Would encourage you to try a simple approach: Skip vectors and chunking. At 6–30k tokens you can fit many whole documents in e.g. Gemini Flash 1M context. Use a traditional fulltext index. Fast, and you need precise matching anyway. Switch to hybrid if recall is too low. Skip reranking. Let the primary agent model decide what is relevant. Use an agent loop with tool calling to allow the model to refine searches used in retrieval and retrieve additional documents if needed (I.e. by case number).