09-12-2024 - Milvus, Vector database used for Sensor Data RAG
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The document provides an overview of vector databases, specifically focusing on Milvus and its application in ingesting air quality data into a retrieval-augmented generation (RAG) application. It outlines the features of vector databases, the process of consuming data, and demonstrates building a local application using tools like Milvus, Langchain, and Python. The agenda also includes a Q&A session to clarify concepts presented in the demonstration.
09-12-2024 - Milvus, Vector database used for Sensor Data RAG
- 1. 1 | © Copyright 2024 Zilliz 1 It's in the Air Tonight. Sensor Data in RAG Tim Spann @ Zilliz
- 2. Slides X
- 3. Overview I will do a quick overview of the basics of Vector Databases and Milvus and then dive into a practical example of how to use one as part of an application. I will demonstrate how to consume air quality data and ingest it into Milvus as vectors and scalars. We will then use our vector database of Air Quality readings to feed our LLM and get proper answers to Air Quality questions. I will show you how to all the steps to build a RAG application with Milvus, LangChain, Ollama, Python and Air Quality Reports. Finally after demos I will answer questions.
- 4. 4 | © Copyright Zilliz 4 A G E N D A Introduction Overview of Vector Databases A Quick Introduction to Milvus Consume and Ingest Air Quality Data Building a local RAG application Q&A
- 5. 5 | © Copyright Zilliz 5 01 Introduction
- 6. 6 | © Copyright 2024 Zilliz 6 6 | © Copyright 10/22/23 Zilliz 6 | © Copyright 2024 Zilliz Tim Spann Principal Developer Advocate, Zilliz tim.spann@zilliz.com https://www.linkedin.com/in/timothyspann/ https://x.com/PaaSDev
- 7. 7 | © Copyright Zilliz 7 Show Me A Demo https://multimodal-demo.milvus.io/ https://milvus.io/milvus-demos/reverse-image-search
- 8. 8 | © Copyright Zilliz 8 osschat.io
- 9. Unstructured Data is Everywhere Unstructured data is any data that does not conform to a predefined data model. Currently, 90% of unstructured data is never analyzed. Images Videos and more! Text
- 10. 10 | © Copyright Zilliz 10 …and cannot process increasingly growing unstructured data Data Source: The Digitization of the World by IDC 20% Other newly generated data in 2025 will be unstructured data 80%
- 11. The challenge of unstructured data ● Problem: Unstructured data comes in lots of forms, no easy way to interact with it all ● Solution: Vector embeddings ● How: Neural networks e.g. embedding models Vector Databases
- 12. 12 | © Copyright Zilliz 12 02 Overview of Vector Databases
- 13. Why a Vector Database? •Vector database • Advanced filtering (filtered vector search, chained filters) • Hybrid search (e.g. full text + dense vector) • Durability (any write in a db is durable, a library typically only supports snapshotting) • Replication / High Availability • Sharding • Aggregations or faceted search • Backups • Lifecycle management (CRUD, Batch delete, dropping whole indexes, reindexing) • Multi-tenancy • Vector search library • High-performance vector search •How do I support different applications? • High query load • High insertion/deletion • Full precision/recall • Accelerator support (GPU, FPGA) • Billion-scale storage Purpose-built to store, index and query vector embeddings from unstructured data.
