Unstructured has 3 extraction options in its cloud. Their Basic option is positioned for plain text documents at $2 / 1,000 pages, their Advanced $20 / 1,000 pages and their Platinum is $30 / 1,000 pages and can handle advanced cases like handwriting.
@avikumart_
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Unstructured.io PDF Library Gains Popularity via LangChain Integration
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1) Unstructured(.)io @UnstructuredIO Unstructured started out as a PDF library, which gained popularity thanks to its early integration with LangChain.
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Top PDF Extractors: Unstructured, LlamaParse, Vectorize
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Blog lists the three most useful PDF extractors such as Unstructured, LlamaParse, and Vectorize. All of these options support many document types beyond PDFs, including Microsoft Word, RTF, JSON, images, Powerpoint, and more.
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Best PDF Extractors Compared for RAG Evaluations
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I found an amazing blog that compares the performance of the 3 best PDF extractors and how they perform on RAG evaluations of your data. Check them out on this blog to learn more:
credits: @Pavan_Belagatti https://
levelup.gitconnected.com/whats-the-best
-pdf-extractor-for-rag-i-tried-llamaparse-unstructured-and-vectorize-4abbd57b06e0
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PDF Extraction Challenges in RAG Systems
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Working with RAG will make you work with the PDF extractors to load the PDF content into your vector databases. Extracting and loading content from PDFs can be difficult sometimes, affecting your RAG systems' performance. Learn more below:
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GPT vs T5 BART: Decoder vs Encoder-Decoder Models
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GPT (Only Decoder) → Best for text generation T5, BART (Full Encoder-Decoder) → Best for translation & summarization
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Encoder-Decoder Architectures: BERT and Advanced NLP Models
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3) Encoders map input sequences to dense representations, while decoders generate meaningful outputs based on context. Popular Models Using These Architectures: BERT (Only Encoder) → Best for understanding text
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Transformers Self-Attention Outperforms RNNs in NLP Tasks
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1) Transformers leverage self-attention to understand the context better than traditional RNNs/LSTMs, making them faster and more scalable. 2) The Encoder-Decoder architecture is crucial for tasks like machine translation, text summarization, and question-answering.
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Transformers Architecture: Foundation of Modern AI Models
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Transformers architecture has revolutionized the modern AI as we know it. Applications like chatGPT, Grok, and deepseek all uses models built on top of this ground-breaking architecture. Find out the basis of this architecture below post:
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OpenAI Abstract vs Grok: AI Model Comparison Analysis
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Openai gave very abstract compared to grok which is surprising tbh