FalkorDB mentions (3)

• Semantic search alone won't solve relational queries in your LLM retrieval pipeline.

  Use a low-latency graph database: Integrate FalkorDB for its sparse matrix representation and optimized linear algebra-based traversals. Queries execute in milliseconds—critical for real-time AI interactions. - Source: dev.to / about 2 months ago

• Graph database vs relational vs vector vs NoSQL

  In vector databases, data is stored as high-dimensional vector embeddings, which are numerical representations generated by machine learning models to capture the features of data. When querying, the input is converted into a vector embedding, and similarity searches are performed between the query vector and stored embeddings using distance metrics like cosine similarity or Euclidean distance to retrieve the most... - Source: dev.to / about 2 months ago

• NoLiMA: GPT-4o achieve 99.3% accuracy in short contexts (<1K tokens), performance degrades to 69.7% at 32K tokens.

  For AI architects, integrating graph-native storage with LLMs isn’t optional—it’s imperative for building systems capable of robust, multi-hop reasoning at scale. - Source: dev.to / 2 months ago

TigerGraph DB mentions (0)