A presentation at Build Club: AI Builder Meetup in in Pune, Maharashtra, India by Manvendra Singh
Harnessing RAG with Vector Databases A Practical Guide.
A a d n e g
Know Your Host 👨💻 Cloud Captain @AWS Cloud Club JECRC • Building @Rel-InDev • 100k+ Impressions • Technical Writer • Developer Advocate • Passionate about Cloud, Content, Community, and Empathy 🥑 💬 👀 I’m a DevOps and Cloud Advocate with a passion for open-source and community building. I help people get into Cloud — Been involved in the DevOps and Cloud Landscape for over 2 years and have strong experience in Public Speaking, Technical Writing, Content Creation, Community Management/Leadership, etc. Hi there! I’m Manvendra!
Let’s know each other… Who’s your Favorite Artist ? What’s your favorite film ? Be Concise What’s your favorite Habit ? What’s your favorite Book ? Be Confident Be Chill ICE BREAKER
Traditional Databases Explained by Kitty Find me songs like the Cloud.
Traditional Databases Explained by Kitty Here’s the track ‘Clouds’ by XYZ Artist
I meant songs that feel like clouds … soft
VECTOR DATABASE Explained by Kitty Find me songs like the Cloud.
VECTOR DATABASE Explained by Kitty Here’s a playlist: lofi beats, chillhop,
What is 1 Vector DB ? 2 A vector database stores and retrieves vector embeddings for fast similarity search and semantic analysis. It supports CRUD operations, metadata filtering, scalability, and serverless capabilities. These databases are essential for AI applications involving large language models and generative AI. LLM Vector DataBase
Why do we need Vectro DB? STATIC DATABASE 1 Store structured data (numbers, text, rows, columns). 2 Retrieval = exact matches (SQL queries, key lookups). 3 Works great for: banking records, inventory, and user profiles. 4 Problem: Can’t understand the Semantics (meaning) between items. VECTOR DATABASE Store vector embeddings (numerical representations of meaning). Retrieval = similarity search (find “closest” vectors). Works great for: Semantic search (find docs with the same meaning). Recommendations (similar products, songs, movies). RAG for LLMs (find relevant context fast). Strength: Handles unstructured data (text, images, audio) by comparing meaning, not just exact words.
ARCHITECTURE WORKING →
ALOGRITHM HOW DOES IT WORK HNSW builds a hierarchical graph of vector clusters. Nodes connect to other nodes with similar vectors. The hierarchy allows both broad exploration at higher levels and fine search at lower levels. During a query, the graph is traversed step by step toward the most relevant vectors. ANN
SERVERLESS VECTOR WORKING Serverless = Next Generation: Firstgen vector DBs are fast and scalable but expensive; serverless models focus on cost-efficiency and elasticity. Separation of Storage & Compute: Uses partitioning to query only relevant parts of the index, lowering cost and latency. Multitenancy: Supports multiple namespaces without letting rarely used data inflate costs. Freshness: Ensures newly inserted vectors become searchable within seconds, even at scale
AWS S3 VECTOR WORKING Storage Backbone: Uses Amazon S3 as the main storage layer for vectors, making it cheap, durable, and highly scalable. Separation of Compute & Storage: Index data lives in S3, while compute nodes are spun up only when queries happen costefficient. Partitioned Search: Vectors in S3 are split into subindices/partitions, so queries only scan the most relevant sections. →
Understanding RAG RETRIEVAL AUGMENTED GENERATION
1 2 Chunking 3 6 Data Intake Embedding Synthesis 5 RAG 4 Retrieval Vector Storage
DEMO TIME !!!!
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THANK YOU!
Large Language Models are only as useful as the information they can access. In this talk, we’ll explore how Retrieval-Augmented Generation (RAG) and Vector Databases enable AI applications to retrieve relevant knowledge, understand context, and generate more accurate responses.
We’ll cover vector embeddings, semantic search, similarity retrieval, and the complete RAG workflow, along with practical use cases and a live demonstration. Whether you’re building AI assistants, internal knowledge tools, or next-generation search experiences, you’ll leave with a clear understanding of how modern AI systems connect language models with your data.
for free. You
can too.