RAG Freshness - Incremental Vector Updates

Rag Embedding Generator

labrat011/rag-embedding-generator

Generate vector embeddings from text or chunked datasets using OpenAI or Cohere. Chains with RAG Content Chunker for end-to-end RAG pipelines. Outputs raw vectors ready for any vector database.

Crawl websites and output clean, chunked content optimized for RAG pipelines, LLM training data, and vector databases. Built for AI knowledge bases and semantic search.

RAG Web Corpus Builder

cynix_dev/rag-web-corpus-builder

Turn public web pages into clean, chunked, metadata-rich Markdown records ready for RAG ingestion and vector stores.

RAG-Ready Web Scraper & Smart Chunker for AI Knowledge Bases

adinfosys-labs/rag-ready-web-scraper-smart-chunker-for-ai-knowledge-bases

RAG-ready web scraper that collects, cleans, deduplicates, filters, and chunks web content into structured datasets for AI pipelines. Generates high-quality knowledge-base data optimized for LLMs, embeddings, and vector databases

Rag Content Chunker

labrat011/rag-content-chunker

Turn raw text, Markdown, or Apify datasets into token-perfect RAG chunks with deterministic IDs, source metadata, and a billing-ready summary—ready for embeddings or vector DBs without extra glue code.

Rag Vector Store Writer

labrat011/rag-vector-store-writer

Apify Actor that writes embedding vectors to Pinecone or Qdrant vector databases. Chains directly with RAG Embedding Generator output or accepts raw vectors with metadata. Handles batching, retries, collection creation, metadata mapping, and ID generation. Bring your own vector DB API key.

Website to RAG Dataset

sebastian-actors/website-to-rag-dataset

Convert public websites, docs, blogs, and XML sitemaps into clean Markdown, structured metadata, and stable chunks for RAG pipelines and vector databases.

Docs To Rag

gabrielaxy/docs-to-rag

Transform documentation websites into RAG-ready chunks with semantic understanding, quality scoring, and direct vector database integration.

Split text into clean, overlapping chunks ready for embeddings, vector databases, RAG and LLM context. Configurable size, overlap, and split strategy.