Vector DB Embedding Batch

Vector Loader — Document Embedding & Vector DB Ingestion helps teams get quick, high-signal results with reliable output, clear fields, and fast setup.

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.

Docs-to-RAG Crawler

automation-lab/docs-rag-crawler

Crawl documentation sites (ReadTheDocs, GitBook, Docusaurus, Mintlify) into RAG-ready Markdown/JSON chunks with stable chunk IDs, heading breadcrumbs, word counts, and token estimates.

RAG-Ready Markdown Converter & Chunker

foxpink/apify-rag-markdown-chunker

Convert raw HTML/text into clean Markdown and split into ready-to-ingest chunks for RAG pipelines, Vector DBs, and LLM fine-tuning workflows.

Ai Memory Engine

ruv/ai-memory-engine

Give your AI persistent memory. Store conversations, search semantically, build knowledge graphs, and watch your AI get smarter with every interaction.

Self Learning Postgres DB

ruv/self-learning-postgres-db

Self-learning vector database with GNN-powered index optimization. Features: vector search, RAG queries, embeddings, clustering, deduplication, batch ops, and data import/export. Scales with Raft consensus.

RAG Pipeline

labrat011/rag-pipeline

One-click RAG pipeline: chunks text, generates embeddings, and stores vectors in Pinecone or Qdrant. Provide your content and API keys -- the orchestrator handles the rest.

Docs to Markdown + AI Embeddings → Vector DB Crawler

badruddeen/docs-to-markdown-ai-embeddings---vector-db-crawler

Turn any documentation site into clean Markdown, intelligently chunked content with embeddings (Azure/OpenAI), and directly upsert into MongoDB Atlas, Pinecone, Weaviate, Qdrant, or Milvus — ready for RAG, AI assistants, and semantic search in minutes.

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.

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.