Two NEW n8n RAG Strategies (Anthropic’s Contextual Retrieval & Late Chunking)

Video Description

👉 Get all of our n8n workflows and learn how to customize them https://www.theaiautomators.com/?utm_source=youtube&utm_medium=video&utm_campaign=tutorial&utm_content=lost_context Struggling with inaccurate RAG results or annoying hallucinations? You've likely hit the "Lost Context Problem". In this video, I break down exactly what this issue is and explore two cutting-edge techniques to dramatically improve your RAG system's accuracy and reduce those frustrating hallucinations.   Traditional RAG systems often lose crucial context when chunking documents, leading to incomplete or irrelevant information retrieval. Forget basic chunking – we're diving deep into:   Late Chunking: Leveraging long-context embedding models (like Jina AI's) to embed before chunking, preserving vital context.   Contextual Retrieval: Using the power of large language models (like Gemini 1.5 Flash with context caching) to add descriptive context to each chunk before embedding.   Watch as I implement both techniques step-by-step in N8N. 🔗 Related Links & Resources: Contextual Retrieval (Anthropic): https://www.anthropic.com/news/contextual-retrieval Jina AI Embeddings: https://jina.ai/embeddings/ Jina AI Late Chunking Article: https://jina.ai/news/late-chunking-in-long-context-embedding-models/ Gemini API Context Caching: https://ai.google.dev/gemini-api/docs/caching Embedding Model Leaderboard: https://huggingface.co/spaces/mteb/leaderboard 💡 What You’ll Learn: Why standard RAG struggles with the "Lost Context Problem".   How Late Chunking works and preserves context using long-context models.   How Contextual Retrieval adds LLM-generated context to chunks.   Implementing Late Chunking in N8N with custom code and models like Jina AI.   Implementing Contextual Retrieval in N8N using LLMs and context caching (e.g., Gemini 1.5 Flash).   Comparing the pros, cons, costs, and performance of each technique.   💬 Which technique do you think is more promising for the future of RAG? Let me know in the comments! Don’t forget to like, subscribe, and hit the bell for more AI automation tutorials! 📌 Timestamps 00:00 The Lost Context Problem 03:16 Late Chunking Strategy 13:42 Contextual Retrieval Strategy