英文字典中文字典


英文字典中文字典51ZiDian.com



中文字典辞典   英文字典 a   b   c   d   e   f   g   h   i   j   k   l   m   n   o   p   q   r   s   t   u   v   w   x   y   z       







请输入英文单字，中文词皆可：


请选择你想看的字典辞典：
单词字典翻译
downcome查看　downcome　在百度字典中的解释百度英翻中〔查看〕
downcome查看　downcome　在Google字典中的解释Google英翻中〔查看〕
downcome查看　downcome　在Yahoo字典中的解释Yahoo英翻中〔查看〕

安装中文字典英文字典查询工具!


中文字典英文字典工具:
选择颜色:
输入中英文单字

































































英文字典中文字典相关资料:


  • Vector store database - Azure AI Search | Microsoft Learn
    Vectors are high-dimensional embeddings that represent text, images, and other content mathematically Azure AI Search stores vectors at the field level, allowing vector and nonvector content to coexist within the same search index A search index becomes a vector index when you define vector fields and a vector configuration To populate vector fields, you can push precomputed embeddings
  • Vector samples - Azure AI Search - GitHub
    Sample Description; JavaScriptVectorDemo: A single folder contains three code samples The azure-search-vector-sample js script calls just Azure OpenAI and is used to generate embeddings for fields in an index The docs-text-openai-embeddings js program is an end-to-end code sample that calls Azure OpenAI for embeddings and Azure AI Seach to create, load, and query an index that contains vectors
  • Azure AI Search as a vector database for OpenAI embeddings
    This notebook provides step by step instuctions on using Azure AI Search (f k a Azure Cognitive Search) as a vector database with OpenAI embeddings Azure AI Search is a cloud search service that gives developers infrastructure, APIs, and tools for building a rich search experience over private, heterogeneous content in web, mobile, and
  • Vector search - Azure AI Search | Microsoft Learn
    The following diagram shows the indexing and query workflows for vector search On the indexing side, Azure AI Search uses a nearest neighbors algorithm to place similar vectors close together in an index Internally, it creates vector indexes for each vector field How you get embeddings from your source content into Azure AI Search depends on
  • Create a vector index - Azure AI Search | Microsoft Learn
    In Azure AI Search, you can store vectors in a search index and send vector queries for matching based on semantic similarity A vector index is defined by an index schema that has vector fields, nonvector fields, and a vector configuration section The Create or Update Index REST API creates the vector index To index vectors in Azure AI
  • Chunk documents in vector search - Azure AI Search
    A fixed-sized chunking and embedding generation sample demonstrates both chunking and vector embedding generation using Azure OpenAI embedding models This sample uses an Azure AI Search custom skill in the Power Skills repo to wrap the chunking step See also Understand embeddings in Azure OpenAI in Azure AI Foundry Models; Learn how to
  • Integrated vectorization - Azure AI Search | Microsoft Learn
    Build a vector store where all of the fields are vector fields, and the document ID (required for a search index) is the only string field Query the vector store to retrieve document IDs, and then send the document's vector fields to another model Combine vector and text fields for hybrid search, with or without semantic ranking
  • Using the Azure AI Search Vector Store connector (Preview)
    Getting started Install semantic kernel with the azure extras, which includes the Azure AI Search SDK pip install semantic-kernel[azure] You can then create a vector store instance using the AzureAISearchStore class, this will use the environment variables AZURE_AI_SEARCH_ENDPOINT and AZURE_AI_SEARCH_API_KEY to connect to the Azure AI Search instance, those values can also be supplied directly
  • Quickstart: Vector Search in the Azure portal - Azure AI Search
    Azure AI Search and your Azure AI resource must be in the same region or configured for keyless billing connections On the Vectorize your images page, specify the kind of connection the wizard should make For image vectorization, the wizard can connect to embedding models in the Azure AI Foundry portal or Azure AI Vision Specify the
  • Configure a vectorizer - Azure AI Search | Microsoft Learn
    In Azure AI Search a vectorizer is a component that performs vectorization using a deployed embedding model on Azure OpenAI or Azure AI Vision It converts text (or images) to vectors during query execution It's defined in a search index, it applies to searchable vector fields, and it's used at query time to generate an embedding for a text or image query input
  • Tutorial: Index mixed content using multimodal embeddings and the . . .
    For more information on vector search, see Vectors in Azure AI Search For more information on semantic ranking, see Semantic ranking in Azure AI Search Create a skillset Create Skillset (REST) creates a search index on your search service An index specifies all the parameters and their attributes





中文字典-英文字典  2005-2009