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laminarflow027

HN member
Joined
Tue, Jun 23, 2020, 6:11 PM UTC
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25 items

About laminarflow027

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Recent public activity

  1. comment
    Comment #48859509

    Very cool, gotta try this out! Curious, how does it handle blob data (e.g., images) -> Is there a way to display the image blobs in the widget too? Kind of like how Hugging Face di…

  2. comment
    Comment #46966041

    Great, thanks for the feedback! I work at LanceDB and will take these points into account (esp. the docs).

  3. comment
    Comment #46963113

    Curious what performance issues you faced. Was that in OSS LanceDB? And what were the challenges?

  4. comment
    Comment #46290481

    Really cool stuff! Looking at the schema of the financial dataset, though, it's really flat, and it doesn't really leverage the power of the BAML schema format (which excels at rep…

  5. comment
    Comment #45680502

    The semantic routing step with intent classification and query rewriting was fascinating. The fact that multiple indexes were used is also understated. Is there anywhere one can ex…

  6. story
    Show HN: Using DSPy to enrich a dataset of the Nobel laureate network

    I've been working a fair bit with DSPy lately, and I did some work in combining the benefits of vector search and LLMs (via a DSPy pipeline) to disambiguate records with a high deg…

  7. story
    Show HN: Tips for getting great Text2Cypher outputs from LLMs for Graph RAG

    For folks working on Graph RAG and trying to get LLMs to generate Cypher queries, I ran some experiments on the LDBC dataset and wrote a blog post about it (code is available in th…

  8. story
    Show HN: Towards agentic Graph RAG: Enhancing graph retrieval with vector search

    In this post, we document the results of some experiments comparing vanilla Graph RAG (just a single pass of text2cypher) vs. a router agent Graph RAG approach that can call vector…

  9. comment
    Comment #43706846

    To me they seem like the pythonic version of dbt! Instead of yaml, you write Python code. That, and a lot of on-the-fly computations to generate an optimized workflow plan.

  10. comment
    Comment #43493981

    Hi! IMO you should definitely check out Kuzu - It's an embeddable, fast, highly scalable graph database with an MIT (highly permissive) open source license. Kuzu was created for br…

  11. comment
    Comment #43484553

    Kuzu is an open source, embedded graph database that supports the Cypher query language. We wrote a blog post that serves the dual role of explaining what MCP is and how the MCP se…

  12. story
  13. comment
    Comment #43327465

    Hi, glad to help! I'm a DevRel advocate at Kuzu, and have spent a decent amount of time in other database paradigms thinking about these things. I'm familiar with SurrealDB too. Al…

  14. comment
    Comment #43322756

    Your concern makes sense, but in the demo we show, all your private data AND the graph database AND the LLM (basically, everything) is confined to your client session in the browse…

  15. comment
    Comment #43322704

    Great point! Several years ago there was a project GRainDB, which along with GraphflowDB (a purely in-memory graph database) formed the ideas of what is now Kuzu :) https://graindb…

  16. comment
    Comment #43322681

    True, but there are likely innovations happening in multiple dimensions all at once: WebGPU improvements that better utilize a device's compute, Wasm64. And of course, LLMs over ti…

  17. comment
    Comment #43322132

    Hi, I work at Kuzu and can offer my thoughts on this. You're making a fair observation here and it's true for any high level query language - SQL and Cypher and interchangeable unl…

  18. comment
    Comment #43269068

    Very interesting! Could you elaborate a bit on the parallels with Keras, other than the API similarities? What aspects of working with LLMs does SynaLinks make easier?

  19. comment
    Comment #41597899

    Really exciting stuff! I can clearly see from the GitHub issue list that you do NOT intend to repeat the mistakes of LangChain, i.e. minimize bloat and abstraction level. How would…

  20. comment
    Comment #39046729

    By this I presume you mean build a search index that can retrieve results based on keywords? I know certain databases use Lucene to build a keyword-based index on top of unstructur…

  21. comment
    Comment #39031059

    Very good points. Have you seen any examples of systems (or projects) that successfully combine multiple backend data sources, including databases, that perform better than the sin…

  22. comment
    Comment #39031011

    Totally agree! The wave of blog posts and examples one sees where it's just text-to-SQL or text-to-Cypher or any other query lang aren't really exploring the topic at any level of …

  23. comment
    Comment #39030924

    This is an excellent article that asks some much-needed questions on the literature that exists connecting LLMs and RAGs on unstructured data, with knowledge graphs in between. We'…

  24. comment
    Comment #39030594

    OpenNRE ( https://github.com/thunlp/OpenNRE ) is another good approach to neural relation extraction, though it's slightly dated. What would be particularly interesting is to combi…

  25. comment
    Comment #23617888

    Ugh, the amount of space wasted on a wider screen is borderline criminal. Whitespace everywhere!! And the tabs above being left-aligned with the main repo being centered below is j…