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Document Querying with Qwen2-VL-7B and JSON Output Complete video: I demonstrate how to perform document queries using Qwen2-VL-7B. By simplifying field names, we streamline the prompts, making them more efficient and reusable across different documents. This approach is similar to running SQL queries on a database, but tailored for...

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Andrej Baranovskij

6,682 subscribers

15,981 görüntüleme • 2 yıl önce •via X (Twitter)

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Rubén Aros profil fotoğrafı
Rubén Aros2 yıl önce

It's the best vllm?

Andrej Baranovskij profil fotoğrafı
Andrej Baranovskij2 yıl önce

At the moment yes definitely- qwen2 is the best open source llm with apache license. China rules 🇨🇳👍

optimizium.base.eth (ξ/e) profil fotoğrafı
optimizium.base.eth (ξ/e)2 yıl önce

Still unable to compile flash attention 2 on my Windows machine. :( So annoying.

Andrej Baranovskij profil fotoğrafı
Andrej Baranovskij2 yıl önce

I believe Windows is not well supported platform. Btw, it looks like new Qwen models will be announced next week -

Z❤️H profil fotoğrafı
Z❤️H2 yıl önce

it hallucinante when it should retrieve a correlated informations from multiple lines

Andrej Baranovskij profil fotoğrafı
Andrej Baranovskij2 yıl önce

Not per my tests, qwen2 7b works excellent. For more complex docs use 72b

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3:31

New course: Document AI: From OCR to Agentic Doc Extraction, built with LandingAI, where I'm executive chairman, and taught by David Park and Andrea Kropp. Much of the world's data is locked in PDFs, JPEGs, and other documents. This short course shows you how to build agentic workflows that process documents accurately: breaking them into parts, examining each piece carefully, and extracting information through multiple iterations. Traditional Optical Character Recognition (OCR) captures text but loses context from table headers, chart captions, or reading order of columns. After exploring OCR's limitations, you’ll use LandingAI's Agentic Document Extraction (ADE) framework to process documents. ADE treats pages as visually -- as images -- to parse information and extract fields. Skills you'll gain: - Build agents to convert unstructured files into structured Markdown/HTML and JSON - Use ADE to parse complex data like forms, handwriting, or equations - Map extracted information to named fields using a specified schema, with bounding boxes for grounding and validation - Deploy RAG applications with event-driven document processing Come learn about the best tools for processing documents like financial invoices, medical records, or academic papers intelligently:

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2:05

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1:01

Quick demo of Zero’s new background queries. Zero’s sync is query-based. Rather than specifying what data you want using rules or some other separate system, you just use queries. Right inside the client app, you do a query using a full sql-style language. You get filters, subqueries , limits, etc. Zero syncs the data backing these queries to the client. It’s important to realize Zero isn’t really a cache. It’s a replica. It’s eagerly replicating a precise snapshot of a slice of your database - the slice covered by the queries you have open. So there is never stale data in Zero. When you close a query we delete the rows uniquely returned by that query because we can no longer keep them up to date. Of course that kind of sucks for the common case of doing a query, navigating, then pressing “back”. Ideally we want that back nav to be fast. To address this Zero 0.17 adds background queries. You can add a ttl to a query and it will keep running and syncing in the background. This is much different than normal caching because this data stays up to date. If you make the same query again, the results will be *instantly* available *and already up to date with server*. If you make a different query the data from the background query is used client-side to answer the new query instantly if possible. This all happens completely automatically. Just by adding the ttl flag.

Aaron Boodman

18,808 görüntüleme • 1 yıl önce

This is, by far, one of the best uses of modern AI. If you don't use embeddings when querying your database, you are definitely leaving a lot on the table. In this video, I'll show you how to run semantic searches using OpenAI and PostgreSQL. It's all thanks to Pgai, an open-source PostgreSQL extension: Here's what will happen: 1. We'll create a simple table with news articles 2. We'll generate embeddings for those articles 3. We'll run queries on top of those embeddings For this video, I generated the embeddings using a simple query, but pgai Vectorizer would do the same automatically as new information makes it into the database. This is awesome! If you have a PostgreSQL database with data you are searching over, you should start experimenting with semantic searches immediately. For most use cases, a combination of full-text search + semantic search is the best approach. If you don't have a PostgreSQL database around, you can try free for 30 days using Timescale: Thanks to the TimescaleDB (by Tiger Data) team for partnering with me on this post!
19:19

This is, by far, one of the best uses of modern AI. If you don't use embeddings when querying your database, you are definitely leaving a lot on the table. In this video, I'll show you how to run semantic searches using OpenAI and PostgreSQL. It's all thanks to Pgai, an open-source PostgreSQL extension: Here's what will happen: 1. We'll create a simple table with news articles 2. We'll generate embeddings for those articles 3. We'll run queries on top of those embeddings For this video, I generated the embeddings using a simple query, but pgai Vectorizer would do the same automatically as new information makes it into the database. This is awesome! If you have a PostgreSQL database with data you are searching over, you should start experimenting with semantic searches immediately. For most use cases, a combination of full-text search + semantic search is the best approach. If you don't have a PostgreSQL database around, you can try free for 30 days using Timescale: Thanks to the TimescaleDB (by Tiger Data) team for partnering with me on this post!

