CTO @ Neo4j. Helping the world to connect all-of-the dots.
San Francisco, CA
Joined June 2009
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Today’s @latentspacepod with @dharmesh is *of course* an awesome listen:
latent.space/p/dharmesh
A thread expanding on the graph database portion the discussion 🧵:
10/ Besides PageRank, one of my favorite graph algorithms is neo4j.com/docs/graph-data-sc…
It’s a great way to resolve anonymous user breadcrumbs into a pseudonymous single identity.
Klarna’s AI journey is rooted in the power of graphs. I agree with Sebastian that the value of Neo4j/knowledge graphs/GraphRAG is in the top line. It’s not about replacing SaaS, but bringing data from the many silos into a graph, and using that for better AI decisions.
Philip Rathle retweeted
Build a knowledge graph agent from scratch 🔥
I'm super excited about this blog post by Tomaz Bratanic from @neo4j - this is probably the most thorough treatment I've seen for building a text-to-cypher powered knowledge graph agent that actually works well.
Tomaz walks through an entire list of potential strategies and evaluates them according to a standardized benchmark:
1. Naive text-to-cypher
2. Text-to-cypher with retry and evaluation
3. Iterative planning system by generating a plan of sub-queries before creating the final query.
All of these can be stitched together as an event-driven system with @llama_index workflows.
Check out the post! llamaindex.ai/blog/building-…
If you want to get your hands dirty on workflows check out our guide here: docs.llamaindex.ai/en/latest…
Dramatically improve the accuracy of your knowledge graph applications by applying agentic strategies with LlamaIndex workflows!
In this comprehensive post by Tomaz Bratanic of @neo4j, he builds up slowly from a naive text2cypher implementation to an agentic approach with error checking, retries and correction, and he has the benchmarks to prove it's a better strategy!
➡️ Implement agentic strategies for text2cypher using LlamaIndex Workflows
➡️ Explore multi-step approaches with retry and self-correction mechanisms
➡️ Understand the benefits of iterative planning for complex queries
➡️ Gain insights on benchmarking and real-world deployment considerations
Check out the full guest post on our blog here:
llamaindex.ai/blog/building-…
Philip Rathle retweeted
GraphRAG with @MistralAI, @CamelAIOrg, and @neo4j:
- Use Mistral Large 2 to extract and structure knowledge graph from a given content source, and store this information in a Neo4j graph database.
- A hybrid approach: combining vector retrieval and knowledge graph retrieval, to query and explore the stored knowledge.
- This hybrid Graph + vector RAG approach increases retrieval accuracy and RAG performance.
Thank you @ttokzzzzz @guohao_li for contributing this great example to Mistral cookbook. Link in 🧵:
Philip Rathle retweeted
🔍 Exploring GraphRAG with Neo4j and LangChain
📝 Check out Tomaz Bratanic's deep dive into "From Local to Global" GraphRAG implementation
Covers how to extract entities & relationships from text and summarize graph structures into natural language.
github.com/tomasonjo/blogs/b…
Philip Rathle retweeted
This weekend, we’re providing a definitive set of tutorials on how to build GraphRAG, step-by-step.
First, check out this video by @fahdmirza on implementing the core components of GraphRAG using an in-memory implementation:
1. Extract entities and relationships using LLMs
2. Partition graph into communities and generate community summaries
3. Query all communities and synthesize into a final answer.
Then, extend this initial in-memory implementation by storing your property graph in a @neo4j graph database. Big shoutout to @ravithejads for the notebook
Video: youtube.com/watch?v=xnoEjczo…
V1 Notebook: github.com/run-llama/llama_i…
V2 notebook (with @neo4j): github.com/run-llama/llama_i…
Source GraphRAG paper by Edge et al. (+ image credits): arxiv.org/pdf/2404.16130
Philip Rathle retweeted
Microsoft GraphRAG Alternative and 10x Cheaper?
🚀 Introducing Sciphi/Triplex
💸 10x Cheaper
🤖 AI Knowledge Graph Extraction
🌟 Higher Accuracy
🛠️ Setup with @huggingface
🖥️ Local Run with @ollama
📊 Data Visualisation @neo4j
Subscribe: youtube.com/@MervinPraison
@sayshrey @ocolegro
If you’re looking for a conversation about GraphRAG to go alongside the GraphRAG Manifesto, look no further than my conversation with Ben Lorica on the Data Exchange podcast: thedataexchange.media/superc…
