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Neo4j
21 posts about Neo4j by 6 contributors.
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- Articles
- 20
- Videos
- 1
- Contributors
- 6
Everything about Neo4j
- Article · LLMs
Debugging LLM Outputs: A Systematic Approach with LangGraph and Observability
Learn how to trace and debug LLM outputs with LangGraph, Graph RAG, Neo4j, FastAPI, and a React observability dashboard for compliance AI workflows.
- Article · Langchain
Semantic Caching in Enterprise LLM Pipelines: Accelerating Intelligence with LangGraph
Build a semantic cache for enterprise LLM pipelines using LangGraph, Redis, Neo4j Graph RAG, and FastAPI to reduce latency and API costs.
- Article · AI Agents
Mastering Token Efficiency in Enterprise LLM Pipelines: A Multi-Agent Approach
Build a token-efficient enterprise RAG pipeline using LangGraph, Neo4j Graph RAG, context compression, and structured LLM output.
- Article · AI Agents
Tool Augmentation in LLMs: Orchestrating Enterprise Action with LangGraph
Build an IT operations AI agent with LangGraph, Graph RAG, Neo4j, and FastAPI. Learn to orchestrate tool calls, preserve state, and require human approval for risky actions.
- Article · AI Agents
Function Calling in LLMs: Bridging Intelligence and Action in Enterprise Systems
Build an enterprise inventory assistant that uses LangGraph, Pydantic, Graph RAG, and FastAPI to validate and orchestrate LLM function calls.
- Article · AI Agents
Implementing PII Anonymization in Enterprise LLM Systems: A Multi-Agent Approach
Build a privacy-aware multi-agent LLM pipeline using LangGraph, Microsoft Presidio, and Graph RAG to detect, anonymize, retrieve, and optionally re-identify PII securely.
- Article · AI Agents
Designing a Scalable LLM API System: An Enterprise Multi-Agent Architecture
Design a scalable enterprise LLM API using FastAPI, LangGraph multi-agent workflows, Graph RAG with Neo4j, and Redis caching and session memory.
- Article · AI Agents
Multi-Hop Reasoning in RAG: Building Enterprise-Grade Intelligent Systems
Build a multi-hop RAG system for enterprise compliance investigations using LangGraph, Neo4j Graph RAG, stateful AI agents, and FastAPI.
- Article · LLMs
Optimizing LLM Pipelines with Advanced Caching Strategies: An Enterprise Multi-Agent Approach
Build an enterprise LLM documentation assistant using LangGraph, Redis semantic caching, Graph RAG, and Neo4j to reduce latency and cost while keeping answers fresh.
- Article · AI Agents
Controlling Costs in Enterprise LLM Systems: A Multi-Agent Architecture with LangGraph
Build a cost-aware enterprise LLM assistant with LangGraph, model routing, Graph RAG, token limits, and usage monitoring.
- Article · AI Agents
Document-Level vs. Chunk-Level Retrieval: Architecting Precision in Enterprise RAG
Build a hybrid enterprise RAG system that uses LangGraph to route queries between chunk-level semantic search and document-level Graph RAG context.
- Article · AI Agents
Securing Enterprise AI: Defending Against Prompt Injection Attacks with Multi-Agent LangGraph
Build a secure LangGraph-based customer support agent that detects prompt injection, routes safe requests, and protects Graph RAG data retrieval with Neo4j and FastAPI.
- Article · .NET Core
Optimizing Retrieval in Manufacturing: A Multi-Agent Graph RAG Approach with Hybrid Search and Reranking
Our system combines vector similarity, keyword matching, and knowledge graph relationships to provide contextually accurate answers while maintaining conversational memory across multiple interactions.
- Article · AI Agents
Accelerating Intelligence: Optimizing Graph RAG Performance for High-Frequency Fintech Applications
Optimizing Graph RAG for fintech requires a shift from simple retrieval to intelligent, parallelized reasoning. By leveraging the structural power of Neo4j, the orchestration capabilities of LangGraph, and the speed of async FastAPI, we can build systems that provide deep, accurate insights in real-time. As financial markets become increasingly complex, the ability to quickly traverse and understand these connections will be a definitive competitive advantage.
- Article · Langchain
Graph-Native Fraud & Credit Risk for Cards
Leverage Graph-RAG Multi-Agent Systems for high-cardinality card fraud detection, enabling low-latency fraud ring identification.
- Article · AI Agents
Building a Low-Latency Multi-Agent RAG System for Loan & Mortgage Exposure
Build a low-latency, multi-agent RAG system for loan & mortgage credit risk analysis using LangGraph and Neo4j.
- Article · AI Agents
Building Enterprise Knowledge Graphs for AI Agents Using Neo4j and C#
Build enterprise AI agents with Neo4j and C#. Learn to model relationships, integrate with ASP.NET Core, and enhance LLM reasoning.
- Video · AI Agents
Agentic RAG with Knowledge Graphs Explained | Neo4j Graph RAG | AI Agents Conference 2025
Unlock Agentic RAG with Knowledge Graphs! Learn how Neo4j enhances AI agents at the 2025 AI Agents Conference. Boost your RAG with graph power!
- Article · LLMs
GraphQA: Building Graph-Aware Question Answering Systems with LLMs
A detailed developer guide to GraphQA — an open-source framework by Catio Tech for integrating graph databases with large language models (LLMs) to enable knowledge-grounded, multi-hop question answering.
- Article · Databases & DBA
Most Popular Graph Databases
Graph databases, or Graph DBMS, are growing in popularity. Neo4j is the most popular graph database. This article lists the most popular graph databases.
- Article · Databases & DBA
What is a Graph Database
Big data, semantic searches, and real-time responses are the reason behind the growing demand for graph databases. This article talks about what a graph database is, why graph databases are popular, and why and when we should use a graph database.
