale—for instance, when searching through the world’s entire scientific research corpus.
3. Manual Workflows and the Need for Intelligent Agents
While AI agents are still maturing, there’s immense potential to automate and abstract away complex components. Specialized research agents can decompose intricate questions, devise search strategies, and address core challenges more effectively.
4. The High Cost of Building It Right
Developing an in-house RAG (Retrieval-Augmented Generation) system is straightforward, but delivering a Google-level search experience is a monumental task—Google has invested over dozens of billions in search R&D over the past decade. While you may not have that budget, your users still expect top-tier performance.
5. Limited Memory
Bolting a vector index onto traditional database architectures does not provide the scalability required by AI workloads. As the scale of your dataset increases, the memory and compute requirements scale linearly. For datasets that grew past 100M, the cost becomes prohibitive to maintain the index in memory.
Deep Lake 4.0: Fast, Accurate, and Cost-Efficient AI Search
Deep Lake offers sub-second latency while being significantly cheaper, thanks to an architecture natively built around object storage, accessed as if it were local. Deep Lake directly stores and maintains the index on the lake without cache. Deep Lake 4.0 is:
- Fast: Achieve sub-second, scalable search.
- Accurate: Utilize multiple indexes (embedding with quantization, lexical, inverted, etc.) for rapid search on object storage with minimal caching, ready for neural search technologies like ColPali.
- Cost-Efficient: Eliminate the need for costly in-memory storage and large clusters. Deep Lake provides rapid, scalable search without the overhead.
Redefining AI Retrieval with Index-on-the-Lake
Traditional multi-modal AI systems rely on expensive compute layers with significant memory and caching requirements. Deep Lake 4.0 disrupts this model by separating compute from storage and offloading indexes to object storage, all while maintaining local-like access. This architecture is 10x more cost-efficient than typical multi-modal systems, without compromising performance.
What’s the alternative to Deep Lake?
Most Multi-Modal AI Systems Look Like This:
with data management, indexing and analysis in expensive compute layers requiring large amounts of memory and/or caching with local storage.
With Deep Lake
To the best of our knowledge, Deep Lake 4.0 is the first to store an index on the lake without requiring a cache, paving the way for a new “Deep Lake” category in database technology alongside data warehouses, lakehouses, and traditional data lakes.
What’s New Compared to 3.0?
In addition to index-on-the-lake, Deep Lake 4.0 introduces:
- Eventual Consistency: Enabling concurrent workloads. Read more here.
- Faster Installation: 5x faster setup by removing all dependencies except NumPy.
- Enhanced Performance: Up to 10x faster reads/writes due to migrating low-level code to C++.
- Cross cloud queries with JOIN operations and User-Defined Functions
- Simplified API: New, more straightforward API with unified documentation, better data typing, and async support.
But wait there’s more…
AI Search Ready: Beyond Embedding and Lexical Indexing
Recent advancements in Visual Language Models (VLMs), as demonstrated in the ColPali paper, show comparable recall on document retrieval benchmarks relative to traditional OCR pipelines. End-to-end learning is set to significantly outperform OCR-based solutions. However, storing the “bag of embeddings” requires 30x more storage than single embeddings. Deep Lake’s format inherently supports n-dimensional arrays, and the 4.0 query engine includes MaxSim operations in alpha support.
Thanks to Deep Lake 4.0’s 10x storage efficiency, you can choose to allocate some of these savings to store rapidly processed PDFs converted into ‘bags of embeddings.’ Although this requires 30x more storage compared to single embeddings, it enables you to capture much richer representations skipping OCR based manual feature engineering pipelines. This trade-off allows for seamless integration into VLM/LLM contexts, resulting in more accurate and truly multi-modal responses.
Deep Lake Benchmarks
Ingestion Time and Cost
Deep Lake significantly reduces ingestion and indexing costs compared to alternatives. For example, ingesting 110 million vectors takes 5 hours on a single machine and compute cost substantially less than the leading serverless vector databases.
Query Cost
Thanks to Deep Lake’s innovative on-lake format, query performance remains exceptional, matching or exceeding competitors despite lower costs.
Accuracy
You can combine MaxSim operation with semantic search and lexical to achieve state of the art retrieval performance on answering scientific questions from papers.
Getting Started
Ready to experience Deep Lake 4.0? Install it now with:
1pip install deeplake
2
Check out our Quickstart guide.
You can easily point to dataset of 247M wikipedia articles:
1import deeplake
2wikipedia = deeplake.open_read_only("s3://activeloopai-db-dev--use1-az6--x-s3/cohere-multilingual")
3wikipedia.summary()
4
1view = wikipedia.query(f"""
2SELECT *
3ORDER BY COSINE_SIMILARITY(embedding, data(embedding, 0)) DESC
4LIMIT 10
5""")
6
Query takes 0.6s after 3 warm up queries on m5.8xlarge and dataset on S3-Express
If you’re transitioning from an existing dataset, follow our migration guide.
Looking Ahead
While Deep Lake 4.0 marks a significant advancement, we’re continually working on improvements, including adding more data types (e.g., image links), upgrading integrations, enabling in-place index updates, scaling MaxSim, and adding more examples in documentation.
Real-World Success Stories
Deep Lake 4.0 is already powering production systems at multiple Fortune 500 companies and unicorn startups across major cloud providers with fine grained access control and SOC2 Type II compliance:
- Bayer: Building a GenAI platform for compliant development of AI-based medical software.
- Flagship Pioneering: Facilitating searches across vast scientific data repositories.
- Matterport: Training multi-modal foundational models for the real estate industry.
- Spotter: Analyzing billions of YouTube videos to identify top influencers.
Join the New Era of Retrieval for AI
This is the beginning of a new era for Deep Lake, redefining AI retrieval with true multi-modality, 10x higher storage efficiency, and AI search readiness. Try Deep Lake 4.0 today.
Deploy Deep Lake 4.0 in Your Enterprise
Ready for secure and compliant deployment of Deep Lake 4.0 in your enterprise? Book a call with us today.