Knowledge Tree
Neo4j
Also known as: Neo4j, Neo4j Graph Database
Facts (18)
Sources
RAG Using Knowledge Graph: Mastering Advanced Techniques procogia.com Jan 15, 2025 9 facts
codellm_transformer = LLMGraphTransformer(llm=llm)
graph_documents = llm_transformer.convert_to_graph_documents(documents)
graph.add_graph_documents(
graph_documents=graph_documents,
include_source=True,
baseEntityLabel=True
)
print("Graph data has been successfully pushed to Neo4j.")
procedureThe process for building a vector retriever model involves sending chunked documents to an embedding model (such as nomic-embed-text) to generate numerical representations (embeddings) that capture semantic meaning, and then storing these embeddings in a vector database like Neo4j for efficient similarity searches.
procedureThe implementation of a hybrid RAG system involves four main steps: (1) building the graph retriever model (extracting entities/relationships, setting up a graph database like Neo4j AuraDB, and implementing querying), (2) building the vector retriever model (generating embeddings, setting up a vectorstore like Chroma/Neo4j/FAISS/Pinecone, and implementing similarity search), (3) combining them into a hybrid model (merging results, creating context aggregation, and integrating with an LLM), and (4) setting up a user interface (e.g., using Gradio).
procedureIn a hybrid RAG architecture, the indexing phase involves splitting documents into smaller passages, creating embeddings for these passages using a retriever model, and storing them in a vectorstore such as Chroma, Neo4j, FAISS, or Pinecone.
procedureThe graph_retriever function in a RAG architecture queries the Neo4j graph database for nodes corresponding to entities detected in user questions, explores the neighborhoods of those nodes to gather contextual information, and formats the relationships as structured strings for response generation.
codeThe Neo4jVector.from_documents method in LangChain stores vector embeddings in a Neo4j database using specified documents, an embedding model, and database credentials.
codeTo configure Neo4j credentials for a RAG application, create a .env file with the following variables: NEO4J_URI=neo4j+s://fb***.databases.neo4j.io, NEO4J_USERNAME=neo4j, and NEO4J_PASSWORD=******************************.
procedureIn a hybrid RAG architecture, a specialized large language model (LLM) converts unstructured text into a knowledge graph by identifying nodes, entities, and relationships, which are then stored in a graph database like Neo4j.
codeimport os
from dotenv import load_dotenv
from langchain_community.graphs import Neo4jGraph
from langchain_experimental.graph_transformers import LLMGraphTransformer
from langchain_ollama import OllamaEmbeddings
from langchain_community.vectorstores import Neo4jVector
from langchain_core.prompts import ChatPromptTemplate
from pydantic import BaseModel, Field
from langchain_core.output_parsers import StrOutputParser
from langchain_core.runnables import RunnablePassthrough
from langchain_community.vectorstores.neo4j_vector import remove_lucene_chars
from langchain_community.document_loaders import TextLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from neo4j import GraphDatabase
from langchain_experimental.llms.ollama_functions import OllamaFunctions
import gradio as gr
load_dotenv(override=True)
neo4j_uri = os.getenv('NEO4J_URI')
neo4j_username = os.getenv('NEO4J_USERNAME')
neo4j_password = os.getenv('NEO4J_PASSWORD')
graph = Neo4jGraph()
Context Graph vs Knowledge Graph: Key Differences for AI - Atlan atlan.com Jan 27, 2026 2 facts
claimKnowledge graphs are built on RDF triple stores or property graphs like Neo4j, whereas context graphs are built on graph databases extended for operational and AI context.
referenceExample platforms for knowledge graphs include Neo4j, Stardog, GraphDB, and Amazon Neptune, while example platforms for context graphs include Atlan (context layer), Glean (enterprise context), and context-aware data catalogs.
Top 10 Use Cases: Knowledge Graphs - Neo4j neo4j.com Feb 1, 2021 2 facts
accountNASA's 'lessons learned' project utilized the Neo4j graph database to identify information regarding the Orion and Apollo eras, which prevented a current issue from escalating, resulting in savings of over two years of work and one million dollars of taxpayer funds.
accountDavid Meza, Chief Knowledge Architect at NASA, utilized Neo4j to connect data across millions of nodes to analyze lessons learned from 50 years of space exploration, replacing a previous system that relied on standard key list searches and separate data silos.
Knowledge Graphs vs RAG: When to Use Each for AI in 2026 - Atlan atlan.com Feb 12, 2026 2 facts
claimKnowledge graph integration requires a graph database such as Neo4j or Amazon Neptune, while RAG integration works with vector stores such as Pinecone or Weaviate.
referenceGraphRAG infrastructure requires graph databases (such as Neo4j or Amazon Neptune), vector stores (such as Pinecone or Weaviate), and integration layers connecting both components.
LLM-KG4QA: Large Language Models and Knowledge Graphs for ... github.com 1 fact
referenceThe Neo4jRAG-QA sample application demonstrates how to implement a Large Language Model (LLM) and Retrieval Augmented Generation (RAG) system using a Neo4j Graph Database.
Combining Knowledge Graphs With LLMs | Complete Guide - Atlan atlan.com Jan 28, 2026 1 fact
claimGraph database infrastructure for knowledge graph-LLM integration requires storage for entities and relationships, with examples including Neo4j, JanusGraph, and cloud-native options.
Knowledge Graphs Enhance LLMs for Contextual Intelligence linkedin.com Mar 10, 2026 1 fact
referenceThe solution guide for integrating Generative AI with graph data combines a Generative AI Agent (such as Google Gemini or OpenAI), a Remote Toolset Service powered by the Model Context Protocol (MCP), and a Neo4j Graph Database containing supply chain data.