Learn how to build a support agent that relies on information from Stack Overflow using the GenAI Stack – Neo4j, LangChain & Ollama in Docker. Read more →
Learn how to overcome the challenges of structured data operations in text embeddings in RAG applications using knowledge graphs. Read more →
Create a Neo4j GraphRAG workflow using LangChain and LangGraph, combining graph queries, vector search, and dynamic prompting for advanced RAG. Read more →
Learn how to combine text extraction, network analysis, and LLM prompting and summarization for improved RAG accuracy. Read more →
Introducing the Neo4j LangChain Starter Kit for Python developers, which generates GenAI answers backed by data stored in a Neo4j Graph Database. Read more →
Optimizing vector retrieval with advanced graph-based metadata filtering techniques using LangChain and Neo4j. Read more →
How to add retrieval-augmented generation (RAG) to your @neo4j/graphql projects using LangChain.js, step-by-step. Read more →
A guide to building LLM applications with the Neo4j GenAI Stack on LangChain, from initializing the database to building RAG strategies. Read more →
Learn how to use LangChain and Neo4j vector index to build a simple RAG application that can effectively answer questions. Read more →
Combining Neo4j knowledge graphs, vector search, and Cypher LangChain templates using LangChain agents for enhanced information retrieval. Read more →
A practical guide to constructing and retrieving information from knowledge graphs in RAG applications with Neo4j and LangChain. Read more →
Learn how to implement a knowledge graph-based RAG application with LangChain to support your DevOps team. Read more →
In this blog, you will learn how to use the neo4j-advanced-rag template in LangServe Playground to implement advanced RAG strategies. Read more →
Learn how to write graph retrieval queries that supplement or ground the LLM’s answer for your RAG application, using Python and Langchain. Read more →
Learn to implement a Mixtral agent with Ollama and Langchain that interacts with a Neo4j graph database through a semantic layer. Read more →
In this tutorial, we’ll extract Youtube data, integrate it into Neo4j, and create an interactive, personalized LLM with LangChain. Read more →
Check out the demonstration of using Langchain v0.1 to update Neo4j & LLM courses on the Neo4j GraphAcademy. Read more →
Learn how to use PDF documents to build a graph and LLM-powered retrieval augmented generation application. Read more →
Neo4j Vector Index and GraphCypherQAChain for optimizing the synthesis of information for informed response generation with Mistral-7b Read more →
Discover how to optimize prompts for Cypher statement generation to retrieve relevant information from Neo4j in your LLM applications. Read more →
