OpenAI o3-pro发布，也许当前的RAG过时了

Group 1 - OpenAI has launched o3-pro, claiming it to be the strongest reasoning AI model with enhanced inference capabilities [1] - The pricing for o3 has been reduced by 80%, aligning it with GPT-4o levels, with input tokens now costing approximately $2 per million and output tokens $8 per million [1] - The context window size for o3-pro is 200k, allowing for input of approximately 150,000 words, which significantly benefits the memory issues in Agent architecture [3][4] Group 2 - The basic RAG (Retrieval-Augmented Generation) framework has limitations, such as fixed retrieval strategies and lack of cross-document reasoning, leading to the development of advanced RAG frameworks [9][10] - Advanced RAG enhances retrieval strategies by incorporating multiple channels and intelligent sorting, improving recall rates and precision [10][13] - GraphRAG further upgrades retrieval to relationship enhancement, allowing for multi-hop reasoning and better understanding of connections between entities [17][18] Group 3 - The introduction of reasoning-type RAG combines reasoning chains with dynamic retrieval, aimed at complex decision-making scenarios [22][23] - The system can dynamically adjust retrieval strategies based on intermediate results, enhancing the overall decision-making process [28][30] - Agentic RAG utilizes intelligent indexing to streamline the retrieval process based on symptoms and conditions, improving efficiency in medical contexts [32] Group 4 - The evolution of models has led to significant improvements in foundational capabilities and context length, with current models supporting context windows of up to 200k [33][39] - Future developments in RAG usage will focus on seamless integration of retrieval and reasoning across diverse data types, moving away from excessive detail-oriented segmentation [40][41]