... best of two worlds: Speed: Classical NLP ( spaCy ) for initial NER (10-50x faster than BERT). Reasoning: State-of-the-art LLMs ( AWS Nova Pro v1 ) to validate complex semantic relationships and refine extraction. 3/ Semantic & Adaptive chunking Forget about arbitrarily cutting text every 500 characters. Our algorithm respects natural paragraph boundaries and evaluates semantic coherence . If a fragment loses the thread, the system detects the topic shift via embedding similarity to decide exactly where ... ... Secret sauce": Context preservation This is where NeuroBlock transforms the data. We don't just store vectors; we build a Contextual Knowledge Graph in Neo4j . We create `NEXT_CHUNK

... Hello, I ve been working on a passion project I called Sapling (use.sapling.mobi) on and off since 2016. I have spent the last 12 months improving it because I kept seeing small charities lose huge chunks of their donations to complex platforms that take high fees or guilt-trip donors into "tipping" the software. It felt wrong, so I decided to build a cleaner, simpler alternative

... still a mess of spaghetti code. So we built Raptor Data to be the "Stripe for RAG Ingestion." What is Raptor Data? Raptor is a developer-first API that turns unstructured documents (PDF, DOCX) into version-controlled, AI-ready chunks. We don't just "parse" files. We treat them like code repositories. The "Git-Like" Magic (How we save you 90%) When you upload a file to Raptor Data, we use Structure-Aware Chunking

... anything radical; it was just using a tool to save time. Little did I know I was participating in a budding movement. As AI coding assistants improved, my vibing only increased. I started letting GitHub Copilot auto-complete whole chunks of code while I casually tabbed through its suggestions. I d begin writing a comment like // function to shuffle a deck of cards and before I could finish my thought, Copilot would offer to generate the entire function ... ... best practices for vibe coding sanely: Break Problems into Bite-Sized Pieces: Don t ask the AI to build your entire app in one go; that s a recipe for disappointment. Instead, split the work into small chunks

... What s New We just launched a native integration between LangChain and Tensorlake! Now you can pass unstructured documents to a LangGraph agent and trust that parsing, chunking, and field-level accuracy are handled by Tensorlake s document engine no hacky pipelines required. Why it matters Many LangChain projects break down when document structure is inconsistent, or field extraction needs to be accurate and explainable. Tensorlake s integration provides: Reliable document ingestion Schema-driven field extraction Native support ... ... pipelines Built-in SDK + Playground for fast iteration It s now super simple to use documents (like SEC filings, contracts, or invoices) as context in LLM apps with structured outputs and markdown chunks

... Text2Note now processes whole books (like The Metamorphosis ) in chunks for detailed, comprehensive notes, all in the background with no clicks needed. Shared context between chunks

... LLMs! Anyone else building cool stuff like PDF Q&A or custom bots with RAG, but finding the context retrieval step... frustrating? Most AI app data stacks these days use vector search (Pinecone, Weaviate, etc.) to grab text chunks for the LLM. But sometimes it feels like it finds stuff that's keyword-similar while totally missing the actual point the user asked for. Leads to those slightly weak or "confidently wrong" LLM answers. So, what's your single ... ... biggest headache right now in getting truly relevant context for your RAG pipeline? Is it nailing the chunking