Namespace LMKit.Retrieval

DocumentIndexingResult: Describes the outcome of loading a document into PdfChat, including how the document was processed and its resource consumption.

DocumentQueryResult: Contains the result of a document query, including the generated response and references to source passages.

DocumentRag: Provides document-centric Retrieval-Augmented Generation (RAG) capabilities with built-in support for multi-page document processing, OCR, and vision-based document understanding.

DocumentRag.DocumentMetadata: Represents metadata associated with a document during import into a DocumentRag instance.

DocumentReference: Represents a reference to a specific location within a document, retrieved during a retrieval operation.

MarkdownChunking: Provides Markdown-aware chunking configuration for retrieval workflows. The underlying splitter favors Markdown structural boundaries (such as headings and paragraph breaks) to produce chunks that preserve semantic coherence.

PartitionSimilarity: Represents the result of a retrieval operation, capturing the similarity between a partition (or vector entry) and a target item.

PdfChat: Provides conversational question-answering over PDF documents by combining intelligent document understanding with grounded response generation.

RagEngine: Provides core functionalities for Retrieval-Augmented Generation (RAG) within a data processing system.

RagEngine.RagReranker: Encapsulates a reranking model and blending factor for adjusting raw similarity scores in RAG workflows.

TextChunking: Implements a recursive chunking strategy for partitioning text into manageable segments, known as "chunks," to support retrieval-augmented generation tasks.
This approach is particularly effective for processing extensive texts, systematically breaking them down into smaller segments that are easier to handle.
Unlike linear chunking methods that sequentially divide text, this recursive strategy dynamically adjusts the segmentation process based on the complexity and structure of the text.
This allows for more nuanced and efficient handling of text data, especially when dealing with nested or hierarchical information.

VectorSearch: Provides methods for searching partitions across one or more data sources by comparing vector embeddings for similarity.

IChunking: Defines configurable settings for text chunking. Implementations control how input text is partitioned into chunks suitable for retrieval and embedding workflows.

DocumentIndexingResult.DocumentIndexingMode: Specifies how a document was processed for retrieval.

PageProcessingMode: Specifies how document pages are interpreted during document preparation.