> For the complete documentation index, see [llms.txt](https://docs.algenta.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.algenta.ai/data-and-retrieval.md).

# Data & retrieval

- [Sparse systems & graph analytics](https://docs.algenta.ai/data-and-retrieval/sparse-and-graph.md): Solve sparse linear systems and analyse graphs with compiled kernels on your own machine.
- [Retrieval & ranking](https://docs.algenta.ai/data-and-retrieval/retrieval.md): Search, fuse, rerank and explain results with compiled kernels on your own machine — and find out when a backend is missing instead of getting an empty list.
- [Retrieval fusion and signal kernels](https://docs.algenta.ai/data-and-retrieval/retrieval-fusion-and-signal-kernels.md): Reciprocal-rank fusion, hybrid blending, MMR reranking and rank-quality ratios over N ranked lists, plus the moving-average, EMA, IIR and spectral-adjacent filters that operate on one numeric series.
- [Retrieval, recommendation and ranking kernels](https://docs.algenta.ai/data-and-retrieval/retrieval-recommendation-and-ranking-kernels.md): RAG-specific fusion and quality scores, user-user collaborative filtering, ground-truth ranking metrics, vector-embedding arithmetic, and credit-style scoring formulas — five independent scalar librar
- [Data quality & feature hygiene](https://docs.algenta.ai/data-and-retrieval/data-quality.md): Expectations that name the failing row and column, a profile that keeps null, NaN and zero apart, duplicates under a key and an equality you declare
- [Geospatial indexing & joins](https://docs.algenta.ai/data-and-retrieval/geospatial.md): A spatial index with a measured selectivity, a three-valued point-in-polygon with no epsilon, coordinate frames that refuse a swapped latitude, and joins
- [Streaming sketches](https://docs.algenta.ai/data-and-retrieval/streaming-sketches.md): Distinct counts, frequencies and quantiles from summaries that fit in constant memory, merge across workers, survive a restart, and report absence rather than zero when there is no answer.
- [Windowed aggregates](https://docs.algenta.ai/data-and-retrieval/streaming-windows.md): Rolling minimum, maximum, range, mean and standard deviation over a sliding window, plus exponentially weighted mean and variance — with the real cost of each one charged before it runs.
- [Columnar queries](https://docs.algenta.ai/data-and-retrieval/columnar-queries.md): Filter, group, sort and rank tabular data with compiled kernels on your own machine.
- [Spreadsheet formula plane](https://docs.algenta.ai/data-and-retrieval/sheet-formula-plane.md): Recalculate real spreadsheet formulas — a tokenizer, dependency graph, cycle detection and 48 census-ranked functions, run as compiled kernels inside the runtime that ships with the SDK.
- [Text processing kernels](https://docs.algenta.ai/data-and-retrieval/text-processing-kernels.md): Fuzzy string distance, a hand-rolled regex engine, generic string utilities, word/sentence tokenization and a lexical column-name scorer, all zero-Python-import Mojo.
- [Text processing and tokenization kernels](https://docs.algenta.ai/data-and-retrieval/text-processing-and-tokenization-kernels.md): Corpus-linguistics readability and diversity statistics, a small from-scratch IR toolkit (TF-IDF, cosine, Jaccard), lexicon-based sentiment scoring, and the arithmetic primitives behind BPE/SentencePi
- [Text similarity and pattern matching kernels](https://docs.algenta.ai/data-and-retrieval/text-similarity-and-pattern-matching-kernels.md): A second, broader string-distance library, a minimal backtracking regex engine distinct from the corpus's other one, and vector-similarity formulas that operate on pre-reduced scalars rather than raw
- [Vector ranking kernels](https://docs.algenta.ai/data-and-retrieval/vector-ranking-kernels.md): SIMD Float32 dot-product and cosine-similarity primitives, a resident candidate-vector table, and the JSON-in/JSON-out column-name and repository-file rankers built on top of them.
- [Graph-based reasoning](https://docs.algenta.ai/data-and-retrieval/graph-based-reasoning-kernels.md): Scalar formulas for compiling an execution graph, scoring decisions walked over a graph under real constraints, and blending ML, simulation and symbolic rules into one reasoning step.


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