This paper introduces **CogRAG**, a new RAG pipeline, for conversational mental health support. Different from conventional RAG that is usually optimized for semantic relevance, CogRAG integrates cognitive factors into both retrieval and response generation stages. To do that, CogRAG uses projections by Appraisal-Theory- and CBT-inspired prompts that are applied symmetrically to patient queries and therapist responses, bridging their lexical asymmetry for retrieval. To deal with crisis cases, it augments safety-planning cards from data that guide generation to follow therapies. Experimental results on two datasets show that cognitive modeling helps improve retrieval and generation performance; gains are most pronounced in counseling quality, while safety-related improvements are modest but consistent. We do not claim to implement clinical CBT or to recover ground-truth cognitive states (Code and data: https://github.com/Cog-AI-Lab/CogRAG).