/**
 * Full-text search operations: tokenization, matching, and relevance scoring.
 *
 * Provides pure functions for text search functionality including
 * tokenization, stop word filtering, and relevance-based ranking.
 */
/**
 * Tokenize text into normalized tokens.
 *
 * Splits on whitespace, strips leading/trailing punctuation characters,
 * lowercases all tokens, and filters out empty strings.
 *
 * @param text - The text to tokenize
 * @returns Array of normalized tokens
 *
 * @example
 * ```ts
 * tokenize("Gibson, William")
 * // => ["gibson", "william"]
 *
 * tokenize("The Left Hand of Darkness")
 * // => ["the", "left", "hand", "of", "darkness"]
 *
 * tokenize("  ")
 * // => []
 *
 * tokenize("")
 * // => []
 * ```
 */
export declare function tokenize(text: string): ReadonlyArray<string>;
/**
 * Tokenize text with optional stop word filtering.
 *
 * Calls `tokenize` then optionally filters out common stop words
 * (articles, prepositions, conjunctions, etc.) that typically don't
 * contribute to search relevance.
 *
 * @param text - The text to tokenize
 * @param removeStopWords - Whether to filter out stop words
 * @returns Array of normalized tokens, optionally without stop words
 *
 * @example
 * ```ts
 * tokenizeWithStopWords("The Left Hand of Darkness", false)
 * // => ["the", "left", "hand", "of", "darkness"]
 *
 * tokenizeWithStopWords("The Left Hand of Darkness", true)
 * // => ["left", "hand", "darkness"]
 * ```
 */
export declare function tokenizeWithStopWords(text: string, removeStopWords: boolean): ReadonlyArray<string>;
/**
 * Compute the relevance score for a single field value against query tokens.
 *
 * Scoring uses three factors combined multiplicatively:
 * 1. **Term coverage**: Fraction of query tokens that matched (0..1)
 * 2. **Term frequency (TF)**: How many times query tokens appear in the field
 * 3. **Field length normalization**: Shorter fields score higher for same match
 *
 * Prefix matches (via startsWith) count toward matching but score slightly lower
 * than exact matches to preserve ranking quality.
 *
 * @param fieldValue - The field value to score
 * @param queryTokens - The tokenized search query
 * @returns Relevance score (0 if no tokens match)
 *
 * @example
 * ```ts
 * computeFieldScore("Dune", ["dune"])
 * // => ~1.44 (exact match, short field)
 *
 * computeFieldScore("The Left Hand of Darkness", ["left", "hand"])
 * // => ~0.29 (2/2 coverage, longer field)
 *
 * computeFieldScore("Neuromancer", ["neuro"])
 * // => ~0.72 (prefix match, reduced score)
 *
 * computeFieldScore("Dune", ["xyz"])
 * // => 0 (no match)
 * ```
 */
export declare function computeFieldScore(fieldValue: string, queryTokens: ReadonlyArray<string>): number;
/**
 * Compute the total relevance score for an entity across multiple fields.
 *
 * Sums the field scores for all specified fields. Fields that are not
 * present on the entity or are not strings are skipped (score 0).
 *
 * @param entity - The entity to score
 * @param queryTokens - The tokenized search query
 * @param fields - The fields to search across
 * @returns Total relevance score (0 if no fields match)
 *
 * @example
 * ```ts
 * const book = { title: "Dune", author: "Frank Herbert", year: 1965 }
 *
 * computeSearchScore(book, ["dune"], ["title", "author"])
 * // => ~1.44 (matches in title only)
 *
 * computeSearchScore(book, ["herbert", "dune"], ["title", "author"])
 * // => ~2.88 (matches in both fields)
 *
 * computeSearchScore(book, ["xyz"], ["title", "author"])
 * // => 0 (no matches)
 * ```
 */
export declare function computeSearchScore(entity: Record<string, unknown>, queryTokens: ReadonlyArray<string>, fields: ReadonlyArray<string>): number;
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