A free Graph / RAG system that works with real data
Iโve tested countless Retrieval-Augmented Generation (RAG) systems, and they all failed to meet my expectations. I found a simple two-stepโฆ
Flexible-GraphRAG
๐๐น๐ฒ๐
๐ถ๐ฏ๐น๐ฒ ๐๐ฟ๐ฎ๐ฝ๐ต๐ฅ๐๐ ๐ผ๐ฟ ๐ฅ๐๐ is now flexing to the max using LlamaIndex, supports ๐ณ ๐ด๐ฟ๐ฎ๐ฝ๐ต ๐ฑ๐ฎ๐๐ฎ๐ฏ๐ฎ๐๐ฒ๐, ๐ญ๐ฌ ๐๐ฒ๐ฐ๐๐ผ๐ฟ ๐ฑ๐ฎ๐๐ฎ๐ฏ๐ฎ๐๐ฒ๐, ๐ญ๐ฏ ๐ฑ๐ฎ๐๐ฎ ๐๐ผ๐๐ฟ๐ฐ๐ฒ๐,
๐๐๐ ๐, Docling ๐ฑ๐ผ๐ฐ ๐ฝ๐ฟ๐ผ๐ฐ๐ฒ๐๐๐ถ๐ป๐ด, ๐ฎ๐๐๐ผ ๐ฐ๐ฟ๐ฒ๐ฎ๐๐ฒ ๐๐๐, ๐๐ฟ๐ฎ๐ฝ๐ต๐ฅ๐๐, ๐๐๐ฏ๐ฟ๐ถ๐ฑ ๐ฆ๐ฒ๐ฎ๐ฟ๐ฐ๐ต, ๐๐ ๐๐ต๐ฎ๐ (shown Hyland products web page data src) ๐๐ฝ๐ฎ๐ฐ๐ต๐ฒ ๐ฎ.๐ฌ ๐ข๐ฝ๐ฒ๐ป ๐ฆ๐ผ๐๐ฟ๐ฐ๐ฒ
๐๐ฟ๐ฎ๐ฝ๐ต: Neo4j ArcadeDB FalkorDB Kuzu NebulaGraph, powered by Vesoft (coming Memgraph and ๐๐บ๐ฎ๐๐ผ๐ป ๐ก๐ฒ๐ฝ๐๐๐ป๐ฒ)
๐ฉ๐ฒ๐ฐ๐๐ผ๐ฟ: Qdrant, Elastic, OpenSearch Project, Neo4j ๐๐ฒ๐ฐ๐๐ผ๐ฟ, Milvus, created by Zilliz (coming Weaviate, Chroma, Pinecone, ๐ฃ๐ผ๐๐๐ด๐ฟ๐ฒ๐ฆ๐ค๐ + ๐ฝ๐ด๐๐ฒ๐ฐ๐๐ผ๐ฟ, LanceDB)
Docling ๐ฑ๐ผ๐ฐ๐๐บ๐ฒ๐ป๐ ๐ฝ๐ฟ๐ผ๐ฐ๐ฒ๐๐๐ถ๐ป๐ด
๐๐ฎ๐๐ฎ ๐ฆ๐ผ๐๐ฟ๐ฐ๐ฒ๐: using LlamaIndex readers: working: Web Pages, Wikipedia, Youtube, untested: Google Drive, Msft OneDrive, S3, Azure Blob, GCS, Box, SharePoint, previous: filesystem, Alfresco, CMIS.
๐๐๐ ๐: ๐๐น๐ฎ๐บ๐ฎ๐๐ป๐ฑ๐ฒ๐
๐๐๐ ๐ (OpenAI, Ollama, Claude, Gemini, etc.)
๐ฅ๐ฒ๐ฎ๐ฐ๐, ๐ฉ๐๐ฒ, ๐๐ป๐ด๐๐น๐ฎ๐ฟ ๐จ๐๐, ๐ ๐๐ฃ ๐๐ฒ๐ฟ๐๐ฒ๐ฟ, ๐๐ฎ๐๐๐๐ฃ๐ ๐๐ฒ๐ฟ๐๐ฒ๐ฟ
๐๐ถ๐๐๐๐ฏ ๐๐๐ฒ๐๐ฒ๐ฟ๐ฒ๐ถ๐ป๐ฒ๐ฟ/๐ณ๐น๐ฒ๐
๐ถ๐ฏ๐น๐ฒ-๐ด๐ฟ๐ฎ๐ฝ๐ต๐ฟ๐ฎ๐ด:
https://lnkd.in/eUEeF2cN
๐ซ.๐ฐ๐ผ๐บ ๐ฃ๐ผ๐๐ ๐ผ๐ป ๐๐น๐ฒ๐
๐ถ๐ฏ๐น๐ฒ ๐๐ฟ๐ฎ๐ฝ๐ต๐ฅ๐๐ ๐ผ๐ฟ ๐ฅ๐๐ ๐บ๐ฎ๐
๐ณ๐น๐ฒ๐
๐ถ๐ป๐ด
https://lnkd.in/gHpTupAr
๐๐ป๐๐ฒ๐ด๐ฟ๐ฎ๐๐ฒ๐ฑ ๐ฆ๐ฒ๐บ๐ฎ๐ป๐๐ถ๐ฐ๐ ๐๐น๐ผ๐ด:
https://lnkd.in/ehpjTV7d
Google Cloud releases new Agentspace Knowledge Graph, built on Spanner Graph
It's great to see the launch of Google Cloud's new Agentspace Knowledge Graph, built on Spanner Graph.
