Vector Databases FAISS, Pinecone, Chroma, Weaviate

Free download скачать Vector Databases FAISS, Pinecone, Chroma, Weaviate
Published 11/2025
Created by Uplatz Training
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All | Genre: eLearning | Language: English | Duration: 12 Lectures ( 12h 28m ) | Size: 6.2 GB

Learn embeddings, ANN search, and vector DBs like FAISS, Pinecone & Chroma to build real AI search, RAG pipelines, apps.
What you'll learn
Understand the mathematical foundations of vector search (linear algebra, probability, ANN optimization).
Generate, evaluate, and work with embeddings using tools like OpenAI, Hugging Face, and sentence-transformers.
Explain how vector databases differ from traditional databases.
Build and query vector indexes using FAISS, Pinecone, Chroma, and Weaviate.
Implement Approximate Nearest Neighbor (ANN) search and compare index types.
Build a semantic search system from scratch using embeddings + vector DB.
Design and deploy RAG (Retrieval-Augmented Generation) pipelines with LLMs.
Compare performance, scalability, and cost of major vector databases.
Integrate vector databases with LangChain, Python, and LLM APIs.
Build production-ready applications such as chatbots, knowledge bases, and recommendation engines.
Optimize query latency, memory usage, and indexing strategies for high-dimensional data.
Understand real-world deployment challenges and best practices.
Requirements
Enthusiasm and determination to make your mark on the world!
Description
A warm welcome to Vector Databases in Action: FAISS, Pinecone, Chroma & Weaviate course by Uplatz.What Are Vector Databases?Vector databases are specialized data systems designed to store and search high-dimensional vectors - numerical representations of data such as text, images, audio, or code. These vectors (embeddings) capture semantic meaning, allowing machines to compare similarity between items using distance metrics like cosine similarity. Unlike traditional databases that search by exact matches or SQL filters, vector databases enable semantic retrieval, powering AI applications such as chatbots, recommendation engines, RAG pipelines, document search, and multimodal understanding.How They WorkWhen data is converted into embeddings (vectors), these are stored in an index optimized for fast Approximate Nearest Neighbor (ANN) search. During a query, the user input is also transformed into a vector, and the database retrieves the most similar vectors based on distance calculations. Various indexing algorithms (e.g., HNSW, IVF, PQ) allow sub-second responses even with millions of vectors. Vector databases can also combine keyword filtering, metadata search, and semantic search for hybrid querying - making them ideal for production-grade AI systems.Popular Vector DatabasesThis course dives deep into the four most widely used vector databases. 1. FAISS, developed by Facebook AI Research, is a high-performance local library ideal for fast similarity search and prototyping. 2. Chroma is a lightweight, open-source vector database built for LLM workflows and integrates smoothly with LangChain. 3. Pinecone is a fully managed cloud platform offering high scalability, enterprise-grade performance, and production-ready infrastructure.4. Weaviate is an open-source vector database with both local and cloud deployment options, featuring GraphQL APIs, hybrid search, schema design, and strong multimodal capabilities. Together, these platforms cover everything from local experimentation to real-world AI deployment at scale.Course DescriptionThe rise of Generative AI and LLMs has made vector databases the new backbone of intelligent applications. Instead of searching by keywords, vector databases enable semantic search - retrieving results based on meaning and context. This course takes you from the mathematical foundations of embeddings all the way to building real-world AI apps using FAISS, Chroma, Pinecone, and Weaviate.You'll learn how embeddings work, how Approximate Nearest Neighbor (ANN) algorithms power high-speed search, and how to design production-ready Retrieval-Augmented Generation (RAG) pipelines with LLMs. By the end of the course, you'll know exactly which vector database to use, when, and why - and how to deploy AI search systems at scale.No outdated theory - this is hands-on, industry-grade content designed for modern AI engineers, ML/LLMOps teams, full-stack developers, and ambitious learners.What You'll Learn (Learning Objectives)Understand how vector databases work and why they are core to AI search and RAG systemsGenerate and evaluate embeddings using OpenAI, Hugging Face, & PythonImplement ANN search and compare indexing strategiesBuild vector indexes using FAISS, Chroma, Pinecone, and WeaviateCreate semantic and multimodal search engines from scratchIntegrate vector DBs with LangChain and LLM APIsDesign and deploy full RAG pipelines with real dataOptimize query speed, memory usage, and scalabilityUnderstand trade-offs between open-source and cloud vector DBsBuild