The Thesis: Is Vector DB Dead?
Retrieval Augmented Generation (RAG) was a patch for small context windows. With Gemini 1.5 Pro and GPT-5 offering 10M+ tokens, do we still need vector databases? The answer is nuanced.
The "Lost in the Middle" Problem
Our benchmarks indicate that while models can ingest 10M tokens, their ability to reason over facts buried in the middle of the context window degrades significantly. We define Recall Accuracy Ar as:
GraphRAG: The Semantic Bridge
Standard Vector RAG fails at multi-hop reasoning. We are seeing massive gains with GraphRAG, which constructs a knowledge graph from the source documents. Instead of just retrieving similar chunks, the agent traverses the graph to find relationships between disparate entities.
The Attention Sink
Filling the context window with irrelevant documents not only costs money; it dilutes the model's attention. Attention is a finite resource.
We advocate for a Hierarchical Retrieval strategy. Layer 1: Use metadata filtering (SQL) to narrow the search space. Layer 2: Use dense vector search (k-NN) to find semantic matches. Layer 3: Use a Cross-Encoder Reranker to grade the relevance of the top 50 chunks. Layer 4: Only feed the top 5 chunks into the LLM context. This "Funnel Architecture" ensures high precision and maximizes the model's reasoning capabilities on the data that actually matters.