antves

7 ms·

1. 31.▲
   by antves 1y ago

   I agree, these mitigations alone can't be sufficient, but they are all necessary within a wider framework. The only way to make this kind of agents safe is to work on every layer. Part of it is teaching the underlying model to see the
2. 32.▲
   by antves 1y ago

   Thanks!
3. 33.▲
   by antves 1y ago

   This is exactly it! We built a browser agent and got awesome results by designing the context in a simplified/compact version + using small/efficient LLMs - it's smooth.sh if you'd like to try
4. 34.▲
   by antves 1y ago

   This is a very valid concern. Here are some of our initial considerations: 1. Security of these agentic system is a hard and important problem to solve. We're indexing heavily on it, but it's definitely still early days and there
5. 35.▲
   by antves 1y ago

   We don't support this yet, but we'd love to talk about it. Feel free to book a demo!
6. 36.▲
   by antves 1y ago

   Thanks for trying it out! We rely on a mix of internal benchmarks and academic benchmarks like WebVoyager.
7. 37.▲
   by antves 1y ago

   Thanks! It all boils down to (1) using small and efficient models, and (2) insisting on good context engineering. We describe the browser state in a way that's both compact and meaningful. This allows us to use tiny LLMs under the hood
8. 38.▲
   by antves 2y ago

   Thanks for sharing this! It sounds very interesting. We experimented with a similar tree setup some time ago and it was giving good results. We eventually decided to move towards graphs as a general case of trees. I think the notion of usin
9. 39.▲
   by antves 2y ago

   Thanks for trying it out! There are two options to use FastGraphRAG for free: (1) Self-hosting our open-source package (2) Using the free tier of the managed service, which includes 100 requests If you wish to upgrade your plan, you can rea
10. 40.▲
    by antves 2y ago

    These are knobs that you can tune to make the graph construction more/less opinionated. Generally speaking, the more we make it opinionated the better it fits the task. At a high-level: (1) Domain: allows you to "talk to the graph
11. 41.▲
    by antves 2y ago

    I completely agree that the README could do a better job explaining the implementation details and our reasoning behind key design choices. For instance, we should elaborate on why we believe using PageRank offers a more effective explorati
12. 42.▲
    by antves 2y ago

    Yes. We already support this feature in our managed service ( https://docs.circlemind.co/essentials/query#include-referenc... ) and we'll include it in the next open-source release too!
13. 43.▲
    by antves 2y ago

    Thanks! It sounds like we should be able to help. I'd love to chat more in detail, feel free to send me a note at antonio [at] circlemind.co.
14. 44.▲
    by antves 2y ago

    Generally speaking RAG comes in the game when it is impractical to use large context windows for three reasons: (1) accuracy drops as you stuff the context windows, (2) currently, context windows do not scale past 1M tokens, and (3) even wi
15. 45.▲
    by antves 2y ago

    HippoRAG is an amazing work and it was a source of inspiration for us as noted in the references. There are a couple of differences: (1) FastGraphRAG allows the user to make the graph construction opinionated and specialized on a given doma
16. 46.▲
    by antves 2y ago

    At the moment this repo is designed to handle more RAG-oriented use cases, i.e. that require to recall the "top pieces of information" relevant to a given question/context. In your specific example, right now, FastGraphRAG wo
17. 47.▲
    by antves 2y ago

    Yes, feel free to try it out! You can specialize the graph for the codebase use-case by configuring the graph prompt, entity types, and example questions accordingly.
18. 48.▲
    by antves 2y ago

    Yes, it works out-of-the-box with any OpenAI compatible API, including Ollama. You can check out our example at https://github.com/circlemind-ai/fast-graphrag/blob/main/exa...
19. 49.▲
    by antves 2y ago

    I think the main bit here is that the knowledge graph is entirely constructed by LLMs. It's not just using a pre-existing knowledge graph. It's creating a knowledge graph on the fly based on your data. Navigating the graph, on the
20. 50.▲
    by antves 2y ago

    You're right! Static knowledge is definitely one of the main limitations of other rag methods. Encoding information that changes over time is such a powerful feature of graphrag
21. 51.▲
    Show HN: Fast-Graphrag(github.com)

    3 points by antves 2y ago|3 comments
22. 52.▲
    by antves 2y ago

    In the past few weeks we’ve been working on implementing a fast and cheap version of graphrag. I hope that it can be helpful for your projects and it’d be great to have feedback to keep improving it and keep moving in the right direction. W
23. 53.▲
    by antves 4y ago

    Is it really annoying though? It takes 5 seconds to respond to a pulse question and it is not disruptive at all. And you will have to respond just once a day. On the flip side, it provides a structural mechanism for sharing feedback about w
24. 54.▲
    Launch – OfficeBird: daily pulse surveys for understanding your team(officebird.co)

    1 points by antves 4y ago|3 comments
25. 55.▲
    by antves 4y ago

    Hi! I’m Antonio, and I’m super excited to launch OfficeBird for the community. OfficeBird helps your team understand what matters to your team and develop your people. The Problem — People are the most valuable asset to your team. OfficeBi