production-grade AI applications for real clientsWho This Course Is ForData scientists and machine learning engineers working with embeddings or RAG pipelinesSoftware/backend/full-stack engineers building chatbots or AI search systemsData engineers and MLOps professionals managing AI infrastructureNLP practitioners focused on similarity and context retrievalResearchers exploring high-dimensional search or ANN algorithmsAI startup founders & product managers planning to integrate vector searchHackathon participants or builders prototyping AI toolsAnyone aiming to master the data layer behind modern generative AIVector Databases in Action: FAISS, Pinecone, Chroma & Weaviate - Course CurriculumModule 1: Linear Algebra FoundationsLecture 1: Linear Algebra Basics(Vectors, matrices, dot product, cosine similarity, vector norms, and their role in embeddings)Module 2: Probability & Statistics for Vector SearchLecture 2: Probability & Statistics for Vector Search(Distributions, similarity measures, distance metrics, and statistical intuition for high-dimensional search)Module 3: Optimization & ANN ConceptsLecture 3: Optimization & Approximate Nearest Neighbor (ANN) Concepts(Gradient descent, loss functions, dimensionality reduction, and ANN algorithms such as HNSW, IVF, PQ)Module 4: Hands-on Python Math LabsLecture 4: Python Math Labs for Vector Search(NumPy-based linear algebra, similarity computations, and visualization of embedding spaces)Module 5: Vector Database FoundationsLecture 5: Introduction to Vector Databases(Concepts, architecture, storage, and retrieval mechanisms)Module 6: Working with EmbeddingsLecture 6: Generating and Using Embeddings(Creating embeddings using OpenAI, Hugging Face, and sentence-transformers; storing and querying)Module 7: FAISS (Facebook AI Similarity Search)Lecture 7: FAISS Overview and SetupLecture 8: Indexing and Searching with FAISSLecture 9: Building a Semantic Search Engine with FAISSModule 8: Chroma - Open-Source Vector DBLecture 10: Introduction to ChromaLecture 11: Creating and Managing CollectionsLecture 12: Using Chroma with LangChain and LLMsModule 9: Pinecone - Managed Cloud Vector DBLecture 13: Overview of PineconeLecture 14: Index Creation and QueryingLecture 15: Building a Semantic Search Pipeline in PineconeModule 10: Weaviate - Open-Source Vector DB with Cloud OptionLecture 16: Introduction to WeaviateLecture 17: Schema Design, Data Ingestion, and QueryingLecture 18: Hybrid Search and GraphQL APIModule 11: Comparing Vector DatabasesLecture 19: Comparing FAISS, Chroma, Pinecone, and Weaviate(Performance, scalability, pricing, and ecosystem trade-offs)Module 12: Real-World ProjectsLecture 20: Project 1 - Building a RAG Pipeline with LLMs and Vector DBsLecture 21: Project 2 - Image Similarity SearchLecture 22: Project 3 - Knowledge Base Chatbot with PineconeReal-World Projects You'll BuildSemantic Search Engine with FAISSRAG Pipeline with LLMs & PineconeKnowledge Base Chatbot Using LangChainImage Similarity Search SystemPerformance Comparison Across Vector DBsHands-on Deployment & OptimizationBy the End of This Course.You'll be able to confidently design, choose, build, and deploy AI-native search and RAG systems using industry-leading vector databases - just like the systems powering ChatGPT, Midjourney, Notion AI, and Google Gemini.Ready to master one of the most important skills in AI today?Enroll now - and start building semantic search, multimodal AI, and intelligent applications with vector databases.
Who this course is for
Data scientists and machine learning engineers working with embeddings, RAG pipelines, and AI search.
Software and backend engineers building chatbots, semantic search, or recommender systems.
Data engineers and MLOps professionals managing AI infrastructure and vector-based retrieval systems.
AI engineers integrating vector search into LLM-based applications.
NLP practitioners interested in improving context retrieval and text similarity.
Full-stack developers wanting to implement RAG or hybrid search in their apps.
Professionals transitioning from traditional databases to AI-native data systems.
Students and researchers in AI, computer science, or data science who want real-world skills in modern AI storage.
Researchers exploring semantic similarity, ANN algorithms, and high-dimensional search.
Researchers working on multimodal search (text, image, audio similarity).
Tech enthusiasts curious about how ChatGPT-style systems retrieve relevant information.
Hackathon participants or AI builders creating prototypes involving vector search.
Engineers evaluating performance trade-offs across different vector DB technologies.
AI startup founders or product managers assessing vector DB integration in production systems.
Developers looking to gain hands-on experience with FAISS, Pinecone, Chroma, and Weaviate.
Anyone aiming to master the data layer behind modern generative AI and search systems.
Homepage
https://www.udemy.com/course/vector-dat … -weaviate/

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