Sources & Further Reading
iSources checked: September 2026
Sources reviewed in September 2026: links checked for availability and extended with references for the current state. As the AI landscape evolves quickly, some details may have changed since then.
Sources
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Lewis, P. et al. (2020). Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks. arXiv:2005.11401. https://arxiv.org/abs/2005.11401
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Edge, D. et al. (2024). From Local to Global: A Graph RAG Approach to Query-Focused Summarization. arXiv:2404.16130. https://arxiv.org/abs/2404.16130
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Pinecone (2025). Pinecone Documentation – Vector Database. https://docs.pinecone.io/
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Weaviate (2025). Weaviate Documentation. https://docs.weaviate.io/weaviate
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Qdrant (2025). Qdrant Documentation. https://qdrant.tech/documentation/
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Muennighoff, N. et al. (2023). MTEB: Massive Text Embedding Benchmark. arXiv:2210.07316. https://arxiv.org/abs/2210.07316
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Pinecone, Weaviate, Qdrant (2026). Official pricing pages. Reference for the cost table in the Vector Databases section. https://www.pinecone.io/pricing/ · https://weaviate.io/pricing · https://qdrant.tech/pricing/
Further Reading
RAG Frameworks
- LlamaIndex Documentation: Comprehensive framework for RAG pipelines and data connectors. https://developers.llamaindex.ai/python/framework/
- LangChain – RAG Tutorial: Step-by-step guide to building RAG systems. https://docs.langchain.com/oss/python/deepagents/rag
Vector Databases & Embeddings
- Hugging Face – MTEB Leaderboard: Up-to-date ranking of embedding models across various tasks. https://huggingface.co/spaces/mteb/leaderboard
- Microsoft – GraphRAG (GitHub): Reference implementation of graph-based RAG. https://github.com/microsoft/graphrag
Deep Dives
- Anthropic – Contextual Retrieval: Techniques for improving chunk quality through contextual enrichment. https://www.anthropic.com/news/contextual-retrieval