ofermend

9 ms·

31.
by ofermend 2y ago
I'm happy to share that today we are releasing the Mockingbird LLM. At <10B parameters it's an LLM trained to provide optimal results for RAG and structured outputs. Although significantly smaller (and thus faster) than GPT-4 o
32.
by ofermend 2y ago
Thank you everyone for the feedback. For those interested in asking questions about articles, here's another nice demo (still in beta): an Agentic RAG chatbot demo (hosted on Huggingface, using streamlit for UI): https://hug
33.
by ofermend 2y ago
This should be fixed now. Thanks for the find.
34.
by ofermend 2y ago
Yes, it's just about 6 months back. If requested by folks here, we can certainly crawl back more years - this was just the first crawl I did.
35.
by ofermend 2y ago
For sure. Will test that.
36.
by ofermend 2y ago
Thank you - these are great suggestions. Will work to add these...
37.
by ofermend 2y ago
Good find. let me check why that occurs.
38.
by ofermend 2y ago
Currently it's indexing the content of stories and comments. Are you suggesting also to index the content of the main story link (which is outside HN)?
39.
Show HN: I made a search engine for Hacker News(hackernews.demo.vectara.com)
211 points by ofermend 2y ago|83 comments
40.
by ofermend 2y ago
Building RAG can be easy for a simple example, but it's much more nuanced than you might think when you try to do it at larger scale. With larger-scale real-world enterprise RAG-based applications, you soon realize the enormous time an
41.
by ofermend 2y ago
Super cool. Clearly many of us see the need here. I have also been working on a similar demo: https://search-hackernews.vercel.app/ 1. Stack: Vectara for RAG, Vercel for hosting 2. Results show the main story and top 3-4 co
42.
by ofermend 2y ago
This model is great. Jumped immediately to 2nd place on HHEM leaderboard: https://github.com/vectara/hallucination-leaderboard
43.
by ofermend 3y ago
Yes agree there, and that was my question too: more focused on how design might change due AI and ChatGPT coming in. My colleague Deryk wrote a nice article about it too: https://vectara.com/blog/user-interfaces-for-ai-
44.
Ask HN: How does AI transform UI design?
17 points by ofermend 3y ago|8 comments
45.
by ofermend 3y ago
have you tried Vectara?
46.
by ofermend 3y ago
Totally agree - the "R" in RAG is about retrieval which is a complex problem and much more than just similarity between embedding vectors.
47.
by ofermend 3y ago
Exciting to see the competition yield better and better LLMs. Thanks Anthropic for this new version of Claude.
48.
by ofermend 3y ago
Nice to see yet another open source approach to LLM/RAG. For those who do not want to meddle with the complexity of do-it-youself, Vectara ( https://vectara.com ) provides a RAG-as-a-service approach - pretty helpful if you w
49.
by ofermend 3y ago
Awesome work on getting this done so quickly. We just added Gemma to the HHEM leaderboard - https://huggingface.co/spaces/vectara/leaderboard , and as you can see there its doing pretty good in terms of low halluci
50.
by ofermend 3y ago
Gemma-7B (instruction tuned version) is now on the Vectara HHEM leaderboard, with 100% answer rate and 7.5% hallucination rate. Pretty good for a model with 7B params. https://huggingface.co/spaces/vectara/leaderbo
51.
by ofermend 3y ago
Somewhat related: I actually think vector databases are not as important as people are led to believe. Read more here: https://vectara.com/blog/vector-database-do-you-really-need-...
52.
by ofermend 3y ago
Curious to hear what criteria each team considers important (top-3) for choosing the VDB they chose. There are so many vector databases available and in my experience it's actually not the most critical component in the overall GenAI&#
53.
by ofermend 3y ago
I think DSPy is more a framework to build LLM programs; this is more of chat functionality as a service.
54.
by ofermend 3y ago
What about data types? Are your RAG pipelines mostly using text data from structured data, from document stores, or unstructured data like PDF files, website content and the like? Or maybe enterprise applications like Salesforce, HR, projec
55.
by ofermend 3y ago
So simplicity and ease-of-use. I assume you mean for builders/developers here, right?
56.
by ofermend 3y ago
We do see that hallucination varies between LLMs https://huggingface.co/spaces/vectara/leaderboard
57.
by ofermend 3y ago
Yes I agree it's complex. So large companies with large tech teams and expertise can handle this and will likely build teams to develop and maintain their own RAG pipelines. I believe simplifying this to non-experts is a necessary next
58.
by ofermend 3y ago
Do you think there is a level of hallucination that may be "acceptable" for an enterprise deployment? if so - what would that be?
59.
Ask HN: Challenges with RAG
1 points by ofermend 3y ago|8 comments
60.
Show HN: Sankofa – Chat with the Browser History(github.com)
5 points by ofermend 3y ago|0 comments

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