How to use One-Hot Encoding for Text Data

🚀 Making Document Retrieval Smarter: How Chunks Pull Context from Their Parent Imagine each document as a tree. Each chunk is a branch, holding its own details. But without context, a branch alone can be misleading. Our approach: we let chunks “pull context” from their parent document, blending their local details with the global picture. How we optimized it: 1️⃣ Precompute Once – All chunks are blended with their parent document embeddings ahead of time, stored in a single ChromaDB collection. No recomputation per query. 2️⃣ Similarity-Weighted Blending – Chunks closer to the document’s core content pull more context, while minor chunks retain their unique info. 3️⃣ Batch Processing – Blending and storing in batches ensures speed and memory efficiency. 4️⃣ Fast Query Retrieval – Queries simply fetch the enhanced chunks. No dynamic computation, no redundant writes. 💡 Why it matters: Each chunk now has both local detail and global context, improving retrieval accuracy. Works especially well in finance, legal, and pharma, where context is key. High-quality retrieval without retraining models or using heavy cross-encoders. #RAG #ChromaDB #AI #MachineLearning #LLM #NLP #VectorDatabases #DocumentRetrieval #KnowledgeManagement #DataScience #EmbeddingOptimization #DeepLearning

𝗩𝗲𝗰𝘁𝗼𝗿 𝘀𝗲𝗮𝗿𝗰𝗵 𝘄𝗮𝗹𝗸𝗲𝗱 𝗶𝗻𝘁𝗼 𝗮 𝗯𝗮𝗿. 𝗜𝘁 𝗰𝗼𝘂𝗹𝗱𝗻'𝘁 𝗳𝗶𝗻𝗱 𝘁𝗵𝗲 𝗲𝘅𝗶𝘁. 🚪 Traditional RAG: "These two sentences FEEL similar, so they must be relevant!" Narrator: They were not. 𝗣𝗮𝗴𝗲𝗜𝗻𝗱𝗲𝘅: "What if we stopped guessing and started reasoning?" The approach? Ditch vectors. Build a document tree. Search like a human would — with actual logic. 𝟵𝟴.𝟳% 𝗮𝗰𝗰𝘂𝗿𝗮𝗰𝘆 on financial docs. No vector DB. No random chunking. No vibes. Just pure, transparent reasoning. The future of RAG doesn't need more embeddings. It needs more common sense. 🧠 🔗 github(.)com/VectifyAI/PageIndex #AI #MachineLearning #NLP #LLM #ReinforcementLearning #OpenSource #DeepLearning #GenerativeAI #DeepSeek #Innovation #Tech #Coding #DataScience #SoftwareEngineering #BigData #RLHF #MITLicense

🚨 Garbage in, Garbage out - even for LLMs/RAG In any LLM-based application, feeding the right data is the key to getting accurate output. If your document parsing is wrong, everything that follows chunking, embeddings, retrieval, generation will also go wrong. For example: If you parse a two-column PDF, most default parsers read left → right & top → bottom That means your content gets mixed up and the LLM will learn or retrieve incorrect context. ✅ Best ways to cross-verify parsed data: 1️⃣ Manual review of a few samples 2️⃣ Compare text count between original & parsed document 3️⃣ Check layout preservation (columns, tables, images) 4️⃣ Validate semantic consistency does the meaning still hold? The first step (parsing) decides the success of the entire pipeline. Get it wrong, and you’ll only amplify garbage. Get it right, and everything downstream performs better. #LLM #RAG #AIEngineering #DataQuality #Parsing #NLP #GenerativeAI #AI #Accuracy

