metadata_filters=None): db = self.embeddingAndVectorDB() if metadata_filters: retriever = db.as_retriever(search_kwargs={"filter": metadata_filters}) # 優化1: 多重查詢 if method == "multiquery": retriever_from_llm = MultiQueryRetriever.from_llm( retriever=db.as_retriever(), llm=self.l...
常规的RAG文本切块如左下图所示,先进行文本切分,分别过Transformer处理,通过meaning pooling得到每个chunk的句向量。假设有如下这段话:“战士金是个程序员。他最近合著出版了《大模型RAG实战》一书。”如果按照句号切分这段话,并分别过transformer处理,模型是并不能感知到第二个chunk里边的“他”是指的谁的。但如果先...
之前有说到RAG中的长文本压缩，现有的上下文压缩方法主要分为基于词汇的压缩（硬提示，如LLMLingua和RECOMP）和基于嵌入的压缩(软提示，如Gist、AutoCompressor和ICAE)。前者通过选择或总结上下文中的重要词或短语来减少上下文大小，后者则通过嵌入模型将上下文转换为较少的嵌入token。但这个对于不同的场景，会有不同的...
具体地,xRAG引入了一个模式投影器W,该投影器被训练以直接将检索特征E投影到语言模型(LLM)的表示空间中。这样,输入到LLM的表示就从传统的嵌入层Emb(D⊕q)变为W(E)⊕Emb(q),大大减少了输入的长度。 2、COCOM 《Context Embeddings for Efficient Answer Generation in RAG》, https://arxiv.org/pdf/2407.09...
Dense Retrievers:These use neural network-based methods to create dense vector embeddings of the text. They tend to perform better when the meaning of the text is more important than the exact wording since the embeddings capture semantic similarities. ...
In the specific example, the Relation is bidirectional, meaning the "Dataset property" appears in the Academic_paper database and links to the Dataset table as a primary key. Conversely, the primary key "Paper" in the Academic_paper database will automatically link to the Dataset table. Now,...
RAG isn’t the only technique used to improve the accuracy of LLM-based generative AI. Another technique is semantic search, which helps the AI system narrow down the meaning of a query by seeking deep understanding of the specific words and phrases in the prompt. ...
Style (style): A commit of this type pertains to formatting, white-space, or other changes that do not affect the meaning of the code. Chore (chore): A commit of this type includes changes that do not relate to a fix or feature and do not modify source or test files. For example,...
| || | | | Additionally, tuples can be used as keys in | tuples are immutable, meaning their elements | | | |
5. The discussion on the meaning of life and the role of science in understanding it. The data presents a wide range of themes, but the top five most prevalent themes can be identified as follows: 1. Conflict and Military Activity: A significant portion of the data revolves around th...