06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
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The document outlines a meetup focused on unstructured data, vector databases, and AI, primarily for professionals like machine learning engineers and data scientists, supported by Zilliz. It highlights Milvus, an open-source vector database for generative AI projects, emphasizing its features such as easy integration, scalability, and diverse search capabilities. Additionally, the document discusses the importance of vector databases in enhancing retrieval augmented generation for various unstructured data types.
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
- 1. 1 | © Copyright 8/16/23 Zilliz 1 | © Copyright 8/16/23 Zilliz Tim Spann | Zilliz Discussion on Vector Databases, Unstructured Data and AI
- 2. 2 | © Copyright 8/16/23 Zilliz 2 | © Copyright 8/16/23 Zilliz Tim Spann Principal Developer Advocate, Zilliz tim.spann@zilliz.com https://www.linkedin.com/in/timothyspann/ https://x.com/paasdev https://github.com/tspannhw Speaker
- 3. 3 | © Copyright 8/16/23 Zilliz 3 | © Copyright 8/16/23 Zilliz 3 Unstructured Data Meetup https://www.meetup.com/unstructured-data-meetup-new-york/ This meetup is for people working in unstructured data. Speakers will come present about related topics such as vector databases, LLMs, and managing data at scale. The intended audience of this group includes roles like machine learning engineers, data scientists, data engineers, software engineers, and PMs.This meetup was formerly Milvus Meetup, and is sponsored by Zilliz maintainers of Milvus.
- 4. 4 | © Copyright 8/16/23 Zilliz 4 | © Copyright 8/16/23 Zilliz 27.5K+ GitHub Stars 25M+ Downloads 250+ Contributors 2,700+ Forks Milvus is an open-source vector database for GenAI projects. pip install on your laptop, plug into popular AI dev tools, and push to production with a single line of code. Easy Setup Pip-install to start coding in a notebook within seconds. Reusable Code Write once, and deploy with one line of code into the production environment Integration Plug into OpenAI, Langchain, LlmaIndex, and many more Feature-rich Dense & sparse embeddings, filtering, reranking and beyond
- 5. 5 | © Copyright 8/16/23 Zilliz 5 | © Copyright 8/16/23 Zilliz The evolution of AI made the semantic search of unstructured data possible Search by Probability Statistical analyses of common datasets established the foundation for processing unstructured data, e.g. NLP, and image classification AI Model Breakthrough The advancements in BERT, ViT, CBT etc. have revolutionized semantic analysis across unstructured data Vectorization Word2Vec, CNNs, Deep Speech pioneered unstructured data embeddings, mapping the words, images, videos into high-dimensional vectors
- 6. 6 | © Copyright 8/16/23 Zilliz 6 | © Copyright 8/16/23 Zilliz What your data looks like
- 7. 7 | © Copyright 8/16/23 Zilliz 7 | © Copyright 8/16/23 Zilliz This new AI breakthrough requires new databases to fully unleash its potential Support multiple use case types Accommodate diverse data requirements, enhancing flexibility and effectiveness in varied operational contexts Scale as needed Enable robust handling of expanding data volumes and search demands Highly performant Ensures swift and accurate query responses, crucial for optimal user experience
- 8. 8 | © Copyright 8/16/23 Zilliz 8 | © Copyright 8/16/23 Zilliz Vector Databases are core component for Retrieval Augmented Generation (RAG)
- 9. 9 | © Copyright 8/16/23 Zilliz 9 | © Copyright 8/16/23 Zilliz …different types of data and schemas needs to be thoroughly planned ahead of time
- 10. 10 | © Copyright 8/16/23 Zilliz 10 | © Copyright 8/16/23 Zilliz Retrieval Augmented Generation (RAG) Expand LLMs' knowledge by incorporating external data sources into LLMs and your AI applications. Match user behavior or content features with other similar ones to make effective recommendations. Recommender System Search for semantically similar texts across vast amounts of natural language documents. Text/ Semantic Search Image Similarity Search Identify and search for visually similar images or objects from a vast collection of image libraries. Video Similarity Search Search for similar videos, scenes, or objects from extensive collections of video libraries. Audio Similarity Search Find similar audios in large datasets for tasks like genre classification or speech recognition Molecular Similarity Search Search for similar substructures, superstructures, and other structures for a specific molecule. Anomaly Detection Detect data points, events, and observations that deviate significantly from the usual pattern Multimodal Similarity Search Search over multiple types of data simultaneously, e.g. text and images …powers searches across various types of unstructured data
- 11. 11 | © Copyright 8/16/23 Zilliz 11 | © Copyright 8/16/23 Zilliz We’ve built technologies for various types of use cases Compute Types Support different types of compute powers, such as AVX512, Neon for SIMD execution, quantization & cache-aware optimization, and GPU Leverage specific strengths of each hardware type efficiently, ensuring high-speed processing and cost-effective scalability for diverse application needs Search Types Provide diverse search types such as top-K ANN, Range ANN, hybrid ANN and metadata filtering Enable unparalleled query flexibility and accuracy, allowing developers to tailor their data retrieval needs Multi-tenancy Enable multi-tenancy through collection and partition management Allow for efficient resource utilization and customizable data segregation, ensuring secure and isolated data handling for each tenant Index Types Offer a diverse range of 11+ index types, including popular ones like HNSW, IVF, PQ, and GPU index Empower developers with tailored search optimizations, catering to specific performance and accuracy needs
- 12. 12 | © Copyright 8/16/23 Zilliz 12 | © Copyright 8/16/23 Zilliz Meta Storage Root Query Data Index Coordinator Service Proxy Proxy etcd Log Broker SDK Load Balancer DDL/DCL DML NOTIFICATION CONTROL SIGNAL Object Storage Minio / S3 / AzureBlob Log Snapshot Delta File Index File Worker Node QUERY DATA DATA Message Storage Access Layer Query Node Data Node Index Node Milvus’ fully distributed architecture is designed scalability and performance
- 13. 13 | © Copyright 8/16/23 Zilliz 13 | © Copyright 8/16/23 Zilliz Tests shows consistent query performance when scaled from 65 million to 1 billion vectors
- 14. 14 | © Copyright 8/16/23 Zilliz 14 | © Copyright 8/16/23 Zilliz ANN Benchmark has recognized Milvus as the performance leader among vector database players
- 15. 15 | © Copyright 8/16/23 Zilliz 15 | © Copyright 8/16/23 Zilliz We provide deployment flexibility for different operational, security and compliance requirements BRING YOUR OWN CLOUD Zilliz BYOC Enterprise-ready Milvus for Private VPCs Deploy in your virtual private cloud Zilliz Cloud Milvus Re-engineered for the Cloud Available on the leading public clouds FULLY MANAGED SERVICE Coming Soon! Coming Soon! Milvus Most widely-adopted open source vector database Self hosted on any machine with community support SELF MANAGED SOFTWARE Local Docker K8s
- 16. 16 | © Copyright 8/16/23 Zilliz 16 | © Copyright 8/16/23 Zilliz Milvus Lite pip install pymilvus
- 17. 17 | © Copyright 8/16/23 Zilliz 17 | © Copyright 8/16/23 Zilliz Embeddings Models
- 18. 18 | © Copyright 8/16/23 Zilliz 18 | © Copyright 8/16/23 Zilliz Questions? Give Milvus a Star! Code on Github github.com/tspannhw github.com/milvus-io/