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Week 9 Master Guide

Week 9: Document Ingestion, Chunking Architectures, Contextual Retrieval & Dense Embeddings

Week 9 Daily Breakdown (7 Days)

Day 1

Week 9, Day 1: Structure-Aware Document Parsing — Markdown Hierarchy, Tables & Cleaning

1 code file(s) • 3 Q&A
Day 2

Week 9, Day 2: Chunking Strategies — Fixed Overlap, Recursive & Parent-Child

1 code file(s) • 3 Q&A
Day 3

Week 9, Day 3: Contextual Retrieval & Metadata Enrichment (*Anthropic Pattern*)

1 code file(s) • 3 Q&A
Day 4

Week 9, Day 4: Dense Embedding Models — `InfoNCE` Contrastive Loss & Matryoshka Embeddings

1 code file(s) • 3 Q&A
Day 5

Week 9, Day 5: Vector Distance Metrics Math — Cosine, Dot Product & L2 Equivalence Proof

1 code file(s) • 3 Q&A
Day 6

Week 9, Day 6: Production Embedding Pipelines — `SHA-256` Content-Addressable Caching & Deduplication

1 code file(s) • 3 Q&A
Day 7

Week 9, Day 7 (Mini-Project): End-to-End Contextual Parent-Child Ingestion & Matryoshka Embedding Pipeline

1 code file(s) • 3 Q&A

Week 9: Document Ingestion, Chunking Architectures, Contextual Retrieval & Dense Embeddings

Month 3 — Enterprise Hybrid RAG, Vector Databases & Information Retrieval Goal: Master the first half of any production RAG system: turning messy enterprise documents into structurally enriched, deduplicated, Matryoshka-embedded parent-child chunks.

Weekly Architecture & Daily Breakdown

  • Day 1: Structure-Aware Document Parsing — Markdown Hierarchy, Tables & Boilerplate Cleaning (day01_document_parsing_markdown_pdf_tables_and_cleaning/)
  • Day 2: Chunking Strategies — Fixed Overlap, Recursive, Semantic & Parent-Child (Small-to-Big) (day02_chunking_strategies_fixed_recursive_semantic_parent_child/)
  • Day 3: Contextual Retrieval & Metadata Enrichment (Anthropic Pattern) (day03_contextual_retrieval_and_metadata_enrichment/)
  • Day 4: Dense Embedding Models — InfoNCE Contrastive Loss & Matryoshka Embeddings (MRL) (day04_dense_embedding_models_contrastive_loss_infonce_matryoshka/)
  • Day 5: Vector Distance Metrics Math — Cosine, Dot Product & L2L_2 Equivalence Proof (day05_distance_metrics_cosine_dot_l2_and_normalization_math/)
  • Day 6: Production Embedding Pipelines — SHA-256 Content-Addressable Caching & Incremental Indexing (day06_embedding_batch_pipeline_caching_and_deduplication/)
  • Day 7: Week 9 Mini-Project — End-to-End Contextual Parent-Child Ingestion & Embedding Engine (day07_week9_mini_project_contextual_ingestion_pipeline/)
Week 9: Week 9: Document Ingestion, Chunking Architectures, Contextual Retrieval & Dense Embeddings | 24-Week AI Engineer Academy · DataArena