In our previous posts (Hybrid Retrieval for GraphRAG Applications Using the GraphRAG Python Package and Enhancing Hybrid Retrieval With Graph Traversal Using the GraphRAG Python Package ), we explored various retrieval strategies to enhance the performance of generative AI models using Neo4j. We also dug into using vector and hybrid retrieval methods, where retrievers used either vector embeddings or full-text embeddings to identify the most relevant data entries. While these approaches have ...
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New Blog: Enhancing Hybrid Retrieval With Graph Traversal Using the Neo4j GraphRAG Package for Python | 0 | 16 | September 26, 2024 | |
New Blog: Hybrid Retrieval for GraphRAG Applications Using the Neo4j GenAI Python Package | 0 | 27 | September 9, 2024 | |
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New Blog: Knowledge Graph vs. Vector RAG: Benchmarking, Optimization Levers, and a Financial Analysis Example | 0 | 79 | June 5, 2024 | |
New Blog: Implementing ‘From Local to Global’ GraphRAG with Neo4j and LangChain: Constructing the Graph | 0 | 86 | July 10, 2024 |