正在加载视频...

视频加载失败

Vision RAG with vector database is all you need. It uses vision language model to embed pages of PDF as directly vectors, without the tedious chunking process. 100% Opensource code.

106,727 次观看 • 1 年前 •via X (Twitter)

11 条评论

Shubham Saboo 的头像
Shubham Saboo1 年前

GitHub Repo: 50+ Step-by-step tutorials of LLM apps with AI Agents and RAG. P.S: Don't forget to subscribe for FREE to access future tutorials.

Shubham Saboo 的头像
Shubham Saboo1 年前

Find all the awesome LLM Apps with AI Agents and RAG in the following Github Repo. P.S: Don't forget to star the repo to show your support 🌟

Shubham Saboo 的头像
Shubham Saboo1 年前

If you find this useful, RT to share it with your friends. Don't forget to follow me @Saboo_Shubham_ for daily tips and tutorials on LLMs, RAG and AI Agents.

PDF GPT 的头像
PDF GPT1 年前

This is my favorite AI tool for reviewing reports. Just upload a report, ask for a summary, and get one in seconds. It's like ChatGPT, but built for documents. Try it for free.

Gargi 的头像
Gargi1 年前

would love a tutorial on this

Shubham Saboo 的头像
Shubham Saboo1 年前

On it!

Kairos Data Labs 的头像
Kairos Data Labs1 年前

This is amazing. Thanks for sharing!

Shubham Saboo 的头像
Shubham Saboo1 年前

You’re welcome!

Jason 的头像
Jason1 年前

That’s an amazing share. Thank you brother

Shubham Saboo 的头像
Shubham Saboo1 年前

You’re welcome!

SAMO 的头像
SAMO1 年前

Thank you for this. Could solve a use case I have of very large pdf docs that needs semantic search.

相关视频

Traditional chunking: cheap but dumb. ColBERT: smart but expensive. 𝗟𝗮𝘁𝗲 𝗰𝗵𝘂𝗻𝗸𝗶𝗻𝗴: the solution we've been waiting for. Here’s a quick evolution of chunking strategies: → 𝗧𝗿𝗮𝗱𝗶𝘁𝗶𝗼𝗻𝗮𝗹 𝗖𝗵𝘂𝗻𝗸𝗶𝗻𝗴 (the basics we all started with) • Token Chunking - split by token count • Sentence Chunking - split by sentence boundaries • Document-Based Chunking - split by sections/paragraphs → 𝗔𝗱𝘃𝗮𝗻𝗰𝗲𝗱 𝗖𝗵𝘂𝗻𝗸𝗶𝗻𝗴 (when things got sophisticated) • Semantic Chunking - split by meaning • LLM-Based Chunking - let the model decide But each chunking method separates text at defined points, meaning context is lost within the document from one chunk to the next. → 𝗘𝗻𝘁𝗲𝗿 𝗟𝗮𝘁𝗲 𝗖𝗵𝘂𝗻𝗸𝗶𝗻𝗴 (the game changer) Traditional approach: Chunk first → Embed each chunk separately Late chunking approach: Embed the entire document → Then chunk with context preserved 𝗪𝗵𝘆 𝗰𝗵𝗼𝗼𝘀𝗲 𝗹𝗮𝘁𝗲 𝗰𝗵𝘂𝗻𝗸𝗶𝗻𝗴? When you chunk first, each piece loses its contextual relationship to the rest of the document. It's like reading a book by randomly picking paragraphs - you miss the flow. With late chunking, every chunk maintains awareness of its neighbors because the embedding happens at the document level first. Mean pooling is done on segments AFTER the full context is embedded. Jina AI tested and saw significant improvements in retrieval quality - chunks that were previously disconnected now maintain their semantic relationships. As documents get longer and context windows expand, late chunking might just become the new standard for high-quality retrieval systems. 𝗪𝗵𝗮𝘁 𝗱𝗼 𝘆𝗼𝘂 𝗻𝗲𝗲𝗱 𝘁𝗼 𝗺𝗮𝗸𝗲 𝘁𝗵𝗶𝘀 𝘄𝗼𝗿𝗸? No modifications to your retrieval pipeline are needed. 1. Long context embedding models (8192+ tokens) 2. Chunking logic that tracks token spans 3. Less than 30 lines of code to implement All you need is to switch the order at which you chunk and embed. Embed FIRST, then chunk, not the other way around. Dive deeper into late chunking:

Femke Plantinga

125,283 次观看 • 10 个月前