Beginner LevelAI, LLMs, Embeddings & MCP
Compare LangChain vs LlamaIndex frameworks for building LLM applications.
❓ Interview Prompt: Compare LangChain and LlamaIndex regarding data ingestion, RAG abstractions, and agent orchestration.
1. LangChain:
•
Focus: Generic application orchestration and multi-step agent workflows.
•
Strengths: Extensive integration ecosystem (hundreds of tools, memory stores, models), flexible chain compositions, and agent execution loops.
2. LlamaIndex (formerly GPT Index):
•
Focus: Specialized Data Ingestion and RAG Search Retrieval.
•
Strengths: Advanced document parsing (LlamaParse), hierarchical index structures, sub-question query engines, and out-of-the-box RAG optimization.
Modern Paradigm (2026):
Developers frequently combine both: using LlamaIndex for RAG ingestion/indexing and LangChain or custom TypeScript code for agent orchestration.
Code Implementation Example
typescript
// LlamaIndex: Quick RAG index creation over documents
import { VectorStoreIndex, SimpleDirectoryReader } from 'llamaindex';
const documents = await new SimpleDirectoryReader().loadData({ directoryPath: './data' });
const index = await VectorStoreIndex.fromDocuments(documents);
const queryEngine = index.asQueryEngine();
const response = await queryEngine.query({ query: 'What is our refund policy?' });💡Creating RAG query engine using LlamaIndex.
🎯 Interview Key Takeaway
Use LlamaIndex for advanced RAG indexing and data retrieval; use LangChain for generic agent workflow orchestration.
#LangChain#LlamaIndex#Frameworks#RAG#Beginner
Back to all AI, LLMs, Embeddings & MCP Questions→