🦛 CHONK docs with Chonkie ✨ — The lightweight ingestion library for fast, efficient and robust RAG pipelines
ChatGPT PROMPTs Splitter. Tool for safely process chunks of up to 15,000 characters per request
Fully neural approach for text chunking
🦛 CHONK your texts with Chonkie ✨ Type-friendly, light-weight, fast and super-simple chunking library
🍱 semantic-chunking ⇢ semantically create chunks from large document for passing to LLM workflows
A sentence splitting (sentence boundary disambiguation) library for Go. It is rule-based and works out-of-the-box.
An agent with human in the loop that can search the web for information while bypassing bot detection for private sites.
JChunk is a lightweight and flexible library designed to provide multiple strategies for text chunking within Java applications
We compared LangChain, Fixie, and Marvin
In this we implements a Retrieval-Augmented Generation (RAG) based conversational AI agent designed for intelligent knowledge extraction from PDF documents. Leveraging LangChain and Google’s Gemini LLM
A lightweight TypeScript text splitter for RAG applications
Generative AI projetc using LangChain for similarity search. Input 3 articles urls and ask something about the topic
An exploration of text splitting and chunking in JavaScript
Sementic chunking algorithm in (mostly) Go
Kardenwort is an intelligent tools for language learners that transforms complex texts and words into simple, clear, and context-rich vocabulary lists
Free AI Prompt Splitter - Split large documents into chunks for ChatGPT, Claude, GPT-4. Supports PDF, TXT, MD files. Smart token counting & overlap control.
A text splitter/chunker to make pasting large text into ChatGPT easier.
Leveraging Langchain for a RAG (Retriever Augmented Generation) project, this implementation enables efficient querying across multiple books, enhancing data retrieval and natural language generation for context-rich answers.
A comprehensive repository to learn and implement Retrieval-Augmented Generation (RAG) from scratch using LangChain. It covers the full RAG pipeline including Document Loaders, Text Splitters, Embeddings, Vector Databases, and Retrievers with practical examples and step-by-step explanations.
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