Preventing Context Overload: Controlled Neo4j MCP Cypher Responses for LLMs | Towards Data Science How timeouts, truncation, and result sanitization keep Cypher outputs LLM-ready

Did some exploration over the weekend on how to enhance @neo4j.com Cypher MCP server. Here are my thoughts:

towardsdatascience.com/preventing-c...

How to Evaluate Graph Retrieval in MCP Agentic Systems | Towards Data Science A framework for measuring retrieval quality in Model Context Protocol agents.

Struggling to evaluate your LLM agents' complex graph queries? Tomaz Bratanic's new article tackles the lack of robust evaluation for GraphRAG in MCP agentic systems. Learn how to build a benchmark for measuring retrieval quality and multi-step reasoning.

Essential GraphRAG Get this comprehensive guide from Manning on how to build a GraphRAG system from scratch. Available now for free!

If you want to learn more about GraphRAG, grab a free copy of the Essential GraphRAG book: neo4j.com/essential-gr...

How to Evaluate Graph Retrieval in MCP Agentic Systems | Towards Data Science A framework for measuring retrieval quality in Model Context Protocol agents.

Everyone’s building agents and spinning up MCP servers. But almost no one is talking about evaluation. In my latest post, I introduce a framework for evaluating graph retrieval in @neo4j.com MCP-based agentic systems, using @langchain.bsky.social

towardsdatascience.com/evaluating-g...

Agentic GraphRAG for Commercial Contracts | Towards Data Science Structuring legal information as a knowledge graph to increase the answer accuracy using a LangGraph agent

In my latest @towardsdatascience.com blog, I've used @langchain.bsky.social to show how to structure legal docs as a knowledge graph in @neo4j.com , and then implement a LangGraph agent with supporting benchmark to demonstrate the power of agentic GraphRAG.

towardsdatascience.com/agentic-grap...

Over the past months, we've been exploring text2cypher agentic flows with @llamaindex.bsky.social . The result? A comprehensive repository featuring multiple @neo4j.bsky.social workflows, a benchmarking framework, and an web UI.

www.llamaindex.ai/blog/buildin...

The Limitations of Text Embeddings in RAG Applications Learn how to overcome the challenges of structured data operations in text embeddings in RAG applications using knowledge graphs.

Text embeddings are great at encoding unstructured text, but they aren’t very good at dealing with structured information and operations such as filtering, sorting, or aggregations.

What do you need? Other tools, such as #KnowledgeGraphs :)

bit.ly/3XMWrjC

#Neo4j #llms

Merry christmas!

Build a Question Answering application over a Graph Database | 🦜️🔗 LangChain In this guide we'll go over the basic ways to create a Q&A chain over a graph database. These systems will allow us to ask a question about the data in a graph database and get back a natural language...

We've added an example how to build an advanced @neo4j.bsky.social text2cypher agent with @langchain.bsky.social 's LangGraph. Would love to hear your thought!

python.langchain.com/docs/tutoria...

I'm learning @llamaindex.bsky.social workflows by implementing an advanced @neo4j.bsky.social text2cypher agent. I'm quite impressed by the ease of implementing various, optionally concurrent, flows.

Building Knowledge Graphs with LLM Graph Transformer A deep dive into LangChain’s implementation of graph construction with LLMs

My last blog post on @towardsdatascience.com was about the underlying implementation of constructing knowledge graphs with @langchain.bsky.social 's LLMGraphTransformer!

towardsdatascience.com/building-kno...