/
TermVectorsRequest.cs
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/
TermVectorsRequest.cs
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/* SPDX-License-Identifier: Apache-2.0
*
* The OpenSearch Contributors require contributions made to
* this file be licensed under the Apache-2.0 license or a
* compatible open source license.
*/
/*
* Modifications Copyright OpenSearch Contributors. See
* GitHub history for details.
*
* Licensed to Elasticsearch B.V. under one or more contributor
* license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright
* ownership. Elasticsearch B.V. licenses this file to you under
* the Apache License, Version 2.0 (the "License"); you may
* not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing,
* software distributed under the License is distributed on an
* "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
* KIND, either express or implied. See the License for the
* specific language governing permissions and limitations
* under the License.
*/
using System;
using System.Runtime.Serialization;
using OpenSearch.Net;
using OpenSearch.Net.Utf8Json;
namespace OpenSearch.Client
{
[MapsApi("termvectors.json")]
public partial interface ITermVectorsRequest<TDocument>
where TDocument : class
{
/// <summary>
/// An optional document to get term vectors for instead of using an already indexed document
/// </summary>
[DataMember(Name = "doc")]
[JsonFormatter(typeof(SourceFormatter<>))]
TDocument Document { get; set; }
/// <summary>
/// Filter the terms returned based on their TF-IDF scores.
/// This can be useful in order find out a good characteristic vector of a document.
/// </summary>
[DataMember(Name = "filter")]
ITermVectorFilter Filter { get; set; }
/// <summary>
/// Provide a different analyzer than the one at the field.
/// This is useful in order to generate term vectors in any fashion, especially when using artificial documents.
/// </summary>
[DataMember(Name = "per_field_analyzer")]
IPerFieldAnalyzer PerFieldAnalyzer { get; set; }
}
public partial class TermVectorsRequest<TDocument>
where TDocument : class
{
/// <inheritdoc cref="ITermVectorsRequest{TDocument}.Document"/>
public TDocument Document { get; set; }
/// <summary>
/// Filter the terms returned based on their TF-IDF scores.
/// This can be useful in order find out a good characteristic vector of a document.
/// </summary>
public ITermVectorFilter Filter { get; set; }
/// <summary>
/// Provide a different analyzer than the one at the field.
/// This is useful in order to generate term vectors in any fashion, especially when using artificial documents.
/// </summary>
public IPerFieldAnalyzer PerFieldAnalyzer { get; set; }
HttpMethod IRequest.HttpMethod => Document != null || Filter != null ? HttpMethod.POST : HttpMethod.GET;
partial void DocumentFromPath(TDocument document)
{
Document = document;
if (Document != null) Self.RouteValues.Remove("id");
}
}
public partial class TermVectorsDescriptor<TDocument> where TDocument : class
{
TDocument ITermVectorsRequest<TDocument>.Document { get; set; }
ITermVectorFilter ITermVectorsRequest<TDocument>.Filter { get; set; }
HttpMethod IRequest.HttpMethod => Self.Document != null || Self.Filter != null ? HttpMethod.POST : HttpMethod.GET;
IPerFieldAnalyzer ITermVectorsRequest<TDocument>.PerFieldAnalyzer { get; set; }
/// <summary>
/// An optional document to get term vectors for instead of using an already indexed document
/// </summary>
public TermVectorsDescriptor<TDocument> Document(TDocument document) => Assign(document, (a, v) => a.Document = v);
/// <summary>
/// Provide a different analyzer than the one at the field.
/// This is useful in order to generate term vectors in any fashion, especially when using artificial documents.
/// </summary>
public TermVectorsDescriptor<TDocument> PerFieldAnalyzer(
Func<PerFieldAnalyzerDescriptor<TDocument>, IPromise<IPerFieldAnalyzer>> analyzerSelector
) =>
Assign(analyzerSelector, (a, v) => a.PerFieldAnalyzer = v?.Invoke(new PerFieldAnalyzerDescriptor<TDocument>())?.Value);
/// <summary>
/// Filter the terms returned based on their TF-IDF scores.
/// This can be useful in order find out a good characteristic vector of a document.
/// </summary>
public TermVectorsDescriptor<TDocument> Filter(Func<TermVectorFilterDescriptor, ITermVectorFilter> filterSelector) =>
Assign(filterSelector, (a, v) => a.Filter = v?.Invoke(new TermVectorFilterDescriptor()));
}
}