20. Production GenAI Deployment: Building an Enterprise PDF/Docs AI Assistant with LangChain & LlamaIndex • Generative AI, Large Language Models (LLMs) & Vector RAG • Rishabh Ki Kaksha

Generative AI, Large Language Models (LLMs) & Vector RAG › 20. Production GenAI Deployment: Building an Enterprise PDF/Docs AI Assistant with LangChain & LlamaIndex

60 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

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

> Generative AI & Vector RAG Notes by Rishabh - Topic 20

---

### 1. The Production Enterprise RAG Pipeline 🏭

```
[ Document Ingestion (PDFs/Markdown) ] ➔ Recursive Chunking (500 Tokens, 50 Overlap)
                                              ⬇
                                  [ Dense Embeddings (BGE-Large) ]
                                              ⬇
                                  [ Hybrid Vector Store (Qdrant) ]
                                              ▲
                                              │ (Hybrid Search: BM25 + Dense)
[ User Query ] ➔ [ Query Rewriter / Sub-Question Decomposition ]
                                              ⬇
                                  [ Cross-Encoder Reranker (Cohere) ] (Top 3 Chunks)
                                              ⬇
                                  [ Prompt Assembly + System Guardrails ]
                                              ⬇
                                  [ Streaming LLM Generation with Citations ]
```

---

### 2. LangChain Expression Language (LCEL) Production Pipeline

```python
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser
from langchain_core.runnables import RunnablePassthrough

template = """You are Rishabh Ki Kaksha AI Assistant. Answer using ONLY the provided context:
Context:
{context}

Question: {question}
Answer:"""

prompt = ChatPromptTemplate.from_template(template)

rag_chain = (
    {"context": retriever | format_docs, "question": RunnablePassthrough()}
    | prompt
    | llm
    | StrOutputParser()
)
```

---

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

1. **LangChain vs LlamaIndex kab choose karenge?** -> LlamaIndex deep data indexing, complex multi-document parsing aur specialized retrieval pipelines ke liye best hai; LangChain multi-step agent tool calling aur orchestration ke liye best hai.
2. **PDF tables aur charts accurate extract karne ke liye kaunsa tool use hota hai?** -> Unstructured.io, LlamaParse ya Vision-based Multimodal LLMs (GPT-4o Vision).
3. **Source Citations (`[Page 4, Doc A]`) UI me accurate kaise map hoti hain?** -> Chunk metadata me `page_number` aur `source_file` store karke generator prompt me strictly chunk index cite karne ka rule enforce karna.

---
**Course Completed:** Generative AI, Large Language Models & Vector RAG (All 20 Lessons Completed!) 🎉🚀🤖🧠⚡🔍💎

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