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Chunking in AI - The Secret Sauce You're Missing
- Chunking is a way of breaking down information into manageable portions, which is crucial for building RAG models that fetch real data from external sources.
- Effective chunking is important for building kick-ass AI applications and getting spot-on answers in text-based AI
- The right chunk size is determined by considering context, natural boundaries such as sentences or paragraphs, and overlap.
- Here is an example of Semantic Chunking in Python using LangChain:
- To build a serverless knowledge base using AWS CDK and Node.js, chunking and indexing are done by retrieving documents from S3 and chunking them using a smart algorithm.
- Lambda function processes the uploaded document in OpenSearch with metadata that later can be queried to find the most relevant chunks for the application of interest.
- Advantages of using AWS services like S3, Lambda and OpenSearch include serverless scalability, pay-per-use pricing, and managed services.
- Building knowledge bases requires choosing chunk size for use case, overlap, and using natural boundaries like paragraphs.
- Effective chunking makes AI applications smarter and efficient as it reduces processing time and increases the relevancy of results.
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