- 14. Vn, 1 … … … 1 2 3 4 5 Transform into Vectors Unstructured Data Images User Generated Content Video Documents Audio Vector Embeddings Perform Approximate Nearest Neighbor Similarity Search Perform Query Get Results Store in Vector Database How Similarity Search Works
- 15. 15 2024 A vector database stores embedding vectors and allows for semantic retrieval of various types of unstructured data. Vector Database: Making Sense of Unstructured Data
- 16. 16 | © Copyright Zilliz 16 03 A Quick Introduction to Milvus
- 17. 17 | © Copyright Zilliz 17 Milvus Features Multi-Tenancy Hardware- Accelerated Compute Support Python, Java, Golang, NodeJS Milvus Lite, K8, Zilliz Cloud, Docker Scalable and Elastic Architecture Diverse Index Support Versatile Search Capabilities Tunable Consistency
- 18. 18 | © Copyright Zilliz 18 Technologies for various types of Use cases Compute Types Designed for various compute powers, such as AVX512, Neon for SIMD, quantization cache-aware optimization and GPU Leverage strengths of each hardware type, ensuring high-speed processing and cost-effective scalability for different application needs Search Types Support multiple types such as top-K ANN, Range ANN, sparse & dense, multi-vector, grouping, and metadata filtering Enable query flexibility and accuracy, allowing developers to tailor their information 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 wide range of 15 indexes support, including popular ones like Hierarchical Navigable Small Worlds HNSW, PQ, Binary, Sparse, DiskANN and GPU index Empower developers with tailored search optimizations, catering to performance, accuracy and cost needs
- 20. 20 | © Copyright Zilliz 20 04 Consume and Ingest Air Quality Data
- 21. 21 | © Copyright Zilliz 21 DATA!!!!
- 22. 22 | © Copyright Zilliz 22 DATA
- 23. 23 | © Copyright Zilliz 23 REST JSON Ingest
- 24. 24 | © Copyright Zilliz 24 Scalars and Vectors in Milvus
- 25. 25 | © Copyright Zilliz 25 05 Building a local RAG application
- 26. 26 Retrieval-Augmented Generation (RAG) 2024 A technique that combines the strength of retrieval-based and generative models: ● Improve accuracy and relevance ● Eliminate hallucination ● Provide domain-specific knowledge
- 27. 27 RAG : an economic perspective 2024 A business model that bridges public data and private data ● Data sovereignty ● You can't and shouldn't give your private data to others
- 28. 28 | © Copyright Zilliz 28 Ollama + Llama 3.1 + Milvus + LangChain = RAG
- 29. 29 | © Copyright Zilliz 29 Ollama + Llama 3.1 + Milvus + LangChain = RAG
- 30. 30 | © Copyright Zilliz 30 Embeddings Models
- 31. 31 | © Copyright Zilliz 31 06 Q & A
- 33. 33 | © Copyright Zilliz 33 | © Copyright Zilliz 33 RESOURCES
- 34. 34 | © Copyright Zilliz 34 Vector Database Resources Give Milvus a Star! Chat with me on Discord! https://github.com/milvus-io/milvus
- 35. 35 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.
- 36. 36 | © Copyright Zilliz 36 https://zilliz.com/learn/generative-ai
- 40. https://medium.com/@tspann/shining-some-light-on-the-new-milvus-lite-5a0565eb5dd9
- 41. https://medium.com/@tspann/unstructured-data-processing-with-a-raspberry-pi-ai-kit-c959dd7fff47 Raspberry Pi AI Kit Hailo Edge AI
- 42. 42 | © Copyright 2024 Zilliz 42 42 This week in Milvus, Towhee, Attu, GPT Cache, Gen AI, LLM, Apache NiFi, Apache Flink, Apache Kafka, ML, AI, Apache Spark, Apache Iceberg, Python, Java, Vector DB and Open Source friends. https://bit.ly/32dAJft https://github.com/milvus-io/milvus AIM Weekly by Tim Spann
- 43. 43 | © Copyright 2024 Zilliz 43 milvus.io github.com/milvus-io/ @milvusio @paasDev /in/timothyspann Connect with me! Thank you!
- 44. 44 | © Copyright 2024 Zilliz 44
- 45. 45 | © Copyright 2024 Zilliz 45
- 46. 46 | © Copyright 2024 Zilliz 46 Join us at our next meetup! meetup.com/unstructured-data-meetup- new-york/
- 47. 47 | © Copyright Zilliz 47 T H A N K Y O U