Last Updated on August 15, 2025

📚 LLM Integration with LangChain & LlamaIndex — Mastery Series

(Production-grade, real-world, GovTech & Enterprise focus)

Module 1: Foundations of LLM Integration

  • Why LangChain & LlamaIndex?
    • LangChain → Orchestration framework for chaining LLM calls, tools, and memory
    • LlamaIndex → Data framework for indexing, retrieval, and connecting LLMs with private datasets
  • LLM Categories: OpenAI, Claude, Gemini, Mistral, LLaMA2, Fine-tuned local models
  • Enterprise Use Cases:
    • RAG-powered Knowledge Portals
    • Legal document search (for Law Firm ERP)
    • Inspection workflow assistants (RDSO)
    • Policy Q&A bots for government portals
  • Setup
    • Python venv
    • Installing langchain, llama_index, openai, pydantic, fastapi
    • API keys & .env management

Module 2: LangChain Core Concepts

  • Prompt Templates & Variables
  • Chains
    • Sequential Chains
    • Router Chains
    • Multi-Tool Agent Chains
  • Memory Types
    • BufferMemory
    • ConversationBufferWindowMemory
    • VectorStoreRetrieverMemory
  • Tools & Agents
    • Custom Tools (API calls, database queries)
    • OpenAI Tools API vs LangChain Agents
  • Practical Demo
    • Chatbot answering from static data

Module 3: LlamaIndex Core Concepts

  • Document Loaders
    • PDF, DOCX, HTML, SQL, API sources
  • Index Types
    • VectorStoreIndex
    • ListIndex
    • TreeIndex
  • Query Engines
    • Simple query
    • Structured query
    • SQL query with LLM reasoning
  • Retrievers
    • BM25
    • Vector search with FAISS / Pinecone / Milvus
  • Practical Demo
    • Build a searchable knowledge base for Railway SOPs

Module 4: Retrieval-Augmented Generation (RAG)

  • Architecture Diagram
  • Chunking strategies
  • Embedding models (text-embedding-ada-002, bge-m3, all-MiniLM-L6-v2)
  • Vector Stores (FAISS, Pinecone, Weaviate, Chroma)
  • Hybrid Search
  • Demo: Ask questions from 10,000+ pages of government tender docs

Module 5: Advanced Agents & Multi-Modal Integration

  • Agent Types: ReAct, Self-Ask, Conversational Agent
  • Multi-Modal Inputs
    • Image Q&A (OCR + LLM)
    • Audio (Speech-to-Text + LLM)
  • Tool Execution
    • Database queries
    • API call chains
  • Demo: An AI agent that can read scanned inspection reports, find relevant data, and update ERP

Module 6: Fine-Tuning & Custom Models

  • Fine-tuning OpenAI GPT models
  • LoRA fine-tuning for LLaMA / Mistral
  • Embedding fine-tuning for domain-specific jargon
  • Demo: Fine-tuned model for Railway inspection terminology

Module 7: Scaling & Deployment

  • LangServe for LangChain APIs
  • FastAPI + LangChain as microservice
  • Dockerization
  • Kubernetes Deployment
  • Load Balancing for LLM APIs
  • Cost Control
    • Token counting
    • Caching with langchain.cache
    • Streaming responses
  • Demo: Deploying a high-availability legal document search service

Module 8: Security & Governance

  • Role-based access to AI tools
  • Audit logs for prompts & responses
  • Data redaction & PII masking
  • Secure embeddings with on-prem vector DB
  • Compliance (ISO 27001, GDPR, DPDP Act India)
  • Demo: RAG bot with RBAC for government departments

Module 9: End-to-End Project

“AI-Powered Knowledge Assistant for Government Tenders”

  • Flow:
    1. Document ingestion (PDF tender docs)
    2. Embedding storage in Pinecone
    3. LangChain chain for Q&A
    4. LlamaIndex query engine for deeper retrieval
    5. FastAPI service with authentication
    6. Docker + Kubernetes deployment
  • Deliverables:
    • Source code
    • Deployment YAMLs
    • API documentation
    • Demo video

