Building RAG Applications With the Neo4j GenAI Stack: A Comprehensive Guide
Apr 25 9 mins read
A guide to building LLM applications with the Neo4j GenAI Stack on LangChain, from initializing the database to building RAG strategies. 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 →
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 →
Discover how to optimize prompts for Cypher statement generation to retrieve relevant information from Neo4j in your LLM applications. Read more →