RAG技术应用
检索增强生成(RAG)技术在GEO中的应用
RAG技术概述
**RAG(Retrieval-Augmented Generation)**结合检索和生成[1]，提供准确且时效的答案[2]。
核心流程
1. 检索(Retrieval)[3]
- 向量检索、语义匹配、相关文档获取
2. 增强(Augmentation)[4]
- 上下文注入、信息整合、知识补充
3. 生成(Generation)[5]
- LLM生成、引用来源、答案合成
优化策略
内容准备[6]
- 文档分块、向量化、元数据标注
检索优化[7]
- 混合检索、重排序、相关性调优
生成优化[8]
- Prompt工程、温度控制、引用格式
应用场景
企业知识库[9]
- 内部文档检索、智能问答、知识管理
客户服务[10]
- 自动回复、问题解答、信息查询
技术栈
RAG框架[11]
- LangChain、LlamaIndex、Haystack
相关资源
参考文献
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Meta AI. (2024). "RAG: Retrieval-Augmented Generation". Research Paper. https://ai.meta.com/blog/retrieval-augmented-generation-streamlining-the-creation-of-intelligent-natural-language-processing-models/
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OpenAI. (2024). "RAG Applications". Use Cases. https://platform.openai.com/docs/guides/
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Pinecone. (2024). "Vector Retrieval". RAG Guide. https://www.pinecone.io/learn/retrieval-augmented-generation/
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LangChain. (2024). "RAG Pipeline". Documentation. https://python.langchain.com/docs/use_cases/question_answering/
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Anthropic. (2024). "Context-Aware Generation". Claude Documentation. https://docs.anthropic.com/
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LlamaIndex. (2024). "Document Processing". RAG Guide. https://docs.llamaindex.ai/
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Cohere. (2024). "Reranking". RAG Optimization. https://docs.cohere.com/docs/reranking
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OpenAI. (2024). "Prompt Engineering". Best Practices. https://platform.openai.com/docs/guides/prompt-engineering
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Microsoft. (2024). "Enterprise RAG". Azure AI. https://azure.microsoft.com/en-us/products/ai-services/
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Zendesk. (2024). "AI Customer Service". Solutions. https://www.zendesk.com/service/ai/
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LangChain. (2024). "RAG Framework". Platform. https://www.langchain.com/
更新日期：2025-11
词条状态：✅ 已完成