243 lines
14 KiB
XML
243 lines
14 KiB
XML
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<?xml version="1.0"?>
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<doc>
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<assembly>
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<name>Telerik.Documents.AI.Core</name>
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</assembly>
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<members>
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<member name="T:Telerik.Documents.AI.Core.Embedding">
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<summary>
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Represents a text embedding consisting of the original text and its associated vector representation.
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</summary>
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</member>
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<member name="M:Telerik.Documents.AI.Core.Embedding.#ctor(Telerik.Documents.AI.Core.IFragment,System.Single[])">
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<summary>
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Initializes a new instance of the Embedding class with the specified text and vector representation.
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</summary>
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<param name="fragment">The source fragment to associate with the embedding. Cannot be null.</param>
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<param name="vector">The vector representation of the text. Cannot be null.</param>
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</member>
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<member name="P:Telerik.Documents.AI.Core.Embedding.Fragment">
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<summary>
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Gets the fragment content associated with this instance.
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</summary>
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</member>
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<member name="P:Telerik.Documents.AI.Core.Embedding.Vector">
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<summary>
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Gets the vector representation as an array of single-precision floating-point values.
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</summary>
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</member>
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<member name="T:Telerik.Documents.AI.Core.IContextFragmentsManager">
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<summary>
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Provides methods for splitting text into context fragments and joining them.
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</summary>
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</member>
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<member name="M:Telerik.Documents.AI.Core.IContextFragmentsManager.SplitIntoFragments(System.String,System.Int32)">
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<summary>
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Splits the specified text into fragments suitable for processing, based on the model and encoding.
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</summary>
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<param name="text">The input text to split.</param>
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<param name="maxTokenSizeOfSingleEmbedding">
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The maximum token size for a single embedding.
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</param>
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<returns>An array of text fragments.</returns>
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</member>
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<member name="T:Telerik.Documents.AI.Core.IContextRetriever">
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<summary>
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Provides methods for retrieving relevant context from embeddings and managing text fragments.
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</summary>
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</member>
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<member name="M:Telerik.Documents.AI.Core.IContextRetriever.GetContextAsync(System.String)">
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<summary>
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Gets the text representation of the relevant embeddings for the provided question.
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</summary>
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<param name="text">The user prompt to get relevant context for.</param>
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<returns>The text representations of the relevant embeddings.</returns>
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</member>
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<member name="M:Telerik.Documents.AI.Core.IContextRetriever.SetContextAsync(System.String,System.Int32)">
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<summary>
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Asynchronously splits the specified text into fragments based on the provided embedding token size.
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</summary>
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<param name="text">The text to be divided into fragments. Cannot be null.</param>
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<param name="embeddingTokenSize">The maximum number of tokens allowed in each fragment. Must be a positive integer.</param>
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<returns>A task that represents the asynchronous operation of fragmenting the text.</returns>
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</member>
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<member name="T:Telerik.Documents.AI.Core.IEmbedder">
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<summary>
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Defines a contract for generating vector embeddings from a collection of text fragments asynchronously.
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</summary>
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<remarks>Implementations of this interface typically interact with machine learning models or external
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services to produce embeddings. The returned embeddings correspond to the input fragments in order.</remarks>
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</member>
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<member name="M:Telerik.Documents.AI.Core.IEmbedder.EmbedAsync(System.Collections.Generic.IList{Telerik.Documents.AI.Core.IFragment})">
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<summary>
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Generates embeddings for the specified collection of text fragments asynchronously.
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</summary>
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<param name="fragments">A list of text fragments for which to generate embeddings. Each fragment represents a separate input to be
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embedded. Cannot be null or contain null elements.</param>
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<returns>A task that represents the asynchronous operation. The task result contains an array of Embedding objects
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corresponding to each input fragment, in the same order as provided.</returns>
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</member>
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<member name="T:Telerik.Documents.AI.Core.IEmbeddingSettings">
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<summary>
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Represents configuration settings for generating embeddings using an AI model.
