Module 1: Introduction to Vector Databases

• What are vector databases and how they differ from relational and NoSQL databases
• The role of vector databases in AI and ML applications
• Key use cases: semantic search, RAG, recommendations, and anomaly detection
• Overview: Pinecone, Weaviate, Chroma, Milvus, and Qdrant

Module 2: Embeddings and Vector Representations

• What are embeddings and how they encode meaning
• Text embeddings: Word2Vec, GloVe, and transformer-based embeddings
• Image and multimodal embeddings overview
• Generating embeddings with OpenAI, Cohere, and Sentence Transformers

Module 3: Vector Search and Similarity Algorithms

• Cosine similarity, dot product, and Euclidean distance explained
• Approximate Nearest Neighbour (ANN) search
• HNSW and IVF indexing algorithms
• Trade-offs between accuracy, speed, and memory in vector search

Module 4: Pinecone – Cloud Vector Database

• Pinecone architecture: indexes, namespaces, and pods
• Creating and managing Pinecone indexes
• Upserting, querying, and filtering vectors
• Metadata filtering and hybrid search with Pinecone

Module 5: Weaviate – Open Source Vector Database

• Weaviate architecture: schemas, classes, and objects
• Setting up Weaviate locally and on the cloud
• Vectorising data with Weaviate modules
• GraphQL queries and hybrid search in Weaviate

Module 6: Building RAG Applications

• RAG pipeline architecture: ingest, embed, store, retrieve, and generate
• Building a document Q&A system with Pinecone and LangChain
• Chunking strategies for optimal retrieval performance
• Evaluating RAG pipeline quality with RAGAS

Module 7: Advanced Topics and Production

• Multi-tenancy and data isolation in vector databases
• Scaling vector databases for large datasets
• Monitoring and maintaining vector database performance
• Cost optimisation strategies for production vector search

Module 8: Hands-on Projects and Assessment

• Building a semantic search engine for a document corpus
• Creating a RAG-powered chatbot with Pinecone and OpenAI
• Comparative project: Pinecone vs Weaviate evaluation
• Final assessment and course certification

Experience in the Industry Learn from AI infrastructure engineers who have designed and deployed vector database solutions for semantic search, recommendation systems, and RAG applications at production scale.

Backgrounds at the Top Our Vector Database trainers have built vector search infrastructure for AI product companies and enterprise knowledge management systems globally.

Clear & Effective Teaching Embeddings, similarity search, HNSW indexing, and vector database operations are explained clearly with practical comparisons across Pinecone, Weaviate, Chroma, and Milvus.

Hands-On Learning Focus Students build complete vector search applications and RAG systems using Pinecone and Weaviate with real document datasets and embedding models.

Up-to-Date Knowledge Trainers cover the latest vector database features, hybrid search capabilities, and integrations with LangChain, LlamaIndex, and OpenAI embeddings.