If you’ve tuned Milvus before or integrated Nomic in a similar workflow, that experience will shine here. I already have a Milvus-backed Retrieval-Augmented Generation setup running with Nomic embeddings, but the answers it returns still feel loose. The whole knowledge base is a single JSON file, and I’m convinced the root cause is the current semantic-chunk approach. I want noticeably better results—tight, high-precision responses that actually match the user query. You’ll be jumping straight into my existing repo. Your main mission is to rethink the chunking strategy, adjust any supporting preprocessing, and, if it helps, tune Milvus search parameters so the search layer stops missing the mark. I’m not tied to the current method; if overlapping, fixed-size, or hybrid chunking gets us there faster, go for it. Deliverables • Updated, well-commented code and notebooks/scripts so I can spin everything up from a clean environment. • A reproducible pipeline that consistently returns the correct answers from the JSON source when queried. If you’ve tuned Milvus before or integrated Nomic in a similar workflow, that experience will shine here. Let’s get this pipeline humming.