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</summary>
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</member>
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<member name="P:Telerik.Documents.AI.Core.IEmbeddingSettings.ModelMaxInputTokenLimit">
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<summary>
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Gets the maximum input token limit the model allows.
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</summary>
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</member>
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<member name="P:Telerik.Documents.AI.Core.IEmbeddingSettings.EmbeddingTokenSize">
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<summary>
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Gets the size in tokens of each embedding that will be generated.
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</summary>
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</member>
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<member name="P:Telerik.Documents.AI.Core.IEmbeddingSettings.TokenizationEncoding">
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<summary>
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Gets the tokenization encoding.
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</summary>
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</member>
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<member name="P:Telerik.Documents.AI.Core.IEmbeddingSettings.ModelId">
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<summary>
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Gets the ID of the model.
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</summary>
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</member>
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<member name="T:Telerik.Documents.AI.Core.IFragment">
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<summary>
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Defines a fragment which can be converted to text representations suitable for context or embedding scenarios.
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</summary>
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</member>
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<member name="M:Telerik.Documents.AI.Core.IFragment.ToContextText">
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<summary>
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Returns a string representation of the object's context suitable for display or logging.
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</summary>
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<returns>A string containing text suitable for adding to an LLM context.</returns>
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</member>
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<member name="M:Telerik.Documents.AI.Core.IFragment.ToEmbeddingText">
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<summary>
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Converts the current object to a plain text representation suitable for use in embedding models.
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</summary>
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<returns>A string containing the embedding-compatible text representation of the object.</returns>
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</member>
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<member name="T:Telerik.Documents.AI.Core.IFragments">
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<summary>
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Provides access to a collection of string fragments.
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</summary>
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</member>
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<member name="P:Telerik.Documents.AI.Core.IFragments.Fragments">
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<summary>
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Gets the collection of fragments associated with the current instance.
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</summary>
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</member>
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<member name="T:Telerik.Documents.AI.Core.ISimilarityCalculator">
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<summary>
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Defines methods for calculating similarity scores between a query and a set of text fragments, enabling
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retrieval of the most relevant fragments based on semantic similarity.
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</summary>
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<remarks>Implementations of this interface typically use vector embeddings to measure semantic
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similarity between the input question and provided fragments. Methods return an array of similarity scores,
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ordered by relevance, which can be used to identify the closest matches. This interface is intended for use in
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scenarios such as search, question answering, or recommendation systems where ranking by semantic similarity is
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required.</remarks>
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</member>
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<member name="M:Telerik.Documents.AI.Core.ISimilarityCalculator.CalculateScores(System.String,System.Collections.Generic.IList{Telerik.Documents.AI.Core.Embedding})">
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<param name="prompt">The input prompt to compare against the provided fragments. Cannot be null.</param>
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<param name="embeddings">A list of embeddings representing the fragments to be compared. Cannot be null or empty.</param>
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<returns>A task that represents the asynchronous operation. The task result contains an array of SimilarityScore objects,
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each representing a fragment and its similarity score. The array will be empty if no fragments are similar.</returns>
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</member>
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<member name="T:Telerik.Documents.AI.Core.ISupportJsonEmbeddings">
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<summary>
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Defines behavior for settings working with embedding of json.
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</summary>
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</member>
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<member name="P:Telerik.Documents.AI.Core.ISupportJsonEmbeddings.ProduceJsonFormattedContext">
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<summary>
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Determines whether the context sent to the model is in JSON or plain text format, if json format is applicable.
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</summary>
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<remarks>Plain text might consume more tokens but should lead to better results when asking the LLM.</remarks>
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</member>
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<member name="P:Telerik.Documents.AI.Core.ISupportJsonEmbeddings.TotalContextTokenLimit">
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<summary>
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Gets the maximum number of tokens allowed in the context to be sent to the model.
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</summary>
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</member>
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<member name="T:Telerik.Documents.AI.Core.ISupportPlainTextEmbeddings">
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<summary>
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Defines behavior for settings working with embedding of plain text.