𝐃𝐚𝐲 𝟏: 𝐁𝐚𝐬𝐢𝐜𝐬 𝐨𝐟 𝐀𝐈 🤖 𝐋𝐋𝐌𝐬 = Next-Token Predictors Computers think in 0s/1s; we speak language. LLMs are the translator between human words and machine logic, enabling natural, human-like replies. 𝐖𝐡𝐚𝐭 𝐢𝐬 𝐆𝐏𝐓? GPT = Generative • Pre-Trained • Transformer 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐯𝐞: Creates new text. 𝐏𝐫𝐞-𝐓𝐫𝐚𝐢𝐧𝐞𝐝: Learned patterns from huge datasets. 𝐓𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐞𝐫: Predicts the next token using self-attention. ChatGPT (OpenAI) and Gemini (Google) are products built on this family. 𝐇𝐨𝐰 𝐲𝐨𝐮𝐫 𝐦𝐞𝐬𝐬𝐚𝐠𝐞 𝐛𝐞𝐜𝐨𝐦𝐞𝐬 𝐚 𝐫𝐞𝐩𝐥𝐲: 👉 𝟏) 𝐓𝐨𝐤𝐞𝐧𝐢𝐳𝐚𝐭𝐢𝐨𝐧 Text → tiny pieces called tokens (could be words, sub-words, or even characters). You’re billed in input tokens (prompt) and output tokens (response). 👉 𝟐) 𝐕𝐨𝐜𝐚𝐛𝐮𝐥𝐚𝐫𝐲 & 𝐈𝐃𝐬 Each token maps to a number (its token ID). The model reads numbers, not letters. 👉 𝟑) 𝐕𝐞𝐜𝐭𝐨𝐫 𝐄𝐦𝐛𝐞𝐝𝐝𝐢𝐧𝐠𝐬 (𝐦𝐞𝐚𝐧𝐢𝐧𝐠) Token IDs → vectors that capture semantic meaning. That’s how the model knows bank ≠ bank (river vs. finance), and king − man + woman ≈ queen. 👉 𝟒) 𝐏𝐨𝐬𝐢𝐭𝐢𝐨𝐧𝐚𝐥 𝐄𝐧𝐜𝐨𝐝𝐢𝐧𝐠 Order matters: “dog chases cat” ≠ “cat chases dog”. 👉 𝟓) 𝐒𝐞𝐥𝐟-𝐀𝐭𝐭𝐞𝐧𝐭𝐢𝐨𝐧 (𝐦𝐮𝐥𝐭𝐢-𝐡𝐞𝐚𝐝) Every word “looks at” other words to decide what’s important. Multiple heads = multiple lenses (who/what/where/when/grammar) all at once. 𝐓𝐰𝐨 𝐩𝐡𝐚𝐬𝐞𝐬: 👉 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐏𝐡𝐚𝐬𝐞: predict the next word → compare to label ( i.e to Desired Output) → compute loss → via backpropagation. Repeat millions/billions of times until predictions are good. 👉𝐈𝐧𝐟𝐞𝐫𝐞𝐧𝐜𝐞 𝐏𝐡𝐚𝐬𝐞 : use learned weights to answer the query (no learning) 𝐖𝐡𝐲 𝐧𝐨𝐰? Faster/cheaper compute, better Transformers, massive data, easy APIs → natural two-way interfaces. 𝐌𝐢𝐧𝐢 𝐞𝐱𝐚𝐦𝐩𝐥𝐞 You: “Hi, my name is Ankur.” Model: “Hey Ankur! How can I help today?” That’s next-token prediction in action. “This is what an LLM does.” 😊 #AI #GenerativeAI #LLM #NLP #Transformers #Embeddings #MLOps #PromptEngineering #RAG #OpenAI #Gemini #AzureAI

