Edo Liberty on Vector Databases for Successful Adoption of Generative AI and LLM based Applications

InfoQ Dev Conference and Ido Liberty

• InfoQ Dev is an upcoming conference in Boston where over 20 senior software practitioners will share their experiences and practical insights on critical topics like generative AI, security, and modern web applications, with plenty of time for attendees to connect with peers and speakers at social events (42s).
• Ido Liberty is the founder and CEO of Pinecone, the company behind the vector database product, and has a background in science and engineering, with a mix of undergraduate studies in physics and computer science, and PhD and postdoctoral work in computer science and applied math (1m34s).
• Ido Liberty's career has focused on Big Data algorithms, machine learning, and theoretical computer science, and he has worked as a scientist and professor at Tel Aviv University, and as a director at Yahoo and AWS, where he built AI services and platforms, including SageMaker (2m36s).

Pinecone and Vector Databases

• In 2019, Ido Liberty founded Pinecone to build a vector database, which was initially met with confusion, but has since gained traction as a critical component in the generative AI space (3m3s).
• Vector databases have gained attention recently due to the adoption of large language models, and are a type of database that deals with vector data, which is the output of machine learning models and generative AI models (3m47s).
• Vector databases are different from traditional databases in that they are used predominantly like a search engine, and deal with vector data, which is a new type of data that requires a new kind of infrastructure (4m1s).
• Vector databases are used to represent anything, whether it's text, images, or other types of data, as vectors in high-dimensional spaces, which allows for efficient search and retrieval of similar data points (4m35s).
• Vector databases are highly specialized to work with vectors and handle complex queries that search and find things by relevance, similarity, and alignment in numerical representations, making them ideal for semantic search, RAG, and other use cases (4m37s).
• The objects worked with in vector databases are complex, such as PDFs, images, and J tickets, and are not rows in a table, requiring a new kind of database to handle them efficiently (5m6s).
• The concept of vector databases is not new, but they have evolved significantly in the last three to five years, driven by the increasing adoption of AI and foundational models (6m1s).
• Vector databases have been used internally at big companies like Facebook, Google, and Amazon for tasks such as ad serving, shopping recommendation, and ranking, but their use has become more widespread with the increasing need for engineers to deal with embeddings (6m38s).
• The demands on vector databases have increased, requiring them to be easier to use, cheaper, and more cost-effective, as well as able to handle larger scales and stricter performance requirements (7m29s).
• The scale and performance requirements of vector databases have become more demanding, with customers now having tens of billions of embeddings in one index, and stricter latency requirements (8m24s).
• The evolution of vector databases has been driven by the need for systems to be extremely performant at many different operating points, which was not a requirement when building systems internally at big companies (8m54s).
• The main differences in how vector databases have evolved are the need for them to be easier to use, cheaper, and more cost-effective, as well as able to handle larger scales and stricter performance requirements (9m7s).
• Vector databases have evolved to address engineering and science issues, particularly in retrieving complex objects such as text, images, and others based on similarity, and they excel at partnering with large language models in applications like retrieval-augmented generation (9m49s).

Types and Optimizations of Vector Databases

• Popular use cases for vector databases include recommendation engines, drug design, chemical compound search, security and abuse prevention, CER prevention, support chats for call centers, and more, with limitless possibilities (11m7s).
• Vector databases are used as a search engine to create context for large language models, and even basic implementations can outperform most systems, with more impressive results achievable with further improvement (10m22s).
• The technology behind vector databases enables semantic similarity search at scale, allowing for various applications across different industries (10m47s).
• Vector embedding refers to a numerical representation of an item in a system, such as a document or part of a text document, created using an embedding model (11m49s).
• A vector index is a piece of code, an algorithm, or a data structure that takes a set of vectors and organizes them to pinpoint the most similar or best matches given a query, often focusing on in-memory algorithms for high performance (12m45s).
• Vector databases are a combination of these components, utilizing vector embeddings and indexes to enable efficient and effective similarity searches (11m42s).
• Vector databases are more complicated objects than vector indexes, requiring the organization of large indexes in disk or blob storage, efficient access, load distribution, and the ability to handle complex queries, including filtering metadata and boosting or sparse boosting for search (13m53s).
• Vector databases need to allow for fresh updates, deletes, and the building of a whole system around them, making them a crucial component in managing vector data (15m6s).
• There are two types of optimizations in vector databases: organizing data and reducing the fraction of data to look at when a query is received, and computing the top matches efficiently (16m0s).
• The first type of optimization involves organizing data into small clumps called clusters, using randomized algorithms, clustering algorithms, and semantic hashing to intelligently figure out which data to look at (16m3s).
• Pinecone uses blob storage to organize data, allowing for efficient querying, and has devised complex versions of clustering algorithms for high-quality and efficient query routing (16m8s).
• The second type of optimization involves computing the top matches efficiently, using indexing, and making tradeoffs between memory, computation, latency, and storage consumption (17m14s).
• Innovations in quantization, compression, dimensional reduction, and deep accelerations of compute with vectorized instructions are also crucial in vector database management (17m43s).

