19. Caching in GenAI: Semantic Caching with GPTCache & Redis to Slash API Costs by 70% • Generative AI, Large Language Models (LLMs) & Vector RAG • Rishabh Ki Kaksha

Generative AI, Large Language Models (LLMs) & Vector RAG › 19. Caching in GenAI: Semantic Caching with GPTCache & Redis to Slash API Costs by 70%

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

# 19. Caching in GenAI: Semantic Caching with GPTCache & Redis to Slash API Costs by 70%

> Generative AI & Vector RAG Notes by Rishabh - Topic 19

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### 1. Semantic Caching vs Exact String Caching 💰

*   Query 1: *"How do I reverse a linked list in Python?"*
*   Query 2: *"Python code to reverse singly linked list"*
*   *Exact Caching:* **Cache Miss** (Different strings).
*   **Semantic Caching (Redis + Embeddings):** Cosine similarity $> 0.95$ detect karke **Cached Response return karta hai in < 5ms with ZERO LLM API COST!**

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### 2. Redis Semantic Cache Architecture

```
[ User Query ] ➔ [ Generate Query Embedding ]
                         ⬇
                 [ Redis Vector Similarity Search ]
                         ├── Cosine Sim >= 0.96? ➔ Return Cached Response! (Cost: $0.00, Latency: 4ms)
                         └── Miss (< 0.96)        ➔ Call LLM ➔ Cache Embedding + Answer in Redis
```

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### 3. Master Comparison Matrix

| Metric | Direct LLM API Call | Semantic Cache Hit |
| :--- | :--- | :--- |
| **Response Latency** | ~2000ms - 5000ms | ⚡ **< 5ms (1000x Faster!)** |
| **API Cost** | $0.03 per request | ⚡ **$0.00 (Zero Dollar Cost)** |

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

1. **Semantic Cache threshold (e.g. 0.95 vs 0.85) set karte waqt kya tradeoff hota hai?** -> Too high threshold (0.99) causes high cache misses; too low threshold (0.80) causes incorrect answers to slightly different questions.
2. **Prompt Templates semantic cache me normalize kaise hoti hain?** -> System prompt aur formatting strip karke sirf raw user semantic intent vector match karna.
3. **Cache Invalidation policy GenAI me kaise set hoti hai?** -> Time-to-Live (TTL e.g. 7 days) + LRU (Least Recently Used) memory eviction policy.

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**Next Topic:** 20. Production GenAI Deployment: Building an Enterprise PDF/Docs AI Assistant with LangChain & LlamaIndex

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