A developer shares seven key lessons learned from building modern AI agents with Neo4j, GraphRAG, Aura Agents, and LLM Mesh. The lessons cover graph databases, persistent memory, GraphRAG vs. traditio…
Cassie Shum, product engineering lead at RelationalAI, presented on GraphRAG at a conference, explaining how knowledge graphs enhance retrieval workflows by adding semantic structure. She contrasted t…
GraphRAG, a retrieval-augmented generation method from Microsoft Research, uses knowledge graphs to connect entities and relationships, improving AI's ability to answer complex queries accurately. Thi…
A developer identified two necessary conditions for FalkorDB's db.idx.vector.queryNodes to work properly: data must be stored as a vector type using vecf32() and a vector index must be created. Common…
Microsoft Research unveiled Memora, a memory system for AI agents that decouples storage from retrieval to provide scalable long-term recall. The system reduces context token usage by up to 98% while …
Microsoft researchers introduced Memora, a scalable memory system for AI agents that decouples stored content from retrieval mechanisms, achieving state-of-the-art performance on long-horizon benchmar…
AI engineering faces a contradiction between paying premium for frontier models' reasoning capabilities and aggressively compressing context to reduce costs, leading to a 'fallacy of context compactio…
A Japanese research team's knowledge graph RAG system achieved a 90% accuracy improvement on scientific paper comprehension tasks by modeling entity relationships alongside raw text retrieval, address…
Intellias achieved the AWS AI Services Competency, a designation recognizing partners with validated capability to deliver production-ready generative AI on AWS infrastructure. The company submitted t…
Airis, a zero-install local AI ecosystem, launches with autonomous PC control via its AgentJo system, which uses a strict JSON ReAct loop for deterministic desktop automation. The open-source project …
Researchers have introduced GraphRAG, a graph-based approach to retrieval-augmented generation that enables large language models to answer global, query-focused summarization questions over entire te…
Neo4j's director of partner marketing explains the differences between graphs, knowledge graphs, and context graphs in a three-part series, highlighting how each builds on the previous to provide conn…
A developer benchmarked seven RAG configurations against real-world banking documents, technical manuals, and internal wikis, finding that Twitter-hyped "graph RAG" approaches like LightRAG performed …
Neo4j announced Neo4j Virtual Graph, a zero-copy architecture that lets enterprises run Cypher queries and graph algorithms directly on data in Snowflake, Databricks, and other databases without movin…
While standard RAG (Retrieval-Augmented Generation) is effective for simple lookup questions, it struggles with complex queries requiring an understanding of relationships between entities. GraphRAG a…
Basic vector search is inadequate for enterprise AI pipelines and promotes GraphRAG, a hybrid retrieval approach combining vector search with graph databases. It describes a Spring AI and Neo4j implem…
Author's journey rebuilding a financial advisory AI system called FinMentor, moving from a naive approach that dumped entire JSON portfolio snapshots into the model's context to a proper Retrieval-Aug…
Poor performance in Retrieval-Augmented Generation (RAG) systems is typically caused by inadequate document chunking or mismatched retrieval architecture, not by the embedding model or LLM. It describ…