Show HN: GibRAM an in-memory ephemeral GraphRAG runtime for retrieval (github.com)

GibRAM, an in-memory knowledge graph server, has been introduced for retrieval augmented generation (RAG) workflows. This innovative tool allows for the storage of a graph structure—comprising entities and their relationships—directly in RAM, facilitating rapid retrieval of relevant information. Its ephemeral nature means that data is stored temporarily, prioritizing short-lived analysis and exploration over persistent storage. By combining a lightweight graph store with vector search capabilities, GibRAM enables users to perform semantic searches and traverse relationships between associated nodes efficiently, ensuring that context-rich information is easily accessible during queries. This development is significant for the AI/ML community as it enhances RAG workflows, allowing for more nuanced and context-aware retrieval of information. GibRAM’s Python SDK simplifies the implementation of graph-aware retrieval processes, making it adaptable for various projects. Users can customize components for indexing, extracting, and embedding data, enabling a flexible workflow tailored to specific needs. With its focus on high-performance retrieval and intuitive usage, GibRAM is poised to support AI researchers and developers as they handle increasingly complex datasets.