Semantic chunking for RAG allows us to build more concise chunks for our RAG pipelines, chatbots, and AI agents. We can pair this with various LLMs and embedding models from OpenAI, Cohere, Anthropic, etc, and libraries like LangChain or CrewAI to build potentially improved Retrieval Augmented Generation (RAG) pipelines.
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00:00 Semantic Chunking for RAG
00:45 What is Semantic Chunking
03:31 Semantic Chunking in Python
12:17 Adding Context to Chunks
13:41 Providing LLMs with More Context
18:11 Indexing our Chunks
20:27 Creating Chunks for the LLM
27:18 Querying for Chunks
#artificialintelligence #ai #nlp #chatbot #openai
Semantic Chunking for RAG
Теги
pythonmachine learningartificial intelligencenatural language processingnlpsemantic searchsimilarity searchvector similarity searchvector searchretrieval augmented generationretrieval augmented generation tutorialsemantic chunkingAI in industryAI pythonai python coderag chatbotpinecone aipinecone raglangchain ragai agentsai in pythonaijames briggsopenaigpt 4gpt 3.5rag airag agentrag langchainsemantic router
