你的 RAG 管道缺失了三分之二的画面

Most RAG pipelines excel at finding text chunks that are semantically similar to a query. However, real customer questions like 'My API calls are failing and I need to upgrade my plan' cannot be answered by similarity alone. A comprehensive approach requires semantic similarity (vector search) to understand intent, keyword precision (text search) to capture exact terms like 'API' and 'upgrade', metadata filtering (SQL conditions) to surface only relevant, high-quality articles, and relationship awareness (graph traversal) to follow the thread from 'API rate limits' to 'plan upgrade' to 'billing FAQ'. Integrating Pinecone, Elasticsearch, and Neo4j to achieve this is an infrastructure nightmare.