Semantic Chunking - 3 Methods for Better RAG

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Semantic Search Topic Vector Similarity Search Technique Vector Search Technique Retrieval Augmented Generation Technique AI Python Use Case AI Agents Vertical OpenAI Company ChatGPT Tool GPT-4o Model Semantic Router Tool Retrieval Augmented Generation Tutorial Format RAG LangChain Topic Pinecone AI Company Developers Audience
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Semantic chunking allows us to build more context-aware chunks of information. We can use this for RAG, splitting video and audio, and much more. In this video, we will use a simple RAG-focused example. We will learn about three different types of chunkers: StatisticalChunker, ConsecutiveChunker, and CumulativeChunker. At the end, we also discuss semantic chunking for video, such as for the new gpt-4o and other multi-modal use cases. 📌 Code: https://github.com/aurelio-labs/semantic-chunkers/blob/main/docs/00-chunkers-intro.ipynb ⭐️ Article: https://www.aurelio.ai/learn/semantic-chunkers-intro 🌟 Build Better Agents + RAG: https://platform.aurelio.ai (use "JBMARCH2025" coupon code for $20 free credits) 👾 Discord: https://discord.gg/c5QtDB9RAP Twitter: https://twitter.com/jamescalam LinkedIn: https://www.linkedin.com/in/jamescalam/ #ai #artificialintelligence #chatbot #nlp 00:00 3 Types of Semantic Chunking 00:42 Python Prerequisites 02:44 Statistical Semantic Chunking 04:38 Consecutive Semantic Chunking 06:45 Cumulative Semantic Chunking 08:58 Multi-modal Chunking

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