Applications and Use Cases of Vector Databases

• Vector databases are used in various applications, including search, filtering metadata, and boosting or sparse boosting, and are a key component in the successful adoption of generative AI and LLM-based applications (13m53s).
• Vector databases are being used to help with RAG (Retrieval Augmented Generation) based applications development, which is one of the most common use cases for vector databases nowadays, allowing people to get better context for LLMs (Large Language Models) and better results without retraining or fine-tuning their models (18m48s).
• RAG enables users to securely and in a data-governed way interact with their proprietary data, which they couldn't do before, making it a very common use case for vector databases (18m52s).
• General pre-trained LLMs are great at what they do, but for businesses, they become somewhat useless if they can't interact with their data, making it essential for LLMs to interact with proprietary data (19m16s).

Security and Data Governance in Vector Databases

• To balance security concerns with the power of AI, it's essential to separate two different kinds of securing: cyber security and data governance (20m0s).
• In terms of cyber security, it's critical not to ship data where it shouldn't be shipped, and investing in security features and being compliant with regulations like GDPR is essential (20m16s).
• Data governance is also a significant issue, as once a model is trained with data, it's impossible to later delete that data from the model, making it essential to think about data governance seriously (20m55s).
• Using a vector database to store data adjacent to the foundation model allows users to decide dynamically what information is available to the model, keeping it fresh and enabling GDPR compliance (21m34s).
• This approach is convenient and one of the main reasons why people choose to use vector databases instead of fine-tuning or retraining their models (22m7s).

Serverless Architecture in Vector Databases

• Vector databases have introduced a serverless architecture, allowing developers to focus on their workload without worrying about managing or provisioning the infrastructure, and enabling faster market entry for generative AI and LLM-based applications (22m12s).
• Serverless architecture is defined as a complete disassociation between the workload and the hardware it runs on, making it the responsibility of the vector database to figure out the hardware and resources needed to run queries efficiently (22m47s).
• This architecture allows users to scale their data and queries without worrying about rescaling, re-provisioning, or moving data around, and they only pay for the resources they use (23m20s).
• Two main problems that serverless architecture solves are planning and cost reduction, as users no longer need to provision for uncertain adoption rates or worry about scaling issues (23m41s).

Cost Reduction and Responsible AI

• Serverless architecture can lead to significant cost savings, with some users experiencing a 50x reduction in total application cost, due to the ability to manage resources more automatically and efficiently (25m35s).
• To create responsible AI solutions, application developers should consider the social side of these technologies and the importance of responsible data, as responsible AI starts with responsible data (26m14s).
• Companies face a balance between moving fast and being responsible when it comes to shipping products, especially in the rapidly evolving field of AI, where there is a high risk of producing actual harm or spectacular backfires (26m38s).
• To mitigate this risk, companies often start by shipping less risky parts of the stack or applications that require less access to sensitive data or high-stakes decision-making, allowing them to progress, learn, and build talent and know-how (27m47s).

Future of AI and Vector Databases

• A recent prediction suggests that within three years, anything not connected to AI will be considered broken or invisible, highlighting the increasing presence of AI in daily life (28m29s).
• AI is expected to play a larger role in work and daily life, with vector databases being part of the ecosystem that enables this evolution by managing data, knowledge, and retrieval (28m44s).
• The integration of AI into daily life is becoming increasingly mundane and practical, with younger generations expecting interfaces that can understand and respond to language and touch (29m14s).
• Companies will need to invest in various technologies, including vector databases, to meet the expected interface of AI-powered products and services (30m44s).

Learning Resources and Best Practices

• Listeners can check out material at Fel to learn more about vector databases, RAG, and related technologies, including how to use LangChain, Open Models from Tropic, Hugging Face, and Open AI (31m5s).
• The material includes notebooks, examples, integrations, and documentation from different technology evangelists, which can be more useful than official documentation for learning (31m50s).
• People who are successful in building with AI are those who start doing it and learn by example, rather than getting bogged down in analysis paralysis or fear of using the wrong technology (32m10s).
• The most common mistakes people make when building with AI are not starting at all or getting stuck in analysis paralysis, and the best approach is to just start building and figure things out as you go (32m21s).
• Building with AI is not as hard as it used to be, and people can start getting something done quickly and have fun with the technology (32m51s).
• AI and generative models bring different perspectives and dimensions to problem-solving, allowing for solutions that humans cannot achieve on their own (33m21s).
• Vector databases are seen as the foundation of the Gen AI and LLM evolution, and listeners can learn more about AI and ML topics through the AI/ML and data engineering community on infoq.com (33m41s).