Unverified50% confidenceTradeoffExact time
相比RAG提交prompt有硬上限，图谱工具调用允许模型按需调用任意次数以挖掘所需信息
1
Sources
50%
Confidence
Long-term
Relevance
7/17/2026
First Seen
Sources
Related Claims
Unverified在RAG流程返回大量候选文档片段时，可先按相关性排序再截取前3-5项传入大模型以控制输入长度74% similarUnverified上下文窗口限制导致模型无法一次性处理几十万行代码，需通过RAG或语义搜索动态注入相关代码片段73% similarUnverifiedTraditional RAG relies on cosine similarity or dot product operations to match text embedding vectors, has limited capability in handling multi-hop questions, and is prone to causing large models to generate hallucinated content.70% similarUnverifiedRAG (Retrieval-Augmented Generation) solutions based on vector databases are better suited for handling document-type knowledge, but often fall short in retrieval precision and real-time performance for highly structured engineering facts.69% similar
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