"𝑾𝒉𝒚 𝒅𝒊𝒅 𝒕𝒉𝒊𝒔 𝒗𝒊𝒅𝒆𝒐 𝒈𝒆𝒕 𝒓𝒂𝒏𝒌𝒆𝒅 𝒇𝒊𝒓𝒔𝒕?" is a question most retrieval systems can’t really answer. I am super excited to share our latest work "𝐗-𝐂𝐨𝐓: 𝐄𝐱𝐩𝐥𝐚𝐢𝐧𝐚𝐛𝐥𝐞 𝐓𝐞𝐱𝐭-𝐭𝐨-𝐕𝐢𝐝𝐞𝐨 𝐑𝐞𝐭𝐫𝐢𝐞𝐯𝐚𝐥 𝐯𝐢𝐚 𝐋𝐋𝐌-𝐛𝐚𝐬𝐞𝐝 𝐂𝐡𝐚𝐢𝐧-𝐨𝐟-𝐓𝐡𝐨𝐮𝐠𝐡𝐭 𝐑𝐞𝐚𝐬𝐨𝐧𝐢𝐧𝐠" published in 𝐄𝐌𝐍𝐋𝐏 𝟮𝟬𝟮𝟱 (Main) Conference. Instead of relying only on cosine similarity scores from embedding models, 𝐗-𝐂𝐨𝐓 𝐚𝐬𝐤𝐬 𝐚𝐧 𝐋𝐋𝐌 𝐭𝐨 𝒕𝒉𝒊𝒏𝒌 𝒕𝒉𝒓𝒐𝒖𝒈𝒉 𝐭𝐡𝐞 𝐫𝐚𝐧𝐤𝐢𝐧𝐠, 𝐫𝐞𝐫𝐚𝐧𝐤 𝐚𝐧𝐝 𝐞𝐱𝐩𝐥𝐚𝐢𝐧 𝘸𝘩𝘺 one video should be preferred over another. The goal is not just higher retrieval metrics, but rankings that come with human-readable reasons. What X-CoT does: - 𝐔𝐬𝐞𝐬 𝐋𝐋𝐌-𝐛𝐚𝐬𝐞𝐝 𝐩𝐚𝐢𝐫𝐰𝐢𝐬𝐞 reasoning to build a full video ranking. - Produces 𝐡𝐮𝐦𝐚𝐧-𝐫𝐞𝐚𝐝𝐚𝐛𝐥𝐞 𝐫𝐚𝐭𝐢𝐨𝐧𝐚𝐥𝐞𝐬 for each comparison, so you can see 𝘸𝘩𝘺 a candidate is above or below another. - Uses the explanations to 𝐬𝐩𝐨𝐭 𝐛𝐚𝐝 𝐨𝐫 𝐛𝐢𝐚𝐬𝐞𝐝 𝐭𝐞𝐱𝐭-𝐯𝐢𝐝𝐞𝐨 𝐩𝐚𝐢𝐫𝐬 and analyze model behavior, not just metrics. Data contributions: - We 𝐞𝐱𝐩𝐚𝐧𝐝 𝐞𝐱𝐢𝐬𝐭𝐢𝐧𝐠 𝐭𝐞𝐱𝐭-𝐭𝐨-𝐯𝐢𝐝𝐞𝐨 𝐛𝐞𝐧𝐜𝐡𝐦𝐚𝐫𝐤𝐬 with extra video annotations that improve semantic coverage. - The dataset is publicly released on HuggingFace to support future work on 𝐞𝐱𝐩𝐥𝐚𝐢𝐧𝐚𝐛𝐥𝐞 𝐯𝐢𝐝𝐞𝐨 𝐫𝐞𝐭𝐫𝐢𝐞𝐯𝐚𝐥 𝐚𝐧𝐝 𝐋𝐋𝐌 𝐫𝐞𝐚𝐬𝐨𝐧𝐢𝐧𝐠. Links and resources: - Paper: https://lnkd.in/ge8XNmgW - Code: https://lnkd.in/gfpfptYe - Project Page: https://lnkd.in/gZzaNGzu - HuggingFace Dataset: https://lnkd.in/gm7i98v7 Grateful to work with an amazing team: Jiamian (Aloes) Wang, Dr. Majid Rabbani, Dr. Sohail Dianat, Dr. Raghuveer Rao, and Dr. Zhiqiang Tao. If you are working on multimodal retrieval, LLM reasoning, or explainable AI, I would love to hear your feedback and thoughts. And if you find X-CoT useful, please try it out, share it, and consider citing it! #EMNLP2025 #ExplainableAI #LLM #ChainOfThought #Multimodal #VideoRetrieval #NLP

Exploring Scikit-LLM - a powerful bridge between classical machine learning and modern large language models. As someone who works with NLP pipelines, I’m genuinely impressed by how Scikit-LLM brings the Scikit-learn API to the world of LLMs. It allows you to integrate models like GPT, Gemini, or Hugging Face endpoints directly into your ML workflows - no need to redesign your stack. The library supports a range of advanced techniques: - Zero-shot, few-shot, and dynamic few-shot classification - leveraging prompt-based learning with minimal labeled data. - Chain-of-thought prompting - adding interpretability and reasoning transparency. - Text-to-text pipelines - for summarization, translation, and other generative tasks. - Text tagging / NER - with automatic parsing and visualization. - Multi-backend support - OpenAI, Vertex AI, Hugging Face, or custom API endpoints. All of this while maintaining Scikit-learn’s familiar fit() / predict() interface - making it easy to prototype, benchmark, and deploy LLM-powered components alongside traditional ML models. It’s open source and actively developed - definitely worth exploring if you’re interested in production-grade LLM integration or hybrid ML+LLM architectures. GitHub: https://lnkd.in/eHYyEVgR #NLP #MachineLearning #LLM #ScikitLearn #OpenSource #AI #DataScience #PromptEngineering

Why DeepSeek-OCR is a big deal (and why people are excited about it) Most LLMs do not handle very long inputs well. Their context window is fixed, and attention gets expensive as tokens grow. This is why models struggle with long PDFs, logs, docs, or multi-page text. DeepSeek-OCR is a new open-source system from DeepSeek-AI (the same team behind DeepSeek-V3 and DeepSeek-Coder). It focuses on taking huge text inputs and turning them into something an LLM can actually process efficiently. Instead of feeding thousands of text tokens into the model, DeepSeek-OCR render the text as an image, compress that image into a small set of “visual tokens”, and only then send those tokens to the language model. This leads to: ✅ fewer tokens ✅ lower compute cost ✅ much larger effective context The system has two parts: #Encoder - turns pages of text into a tiny number of vision tokens (massive compression). #Decoder - reads those tokens and outputs text, like a standard LLM. Why is this cool? It shows we can handle massive documents by compressing them visually instead of trying to expand context windows forever. Use-cases: ✨ Large PDFs ✨ OCR ✨ Tables, charts, layouts ✨ Long-context document understanding ✨ Potentially: LLM memory via visual compression The full paper is available here: https://lnkd.in/dTQ_V7TV The DeepSeek-OCR github repo: https://lnkd.in/dVaBK7Wj #DeepSeek #DeepSeekOCR #LLM #AI #MachineLearning #OCR #LongContext #AIResearch #VisionLLM #DeepLearning #NLP

