Unverified50% confidenceFactExact time
Embedding models map queries and documents into the same vector space and match them using measures such as cosine similarity; when a query uses domain abbreviations, internal codenames, or wording whose distribution differs greatly from the training corpus, the correct document may be ranked very far down or may not even appear in the candidate set.
1
Sources
50%
Confidence
Long-term
Relevance
10/12/2026
First Seen
Sources
Related Entities
Related Claims
Unverified索引阶段和查询阶段必须使用同一个Embedding模型,否则向量空间不一致会导致检索完全失效76% similarUnverified端到端模型在代表性充足与代表性不足口音之间的WER差距并未按比例缩小,属于数据分布问题而非架构问题65% similarUnverifiedIn the RAG retrieval process, the user's question is first passed to an Embedding model to be converted into a vector, then similarity calculations are performed in a vector database to retrieve the top-K most relevant documents, which are then concatenated with the original question and passed to the large model to generate an answer.64% similarUnverifiedembedding模型的版本更新可能导致语义向量空间发生偏移,使原本高排名的相关文档被不太相关的文档取代64% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1005611API
curl https://kongchang.com/api/v1/knowledge/claims/1005611MCP
get_claim(id=1005611)