""" # 使用 LLM 判斷該片段是否相關 response = self.llm(prompt).strip().lower() if response == "yes": relevant_results.append(doc) return relevant_results # 修改 askAndFindFiles 方法,加入篩選邏輯 def askAndFindFiles(self, question, method, metadata_filters=None): db = self.embeddingAndVe...
LLM context length LLM上下文长度 LLMs have finite context windows. Chunk size directly affects how much context can be fed into the LLM. Due to context length limitations, large chunks force the user to keep the top k in retrieval as low as possible. LLMs具有有限的上下文窗口。块大小直接影响...
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...
我们使用 LLM、文档拆分、嵌入模型和 Pinecone 索引名称从TestsetGenerator 类初始化一个对象。from langchain.embeddings import VertexAIEmbeddingsfrom langchain.llms import VertexAIfrom testset_generator import TestsetGeneratorgenerator_llm = VertexAI( location="europe-west3", max_output_tokens=256, ...
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。
One core principle of RAG is theseamless integration of retrieval and generation, which hinges on the quality of the retrieval mechanism. Effective retrieval depends on vector embeddings that accurately represent semantic meaning. For instance, dense retrieval models like DPR (Dense Passage Retrieval) ...
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。
They tend to perform better when the meaning of the text is more important than the exact wording since the embeddings capture semantic similarities. Sparse Retrievers: These rely on term-matching techniques like TF-IDF or BM25. They excel at finding documents with exact keyword matches which can...
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...
前文提到,GraphRAG与 RAPTOR 类似,需要预先对文档进行处理,进行分层聚类和总结,在 Query 时使用构建出的数据放入 LLM 上下文进行推理。GraphRAG可以分为 Indexing 和 Query 两个部分。 2.1 Indexing 2.1.1 基本流程 类似于基于倒排算法的搜索引擎,搜索引擎需要对所有爬取到的文档进行切词、构建倒排索引,以便后续的关...