Santiago

110,133 görüntüleme • 1 yıl önce

Today, we’re excited to announce Deep Extract. We’re pushing the boundary of complex document extraction by utilizing an agent harness approach, iterating and verifying outputs until they are at human-level accuracy. The results speak for themselves: we’ve handled documents exceeding 2,500 pages, extracting a total of 30M+ fields in the last couple weeks with results in our production beta reaching 99-100% accuracy. It’s particularly effective on complex documents with long lists, such as invoice line items, brokerage statement transactions, equipment manifests, and more. See more on how we built it, how to use it, and more👇
0:59

Today, we’re excited to announce Deep Extract. We’re pushing the boundary of complex document extraction by utilizing an agent harness approach, iterating and verifying outputs until they are at human-level accuracy. The results speak for themselves: we’ve handled documents exceeding 2,500 pages, extracting a total of 30M+ fields in the last couple weeks with results in our production beta reaching 99-100% accuracy. It’s particularly effective on complex documents with long lists, such as invoice line items, brokerage statement transactions, equipment manifests, and more. See more on how we built it, how to use it, and more👇

Reducto

23,949,618 görüntüleme • 5 ay önce

Introducing DocJev - a lightning-fast OSS library for document classification and splitting with jev ⚡️ Give a document alongside some natural language category rules. Jev will predict the document category (classify) or the boundaries between sub-documents (split). It is 6x faster than gpt-5.6-luna with equivalent accuracy. You can choose between different OCR backends: liteparse: the fastest/most accurate free+OSS text parser out there. Perfect for digitalized documents. The speedups vs. luna above are inclusive of liteparse times. LlamaParse: the most advanced VLM-based solution for complex docs. Using this will of course add latency on document preprocessing. But it’s a good option if you want to cache the digitalized representations of documents for a variety of downstream tasks. Check it out: If you want to dive deeper into liteparse or LlamaParse check the links below: liteparse: LlamaParse:
0:24

Introducing DocJev - a lightning-fast OSS library for document classification and splitting with jev ⚡️ Give a document alongside some natural language category rules. Jev will predict the document category (classify) or the boundaries between sub-documents (split). It is 6x faster than gpt-5.6-luna with equivalent accuracy. You can choose between different OCR backends: liteparse: the fastest/most accurate free+OSS text parser out there. Perfect for digitalized documents. The speedups vs. luna above are inclusive of liteparse times. LlamaParse: the most advanced VLM-based solution for complex docs. Using this will of course add latency on document preprocessing. But it’s a good option if you want to cache the digitalized representations of documents for a variety of downstream tasks. Check it out: If you want to dive deeper into liteparse or LlamaParse check the links below: liteparse: LlamaParse:

Jerry Liu

213,302 görüntüleme • 5 gün önce

Databases are far from dead. Hot take within the vibe-coding community, but you can't build a reliable agentic memory system using files alone. The filesystem is a great interface for agents, but for complex, distributed, production applications, databases win hands down. I recorded a video to show you the benchmarks. Large Language Models know how to navigate and work with the filesystem, but as soon as you add complexity, files will fall short. You need databases whenever any of the following happens: 1. You have concurrent writes from multiple agents or users 2. You need semantic retrieval at scale 3. You need ACID guarantees for shared state 4. You need audit trails and row-level access control 5. You need indexed queries over growing memory In the attached video, I'm running a notebook comparing a filesystem-backed agent with a database-backed agent. The three most important findings: • Filesystem = Database with small corpus, keyword-friendly queries • Databases > Filesystem with large corpus, fuzzy queries • Databases > Filesystem with concurrent writes without locking Numbers don't lie. You can run the benchmarks yourself.
8:11

Databases are far from dead. Hot take within the vibe-coding community, but you can't build a reliable agentic memory system using files alone. The filesystem is a great interface for agents, but for complex, distributed, production applications, databases win hands down. I recorded a video to show you the benchmarks. Large Language Models know how to navigate and work with the filesystem, but as soon as you add complexity, files will fall short. You need databases whenever any of the following happens: 1. You have concurrent writes from multiple agents or users 2. You need semantic retrieval at scale 3. You need ACID guarantees for shared state 4. You need audit trails and row-level access control 5. You need indexed queries over growing memory In the attached video, I'm running a notebook comparing a filesystem-backed agent with a database-backed agent. The three most important findings: • Filesystem = Database with small corpus, keyword-friendly queries • Databases > Filesystem with large corpus, fuzzy queries • Databases > Filesystem with concurrent writes without locking Numbers don't lie. You can run the benchmarks yourself.

