> ## Documentation Index > Fetch the complete documentation index at: https://docs.ardie.ai/llms.txt > Use this file to discover all available pages before exploring further. # Resources > RAG models, pipelines, tools, and educational content to help you build better retrieval-augmented generation systems ## RAG Models & Pipelines Explore popular RAG architectures and pre-built pipelines to accelerate your development. Build RAG applications with LangChain's modular framework for document loading, splitting, embedding, and retrieval. Data framework for LLM applications with advanced indexing and query capabilities. Open-source framework for building production-ready NLP pipelines with RAG support. Easy-to-use library for state-of-the-art ColBERT-based retrieval. ## Embedding Models Choose the right embedding model for your use case. text-embedding-3-small and text-embedding-3-large for high-quality semantic search. Multilingual embeddings optimized for search and retrieval. Open-source models for generating sentence and document embeddings. Domain-specific embeddings for legal, finance, code, and more. ## Vector Databases Store and query your embeddings efficiently. Managed vector database with hybrid search. Open-source vector search engine. High-performance vector similarity search. AI-native open-source embedding database. Scalable vector database for AI applications. Vector similarity search for PostgreSQL. ## Ardie Resources Tools and guides we've created to help you succeed with RAG. Our comprehensive guide to building production-ready RAG systems. How to split documents for optimal retrieval performance. Measure and improve your RAG pipeline's accuracy. Combine keyword and semantic search for better results. *** ## Blog Posts Read our latest articles on RAG, knowledge bases, and AI-powered search. Explore all our articles on RAG architecture, implementation tips, and industry use cases. Have a resource suggestion? [Let us know](mailto:support@ardie.ai) and we'll consider adding it to this page.