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name: Rust
name: CI (main)

on:
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# Search <img alt="Rust's Crab" width="25px" src="https://rustacean.net/assets/rustacean-flat-noshadow.png"/>
![web.png](misc%2Fweb.png)

# Search-rs
An on-disk Search Engine with boolean and free text queries and spelling correction.

[![CI (main)](https://github.com/tomfran/search-rs/actions/workflows/rust.yml/badge.svg)](https://github.com/tomfran/search-rs/actions/workflows/rust.yml)

## Architecture

Here is an high level overview of the project architecture, you can
find a more detailed presentation in the following [Medium article](https://medium.com/itnext/building-a-search-engine-in-rust-c945b6e638f8).

### Inverted index

The backbone of the engine is an inverted index. The main
idea is to have, for each word appearing in the documents, a list
of document IDs.
This allows us to quickly find documents containing a given word.

More specifically, for each term we save a postings list as follows:
$$\text{n}\\;|\\;(\text{id}_i, f_i, [p_0, \dots, p_m]), \dots$$

Where $n$ is the number of documents the term appears in, id is the
doc id, $f$ is the frequency, and $p_j$ are the positions where
the term appears in the document $i$.


Delta encoding is used to represent document IDs, as they are strictly increasing, the same goes for the term positions. All those integers are written with [Gamma coding](https://en.wikipedia.org/wiki/Elias_gamma_coding).
Generic integers, such as list lengths are written in [VByte encoding](https://nlp.stanford.edu/IR-book/html/htmledition/variable-byte-codes-1.html#:~:text=Variable%20byte%20(VB)%20encoding%20uses,gap%20and%20to%200%20otherwise.).

### Vocabulary

The vocabulary is written on disk using prefix compression.
The idea is to sort terms and then write them as "matching prefix length", and suffix.

Here is an example with three words:
$$\text{watermelon}\\;\text{waterfall}\\;\text{waterfront}$$
$$0\\;\text{watermelon}\\;5\\;\text{fall}\\;6\\;\text{ront}$$

Spelling correction is used before answering queries. Given a
word $w$, we use a trigram index to find terms in the vocabulary
which shares at least a trigram with it.
We then select the one with the lowest [Levenshtein Distance](https://en.wikipedia.org/wiki/Levenshtein_distance).

$$
\text{lev}(a, b) = \begin{cases}
|a| & \text{if}\\;|b| = 0, \\
|b| & \text{if}\\;|a| = 0, \\
1 + \text{min} \begin{cases}
\text{lev}(\text{tail}(a), b) \\
\text{lev}(a, \text{tail}(b)) \\
\text{lev}(\text{tail}(a), \text{tail}(b)) \\
\end{cases} & \text{otherwise} \\
\end{cases}
$$

### Query processing

You can query the index with boolean or free test queries. In the first case you can use the usual boolean operators to compose a query, such as:
$$\text{gun}\\;\text{AND}\\;\text{control}$$

In the second case, you just enter a phrase and receive a ranked collection of documents matching the query, ordered by [BM25 score](https://en.wikipedia.org/wiki/Okapi_BM25).

$$\text{BM25}(D, Q) = \sum_{i = 1}^{n} \\; \text{IDF}(q_i) \cdot \frac{f(q_i, D) \cdot (k_1 + 1)}{f(q_i, D) + k_1 \cdot \Big (1 - b + b \cdot \frac{|D|}{\text{avgdl}} \Big )}$$

$$\text{IDF}(q_i) = \ln \Bigg ( \frac{N - n(q_i) + 0.5}{n(q_i) + 0.5} + 1 \Bigg )$$

A window score is also computed, as the cardinality of
the user query, divided by the minimum size windows where all query terms appears in a document, or plus infinity if they don't appear all togheter.

$$\text{window}(D, Q) = \frac{|Q|}{\text{min. window}(Q, D)}$$

Finally they are combined with the following formula:

$$\text{score}(D, Q) = \alpha \cdot \text{window}(D, Q) + \beta \cdot \text{BM25}(D, Q)$$

Search engine written in Rust, based on an inverted index on disk.

## Commands

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You can then visit `http://0.0.0.0:3000` to find a web interface to enter free text and boolean queries.

![web.png](misc%2Fweb.png)

**Query Syntax**

You can perform Google-like free test queries, results will
be ranked via [BM25](https://en.wikipedia.org/wiki/Okapi_BM25) scoring.
You can perform Google-like free test queries.

You can also specify boolean queries with `"b: "` prefix such as:
```
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