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RAG Models & Pipelines

Explore popular RAG architectures and pre-built pipelines to accelerate your development.

LangChain RAG

Build RAG applications with LangChain’s modular framework for document loading, splitting, embedding, and retrieval.

LlamaIndex

Data framework for LLM applications with advanced indexing and query capabilities.

Haystack

Open-source framework for building production-ready NLP pipelines with RAG support.

RAGatouille

Easy-to-use library for state-of-the-art ColBERT-based retrieval.

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Embedding Models

Choose the right embedding model for your use case.

OpenAI Embeddings

text-embedding-3-small and text-embedding-3-large for high-quality semantic search.

Cohere Embed

Multilingual embeddings optimized for search and retrieval.

Sentence Transformers

Open-source models for generating sentence and document embeddings.

Voyage AI

Domain-specific embeddings for legal, finance, code, and more.

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Vector Databases

Store and query your embeddings efficiently.

Pinecone

Managed vector database with hybrid search.

Weaviate

Open-source vector search engine.

Qdrant

High-performance vector similarity search.

Chroma

AI-native open-source embedding database.

Milvus

Scalable vector database for AI applications.

pgvector

Vector similarity search for PostgreSQL.

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Ardie Resources

Tools and guides we’ve created to help you succeed with RAG.

RAG Best Practices

Our comprehensive guide to building production-ready RAG systems.

Chunking Strategies

How to split documents for optimal retrieval performance.

Evaluation Metrics

Measure and improve your RAG pipeline’s accuracy.

Hybrid Search Guide

Combine keyword and semantic search for better results.

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Blog Posts

Read our latest articles on RAG, knowledge bases, and AI-powered search.

Visit the Ardie Blog

Explore all our articles on RAG architecture, implementation tips, and industry use cases.
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