Show HN: GibRAM an in-memory ephemeral GraphRAG runtime for retrieval

7 comments

• VoidWhisperer 19 days ago
  Out of curiosity, did you settle on that name before or after the RAM availability/price issues?
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  • ktyptorio 19 days ago
    Actually, the name definitely came after noticing RAM prices. Though the idea where the graph-in-memory only for ephemeral RAG sessions came first, we won't pretend the naming wasn't influenced by RAM being in the spotlight.
  • mirekrusin 19 days ago
    GrrHDD
• zwaps 19 days ago
  Very cool, kudos

  Where might one see more about what type of indexing you do to get the graph?

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  • threecheese 19 days ago
    Appears to be: https://github.com/gibram-io/gibram/blob/main/sdk/python/gib...
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    • ktyptorio 19 days ago
      Exactly, thank you. Still in LLM-based extraction.
• ekianjo 19 days ago
  how do you search the graph network?
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  • ktyptorio 19 days ago
    There are two steps:

    Vector search (HNSW): Find top-k similar entities/text units from the query embedding

    Graph traversal (BFS): From those seed entities, traverse relationships (up to 2 hops by default) to find connected entities

    This catches both semantically similar entities AND structurally related ones that might not match the query text.

    Implementation: https://github.com/gibram-io/gibram/blob/main/pkg/engine/eng...

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    • kordlessagain 19 days ago
      This is how I did it a few years back while working for a set store company. It works well.
• nirdiamant 19 days ago
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• Agent_Builder 17 days ago
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