You can build a production RAG pipelines with 18 lines of YAML with CloudQuery?!?! Checkout this great post to learn how!
Build production RAG pipelines with 18 lines of YAML 🚀 RAG applications need data from various sources moved into vector stores. Manual API integration means writing boilerplate for rate limiting, pagination, and error handling instead of building AI. CloudQuery handles the entire data-to-embeddings pipeline with declarative YAML config and native pgvector support. Key benefits: • Pre-built connectors for AWS, GCP, Azure, and 100+ platforms • Sync state persistence with incremental processing and automatic schema evolution • Built-in PII removal, column obfuscation, and data cleaning for compliance • Native pgvector support: text splitting, embeddings, semantic indexing for RAG Plus, CloudQuery is open source! Install it with "pip install cloudquery". #DataEngineering #ELT #Colaboration #DataPipelines
