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RAG Pipelines — Pinecone & Semantic Chunking
Retrieval-augmented generation (RAG) answers questions from your own content: relevant passages are retrieved from a vector database at question time and handed to the model, so answers are grounded in your data rather than in the model's training. I build the full pipeline — ingestion, chunking, embedding, retrieval and generation — on Pinecone.
Semantic chunking with overlap
Content is split along meaning rather than at fixed character counts, and neighbouring chunks overlap, so an idea that straddles a boundary can still be retrieved whole. Chunking decides what the model can ever see, so it gets designed for the corpus rather than left on defaults.
Pinecone as the vector store
Each chunk is embedded and indexed in Pinecone. A question is embedded the same way at query time and matched against the index by similarity, returning the passages most likely to contain the answer.
Custom retrieval and generation
Retrieval and generation are written for the content rather than taken from a stock chain: what gets retrieved, how much of it, and how it is presented to the model so the answer stays tied to the source material.
Stack
Have a system like this in mind?
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