Fix node-gyp compatibility: move to dependencies for proper version control

- Move node-gyp from devDependencies to dependencies
- Ensures v11.2.0 is used when installed as dependency
- Fixes Visual Studio detection issues in consuming projects
- Resolves shopApi build failures with old node-gyp v8.4.1

.gitignore

@@ -22,4 +22,6 @@ build/
 .Spotlight-V100
 .Trashes
 ehthumbs.db
-Thumbs.db 
+Thumbs.db
+
+package-lock.json

README.md

@@ -2,18 +2,24 @@
 
 A Node.js module that performs word order independent similarity search on strings.
 
-This module is built as a native addon that uses C code for fast similarity computations. It uses Jaccard similarity between word sets to find matches regardless of word order.
+This module is built as a native addon that uses C code for fast similarity computations. It uses a sophisticated similarity metric that combines fuzzy matching, prefix matching, and word-level comparisons to find matches regardless of word order.
 
 ## Installation
 
 ```bash
-npm install
+npm install similarity-search
 ```
 
+## Dependencies
+
+- Node.js (with node-gyp for building native addons)
+- nan (^2.22.2)
+- node-addon-api (^6.0.0)
+
 ## Usage
 
 ```javascript
-const SimilaritySearch = require('./index');
+const SimilaritySearch = require('similarity-search');
 
 // Create a new search index with default capacity (500)
 const index = new SimilaritySearch();
@@ -45,20 +51,21 @@ results.forEach(match => {
 Creates a new search index.
 
 - `capacity` (optional): Initial capacity for the index. Default: 500.
+- Returns: A new SimilaritySearch instance.
 
 ### `addString(str)`
 
 Adds a string to the index.
 
 - `str`: The string to add.
-- Returns: Boolean indicating success.
+- Returns: Boolean indicating success (true if successful, false otherwise).
 
 ### `addStrings(strings)`
 
 Adds multiple strings to the index.
 
 - `strings`: Array of strings to add.
-- Returns: Boolean indicating if all adds were successful.
+- Returns: Boolean indicating if all adds were successful (true if all successful, false if any failed).
 
 ### `search(query, [cutoff])`
 
@@ -66,7 +73,9 @@ Searches the index for strings similar to the query.
 
 - `query`: The search query.
 - `cutoff` (optional): Similarity threshold between 0.0 and 1.0. Default: 0.2.
-- Returns: Array of matching results, sorted by similarity (descending).
+- Returns: Array of matching results, sorted by similarity (descending). Each result is an object with:
+  - `string`: The matching string
+  - `similarity`: The similarity score (0.0 to 1.0)
 
 ### `size()`
 
@@ -82,6 +91,7 @@ Creates a test index with random data.
 
 - `size` (optional): Number of strings to generate. Default: 500.
 - Returns: A new SimilaritySearch instance with random data.
+- Note: The first 5 strings are fixed test cases, followed by randomly generated strings.
 
 ### `SimilaritySearch.benchmark(index, queries, [cutoff])`
 
@@ -90,30 +100,31 @@ Benchmarks the search performance.
 - `index`: The index to benchmark.
 - `queries`: Array of search queries.
 - `cutoff` (optional): Similarity threshold. Default: 0.2.
-- Returns: Benchmark results.
+- Returns: Array of benchmark results, each containing:
+  - `query`: The search query
+  - `matches`: Number of matches found
+  - `timeMs`: Search time in milliseconds
+  - `topResults`: Top 5 matching results
 
 ## How It Works
 
-The similarity search uses Jaccard similarity between word sets:
+The similarity search uses a sophisticated multi-stage matching algorithm:
 
-```
-similarity = (number of matching words) / (total unique words)
-```
+1. **Word-level Matching**: The algorithm first splits both the query and target strings into words.
 
-This means word order doesn't matter - "bio bizz" will match with "bizz bio" with 100% similarity.
+2. **Word Similarity Calculation**: For each word pair, similarity is calculated using:
+   - Levenshtein distance for fuzzy matching
+   - Special handling for short words (3 chars or less require exact match)
+   - Prefix matching for significantly different length words
+   - Length-based similarity adjustments
 
-## Building
+3. **Overall Similarity Score**: The final similarity score is a weighted combination of:
+   - Word match score (70% weight): Percentage of query words that have a good match
+   - Average word similarity (30% weight): Average similarity of the best matching word pairs
 
-To rebuild the native addon:
