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Memgraph
Memgraph @memgraphdb ·
With local graph search you can find the right node, expand the graph, filter the noise, return the context that matters. Why Atomic GraphRAG helps: ✓ less code to review ✓ smaller context ✓ better accuracy Full walkthrough 👉 memgraph.com/blog/local-gra… #GraphRAG #Memgraph
How Local Graph Search Works in Atomic GraphRAG

Explore the local graph search pattern in Atomic GraphRAG and see how it retrieves the right neighborhood for context-rich retrieval.

From memgraph.com
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Memgraph
Memgraph @memgraphdb ·
A leading retail bank, Capitec Bank, used #Memgraph to surface the network patterns behind APP scams, then ran the pipeline daily at scale. Results? Runs 3.5M+ scores daily in ~2 hours. Full walkthrough 👉memgraph.com/webinars/how-c…Z Key highlights �memgraph.com/blog/graph-mac…Xx
How a Leading Retail Bank Built a GraphML Pipeline for Higher-Precision Fraud Scoring

See how a leading retail bank uses Memgraph to strengthen its machine learning models for higher-precision fraud scoring.

From memgraph.com
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Memgraph
Memgraph @memgraphdb ·
GraphRAG often fails due to pipeline sprawl, not retrieval. Atomic GraphRAG: run the pipeline as a single Cypher query in the DB, with 10x less orchestration code. Explainer by @mbudiselicbuda 👉 memgraph.com/blog/atomic-gr… #GraphData #Memgraph
Atomic GraphRAG Explained: The Case for a Single-Query Pipeline

Learn what exactly is GraphRAG, the three common question types GraphRAG systems faces, and why Atomic GraphRAG’s single query execution layer is truly a game changer.

From memgraph.com
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Memgraph
Memgraph @memgraphdb ·
Need vector search that scales without doubling storage? In this blog, David Iveković breaks down how Memgraph avoids duplicate vector copies and improves memory efficiency. Full write-up 👉 memgraph.com/blog/single-st… #Memgraph #VectorSearch #VectorIndex #GraphData #GraphRAG #RAG
Single-Store Vector Index: Architecture and Memory Efficiency

Learn how the vector index is built, how it stays in sync with the graph without duplicating data, and why the latest version uses significantly less RAM for the same workload.

From memgraph.com
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Memgraph
Memgraph @memgraphdb ·
LLM apps don’t fail because prompts are bad. They fail because context is missing. This beginner’s guide explains what #ContextEngineering actually is and how developers use it to ship reliable RAG and agent workflows. Have a look 👉 memgraph.com/blog/context-e… #GraphRAG #Memgraph
Context Engineering for Beginners: A Developer’s Guide

This beginner’s guide explains what Context Engineering actually is and how developers use it to ship reliable RAG and agent workflows.

From memgraph.com
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Memgraph
Memgraph @memgraphdb ·
#Memgraph 3.8 is out 🎉 ✨ Parallel Runtime ✨ Concurrent Edge WRITES on Supernodes ✨ Atomic GraphRAG ✨ Single Store Vector Index Release highlights 👉 memgraph.com/blog/memgraph-… #GraphRAG #Cypher #VectorSearch
Memgraph 3.8 is Out: Atomic GraphRAG + Vector Single Store With Major Performance Upgrades

Parallel runtime and concurrent edge WRITES on supernodes for faster ingestion, flexible one-query pipelines and 85% vector memory savings.

From memgraph.com
250
Memgraph
Memgraph @memgraphdb ·
Nothing like turning a massive codebase into something you can query. #Linux kernel into #Memgraph with Code-Graph, around 1M entities. Let's see how the @rustlang footprint evolve 🦀github.com/vitali87/code-…L
GitHub - vitali87/code-graph-rag: The ultimate RAG for your monorepo. Query, understand, and edit...
From github.com
Ante Javor Ante Javor @AnteJavor ·
I just used Code-Graph to ingest the Linux Kernel into @memgraphdb. The knowledge graph has around a million graph entities representing code structure. It would be interesting to see how much @rustlang will eat into the kernel over time. 🤔 �OC
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