redskyluan

10 ms·

61.
Cagra: Next-Gen GPU-Powered Vector Search by Nvidia and Zilliz(zilliz.com)
2 points by redskyluan 3y ago|0 comments
62.
Nvidia Launches GenAI Microservices on CUDA(nvidianews.nvidia.com)
2 points by redskyluan 3y ago|0 comments
63.
by redskyluan 3y ago
This seems not be a repo ready to open source. You only get weights, very less information about how the weights is trained and finetuned. But anyway, it always great to see more LLM weigts available.
64.
Gemini and Claudes are killing RAG?(zilliz.com)
38 points by redskyluan 3y ago|19 comments
65.
Will RAG Be Killed by Long-Context LLMs?(zilliz.com)
3 points by redskyluan 3y ago|0 comments
66.
by redskyluan 3y ago
Lessons learned: The Importance of Model Agnosticism: With the rapid evolution of AI models, building applications that are model-agnostic has become more critical than ever. Control and Interpretability Matter: Relying solely on large lang
67.
Building Zilliz Cloud from scratch in 18 months(zilliz.com)
2 points by redskyluan 3y ago|0 comments
68.
Lessons learned while creating Vector Search Service on cloud(zilliz.com)
2 points by redskyluan 3y ago|0 comments
69.
by redskyluan 3y ago
As an open source database Milvus contributor, I am deeply grateful for the contributions brought by all non-code contributors to the project. However, in my view, this is far from sufficient. Without the project maintainers or dedicated do
70.
by redskyluan 3y ago
Regarding scale, PostgreSQL may not cover all bases. Though I'm a PostgreSQL fan, I prefer specialized services for specific tasks. Using PG-based plugins could help, but a dedicated SQL-compatible database is often a better fit. For v
71.
Easiest Way to Leverage Vector Search: Free, One-Click(zilliz.com)
1 points by redskyluan 3y ago|0 comments
72.
by redskyluan 3y ago
this is much cheaper than S3 glacier. Any explanation on how to keep data safe?
73.
What I Learned from Building Google Search and How It Inspires RAG Development(medium.com)
8 points by redskyluan 3y ago|2 comments
74.
by redskyluan 3y ago
We also built a similar semantic search engine for retrieving open-source projects. We experimented with HyDE and guessing user queries based on a corpus, both of which were very successful. For embeddings, we chose BGE, but found they seem
75.
by redskyluan 3y ago
Just stumbled upon this blog it's absolutely intriguing! As a Python enthusiast, it's like finding a hidden treasure that challenges the usual norms of Python's capabilities. Thinks of writing a Pure python implementation of
76.
by redskyluan 3y ago
The inclusion of the OpenAI dataset in this benchmark adds a layer of realism that's often missing in standardized tests with datasets like SIFT and DEEP
77.
by redskyluan 3y ago
I think we need vectordb bench on 100M level. If you don't have 100M data, and you don't care about things like filtering and streaming insertion, I vote for PGVector since SQL is convenient enough. However, for large dataset dep
78.
by redskyluan 3y ago
I think user has to test by themselves. Vectorbenchmark support you to run the test by yourself on any cloud serivce or opensource deployment. One of my guess is qdrant tune their parameters crazily on their benchmark.
79.
by redskyluan 3y ago
how do you define chroma as a vector db? a wrapper on top of other databases and hnsw? What about a billion vectors in house~ is chroma all we need?
80.
by redskyluan 3y ago
That's exactly what I'm trying to do. Play with prompts, generate multiple questions and cluster questions and pick some of the centroid questions to do embeddings
81.
by redskyluan 3y ago
They generate questions like: where did you go this morning? When did you woke up this morning. What did you do after breakfast? What did you do today at Golden Gate Park. GPT is all about probabilities. So the LLM know what might be most r
82.
by redskyluan 3y ago
i have an opposite way on doing this. Tried to generate questions based on doc chunks and embedding on questions. It works perfect!
83.
by redskyluan 3y ago
I would vote for finetuning, prompt engineering, rather than only add domain specific knowledge. Others are playing detective ,digging into the ethical conundrums of AI-generated content.
84.
by redskyluan 3y ago
Can't Agree with that more. LLMs should not be trained to simply memorize information. Instead, they should be designed to understand and identify patterns in the data, and use the knowledge stored in vector databases to organize and s
85.
OSSChat Now Support OpenCV(osschat.io)
1 points by redskyluan 3y ago|2 comments
86.
by redskyluan 3y ago
OSSChat is a free chatbot that helps you learn more about open source software. It is powered by ChatGPT, Langchain, and Milvu and supported community like pytorch, huggingface and ray. We are pleased to announce that we now support OpenCV.
87.
by redskyluan 3y ago
Although LLMs are impressive, we should keep an eye on the potential risks. They have the ability to generate hallucinations and should be monitored closely. It's not just the tech industry that's worried, governments all over the
88.
by redskyluan 3y ago
Totally agree with that. 8G might be too much to make the open source product popular. I would say 4G may more sense to me, I know how much engineering effort it requires though. LOL
89.
Know more about AutoGPT? Try OSSChat(osschat.io)
1 points by redskyluan 3y ago|1 comments
90.
by redskyluan 3y ago
OSSChat is a ChatGPT and Vector database enhanced chatbot about open source software. Now we support AutoGPT!

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