+This approach provides robust matching that:
+- Handles typos and slight variations in words
+- Requires exact matches for short words to avoid false positives
+- Recognizes prefix matches (e.g., "bio" matches "biology")
+- Considers both word presence and character-level similarity
 
-```bash
-npm install
-```
-
-## Testing
-
-Run the test script:
-
-```bash
-npm test
-``` 

package.json

@@ -1,23 +1,59 @@
 {
   "name": "similarity-search",
-  "version": "1.0.0",
+  "version": "1.0.2",
   "description": "A Node.js module for word order independent string similarity search",
   "main": "index.js",
+  "engines": {
+    "node": ">=14.0.0"
+  },
   "scripts": {
     "install": "node-gyp rebuild",
-    "test": "node test.js"
+    "rebuild": "node-gyp rebuild",
+    "build": "node-gyp rebuild",
+    "clean": "node-gyp clean",
+    "configure": "node-gyp configure",
+    "test": "node test.js",
+    "pretest": "npm run build"
   },
   "keywords": [
     "search",
     "similarity",
     "string",
-    "fuzzy"
+    "fuzzy",
+    "native",
+    "addon",
+    "c++",
+    "performance"
   ],
   "author": "",
   "license": "MIT",
   "dependencies": {
     "nan": "^2.22.2",
-    "node-addon-api": "^6.0.0"
+    "node-addon-api": "^6.0.0",
+    "node-gyp": "^11.2.0"
   },
-  "gypfile": true
+  "devDependencies": {
+  },
+  "repository": {
+    "type": "git",
+    "url": ""
+  },
+  "files": [
+    "index.js",
+    "binding.gyp",
+    "similarity_search.c",
+    "similarity_search.h",
+    "similarity_search_addon.cc",
+    "README.md"
+  ],
+  "gypfile": true,
+  "os": [
+    "win32",
+    "darwin",
+    "linux"
+  ],
+  "cpu": [
+    "x64",
+    "arm64"
+  ]
 }

similarity_search.c

@@ -3,6 +3,13 @@
 #include <string.h>
 #include <time.h>
 #include <ctype.h>
+#include <math.h>
+#include <stdbool.h>
+#ifdef _WIN32
+#include <malloc.h>  // For alloca on Windows
+#else
+#include <alloca.h>  // For alloca on Unix-like systems
+#endif
 #include "similarity_search.h"
 
 // Case insensitive string comparison
@@ -20,72 +27,139 @@ int str_case_cmp(const char *s1, const char *s2) {
 }
 
 // Split a string into words
-int split_into_words(const char *string, char *words[MAX_WORDS]) {
-    if (!string || strlen(string) >= MAX_STRING_LEN) {
-        return 0;
+int split_into_words(const char *s,
+                     char  *words[MAX_WORDS],
+                     char **storage)          /* NEW OUT PARAM            */
+{
+    if (!s || strlen(s) >= MAX_STRING_LEN) return 0;
+
+    char *buf = strdup(s);                    /* one single allocation    */
+    if (!buf) return 0;
+    *storage = buf;                           /* hand ownership to caller */
+
+    int n = 0;
+    for (char *tok = strtok(buf, " \t\n"); tok && n < MAX_WORDS;
+         tok = strtok(NULL, " \t\n"))
+    {
+        words[n++] = tok;                     /* pointers into buf        */
     }
-    
-    char temp[MAX_STRING_LEN];
-    strncpy(temp, string, MAX_STRING_LEN - 1);
-    temp[MAX_STRING_LEN - 1] = '\0';
-    
-    int word_count = 0;
-    char *token = strtok(temp, " \t\n");
-    
-    while (token != NULL && word_count < MAX_WORDS) {
-        words[word_count] = strdup(token);
-        if (!words[word_count]) {
-            // Free any already allocated words on error
-            for (int i = 0; i < word_count; i++) {
-                free(words[i]);
-            }
-            return 0;
-        }
-        word_count++;
-        token = strtok(NULL, " \t\n");
-    }
-    
-    return word_count;
+    return n;
 }
 
 // Free memory allocated for words
-void free_words(char *words[], int word_count) {
-    for (int i = 0; i < word_count; i++) {
-        free(words[i]);
+void free_words(char *storage) {              /* simplified               */
+    if (storage) {                            /* check for NULL           */
+        free(storage);                        /* single free, if any      */
     }
 }
 
+// Calculate Levenshtein distance between two strings
+int levenshtein_distance(const char *a, const char *b)
+{
+    size_t m = strlen(a), n = strlen(b);
+    if (m < n) { const char *t=a; a=b; b=t; size_t tmp=m; m=n; n=tmp; }
+
+    int *row0 = alloca((n + 1) * sizeof(int));
+    int *row1 = alloca((n + 1) * sizeof(int));
+
+    for (size_t j = 0; j <= n; ++j) row0[j] = j;
+    for (size_t i = 1; i <= m; ++i) {
+        row1[0] = i;
+        for (size_t j = 1; j <= n; ++j) {