Module 10: Bonus — Domain-Specific Templates

  • Legal Case Search Assistant
  • Urban Governance Scheme Q&A Bot
  • Railway Safety SOP Copilot
  • AI-powered Citizen Complaint Triage

we’ll embed IREPS (Indian Railways e-Procurement System) and TPI (Third Party Inspection) as real-world enterprise/GovTech use cases inside the LangChain & LlamaIndex Mastery Series

📚 LLM Integration with LangChain & LlamaIndex — Mastery Series (GovTech + Enterprise)

(IREPS, TPI, RIMS, Law ERP-ready)

Module 1: Foundations of LLM Integration

  • Why LangChain & LlamaIndex for GovTech?
    • IREPS: AI-powered tender clause Q&A, bidder support, and compliance checks
    • TPI: Inspection report summarization, defect classification, and compliance tagging
  • LLM Categories: OpenAI, Claude, Gemini, LLaMA2, domain fine-tunes
  • Setup:
    • Python venv, installing langchain, llama_index, faiss, fastapi
    • Secure API key management (.env + vault integration)

Module 2: LangChain Core Concepts

  • Prompt templates for tender queries (IREPS)
  • Chains for inspection workflows (TPI)
  • Memory to retain bidder interactions
  • Custom Tools:
    • Tender clause search API
    • Inspection checklist validation API
  • Demo: “Ask about Earnest Money Deposit clause in IREPS tender”

Module 3: LlamaIndex Core Concepts

  • Loaders for:
    • Bulk PDF tenders (IREPS)
    • TPI inspection reports (scanned + OCR)
  • VectorStoreIndex for tender clauses
  • TreeIndex for multi-section inspection manuals
  • Demo: Search across thousands of IREPS tenders in seconds

Module 4: Retrieval-Augmented Generation (RAG)

  • RAG for:
    • Tender clarification bot (IREPS)
    • TPI report anomaly detection
  • Chunking strategies for large bid documents
  • Hybrid Search for technical & commercial terms
  • Demo: AI explains why a tender bidder was disqualified

Module 5: Advanced Agents & Multi-Modal Integration

  • Multi-tool agent:
    • Reads scanned TPI reports → extracts key points → updates ERP
  • Image + Table extraction from inspection forms
  • Demo: Agent uploads signed inspection report, detects defects, sends alert to QA team

Module 6: Fine-Tuning & Custom Models

  • Fine-tune for:
    • IREPS tender terminology (Railway procurement jargon)
    • TPI inspection defect categories
  • LoRA fine-tuning for on-prem LLaMA2 models
  • Embedding fine-tuning for railway-specific compliance codes

Module 7: Scaling & Deployment

  • Deploy as GovCloud microservices
  • LangServe API for RAG services
  • Dockerized AI Tender Assistant for IREPS
  • K8s scaling with rate-limiting to control API costs

Module 8: Security & Governance

  • RBAC:
    • Procurement officers see all data
    • Bidders only see public tender info
  • Audit logging for inspection workflows (TPI)
  • DPDP compliance for vendor data
  • Secure embeddings on on-prem servers

Module 9: End-to-End Project

“AI-Powered Tender & Inspection Copilot”

  • Flow:
    1. IREPS tender ingestion → Vector DB
    2. AI clause search & compliance Q&A
    3. TPI inspection report OCR & classification
    4. Alerts for non-compliance in ERP
  • Deliverables:
    • Full code repo
    • Docker/K8s configs
    • API documentation

Module 10: Bonus Templates

  • Tender Q&A Bot (IREPS)
  • Inspection Workflow Assistant (TPI)
  • RIMS Safety Protocol Copilot
  • AI-driven Vendor Helpdesk

Pranu, if we develop this mastery series as a PDF + code package, we can have:

  • IREPS demo: Live RAG bot answering tender queries
  • TPI demo: AI extracts defects from inspection reports
  • Fully deployable microservices for both