Santiago

29,445 görüntüleme • 4 ay önce

GPT-4o is bad at processing PDF documents. Whoever tells you otherwise is not living in the real world. In 2024, people fill out forms using pen and paper. Try to answer questions from those forms using modern models, and you'll be disappointed. I recorded a video to show you how to fix this. The answer is simple: stop letting the model see your PDF document. Instead, preprocess it and stick to showing the model text. Watch the video. You'll go from "THIS IS CRAP" to "EXCELLENT" in no time. I'm using Unstract to turn the documents into text while keeping the original format (this is crucial!) You can use them to process up to 100 pages for free. They collaborated with me on this post. You can find the code I wrote right here:
6:27

GPT-4o is bad at processing PDF documents. Whoever tells you otherwise is not living in the real world. In 2024, people fill out forms using pen and paper. Try to answer questions from those forms using modern models, and you'll be disappointed. I recorded a video to show you how to fix this. The answer is simple: stop letting the model see your PDF document. Instead, preprocess it and stick to showing the model text. Watch the video. You'll go from "THIS IS CRAP" to "EXCELLENT" in no time. I'm using Unstract to turn the documents into text while keeping the original format (this is crucial!) You can use them to process up to 100 pages for free. They collaborated with me on this post. You can find the code I wrote right here:

Santiago

196,868 görüntüleme • 2 yıl önce

We’re excited to officially launch LlamaParse, the first genAI-native document parsing solution. Not only is it better at parsing out images/tables/charts 📊📈 than virtually every other parser, it is now steerable through natural language instructions - output the document in whatever format you desire! It is also the only parsing solution that seamlessly allows you to build accurate RAG over complex documents, free of hallucinations 🔥 We launched it in private preview a few weeks ago and hit 2k users, 1M total PDF pages parsed. And now it’s better than ever. LlamaParse contains the following killer features: ✅ SOTA table/chart extraction ✅ Seamless integration with LlamaIndex 🦙 advanced RAG/agents ✅✨ Natural language Parsing Instructions ✅✨JSON mode and image extraction ✅✨Support for ~10 document types (.pdf, .pptx, .docx, .xml) and more Our pricing is simple: 1k free per day, and additional pages at 0.3c a page, or $3 for 1k pages. If you want advanced document RAG and/or private deployments, come get in touch with us to chat about LlamaCloud. Check out our full blog post here: LlamaParse client repo: Signup at 🦙☁️: Come talk to us:
5:07

We’re excited to officially launch LlamaParse, the first genAI-native document parsing solution. Not only is it better at parsing out images/tables/charts 📊📈 than virtually every other parser, it is now steerable through natural language instructions - output the document in whatever format you desire! It is also the only parsing solution that seamlessly allows you to build accurate RAG over complex documents, free of hallucinations 🔥 We launched it in private preview a few weeks ago and hit 2k users, 1M total PDF pages parsed. And now it’s better than ever. LlamaParse contains the following killer features: ✅ SOTA table/chart extraction ✅ Seamless integration with LlamaIndex 🦙 advanced RAG/agents ✅✨ Natural language Parsing Instructions ✅✨JSON mode and image extraction ✅✨Support for ~10 document types (.pdf, .pptx, .docx, .xml) and more Our pricing is simple: 1k free per day, and additional pages at 0.3c a page, or $3 for 1k pages. If you want advanced document RAG and/or private deployments, come get in touch with us to chat about LlamaCloud. Check out our full blog post here: LlamaParse client repo: Signup at 🦙☁️: Come talk to us:

LlamaIndex 🦙

143,189 görüntüleme • 2 yıl önce

New short course with Mistral AI ! Mistral's open-source Mixtral 8x7B model uses a "mixture of experts" (MoE) architecture. Unlike a standard transformer, an MoE model has multiple expert feed-forward networks (8 in this case), with a gating network selecting two experts at inference time. This enables MoE to match the performance of a large model but faster inference. Mixtral 8x7B has 46.7B parameters but activates only 12.9B at inference to predict the next token. In Getting Started with Mistral, you’ll learn from Mistral’s Sophia Yang, Ph.D. to: - Explore Mistral's open-source models (Mistral 7B, Mixtral 8x7B) and commercial models via API calls and Mistral AI's Le Chat website - Implement JSON mode to generate structured outputs to integrate directly into larger software systems. - Use function calling for Tool Use, such as calling custom Python code that queries tabular data - Ground your LLM's response with external knowledge sources using RAG - Build a Mistral-powered chat interface that can reference external documents This course will help deepen your prompt engineering skills. Please sign up here:
1:55

New short course with Mistral AI ! Mistral's open-source Mixtral 8x7B model uses a "mixture of experts" (MoE) architecture. Unlike a standard transformer, an MoE model has multiple expert feed-forward networks (8 in this case), with a gating network selecting two experts at inference time. This enables MoE to match the performance of a large model but faster inference. Mixtral 8x7B has 46.7B parameters but activates only 12.9B at inference to predict the next token. In Getting Started with Mistral, you’ll learn from Mistral’s Sophia Yang, Ph.D. to: - Explore Mistral's open-source models (Mistral 7B, Mixtral 8x7B) and commercial models via API calls and Mistral AI's Le Chat website - Implement JSON mode to generate structured outputs to integrate directly into larger software systems. - Use function calling for Tool Use, such as calling custom Python code that queries tabular data - Ground your LLM's response with external knowledge sources using RAG - Build a Mistral-powered chat interface that can reference external documents This course will help deepen your prompt engineering skills. Please sign up here:

Andrew Ng

387,172 görüntüleme • 2 yıl önce