Vector Databases & Embeddings Certification

Module 1: Introduction to Embeddings

• Understanding embeddings
• Vector representations
• Semantic meaning
• Embedding dimensions
• Common AI applications

Module 2: Vector Fundamentals

• Vectors and dimensions
• Vector spaces
• Numerical representations
• Vector relationships
• High-dimensional data

Module 3: Understanding Embedding Models

• Embedding model concepts
• Text embedding models
• Model dimensions
• Model selection
• Comparing embedding models

Module 4: Generating Embeddings

• Preparing input data
• Calling embedding models
• Processing responses
• Batch embedding
• Storing generated vectors

Module 5: Similarity and Distance Metrics

• Cosine similarity
• Euclidean distance
• Dot product
• Comparing vectors
• Selecting similarity metrics

Module 6: Introduction to Vector Databases

• Vector database concepts
• Traditional versus vector databases
• Vector storage
• Similarity search
• Common use cases

Module 7: Vector Database Architecture

• Collections and indexes
• Vector records
• Metadata
• Query processing
• Storage architecture

Module 8: Creating Vector Collections

• Defining collections
• Configuring dimensions
• Selecting metrics
• Adding vectors
• Managing records

Module 9: Vector Indexing

• Purpose of indexing
• Exact search
• Approximate nearest neighbours
• Index structures
• Search-performance trade-offs

Module 10: Semantic Search

• Keyword versus semantic search
• Query embeddings
• Similarity matching
• Retrieving relevant results
• Search result ranking

Module 11: Metadata and Filtering

• Adding metadata
• Metadata schemas
• Filtering search results
• Combining filters with similarity
• Improving retrieval precision

Module 12: Document Processing

• Document ingestion
• Text extraction
• Cleaning content
• Preserving metadata
• Preparing documents for retrieval

Module 13: Chunking Strategies

• Why chunking matters
• Fixed-size chunking
• Semantic chunking concepts
• Chunk overlap
• Choosing chunk size

Module 14: Building an Embedding Pipeline

• Data ingestion
• Chunk generation
• Embedding creation
• Vector storage
• Pipeline validation

Module 15: Query Processing

• Preparing user queries
• Query embeddings
• Search parameters
• Retrieving candidates
• Processing retrieved results

Module 16: Top-K Retrieval

• Understanding Top-K
• Number of retrieved results
• Relevance trade-offs
• Retrieval thresholds
• Tuning search results

Module 17: Hybrid Search

• Semantic search
• Keyword search
• Combining retrieval methods
• Result fusion
• Hybrid search use cases

Module 18: Reranking

• Why reranking is useful
• Candidate retrieval
• Reranking models
• Relevance scoring
• Improving result quality

Module 19: Retrieval-Augmented Generation

• RAG architecture
• Retrieval pipeline
• Context augmentation
• Grounded generation
• Vector databases within RAG

Module 20: Building a RAG Retrieval Layer

• Preparing knowledge sources
• Creating embeddings
• Indexing vectors
• Querying knowledge
• Supplying retrieved context

Module 21: Conversational Retrieval

• Follow-up queries
• Conversation context
• Query rewriting
• Context-aware retrieval
• Multi-turn search

Module 22: Multimodal Embeddings

• Text embeddings
• Image embeddings
• Shared embedding spaces
• Cross-modal similarity
• Multimodal retrieval

Module 23: Managing Vector Data

• Adding new records
• Updating vectors
• Deleting records
• Managing metadata
• Maintaining collections

Module 24: Updating Knowledge Bases

• Incremental ingestion
• Re-embedding content
• Detecting changed documents
• Removing outdated content
• Keeping indexes current

Module 25: Evaluating Retrieval Quality

• Retrieval relevance
• Precision concepts
• Recall concepts
• Evaluation datasets
• Comparing retrieval configurations

Module 26: Debugging Retrieval Systems

• Irrelevant results
• Missing results
• Poor chunking
• Embedding mismatches
• Metadata issues

Module 27: Improving Retrieval Performance

• Better chunking
• Query transformation
• Metadata filtering
• Hybrid retrieval
• Reranking strategies

Module 28: Vector Search Performance

• Query latency
• Index configuration
• Search accuracy
• Resource usage
• Performance trade-offs

Module 29: Scaling Vector Databases

• Growing vector collections
• Distributed storage concepts
• High query volumes
• Replication concepts
• Capacity planning

Module 30: Security and Access Control

• Authentication
• Authorization
• Collection access
• Protecting stored data
• Secure application integration

Module 31: Building a Semantic Search Application

• Preparing source content
• Generating embeddings
• Creating a vector index
• Implementing search
• Evaluating results

Module 32: Designing Production-Ready Vector Retrieval

• Architecture planning
• Embedding strategy
• Retrieval configuration
• Monitoring performance
• Continuous optimization