💡 Mastering LLM Sampling: Controlling Randomness for Better AI Outputs Every response a Large Language Model (LLM) generates is the result of a weighted random choice. Behind every token, there’s a probability distribution – sometimes confident (a sharp spike for one token) and sometimes uncertain (a flat curve where many tokens compete). Tuning this randomness is the key to controlling your LLM’s behavior. Here’s how: 🔹 Greedy Decoding – Always pick the highest probability token. • Deterministic & predictable (great for coding or debugging) • Can lead to repetitive or “stilted” text 🔹 Temperature – Your creativity dial. • 0 → Greedy & precise • ~1 → Balanced randomness • >1 → More exploratory & creative 🔹 Top-k & Top-p Sampling – Limit token choices smartly. • Top-k: Pick from the top k tokens every step • Top-p (nucleus sampling): Dynamically pick tokens until cumulative probability hits p% • Keeps responses varied but sensible 🔹 Repetition Penalty & Logit Biasing • Reduce repeated words for natural flow • Boost or suppress specific tokens to guide outputs 💡 Pro tip: Use low temperature + low top-p for factual or code tasks Use higher temperature + top-p for creative writing or brainstorming Layer repetition penalties if outputs loop or sound robotic #AI #MachineLearning #LLM #RAG #PromptEngineering #contextengineering #GenerativeAI #ArtificialIntelligence #SamplingStrategies #OpenAI #NLP #DataScience

Ever heard of **Retrieval-Augmented Generation (RAG)**? If you haven’t, it’s high time to get familiar — especially if you’re curious about where AI language models are headed next. Here’s the gist: traditional large language models (LLMs) like GPT-4 generate text solely based on patterns in their pre-trained parameters. They’re incredibly powerful but sometimes prone to “hallucinating” facts or giving outdated info. That’s where RAG shines. **What is RAG?** It’s a clever combo of two worlds: retrieval and generation. Instead of only relying on the model’s internal knowledge, RAG searches through an external knowledge base or document store in real-time to fetch relevant info. Then the LLM uses that retrieved data to generate answers. Imagine it as a librarian helping an author by providing up-to-date references — so the output is not just creative but also *grounded* and accurate. **Why does this matter?** - **Up-to-date answers:** Your AI assistant can access fresh data beyond its training cutoff. - **Reduced hallucinations:** By grounding responses in reliable sources, the AI tends to “make stuff up” less. - **Custom knowledge:** You can plug in specialized corpora — company docs, medical databases, legal texts — making AI far more domain-aware. **How can developers start exploring RAG?** Open-source frameworks like Hugging Face’s `transformers` and `datasets` now have tools for RAG-style pipelines. You can combine vector search libraries (like FAISS or Pinecone) with language models to build your own knowledge-augmented assistants or chatbots tailored to your data. As AI continues to reshape software development, understanding and leveraging retrieval-augmented models is an incredibly practical skill. Not only for researchers, but for engineers building smarter, more reliable, and context-aware tools. If you’re building AI-powered apps or curious about staying ahead in the AI game, give RAG a look. It’s where smart retrieval meets creative generation — and that combo is only going to get bigger. #AI #MachineLearning #NLP #RetrievalAugmentedGeneration #TechTrends #DeveloperTools #ArtificialIntelligence #Innovation

From Upload to Usable Index in Minutes: Exploring RAG with LlamaCloud I recently experimented with Retrieval Augmented Generation (RAG) to understand how language models can answer questions using external knowledge. To get started quickly, I tried out LlamaCloud, and the setup turned out to be very straightforward. I used the UI to configure the pipeline, uploaded my structured documents, selected an embedding model, and set a chunk size. After that, LlamaCloud handled the rest: chunking, embedding, and indexing. Within five to six minutes, I had a working index that I could query and test. Now that I’ve tried the hosted setup, I want to explore the local implementation as well. Running everything locally will give me more control over the choice of embedding models, storage layer, and retrieval strategy, and also help me understand the fundamentals of RAG more deeply. Overall, a simple experiment that turned into a solid learning experience. Looking forward to the next steps. Great thanks to Siddhant Goswami, Ashhar Akhlaque for their guidance. #RAG #RetrievalAugmentedGeneration #LlamaCloud #VectorSearch #GenerativeAI #MachineLearning #AIEngineering #LLMs #NLP #LearningByBuilding #0to100xEngineers