LangChain Explained: Models, Prompts, Chains, Memory, Indexes and Agents
Expert-level deep dive: LangChain Explained: Understanding Models, Prompts, Chains, Memory, Indexes, and Agents
10. Production RAG - Everything Together
Nine articles, nine patterns. Now build the service that ships: Pydantic config, request-ID middleware, async document ingestion, a unified multi-mode query endpoint, K8s health probes, and a multi-stage Dockerfile.
9. RAG with Structured Outputs - JSON Mode + Pydantic
Force your RAG pipeline to always return valid, typed data. Combine OpenAI JSON mode, Pydantic v2 schemas, and LangChain output parsers to build a pipeline where every response is schema-validated.
8. Corrective RAG (CRAG) - Self-Correcting Retrieval
Implement CRAG: a retrieval pipeline that grades its own results and automatically corrects poor retrievals before generating. Combines LangGraph state machines with a web search fallback.
7. Agentic RAG - ReAct Agent with Tool-Calling
Give your RAG system the ability to reason, act, and self-correct. Build a ReAct agent that decides when to retrieve, when to search the web, and when to compute — using LangGraph and custom tools.
6. GraphRAG - Multi-Hop Reasoning with a Local Knowledge Graph
Go beyond flat vector search. Build a knowledge graph from your documents, run multi-hop queries that traverse entity relationships, and combine graph traversal with vector retrieval for richer answers.
5. Evaluating RAG Quality with RAGAS
Stop guessing whether your RAG pipeline is good. Use RAGAS to measure faithfulness, answer relevancy, context precision and recall — and learn to interpret the metrics to drive concrete improvements.
4. Streaming RAG Chat - FastAPI SSE + React
Add token-by-token streaming to your RAG pipeline. Server-Sent Events from FastAPI, a React client with AbortController, and the nuances of streaming structured retrieval responses.
3. Multi-Tenant Document Q&A API
Build a multi-tenant RAG API where each tenant's documents are fully isolated. FastAPI, per-tenant ChromaDB collections, JWT authentication, and a clean REST interface for document ingestion and querying.
2. Build Your First RAG System from Scratch
Strip away every framework and build RAG with raw Python — numpy cosine similarity, direct OpenAI calls, no LangChain. Understand exactly what the abstractions hide before you use them.
1. Document Analysis with LLMs
Build a production-ready document Q&A pipeline from scratch. Load PDFs, Word files and web pages, split them into semantic chunks, embed with open-source models, store in a vector DB, and answer questions with full source attribution — zero paid APIs required.
Your AI Agent Works Perfectly in the Demo. Here Are the 6 Ways It Dies in Production with AI Automation
AI agents rarely fail in demos - they fail in production. Discover the 6 critical reasons AI automation systems break at scale: model drift, weak governance, poor observability, data quality issues, scalability bottlenecks, and security gaps.