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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... show more

Andrej Baranovskij

6,686 subscribers

15,976 Aufrufe • vor 1 Jahr •via X (Twitter)

Anya Rossi• Live Now

Private livecam show

6 Kommentare

Profilbild von Rubén Aros

Rubén Arosvor 1 Jahr

It's the best vllm?

Profilbild von Andrej Baranovskij

Andrej Baranovskijvor 1 Jahr

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

Profilbild von optimizium.base.eth (ξ/e)

optimizium.base.eth (ξ/e)vor 1 Jahr

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

Profilbild von Andrej Baranovskij

Andrej Baranovskijvor 1 Jahr

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

Profilbild von Z❤️H

Z❤️Hvor 1 Jahr

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

Profilbild von Andrej Baranovskij

Andrej Baranovskijvor 1 Jahr

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

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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 Timescale (now TigerData) team for partnering with me on this post!

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109,517 Aufrufe • vor 1 Jahr

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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👇

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29,402 Aufrufe • vor 3 Monaten

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:

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

196,843 Aufrufe • vor 1 Jahr

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:

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 🦙

143,136 Aufrufe • vor 2 Jahren

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:

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

387,018 Aufrufe • vor 2 Jahren

President of Ukraine: We did indeed have a positive meeting with President Trump and his team. First of all, to be honest, I raised the issue of air defense. And I think that the US will return with a positive result. I will definitely talk about it when the result is in Ukraine. Second, our security guarantees. They are indeed ready, and the agreement is ready for signing. Of course, there will be many different technical details. And based on this document, additional documents will appear, but the main, core agreement on security guarantees is in place. Now I am waiting for President Trump to set the date and place. It's up to him. We are ready to sign such an important [document] for us. I emphasize once again that I am confident this is a historic document. Why did I mention the many details? You know that such a document awaits ratification by the US Congress and the Ukrainian Parliament. Therefore, there are still many steps ahead on this path.

President of Ukraine: We did indeed have a positive meeting with President Trump and his team. First of all, to be honest, I raised the issue of air defense. And I think that the US will return with a positive result. I will definitely talk about it when the result is in Ukraine. Second, our security guarantees. They are indeed ready, and the agreement is ready for signing. Of course, there will be many different technical details. And based on this document, additional documents will appear, but the main, core agreement on security guarantees is in place. Now I am waiting for President Trump to set the date and place. It's up to him. We are ready to sign such an important [document] for us. I emphasize once again that I am confident this is a historic document. Why did I mention the many details? You know that such a document awaits ratification by the US Congress and the Ukrainian Parliament. Therefore, there are still many steps ahead on this path.

Anton Gerashchenko

27,842 Aufrufe • vor 6 Monaten

SQL has levels to it: - level 1 SELECT, FROM, WHERE, GROUP BY, HAVING, LIMIT Master these basic keywords and you’ll be well on your way to mastering SQL. - level 2 Mastering JOINs: Most common JOINs: INNER and LEFT Less common JOINs: FULL OUTER Joins you should avoid almost always: RIGHT and CROSS JOIN Mastering common table expressions (CTEs). The WITH keyword defines a CTE which you can imagine as a “variable” that you can query later. Using variables like this you can master algorithm techniques like recursion, breadth first search and more! CTEs also make your SQL much more readable and make your coworkers hate you less compared to nested sub queries. - level 3 Mastering window functions Window functions have 3 pieces: The function (i.e. SUM, RANK, AVG) The over clause to start the window The window definition which has 3 pieces: - how to split the window up with PARTITION BY - how to order the window with ORDER BY - how to restrict the window size with ROWS clause (useful for rolling monthly averages) Understand RANK vs DENSE_RANK vs ROW_NUMBER, I have been asked this in interviews a million times. - level 4 You understand table scans, b-tree indexes, and partitioning schemes to increase performance. Doing something like COUNT(CASE WHEN) is much better than doing multiple queries with a UNION ALL. UNION ALL is terrible for all sorts of reasons that I don’t want to get into in this post. B-trees indexes allow for efficient scanning of data in the WHERE clause. Use explain plans to understand if an index is actually being used or not! Partitioning is similar to indexes except it’s a “poor mans” index. It just keeps data in specific folders and skips the folders that don’t include the data I question. What else did I miss for mastering SQL?

SQL has levels to it: - level 1 SELECT, FROM, WHERE, GROUP BY, HAVING, LIMIT Master these basic keywords and you’ll be well on your way to mastering SQL. - level 2 Mastering JOINs: Most common JOINs: INNER and LEFT Less common JOINs: FULL OUTER Joins you should avoid almost always: RIGHT and CROSS JOIN Mastering common table expressions (CTEs). The WITH keyword defines a CTE which you can imagine as a “variable” that you can query later. Using variables like this you can master algorithm techniques like recursion, breadth first search and more! CTEs also make your SQL much more readable and make your coworkers hate you less compared to nested sub queries. - level 3 Mastering window functions Window functions have 3 pieces: The function (i.e. SUM, RANK, AVG) The over clause to start the window The window definition which has 3 pieces: - how to split the window up with PARTITION BY - how to order the window with ORDER BY - how to restrict the window size with ROWS clause (useful for rolling monthly averages) Understand RANK vs DENSE_RANK vs ROW_NUMBER, I have been asked this in interviews a million times. - level 4 You understand table scans, b-tree indexes, and partitioning schemes to increase performance. Doing something like COUNT(CASE WHEN) is much better than doing multiple queries with a UNION ALL. UNION ALL is terrible for all sorts of reasons that I don’t want to get into in this post. B-trees indexes allow for efficient scanning of data in the WHERE clause. Use explain plans to understand if an index is actually being used or not! Partitioning is similar to indexes except it’s a “poor mans” index. It just keeps data in specific folders and skips the folders that don’t include the data I question. What else did I miss for mastering SQL?

Zach Wilson

79,578 Aufrufe • vor 1 Jahr