Principle:Neuml Txtai Document Indexing
| Knowledge Sources | |
|---|---|
| Domains | Semantic_Search, NLP, Information_Retrieval |
| Last Updated | 2026-02-09 00:00 GMT |
Overview
Document indexing is the process of transforming a collection of text documents into dense vector representations and organizing them into an efficient data structure for fast similarity retrieval.
Description
The indexing phase is the computational core of any semantic search system. Given a corpus of documents, the system must perform three key operations: normalization (converting heterogeneous input formats into a uniform stream), vectorization (encoding each document through a neural embedding model to produce a fixed-length dense vector), and index construction (building an Approximate Nearest Neighbor data structure over the resulting vectors).
Document indexing is designed to handle diverse input formats transparently. Documents may arrive as simple strings, as (id, data) tuples, as (id, data, tags) triples, or as dictionaries with named fields. The normalization layer standardizes these into a consistent stream before they reach the vectorization layer. This flexibility is essential for real-world pipelines where data comes from heterogeneous sources such as databases, file systems, or APIs.
After vectorization, the resulting matrix of embedding vectors is passed to an ANN index construction algorithm. Optionally, a dimensionality reduction step (such as principal component analysis) may be applied first to remove noise and reduce storage requirements. The indexing phase also populates auxiliary structures including the document database (for content retrieval), the scoring index (for sparse keyword search), subindexes (for multi-model configurations), and graph indexes (for relationship-based queries).
Usage
Use document indexing when you have a collection of documents that you want to make searchable by semantic meaning. This is a batch operation that builds the entire index from scratch, overwriting any previously existing index. For incremental additions to an existing index, use the upsert operation instead.
Theoretical Basis
1. Document Normalization: The stream function S maps heterogeneous input formats to a canonical form:
S: (string | tuple | dict)* -> (id, text, tags)*
This ensures downstream components operate on a consistent interface regardless of how the caller provides data.
2. Neural Encoding: Each document text is passed through a pretrained transformer model M to produce an embedding vector:
v_i = M(text_i), where v_i in R^d
The model M is typically a sentence transformer fine-tuned on semantic similarity tasks, producing vectors where cosine similarity correlates with semantic relatedness.
3. Dimensionality Reduction (Optional): When PCA is enabled, a linear transformation is applied:
v_i' = W * (v_i - mu)
where W is the projection matrix retaining the top-k principal components and mu is the mean vector. This reduces the dimensionality from d to k, removing noise in directions of low variance.
4. ANN Index Construction: The set of vectors {v_1, v_2, ..., v_n} is organized into a data structure that supports approximate nearest neighbor queries:
ANN.build({v_1, ..., v_n}) such that ANN.query(q, k) returns the approximate top-k vectors by similarity to q in sublinear time.
Common algorithms include HNSW (Hierarchical Navigable Small World graphs), IVF (Inverted File indexes), and random projection trees.
5. Auxiliary Index Construction: In parallel with the dense vector index, auxiliary structures are built:
- Scoring index: For sparse term-based retrieval (e.g., BM25 or sparse vectors)
- Document database: For storing and filtering original content
- Graph index: For capturing relationships between documents