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[CH03.02.01] 03. Langchain으로 구현하는 Basic, Agentic RAG - Ch03.LLM 어플리케이션의 기본, RAG

실습 강의 중에 ' RAG 에이전트 테스트 - 단일 검색이 필요한 질문' 구간에서 실행하면

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❓ 질문: DeepSeek-OCR의 압축률(compression ratio)은 얼마인가요? 🤖 에이전트 응답 과정: ================================ Human Message ================================= DeepSeek-OCR의 압축률(compression ratio)은 얼마인가요?

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================================== Ai Message ================================== Tool Calls: retrieve_context (call_kvGaPiEQE87OcA4UrzIJeARW) Call ID: call_kvGaPiEQE87OcA4UrzIJeARW Args: query: DeepSeek-OCR 압축률

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================================= Tool Message ================================= Name: retrieve_context [페이지 0] DeepSeek-OCR: Contexts Optical Compression Haoran Wei, Yaofeng Sun, Yukun Li DeepSeek-AI Abstract We present DeepSeek-OCR as an initial investigation into the feasibility of compressing long contexts via optical 2D mapping. DeepSeek-OCR consists of two components: DeepEncoder and DeepSeek3B-MoE-A570M as the decoder. Specifically, DeepEncoder serves as the core engine, designed to maintain low activations under high-resolution input while achieving high compression ratios to ensure an optimal and manageable number of vision tokens. Experiments show that when the number of text tokens is within 10 times that of vision tokens (i.e., a compression ratio < 10×), the model can achieve decoding (OCR) precision of 97%. Even at a compression ratio of 20×, the OCR accuracy still remains at about 60%. This shows considerable promise for research areas such as historical long-context compression and memory forgetting --- [페이지 0] compression ratio of 20×, the OCR accuracy still remains at about 60%. This shows considerable promise for research areas such as historical long-context compression and memory forgetting mechanisms in LLMs. Beyond this, DeepSeek-OCR also demonstrates high practical value. On OmniDocBench, it surpasses GOT-OCR2.0 (256 tokens/page) using only 100 vision tokens,

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data covering about 100 languages from the Internet, with Chinese and English accounting for approximately 25M and other languages accounting for 5M. For this data, we create two types of ground truth: coarse annotations and fine annotations. Coarse annotations are extracted 7

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================================== Ai Message ================================== DeepSeek-OCR의 압축률(compression ratio)은 10배 미만(< 10×)일 때 OCR 정확도(precision)가 97%에 달하며, 압축률이 20배(20×)일 때도 OCR 정확도가 약 60% 수준을 유지합니다. 즉, DeepSeek-OCR은 10배 이하의 압축률에서 매우 높은 정확도를 보이고, 20배의 압축률에서도 상당한 수준의 성능을 나타냅니다.

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