AI Agents & RAG

13 pages in this section.

The RAG Pipeline and Agent Loop Mental Model

The two mental models underneath every AI agents and RAG page: the chunk-embed-retrieve-rerank-generate pipeline, and the perceive-decide-act agent loop.

Explainer Python Rag Agents

RAG Basics

Learn RAG fundamentals with 10 Python examples, covering document chunking, embedding storage with ChromaDB, and retrieving relevant information.

Python Ai Agents Rag Rag Basics

Chunking & Ingestion

Learn how to transform raw documents into searchable chunks with embeddings for RAG, covering chunk size, overlap, and metadata.

Python Ai Agents Rag Chunking Ingestion

Vector Databases

Python Ai Agents Rag Vector Db Pgvector

Retrieval & Re-ranking

Python Ai Agents Rag Retrieval Reranking

LangChain

Learn to build LLM applications with LangChain, using models, retrievers, tools, and chains. Connect steps into pipelines with LCEL pipe syntax.

Python Ai Agents Rag Langchain Chains

LlamaIndex

Learn how LlamaIndex builds data-centric RAG pipelines. Ingest, index, and query private data with LLMs using Python examples.

Python Ai Agents Rag Llamaindex Data

LangGraph & Agent Loops

Python Ai Agents Rag Langgraph Agents

Tool Use & Function Calling

Learn how to enable LLMs to execute functions safely. Define tools with schemas, validate arguments, and return results to the model.

Python Ai Agents Rag Tools Function Calling

Model Context Protocol (MCP)

Python Ai Agents Rag Mcp Protocol

Evaluation & Guardrails

Python Ai Agents Rag Evaluation Guardrails

AI Agents Best Practices

Learn best practices for building grounded, observable, and controllable RAG systems and AI agents, covering retrieval, generation, and safety.

Python Ai Agents Rag Best Practices List

AI Agents & RAG Highlights Summary

A single-page roundup of every highlight bullet from the 12 pages in the AI Agents & RAG section, grouped by source page so you can scan all 50 takeaways without opening each article individually.

Highlights Summary Ai Agents Rag

AI Agents & RAG

Search Documentation

AI Agents & RAG