Power GenAI Apps with Your Own Data using GraphRAG
Intelligence with Your Context
With GraphRAG, you combine Memgraph's in-memory graph database, LLMs, and Retrieval-Augmented Generation (RAG) to deliver accurate, context-rich answers—grounded in your own proprietary data.
Search, Retrieve, and Discover
Why GraphRAG with Memgraph?
Memgraph Tools to build GraphRAG
Memgraph as Your GraphDB
Use Memgraph as the backbone for your GraphRAG apps. It's built for scale, performance, and can handle complex queries across large knowledge graphs—whether you're working with millions of nodes or performing real-time calculations.
Integrated Algorithms
Memgraph's powerful algorithms—Community Detection, PageRank, and graph traversals—ensure your AI isn't just making things up. It responds based on real, structured relationships embedded within your data.
GraphChat
Query your graph with plain English. GraphChat is your direct line to your graph database inside Memgraph Lab. Forget about writing complex queries—just ask. GraphChat translates your natural language question into a Cypher query, runs it on Memgraph, and provides you with the best possible answer in human language.
This two-phase generative AI app gives you answers grounded in the context of your knowledge graph.
LangChain and LlamaIndex Integrations
Memgraph integrates with Langchain and LLamaIndex, allowing for multi-hop retrieval to answer complex questions by connecting data from different sources. You can easily integrate Memgraph with your existing LLM workflows to power advanced knowledge extraction.
Upcoming Features
Vector Search
Typically used in the first step of finding and extracting relevant information (pivot search), vector search enables similarity search alongside relevance-based graph search in a single, high-performance system.
Leiden Community Detection
An improved, faster version of the Louvain algorithm, Leiden guarantees well-connected communities and is used in the second step of finding and extracting relevant information (relevance expansion). It's proven to be more effective and reliable in uncovering relationships.
But here's the thing: GraphRAG already works without a vector database. In the relevance phase, graph algorithms like community detection are key. Alternatives like fuzzy search, full-text search, and geo search can also be used to find the right pivot points, depending on your use case.
GraphChat Updates
The upcoming release will bring major improvements to GraphChat in Memgraph Lab, making it even easier to query your graph data with natural language and receive detailed, context-rich responses.
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