Module 1: พื้นฐาน Vector DB & Distance Metrics
Mathematical foundations and distance metrics. Days 1–3 มีบทเรียนแล้ว
เปิดแผน Module 1 →
Module 2: Chroma DB Architecture, HNSW & Dual-Filtering
- Day 8: Chroma CRUD Data Pipeline & Ingestion
- Day 9: HNSW Graph Mechanics & Multi-Layer Concept
- Day 10: จูน HNSW Parameters (M, ef_construction, ef_search)
- Day 11: Metadata Filtering (where operators: $eq, $gt, $in)
- Day 12: Document Full-Text Filtering (where_document: $contains)
- Day 13: Hybrid Filtering (Metadata + Document Search)
- Day 14: สรุปทบทวน & แบบฝึกหัดรวม
Module 3: Advanced Retrievers (LangChain & LlamaIndex)
- Day 15: LangChain Retrievers (Similarity, MMR, Threshold)
- Day 16: Multi-Query & Self-Querying Retrievers
- Day 17: Parent Document Splitter & Context Expansion
- Day 18: LlamaIndex Core Indices (VectorStore, DocumentSummary, KeywordTable)
- Day 19: BM25 Lexical Search (k1, b) & Auto-Merging
- Day 20: Recursive Retriever & Query Fusion (RRF)
- Day 21: สรุปทบทวน & แบบฝึกหัดรวม
Module 4: Enterprise Indexing, FAISS Scaling & Production RAG
- Day 22: FAISS Index Types (Flat, IVF, LSH, HNSW)
- Day 23: Tool Comparison & Milvus Distributed Scaling
- Day 24: 8-Stage Production RAG Pipeline Architecture
- Day 25: Failure Modes (Embedding Mismatch, Chunking Errors)
- Day 26: Collection Migration, Re-indexing & Index Failover
- Day 27: Production Optimization & Benchmarking
- Day 28: Graduation Review