+            int cost = (tolower((unsigned)a[i-1]) ==
+                        tolower((unsigned)b[j-1])) ? 0 : 1;
+            int del  = row0[j]   + 1;
+            int ins  = row1[j-1] + 1;
+            int sub  = row0[j-1] + cost;
+            row1[j] = (del < ins ? (del < sub ? del : sub)
+                                 : (ins < sub ? ins : sub));
+        }
+        int *tmp = row0; row0 = row1; row1 = tmp;
+    }
+    return row0[n];
+}
+
+// Calculate similarity between two words based on Levenshtein distance
+float word_similarity(const char *word1, const char *word2) {
+    int len1 = strlen(word1);
+    int len2 = strlen(word2);
+    
+    // For very short words (2 chars or less), require exact match
+    if (len1 <= 2 || len2 <= 2) {
+        return str_case_cmp(word1, word2) == 0 ? 1.0f : 0.0f;
+    }
+    
+    // Calculate Levenshtein distance
+    int distance = levenshtein_distance(word1, word2);
+    int max_len = len1 > len2 ? len1 : len2;
+    
+    // Simple linear scoring: 1.0 for exact match, 0.9 for one char difference, etc.
+    float similarity = 1.0f - (float)distance / max_len;
+    
+    // Boost similarity for small differences
+    if (distance <= 1) {
+        similarity = 0.9f + (similarity * 0.1f);
+    }
+    
+    return similarity;
+}
+
 // Calculate similarity between query and target string
 float calculate_similarity(const char *query, const char *target, float cutoff) {
     // Split strings into words
-    char *query_words[MAX_WORDS] = {0};
-    char *target_words[MAX_WORDS] = {0};
+    char *query_buf = NULL, *target_buf = NULL;
+    char *query_words[MAX_WORDS], *target_words[MAX_WORDS];
     
-    int query_word_count = split_into_words(query, query_words);
-    int target_word_count = split_into_words(target, target_words);
+    int query_word_count = split_into_words(query,  query_words,  &query_buf);
+    int target_word_count = split_into_words(target, target_words, &target_buf);
     
     if (query_word_count == 0 || target_word_count == 0) {
-        free_words(query_words, query_word_count);
-        free_words(target_words, target_word_count);
+        free_words(query_buf);
+        free_words(target_buf);
         return 0.0;
     }
     
-    // Count matches
-    int matches = 0;
+    // Track best matches for each query word
+    float best_word_similarities[MAX_WORDS] = {0.0f};
+    int query_words_found = 0;
+    
+    // For each query word, find its best match in target words
     for (int i = 0; i < query_word_count; i++) {
+        float best_similarity = 0.0f;
+        
         for (int j = 0; j < target_word_count; j++) {
-            if (str_case_cmp(query_words[i], target_words[j]) == 0) {
-                matches++;
-                break;
+            float similarity = word_similarity(query_words[i], target_words[j]);
+            if (similarity > best_similarity) {
+                best_similarity = similarity;
             }
         }
+        
+        best_word_similarities[i] = best_similarity;
+        if (best_similarity >= 0.4f) {
+            query_words_found++;
+        }
     }
     
-    // Calculate Jaccard similarity (intersection over union)
-    float similarity = (float)matches / (query_word_count + target_word_count - matches);
+    // Calculate average word similarity
+    float avg_word_similarity = 0.0f;
+    for (int i = 0; i < query_word_count; i++) {
+        avg_word_similarity += best_word_similarities[i];
+    }
+    avg_word_similarity /= query_word_count;
     
-    free_words(query_words, query_word_count);
-    free_words(target_words, target_word_count);
+    // Calculate word match ratio
+    float word_match_ratio = (float)query_words_found / query_word_count;
+    
+    // Final score is the average of word match ratio and average word similarity
+    float similarity = (word_match_ratio + avg_word_similarity) / 2.0f;
+    
+    // Boost score if all words are found
+    if (query_words_found == query_word_count) {
+        similarity = 0.8f + (similarity * 0.2f);
+    }
+    
+    free_words(query_buf);
+    free_words(target_buf);
     
     return similarity;
 }
@@ -232,24 +306,26 @@ SearchResult* search_index(SearchIndex* index, const char* query, float cutoff,
         }
     }
     
+    // If no results found, return NULL properly
+    if (*num_results == 0) {
+        free(temp_results);
+        return NULL;
