page_index

5 ms·

1.
by page_index 11mo ago
This vectorless, vision RAG notebook passes PDF page images directly to Vision Language Models without OCR or embeddings. This eliminates the text extraction pipeline entirely, no layout detection, no character recognition, no vector search
2.
Vectorless, Vision-Based RAG(colab.research.google.com)
5 points by page_index 11mo ago|1 comments
3.
by page_index 11mo ago
In modern document question answering (QA) systems, OCR serves an important role by converting PDF pages into text that can be processed by Large Language Models (LLMs). The resulting text can provide contextual input that enables LLMs to p
4.
by page_index 11mo ago
In modern document question answering (QA) systems, OCR serves an important role by converting PDF pages into text that can be processed by Large Language Models (LLMs). The resulting text can provide contextual input that enables LLMs to p
5.
Show HN: Vision-Based, Vectorless RAG for Long Douments(github.com)
6 points by page_index 11mo ago|0 comments
6.
CausalRAG: Integrating Causal Graphs into RAG(arxiv.org)
2 points by page_index 11mo ago|1 comments
7.
by page_index 11mo ago
Large language models (LLMs) have revolutionized natural language processing (NLP), particularly through Retrieval-Augmented Generation (RAG), which enhances LLM capabilities by integrating external knowledge. However, traditional RAG syste
8.
Claude researcher explains why agentic search beats RAG for code generation(officechai.com)
2 points by page_index 11mo ago|0 comments
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RAG Is Over: RL Agents Are the New Retrieval Stack(inference.net)
8 points by page_index 11mo ago|0 comments
10.
by page_index 1y ago
The word index originally came from how humans retrieve information: book indexes and tables of contents that guide us to the right place. Computers later borrowed the term for data structures such as B-trees, hash tables, and more recently
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AgentScope: Lego-style Agent Building Platform from Alibaba Qwen(github.com)
2 points by page_index 1y ago|0 comments
12.
by page_index 1y ago
Lol you mean vector db is more like hash_map. map is more tree based
13.
by page_index 1y ago
I have page index in my book :)
14.
by page_index 1y ago
I guess the major foucs in certain uses cases is not speed but accuracy and retrieval quality.
15.
by page_index 1y ago
curious about the other attempt you see
16.
by page_index 1y ago
LOL ctrl-f feels like bm25 vector search
17.
by page_index 1y ago
In specific domains, accuracy matters more than than speed. Document structure and reasoning bring better retrieval than semantic search which retrieves "similar" but not "relevant" results.
18.
by page_index 1y ago
you can use document search straedgies (like SQL metadata search, semantic search etc, doc descrption search by LLM) to narrow down the doc candidates first.
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Show HN: PageIndex – Vectorless RAG(github.com)
192 points by page_index 1y ago|128 comments
20.
by page_index 1y ago
github repo: github.com/VectifyAI/PageIndex
21.
Human-like RAG – with no vectors
6 points by page_index 1y ago|1 comments
22.
Show HN: Human-like RAG — no vectors(github.com)
11 points by page_index 1y ago|0 comments