10. Document Chunking Strategies: Fixed Size, Recursive Character, Semantic Chunking & Parent Document Retrievers • Generative AI, Large Language Models (LLMs) & Vector RAG • Rishabh Ki Kaksha

Generative AI, Large Language Models (LLMs) & Vector RAG › 10. Document Chunking Strategies: Fixed Size, Recursive Character, Semantic Chunking & Parent Document Retrievers

45 mins

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Course Syllabus

Module 1: Large Language Model (LLM) Fundamentals & Transformer Architecture

01. Transformer Architecture Deep Dive: Self-Attention Mechanism, Multi-Head Attention & Positional Encoding 02. Autoregressive Generation: Tokenization (BPE, WordPiece, SentencePiece), Sampling (Temperature, Top-K, Top-P) 03. Prompt Engineering Architecture: Few-Shot Prompting, Chain-of-Thought (CoT), ReAct & System Prompt Hardening

Part 1: LLM Internals & Vector RAG Pipelines

Module 2: Embeddings, Vector Spaces & Similarity Mathematics

04. Vector Embeddings: Dense vs Sparse Vectors (OpenAI text-embedding-3, BGE-Large, BM25) 05. Similarity Metrics: Cosine Similarity, Dot Product, Euclidean Distance & Manifold Geometry 06. Approximate Nearest Neighbor (ANN) Algorithms: HNSW (Hierarchical Navigable Small World) & IVF-PQ Indexes

Module 3: Vector Databases: Pinecone, Qdrant, Milvus, Chroma & pgvector

07. Vector Database Architecture: Comparing Pinecone, Qdrant, Weaviate, Milvus & PostgreSQL with pgvector 08. Metadata Filtering & Payload Indexing for High-Performance Multitenant Vector Search 09. Embedded Vector Stores: ChromaDB, DuckDB & In-Memory FAISS (Facebook AI Similarity Search)

Module 4: Advanced RAG Architecture: Chunking, Reranking & Hybrid Search

10. Document Chunking Strategies: Fixed Size, Recursive Character, Semantic Chunking & Parent Document Retrievers 11. Hybrid Search Architecture: Combining Keyword Search (BM25) + Dense Vector Search with Reciprocal Rank Fusion (RRF) 12. Cross-Encoder Reranking: Integrating Cohere Rerank / BGE-Reranker for Pinpoint Context Relevance

Module 5: Context Window Optimization, Multi-Query & Graph RAG

13. Multi-Query Generation, Sub-Question Decomposition & Step-Back Prompting for Complex Questions 14. Context Compression & Lost-in-the-Middle Mitigation Strategies 15. Graph RAG Architecture: Combining Knowledge Graphs (Neo4j / NetworkX) with Vector Search for Relational Insights

Module 6: RAG Evaluation (RAGAS), Guardrails & Production Deployment

16. RAG Evaluation Framework (RAGAS): Faithfulness, Answer Relevance, Context Precision & Context Recall 17. Guardrails & Safety: NeMo Guardrails, Llama Guard & Prompt Injection Defense Mechanisms 18. Streaming Output Architecture with Server-Sent Events (SSE) & WebSocket RAG Responses 19. Caching in GenAI: Semantic Caching with GPTCache & Redis to Slash API Costs by 70% 20. Production GenAI Deployment: Building an Enterprise PDF/Docs AI Assistant with LangChain & LlamaIndex

Interactive Lesson Studio

# 10. Document Chunking Strategies: Fixed Size, Recursive Character, Semantic Chunking & Parent Document Retrievers

> Generative AI & Vector RAG Notes by Rishabh - Topic 10

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### 1. The 4 Document Chunking Paradigms 📄

1. **Fixed-Size Chunking (e.g. 500 characters, 50 overlap):** Naive; words aur sentences ko beech me kaat deta hai.
2. **Recursive Character Chunking (LangChain Standard):** Paragraphs (`\n\n`) $\rightarrow$ Sentences (`\n`, `.`) $\rightarrow$ Words me hierarchical split karta hai.
3. **Semantic Chunking:** Sentence embeddings ke cosine distance me sharp drop hone par natural topic transition detect karke split karta hai!
4. **Parent Document Retriever (Small2Big RAG 👑):** Search ke liye **Small Chunks (100 tokens)** index karta hai, lekin LLM ko context pass karte waqt **Parent Document (2000 tokens)** retrieve karta hai!

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### 2. Master Comparison Matrix

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### 3. Interview Ke 3 Sawal Is Topic Se

1. **Chunk Overlap (e.g. 10-15%) kyu zaroori hota hai?** -> Taaki sentence boundaries par cross-chunk semantic context disconnect na ho.
2. **Parent Document Retriever RAG hallucination kaise kam karta hai?** -> Chhota chunk embedding similarity match fast karta hai jabki parent context LLM ko poora background provide karke incomplete answer generate hone se rokkta hai.
3. **Chunk size (256 vs 1024 tokens) ka tradeoff kya hota hai?** -> Chhote chunks granular precision dete hain par global context lose karte hain; bade chunks noise introduce karte hain aur attention dilution create karte hain.

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**Next Topic:** 11. Hybrid Search Architecture: Combining Keyword Search (BM25) + Dense Vector Search with Reciprocal Rank Fusion (RRF)

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