Developer Builds Private Notes Assistant Using RAG, Amazon Bedrock and Pinecone

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A developer built a personal notes assistant that answers plain-English questions using only the content from privately uploaded text files. The system uses Retrieval-Augmented Generation (RAG), which adds a search step before querying an AI model, allowing it to draw answers from personal documents rather than generic training data. The tech stack includes Python, Flask, Amazon S3, Pinecone, AWS Lambda, API Gateway, and Amazon Bedrock's Nova Lite model. When a note is uploaded, it is chunked into overlapping snippets and converted into numerical vectors via Amazon Titan, then stored in Pinecone for semantic similarity search. At query time, the user's question is similarly vectorized, the top three matching note snippets are retrieved, and the AI generates a grounded answer from that context.

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