Today in Edworking News we want to talk about Why your brain is 3 million more times efficient than GPT-4 - dead simple introduction to Embeddings, HNSW, ANNS, Vector Databases and their comparison based on experience from production project Recently I had to go on journey into the Vector Database world and pick one for a particular project. And oh boy, was it a ride.
Vector Databases have recently gained significant attention owing to the rise of Large Language Models (LLMs). Although they appear as new, untested technologies, vector databases have a substantial engineering and scientific foundation. As the founder of a startup navigating this landscape, I embarked on a comprehensive exploration of vector databases, comparing and contrasting them to deduce the most suitable option for a production project.
Introduction to Essential Concepts
First, let's break down some foundational concepts. Computers primarily understand binary language, which translates the high/low current (1s and 0s) into data. This binary language allows numbers to be represented, albeit without inherent meaning.
Contextualized Word Embeddings come into play here, translating words into unique numbers based on their context and semantic meaning. They've revolutionized how machines interpret language by providing context-sensitive meanings. For example, 'dust the furniture' and 'dust on the furniture' would have vastly different numerical representations in a contextualized word embedding system.
To delve deeper, the study of Linear Algebra and vector spaces is crucial. Vectors, which are essentially numbers in n-dimensional space, represent words and their meanings in high-dimensional spaces. For instance, OpenAI's models operate in a 1536-dimensional space, accommodating a broad range of contextual meanings.
Efficiency Challenge
Given how Large Language Models like GPT-4 operate on vector databases, there's an inherent computational and economic challenge. Processing, storing, and retrieving these high-dimensional vectors calls for advanced techniques like Approximate Nearest Neighbour Search (ANNS) and Hierarchical Navigable Small World (HNSW). These methods allow the traversal and management of vast data networks effectively.
However, it's crucial to emphasize that while our brains achieve similar feats at a significantly higher efficiency (just 24 Watts of power per hour), LLMs require extensive data-center resources, showcasing our brain's magnificence.
Exploring Vector Databases
Next is my journey into Vector Databases (VDBs). I required a reliable VDB for a Question & Answer app. After exploring various options, here’s a summary based on my experiences with some popular choices:
ChromaDB
My initial choice, chromaDB, was straightforward and perfect for a Proof of Concept (PoC). However, it wasn't suitable for production environments due to challenges like long build times and suboptimal scaling. Since then, chromaDB has made significant progress, addressing many initial concerns, thereby becoming a strong contender for evaluation again.
Image description: Diagram showing integration of ChromaDB
Pinecone
Pinecone is a VDB-as-a-Service that’s easy to implement but inherently proprietary. For anyone prioritizing data control and compliance, it's less suitable.
Faiss
Faiss offers core VDB functionalities but lacks comprehensive database features. It’s best suited for small-scale PoCs rather than full-scale deployments.
Milvus
Initially promising, Milvus posed usability challenges and integration bugs. Despite being decent for local setups, it didn’t meet my expectations under production loads.
pgvector
pgvector integrates vector functionality into PostgreSQL but suffers from performance and concurrency issues. It’s better suited for small-scale applications without heavy concurrent access.
Redis
Redis offers standard infrastructure and acceptable performance but lacks specialized vector features crucial for more complex applications.
Qdrant
Qdrant stood out with its open-source nature, self-hosting options, high performance, scalability, and excellent community support. It offered a seamless experience, making it my top choice. The availability of tutorials, integration with popular libraries, and a supportive community were significant advantages.
Weaviate
Weaviate seems promising, particularly in terms of features, but I haven’t personally tested it. Feedback from others suggests it’s a viable option to explore.
Conclusion
The journey through various vector databases highlighted the importance of understanding one's project requirements and choosing the appropriate tool accordingly. Qdrant emerged as the most holistic solution, blending performance, scalability, usability, and community support.
Remember these 3 key ideas for your startup:
Understand Your Requirements: Before diving into any technology, evaluate your project's specific needs. Consider factors like scaling, ease of use, and integration capabilities.
Community and Support Matter: Choose solutions with active communities and robust support. Platforms like Qdrant excel not just in technology but also in offering valuable resources and assistance.
Flexibility and Future-Proofing: Opt for technologies that provide flexibility and scalability. This ensures you can adapt as your startup grows without overhauling your tech stack.
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