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...
machine-learning#artificial-intelligence#llms#rag-architecture#rag#how-does-rag-work#retrieval-augmented-generation#workflow-of-a-rag-system#applications-of-rag THIS ARTICLE WAS FEATURED IN... Arweave Terminal Lite Workwonders Briefly Thetechstreetnow RELATED STORIES Boost your HackerNoon story @ $159....
LLM hallucinations LLM幻觉 While adding more context may seem better, excessive context can lead tohallucinations in LLMs. Choosing the right chunking size for the job plays a large role in determining the quality of generated content. Balancing contextual richness with retrieval precision is essential...
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...
| || | | | Additionally, tuples can be used as keys in | tuples are immutable, meaning their elements | | | |
response = retrieval_chain.invoke({"input":"What is REACT in machine learning meaning?"}) 作为响应,我们将收到一个包含三个变量的对象: input - 我们的查询; context - 我们作为上下文传递给提示的文档(块)数组; answer - 由大型语言模型 (LLM) 生成的查询的答案。
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。
Many complex operations need to be performed - such as generating embeddings, comparing the meaning between different pieces of text, and retrieving data in real-time. These tasks are computationally intensive and can slow down the system as the size of the source data increases. To address this...
The challenge remains, however, for query-focused abstractive summarization over an entire corpus. Such volumes of text can greatly exceed the limits of LLM context windows, and the expansion of such windows may not be enough given that information can be “lost in the middle” of longer contex...
Large Language Models (LLMs) are often surprisingly knowledgeable about a wide range of topics but they are limited to only the data they were trained on. This means that clients looking to use LLMs with private or proprietary business information cannot