mingtianzhang

8 ms·

91.
by mingtianzhang 1y ago
Yeah I should call it 'Vibe RAG' lol.
92.
Do You Like Vibe Retrieval (RAG)?
3 points by mingtianzhang 1y ago|11 comments
93.
by mingtianzhang 1y ago
Exactly — thanks for the insightful comments! The goal is to generate an “LLM-friendly table of contents” for retrieval, rather than relying on vector-based semantic search. We think it’s closer to how humans approach information retrieval.
94.
Show HN: Vectorless RAG(colab.research.google.com)
11 points by mingtianzhang 1y ago|6 comments
95.
by mingtianzhang 1y ago
Now all the headings can be rendered properly in your markdown visualization if using this OCR tool.
96.
by mingtianzhang 2y ago
Hi, I think our approach can benefit from these OCR tools to generate a better tree for search. We will update more on this point in our GitHub repo, thanks for raising this question!
97.
by mingtianzhang 2y ago
Hi Imanari! That’s essentially one of the key challenges we’re aiming to address with our PageIndex package. We’ve designed two LLM functions: a. LLM Function 1: init_content -> initial_structure b. LLM Function 2: (previous_structure, c
98.
by mingtianzhang 2y ago
Hi Bsenftner, thanks for your interest. The motivation behind building PageIndex is to build a reasoning-based RAG. When we previously designed a RAG system for financial documents, we encountered two main challenges: 1. Traditional embeddi
99.
by mingtianzhang 3y ago
Hi, thanks for the comment! I agree with you that the sections are very useful. In the end of the blog, we did mention that "In practice, an improved chunking strategy should also consider the boundaries of sentences or paragraphs. We&
100.
How to choose your chunk size for summarizing large documents using LLM(vectify.ai)
29 points by mingtianzhang 3y ago|6 comments
101.
by mingtianzhang 3y ago
1. The limited context window of LLM poses challenges when summarizing large documents. A common strategy (provided by LangChain or LlamaIndex) is to: (i) divide the document into chunks, (ii) summarize each chunk using LLM, and (iii) combi