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</summary>
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</member>
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<member name="P:Telerik.Documents.AI.Core.ISupportPlainTextEmbeddings.MaxNumberOfEmbeddingsIncludedInContext">
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<summary>
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Gets or sets the maximum number of embeddings to be sent to the model.
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</summary>
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</member>
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<member name="T:Telerik.Documents.AI.Core.ITokensCounter">
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<summary>
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Defines a contract for counting tokens in a given input.
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</summary>
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<remarks>Implementations of this interface are responsible for analyzing input data and determining
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the number of tokens it contains. The definition of a "token" may vary depending on the specific implementation
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(e.g., words, characters, or other units of text).</remarks>
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</member>
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<member name="M:Telerik.Documents.AI.Core.ITokensCounter.Count(System.String)">
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<summary>
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Counts the number of tokens in the specified input.
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</summary>
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<param name="input">The input data to analyze.</param>
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<returns>The number of tokens in the input.</returns>
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</member>
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<member name="T:Telerik.Documents.AI.Core.SimilarityScore`1">
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<summary>
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Represents a score indicating the similarity of an item to a reference point.
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</summary>
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<remarks>This structure is used to encapsulate the similarity score of an item, along with the item
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itself. It is particularly useful in scenarios where items need to be ranked or sorted based on their similarity
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to a reference point. The <see cref="F:Telerik.Documents.AI.Core.SimilarityScore`1.Comparer"/> ensures that items with the same similarity score are
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distinguished by a unique index, which is useful for operations that require distinct results.</remarks>
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<typeparam name="T">The type of the item being compared.</typeparam>
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<param name="similarity">The similarity score indicating how closely the item matches the comparison criteria. Higher values represent
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greater similarity.</param>
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<param name="item">The item of type T that is associated with the similarity score.</param>
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</member>
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<member name="M:Telerik.Documents.AI.Core.SimilarityScore`1.#ctor(System.Single,`0)">
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<summary>
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Represents a score indicating the similarity of an item to a reference point.
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</summary>
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<remarks>This structure is used to encapsulate the similarity score of an item, along with the item
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itself. It is particularly useful in scenarios where items need to be ranked or sorted based on their similarity
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to a reference point. The <see cref="F:Telerik.Documents.AI.Core.SimilarityScore`1.Comparer"/> ensures that items with the same similarity score are
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distinguished by a unique index, which is useful for operations that require distinct results.</remarks>
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<typeparam name="T">The type of the item being compared.</typeparam>
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<param name="similarity">The similarity score indicating how closely the item matches the comparison criteria. Higher values represent
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greater similarity.</param>
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<param name="item">The item of type T that is associated with the similarity score.</param>
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</member>
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<member name="P:Telerik.Documents.AI.Core.SimilarityScore`1.Similarity">
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<summary>
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Gets the similarity score between two compared entities.
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</summary>
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</member>
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<member name="P:Telerik.Documents.AI.Core.SimilarityScore`1.Item">
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<summary>
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Gets the item of type <typeparamref name="T"/>.
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</summary>
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</member>
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<member name="F:Telerik.Documents.AI.Core.SimilarityScore`1.Comparer">
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<summary>
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Gets a comparer that orders <see cref="T:Telerik.Documents.AI.Core.SimilarityScore`1"/> instances by descending similarity, using the
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unique index as a tiebreaker.
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</summary>
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<remarks>This comparer is useful for sorting collections of <see cref="T:Telerik.Documents.AI.Core.SimilarityScore`1"/>
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so that items with higher similarity values appear first. If two items have the same similarity, the one
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with the lower unique index is considered less than the other.</remarks>
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</member>
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<member name="M:Telerik.Documents.AI.Core.SimilarityScore`1.ToString">
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<summary>
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Returns a string that represents the current object, including its similarity value and item.
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</summary>
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<returns>A string containing the similarity value and item of this instance, formatted for readability.</returns>
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</member>
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</members>
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</doc>
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