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.

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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.

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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

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01 Introduction

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Tim Spann
Principal Developer
Advocate, Zilliz
tim.spann@zilliz.com
https://www.linkedin.com/in/timothyspann/
https://x.com/PaaSDev

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Show Me A Demo
https://multimodal-demo.milvus.io/
https://milvus.io/milvus-demos/reverse-image-search

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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

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…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

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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
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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

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A vector database stores
embedding vectors and
allows for semantic
retrieval of various types
of unstructured data.
Vector Database: Making Sense of Unstructured Data

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03 A Quick Introduction to Milvus

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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

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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

19. https://docs.voxel51.com/integrations/milvus.html
FiveOne + Milvus
https://milvus.io/docs/integrate_with_voxel51.md

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04 Consume and Ingest Air Quality
Data

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DATA!!!!

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DATA

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REST JSON Ingest

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Scalars and Vectors in Milvus

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05 Building a local RAG application

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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

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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

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Ollama + Llama 3.1 + Milvus + LangChain = RAG

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Ollama + Llama 3.1 + Milvus + LangChain = RAG

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Embeddings Models

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06 Q & A

32. https://medium.com/@tspann/whats-in-the-air-tonight-mr-milvus-fbd42f06e482

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RESOURCES

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Vector Database Resources
Give Milvus a Star! Chat with me on Discord!
https://github.com/milvus-io/milvus

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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.

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https://zilliz.com/learn/generative-ai

38. https://medium.com/@tspann/unstructured-street-data-in-new-york-8d3cde0a1e5b

39. https://medium.com/@tspann/not-every-field-is-just-text-numbers-or-vectors-976231e90e4d

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

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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

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milvus.io
github.com/milvus-io/
@milvusio
@paasDev
/in/timothyspann
Connect with me!
Thank you!

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Join us at our next meetup!
meetup.com/unstructured-data-meetup-
new-york/

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T H A N K Y O U