Fix RAG Retrieval Errors with CRAG, LangGraph, and Milvus

This post details the implementation of a Corrective Retrieval-Augmented Generation (CRAG) system using LangGraph's middleware pattern and Milvus as the vector database. It addresses common RAG failures such as retrieval mismatch, stale content, and memory contamination by introducing an evaluation and correction step between retrieval and generation. The post highlights Milvus's suitability for CRAG due to its multi-tenant isolation via Partition Key, native hybrid retrieval capabilities (dense, sparse, scalar), and flexible schema support with dynamic JSON fields. It provides a practical example of building the CRAG system, including environment setup, data preparation with a specific Milvus schema, and the core workflow involving retrieval, evaluation, correction, and injection of finalized context into the LLM via a dynamic system prompt. The middleware approach in LangGraph is presented as a more maintainable and debuggable alternative to state graphs for complex CRAG pipelines.