Agentspace Knowledge Graph (https://lnkd.in/gYM6xZQS) allows an AI agent to understand the real-world context of your organizationโthe web of relationships between people, projects, and products. This is the difference between finding a document and understanding who wrote it, what team they're on, and what project it's for.
Because this context is a network, the problem is uniquely suited for a graph model. Spanner Graph (https://lnkd.in/gkwbGFbS) provides a natural way to model this reality, allowing an AI agent to instantly traverse complex connections to find not just data, but genuine insight.
This is how we move from AI that finds information to AI that understands it. The ability to reason over the "why" behind the data is a true game-changer.
#GoogleCloud #GenAI #Agentspace #SpannerGraph #KnowledgeGraph
Because this context is a network, the problem is uniquely suited for a graph model. Spanner Graph (https://lnkd.in/gkwbGFbS) provides a natural way to model this reality, allowing an AI agent to instantly traverse complex connections to find not just data, but genuine insight.
GraphRAG infrastructure for generative AI using Vertex AI and Spanner Graph | Cloud Architecture Center | Google Cloud
Design infrastructure for a generative AI application with GraphRAG using Spanner Graph.
Why Knowledge Graphs are Critical to Agent Context
How should we organize knowledge to provide the best context for agents? We show how knowledge graphs could play a key role in enhancing context for agents.
The Developer's Guide to GraphRAG
Find out how to combine a knowledge graph with RAG for GraphRAG. Provide more complete GenAI outputs.
Youโve built a RAG system and grounded it in your own data. Then you ask a complex question that needs to draw from multiple sources. Your heart sinks when the answers you get are vague or plain wrong.ย ย
How could this happen?
Traditional vector-only RAG bases its outputs on just the words you use in your prompt. It misses out on valuable context because it pulls from different documents and data structures. Basically, it misses out on the bigger, more connected picture.
Your AI needs a mental model of your data with all its context and nuances. A knowledge graph provides just that by mapping your data as connected entities and relationships. Pair it with RAG to create a GraphRAG architecture to feed your LLM information about dependencies, sequences, hierarchies, and deeper meaning.
Check out The Developerโs Guide to GraphRAG. Youโll learn how to:
Prepare a knowledge graph for GraphRAG
Combine a knowledge graph with native vector search
Implement three GraphRAG retrieval patterns
Announcing general availability of Amazon Bedrock Knowledge Bases GraphRAG with Amazon Neptune Analytics | Amazon Web Services
Today, Amazon Web Services (AWS) announced the general availability of Amazon Bedrock Knowledge Bases GraphRAG (GraphRAG), a capability in Amazon Bedrock Knowledge Bases that enhances Retrieval-Augmented Generation (RAG) with graph data in Amazon Neptune Analytics. In this post, we discuss the benefits of GraphRAG and how to get started with it in Amazon Bedrock Knowledge Bases.
Graphiti: Knowledge Graph Memory for a Post-RAG Agentic World
An open-source Python framework for real-time, graph-based knowledge in Neo4j
Google Cloud & Neo4j: Teaming Up at the Intersection of Knowledge Graphs, Agents, MCP, and Natural Language Interfaces - Graph Database & Analytics
Weโre thrilled to announce new Text2Cypher models and Googleโs MCP Toolbox for Databases from the collaboration between Google Cloud and Neo4j.
Build your hybrid-Graph for RAG & GraphRAG applications using the power of NLP | LinkedIn
Build a graph for RAG application for a price of a chocolate bar! What is GraphRAG for you? What is GraphRAG? What does GraphRAG mean from your perspective? What if you could have a standard RAG and a GraphRAG as a combi-package, with just a query switch? The fact is, there is no concrete, universal
Multimodal Graph RAG (mmGraphRAG): Incorporating Vision in Search and Analytics - Enterprise Knowledge
David Hughes presented โUnleashing the Power of Multimodal GraphRAG: Integrating Image Features for Deeper Insightsโ at Data Day Texas 2025.
Turn Your CSVs Into Graphs Using LLMs
Learn how to turn CSV files into graph models using LLMs, simplifying data relationships, enhancing insights, and optimizing workflows.