+    }
+    
     // Sort results by similarity
     qsort(temp_results, *num_results, sizeof(SearchResult), compare_results);
     
-    // Allocate final result array with exact size
-    SearchResult* results = (SearchResult*)malloc(*num_results * sizeof(SearchResult));
+    // Shrink temp_results to exact size and return it directly
+    SearchResult* results = (SearchResult*)realloc(
+        temp_results, *num_results * sizeof(SearchResult));
     if (!results) {
-        // Free all strings in temp_results
+        // realloc failure – temp_results unchanged, clean up
         for (int i = 0; i < *num_results; i++) {
             free(temp_results[i].string);
         }
         free(temp_results);
         return NULL;
     }
-    
-    // Copy results to final array
-    memcpy(results, temp_results, *num_results * sizeof(SearchResult));
-    free(temp_results);
-    
     return results;
 }
 
@@ -262,4 +338,4 @@ void free_search_results(SearchResult* results, int num_results) {
         free(results[i].string);
     }
     free(results);
-} 
+}

similarity_search.h

@@ -5,8 +5,8 @@
 extern "C" {
 #endif
 
-#define MAX_STRING_LEN 100
-#define MAX_WORDS 20
+#define MAX_STRING_LEN 1000
+#define MAX_WORDS 100
 
 // Public API
 
@@ -19,7 +19,7 @@ typedef struct {
 
 // Structure to hold a search result
 typedef struct {
-    const char *string;
+    char *string;
     float similarity;
 } SearchResult;
 

similarity_search_addon.cc

@@ -41,7 +41,7 @@ SearchIndexWrapper::SearchIndexWrapper(const Napi::CallbackInfo& info)
   Napi::Env env = info.Env();
   Napi::HandleScope scope(env);
   
-  int capacity = 500; // Default capacity
+  int capacity = 10000; // Increased default capacity from 500 to 10000
   if (info.Length() > 0 && info[0].IsNumber()) {
     capacity = info[0].As<Napi::Number>().Int32Value();
   }
@@ -67,6 +67,12 @@ Napi::Value SearchIndexWrapper::AddString(const Napi::CallbackInfo& info) {
   
   std::string str = info[0].As<Napi::String>().Utf8Value();
   
+  // Check if string is empty
+  if (str.empty()) {
+    Napi::Error::New(env, "Empty string not allowed").ThrowAsJavaScriptException();
+    return env.Null();
+  }
+  
   // Check if string is too long
   if (str.length() >= MAX_STRING_LEN) {
     Napi::Error::New(env, "String too long").ThrowAsJavaScriptException();
@@ -99,6 +105,12 @@ Napi::Value SearchIndexWrapper::Search(const Napi::CallbackInfo& info) {
   
   std::string query = info[0].As<Napi::String>().Utf8Value();
   
+  // Check if query is empty
+  if (query.empty()) {
+    Napi::Error::New(env, "Empty query not allowed").ThrowAsJavaScriptException();
+    return env.Null();
+  }
+  
   // Check if query string is too long
   if (query.length() >= MAX_STRING_LEN) {
     Napi::Error::New(env, "Query string too long").ThrowAsJavaScriptException();
@@ -117,9 +129,9 @@ Napi::Value SearchIndexWrapper::Search(const Napi::CallbackInfo& info) {
   int num_results = 0;
   SearchResult* results = search_index(this->index_, query.c_str(), cutoff, &num_results);
   
-  if (!results) {
-    Napi::Error::New(env, "Search failed").ThrowAsJavaScriptException();
-    return env.Null();
+  // If no results found, return empty array instead of throwing error
+  if (!results || num_results == 0) {
+    return Napi::Array::New(env, 0);
   }
   
   Napi::Array result_array = Napi::Array::New(env, num_results);

test.js

@@ -43,14 +43,22 @@ customIndex.addString('bizz bio mix light');
 // Add multiple strings at once
 customIndex.addStrings([
   'plant growth bio formula',
-  'garden soil substrate'
+  'garden soil substrate',
+  'plagron lightmix',
+  'Anesia Seeds Imperium X Auto 10',
+  'anesi'
 ]);
 
 console.log(`Custom index created with ${customIndex.size()} strings`);
 
 // Search with a higher similarity threshold
-console.log('\nSearching with higher similarity threshold (0.3):');
-const results = customIndex.search('bio bizz', 0.3);
+console.log('\nSearching with higher similarity threshold (0.1) for "amnesia":');
+const results = customIndex.search('amnesia haze', 0.1);
 results.forEach(match => {
   console.log(`  ${match.similarity.toFixed(2)}: ${match.string}`);
+}); 
+console.log('\nSearching with higher similarity threshold (0.1) for "mix light":');
+const results2 = customIndex.search('mix light', 0.1);
+results2.forEach(match => {
+  console.log(`  ${match.similarity.toFixed(2)}: ${match.string}`);
 });