text = "Sentence chunking is a method used in natural language processing. It involves breaking down a text into individual sentences. This approach ensures that each chunk is a complete sentence, preserving the semantic integrity." # 进行句子分块 chunks = sentence_chunking(text) for i, chunk ...
Uses an ORM-style approach to document extraction, combining OCR with LLMs for performance and agnostic usability.ExtractThinker 是一个旨在将文档智能引入LLMs 的库。它基于之前的 NET 项目,迁移并实现为 Python。使用 ORM 风格的方法进行文档提取,将 OCR 与 LLMs 相结合,以提高性能和不可知的可用性。 As...
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, ...
构建自己LLM:构建自己的 LLM 可让您完全控制架构、数据和部署,从而获得量身定制的解决方案。但是,这大大增加了基础设施和培训的成本,因此对大多数组织来说并不现实。 为您的使用案例选择正确的LLM解决方案 选择利用 LLM 的最佳解决方案取决于您的具体要求、可用资源和预期结果。本节将根据性能、成本和实施复杂性探讨...
GraphRAG的主要流程:利用LLM从知识库中提取实体以及实体关系;利用LLM对实体联系进行聚类,生成社区摘要;在回答用户问题时利用LLM结合社区摘进行回答。所以基于GraphRAG的问答是提供了更丰富的上下文和关系信息,提升生成RAG的检索生成效果,整个pipline如下图所示。 paper: https://arxiv.org/pdf/2404.16130 code: https:...
If the search function can find the right results in the index (assuming the answer is somewhere in the index), then the LLM can typically do a pretty good job of synthesizing the answer from the sources. Unstructured queries This simple RAG approach works best for "unstru...
LLMs之GraphRAG:《From Local to Global: A Graph RAG Approach to Query-Focused Summarization》翻译与解读 导读:该论文提出了一种基于图结构的知识图谱增强生成(Graph RAG)方法,用于回答用户针对整个文本集合的全局性质询问,以支持人们对大量数据进行全面理解。
(LLMs) efficiently. Techniques likegradient checkpointing—which recomputes intermediate results instead of storing them—have emerged as a solution, reducing memory usage by up to 40% during inference. This approach is particularly effective in applications like real-time language translation, where ...
Figure 1 shows how RAG systems layer new information into the world knowledge LLMs already know. To see an example of RAG applied to a real-world use case, check outhow we built a safe AI assistant for a financial services firm.
Why Use RAG to Improve LLMs? An Example To better demonstrate what RAG is and how the technique works, let’s consider a scenario that many businesses today face. Imagine you are an executive for an electronics company that sells devices like smartphones and laptops. You want to create a ...