RAG在NQ数据集上的评估 讨论 稀疏与密集向量索引之间的权衡 没有元数据的混合检索器 结论 原文地址 当前RAG系统的局限性 目前在RAG流程中采用的多数检索方法依赖于关键词搜索和相似性搜索,这可能会影响RAG系统的整体准确度。表1总结了目前检索器准确度的基准数据。 表1 | 当前检索器基准 尽管过去提高RAG准确度的努...
Blended RAG: Improving RAG (Retriever-Augmented Generation) Accuracy with Semantic Search and Hybrid Query-Based Retrievers BM25 索引:BM25 索引擅长利用全文本搜索功能,并通过模糊匹配技术增强搜索效果,为更复杂的查询操作奠定基础。 密集向量索引:我们构建了一个由句子transformer支持的密集向量索引。它识别来自文档...
; additionalInfo.Clear; // 从向量数据库中找到跟提问最为相近的3条信息,将其添加到对话历史中 await foreach (var hit in memory.SearchAsync(CollectionName, question, limit: 3)) { additionalInfo.AppendLine(hit.Metadata.Text); } var contextLinesToRemove = -1; if (additionalInfo.Length != 0) ...
下面我们直接使用Semantic Kernel,通过RAG来增强模型应答。 首先,在Azure OpenAI Studio中,按照上文的步骤,部署一个text-embedding-3-small的模型,同样将终结点URI和API Key记录下来,然后,在项目中添加Microsoft.SemanticKernel.Plugins.MemoryNuGet包的引用,因为我们打算先使用基于内存的文本向量数据库来运行我们的代码。
I am working on a project for a publishing house that involves implementing semantic search across an archive of approximately 50,000 articles, each averaging 15 pages in length. My understanding is that I need to use a Retrieval Augmented Generation (RAG) approach to achieve this...
and one of the leading ways to reduce it is to build systems that can retrieve relevant information and provide it to the llm to aid it in generating more factual answers. this method, called rag, is one of the most popular ... get hands-on large language models now with the o'...
Leave your feedback and questions in the comments section. References Building a Semantic Search Engine and RAG Applications with Vector Databases and Large Language Models How to Build a Semantic Search Engine With Transformers and Faiss Semantic and Vector Search with Am...
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Vector database in Azure Cosmos DB for MongoDB Related concepts Vector search overview Retrieval Augmented Generation (RAG) Tokens Vector embeddings Distance functions kNN vs ANN Semantic cache Multi-tenancy for vector search AI Applications Quickstart - build a RAG chatbot Ingest and vectorize document...
To use neural search for semantic search in OCI Search with OpenSearch, you need to: Register and deploy your choice of model to the cluster. Create an index and set up an ingestion pipeline using the deployed model. Use the ingestion pipeline to ingest documents into the index. Run semantic...