| S.No |
Topic |
Sub-Topics |
| 1 | Vector Databases | What is a vector, embeddings basics, similarity search, use cases, traditional DB vs vector DB |
| 2 | Mathematics Behind Vectors | Linear algebra basics, cosine similarity, Euclidean distance, dot product, normalization |
| 3 | Embeddings Fundamentals | Text embeddings, image embeddings, dimensionality, dense vs sparse vectors, embedding quality |
| 4 | Embedding Models | Word2Vec, GloVe, FastText, Sentence Transformers, OpenAI embeddings |
| 5 | Vector Similarity Metrics | Cosine similarity, L2 distance, inner product, Hamming distance, trade-offs |
| 6 | Indexing Techniques | Flat index, inverted index, IVF, HNSW, PQ |
| 7 | Approximate Nearest Neighbor (ANN) | Why ANN, accuracy vs speed, recall, latency, scalability |
| 8 | HNSW Algorithm | Graph layers, insertion, search process, parameters, performance tuning |
| 9 | IVF and PQ Indexes | Clustering, centroids, compression, memory optimization, search flow |
| 10 | Index Optimization | Re-indexing, shard sizing, dimension reduction, caching, pruning |
| 11 | Popular Vector Databases | Pinecone, Weaviate, Milvus, Qdrant, FAISS overview |
| 12 | Pinecone Architecture | Indexes, namespaces, pods, metadata filtering, scaling |
| 13 | Weaviate Architecture | Schema, classes, modules, GraphQL API, hybrid search |
| 14 | Milvus Architecture | Collections, partitions, segments, coordinators, storage layers |
| 15 | Open Source vs Managed Vector DBs | Cost, scalability, maintenance, performance, use cases |
| 16 | Data Modeling in Vector DBs | Vector schema, metadata fields, hybrid models, versioning, updates |
| 17 | CRUD Operations | Insert vectors, update vectors, delete vectors, batch operations, upserts |
| 18 | Metadata Filtering | Structured filters, boolean logic, range queries, hybrid search, performance impact |
| 19 | Hybrid Search | Keyword + vector search, BM25, re-ranking, score fusion, use cases |
| 20 | Vector Search APIs | REST APIs, SDKs, query parameters, pagination, result tuning |
| 21 | Scalability & Sharding | Horizontal scaling, replicas, partitioning, load balancing, fault tolerance |
| 22 | Performance Tuning | Latency optimization, recall tuning, batch queries, memory usage, caching |
| 23 | Consistency & Durability | Replication, WAL, backups, recovery, data integrity |
| 24 | Security in Vector Databases | Authentication, authorization, encryption, network security, compliance |
| 25 | Monitoring & Observability | Metrics, logging, tracing, alerts, capacity planning |
| 26 | Vector DB + LLM Integration | RAG pattern, prompt injection, context windowing, retrieval tuning, pipelines |
| 27 | Semantic Search Applications | Search engines, document retrieval, FAQs, chatbots, recommendation systems |
| 28 | Multimodal Vector Search | Text-image search, audio embeddings, video embeddings, fusion strategies, use cases |
| 29 | Production Best Practices | Schema evolution, re-embedding, cost optimization, SLA planning, testing |
| 30 | Advanced & Future Trends | Vector + graph DBs, real-time embeddings, edge deployment, AI agents, research trends |
