Document Chunking & Indexing Principles
Understanding how to break down documents and organize them for efficient retrieval is a foundational skill in Retrieval-Augmented Generation. When you process documents for retrieval, you must decide how to split the text into manageable pieces, called chunks. The principles of document chunking focus on three key factors: chunk size, chunk overlap, and information preservation.
Chunk size determines how much text is included in each chunk. If chunks are too large, retrieval may return irrelevant or overly broad content. If chunks are too small, important context might be lost, making it harder for the model to generate meaningful answers. Chunk overlap refers to repeating a portion of text at the boundaries of consecutive chunks. Overlap helps preserve context that might otherwise be split between chunks, ensuring that relevant information is not missed during retrieval. Information preservation is the goal of chunking: you want the chunks to retain enough context and coherence so that, when retrieved, they provide useful, self-contained information.
Document-level metadata includes attributes such as title, author, date, and source. Document-level metadata helps filter or rank documents during retrieval.
Chunk-level metadata refers to details attached to individual chunks, like their position within the document, section headings, or paragraph numbers. Chunk-level metadata enables more precise filtering and relevance scoring.
Semantic metadata describes the content or topic of the chunk, such as keywords, tags, or detected entities. Semantic metadata enhances retrieval by allowing for topic-based filtering or semantic search.
Provenance metadata provides information about the origin, trustworthiness, or version of the document or chunk. Provenance metadata supports quality control and compliance in retrieval systems.
Once your documents are chunked and enriched with metadata, they must be organized in a way that supports fast and accurate retrieval. Indexing structures are conceptual frameworks that map your chunks and their metadata into a searchable format. The most common indexing structures include inverted indexes and vector indexes.
An inverted index maps terms or tokens to the chunks in which they appear, enabling fast keyword-based search. Vector indexes, on the other hand, map chunks into a high-dimensional vector space, supporting similarity search based on embeddings. The choice of indexing structure impacts both retrieval speed and quality. Inverted indexes are efficient for exact keyword matches but may struggle with semantic similarity. Vector indexes enable semantic retrieval but may be slower or require more resources for large collections. By understanding the strengths and limitations of each structure, you can design a system that balances speed, relevance, and scalability.
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Understanding how to break down documents and organize them for efficient retrieval is a foundational skill in Retrieval-Augmented Generation. When you process documents for retrieval, you must decide how to split the text into manageable pieces, called chunks. The principles of document chunking focus on three key factors: chunk size, chunk overlap, and information preservation.
Chunk size determines how much text is included in each chunk. If chunks are too large, retrieval may return irrelevant or overly broad content. If chunks are too small, important context might be lost, making it harder for the model to generate meaningful answers. Chunk overlap refers to repeating a portion of text at the boundaries of consecutive chunks. Overlap helps preserve context that might otherwise be split between chunks, ensuring that relevant information is not missed during retrieval. Information preservation is the goal of chunking: you want the chunks to retain enough context and coherence so that, when retrieved, they provide useful, self-contained information.
Document-level metadata includes attributes such as title, author, date, and source. Document-level metadata helps filter or rank documents during retrieval.
Chunk-level metadata refers to details attached to individual chunks, like their position within the document, section headings, or paragraph numbers. Chunk-level metadata enables more precise filtering and relevance scoring.
Semantic metadata describes the content or topic of the chunk, such as keywords, tags, or detected entities. Semantic metadata enhances retrieval by allowing for topic-based filtering or semantic search.
Provenance metadata provides information about the origin, trustworthiness, or version of the document or chunk. Provenance metadata supports quality control and compliance in retrieval systems.
Once your documents are chunked and enriched with metadata, they must be organized in a way that supports fast and accurate retrieval. Indexing structures are conceptual frameworks that map your chunks and their metadata into a searchable format. The most common indexing structures include inverted indexes and vector indexes.
An inverted index maps terms or tokens to the chunks in which they appear, enabling fast keyword-based search. Vector indexes, on the other hand, map chunks into a high-dimensional vector space, supporting similarity search based on embeddings. The choice of indexing structure impacts both retrieval speed and quality. Inverted indexes are efficient for exact keyword matches but may struggle with semantic similarity. Vector indexes enable semantic retrieval but may be slower or require more resources for large collections. By understanding the strengths and limitations of each structure, you can design a system that balances speed, relevance, and scalability.
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