Topic
Framework Tutorials: E-commerce Search & Recommendations
12 posts on framework tutorials: e-commerce search & recommendations — part of framework tutorials on the n4n AI blog.
Visual product search with LangChain and multi-modal models
Build a visual product search system using LangChain and multi-modal embeddings — from image ingestion to vector search with runnable code at each step.
Personalized recommendations with CrewAI agents
Build a personalized recommendation engine using CrewAI agents with user profiling, candidate generation, and ranking stages — complete with runnable code and output examples.
Building a price comparison agent across product feeds
Build a production-ready price comparison agent that ingests multiple product feeds, normalizes messy data, and surfaces the best deals across merchants.
Building a cart-recovery agent with LangGraph
Build a production-ready cart recovery agent with LangGraph — state machines, conditional routing, checkpointing, and real integration patterns.
E-commerce search: filtering and reranking in LlamaIndex
Build production e-commerce search with LlamaIndex: metadata filtering, hybrid retrieval, and cross-encoder reranking for relevant product results.
E-commerce hybrid search: keyword plus vector in LangChain
Build production-ready hybrid search for e-commerce using LangChain — combine BM25 keyword matching with vector embeddings, add cross-encoder reranking, and evaluate with real metrics.
E-commerce chatbot: from search to checkout with LangChain
Hands-on ecommerce chatbot search to checkout langchain tutorial: build semantic search, tool-calling agent, cart and checkout with runnable Python code.
Building semantic product search with LlamaIndex
Step-by-step tutorial to build semantic product search with LlamaIndex using local embeddings, metadata filters, and persistence for e-commerce catalogs.
Building a shopping assistant agent with LangGraph
Build a shopping assistant agent langgraph in this hands-on tutorial: product search, recommendation reasoning, and tool-calling with LangGraph.
Building a find-similar-products feature with LlamaIndex
Hands-on tutorial: implement a find similar products LlamaIndex feature for e-commerce using vector embeddings, metadata filters, and persistence.
AI-powered product recommendations with LangChain
Hands-on tutorial: build AI-powered product recommendations with LangChain using a vector store and LLM chain, from catalog embedding to ranked output.
A product Q&A chatbot from your catalog with LangChain
Build a product Q&A chatbot from your catalog with LangChain using RAG. Step-by-step tutorial with runnable code for ecommerce retrieval and chat.
More topics in framework tutorials
- Building Chatbots with Vercel AI SDK & Next.js15
- LangChain RAG with Vector Databases15
- LangGraph Multi-Agent Workflows15
- LlamaIndex Query Engines for RAG15
- AutoGen Multi-Agent Conversations & Group Chat14
- Choosing an AI Framework by Use Case14
- CrewAI Agent Roles & Task Design14
- CrewAI Real-World Crew Examples14
- Haystack RAG Pipelines14
- LangChain Agents & Tool Calling14
- LangChain Multi-Model Fallback & Routing14
- LangChain.js for Node & TypeScript14