diff --git a/README.md b/README.md index ecc7201..97bdcb1 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,6 @@


- Search Library + Search Library

@@ -15,7 +15,7 @@

Web's fastest and most memory-flexible full-text search library with zero dependencies.

-When it comes to raw search speed FlexSearch outperforms every single searching library out there and also provides flexible search capabilities like multi-word matching, phonetic transformations or partial matching. +When it comes to raw search speed FlexSearch outperforms every single searching library out there and also provides flexible search capabilities like multi-word matching, phonetic transformations or partial matching. Depending on the used options it also providing the most memory-efficient index. Keep in mind that updating and/or removing existing items from the index has a significant cost. When your index needs to be updated very often then BulkSearch may be a better choice. FlexSearch also provides you a non-blocking asynchronous processing model as well as web workers to perform any updates or queries on the index in parallel through dedicated balanced threads. @@ -28,8 +28,8 @@ Supported Platforms: > FlexSearch Server is also available here: https://github.com/nextapps-de/flexsearch-server Library Comparison: -- Benchmark "Gulliver's Travels" -- Relevance Scoring +- Benchmark "Gulliver's Travels" +- Relevance Scoring - Memory Consumption Get Latest (Stable Release): @@ -186,7 +186,7 @@ All Features: ## Benchmark Ranking -Comparison: Benchmark "Gulliver's Travels" +Comparison: Benchmark "Gulliver's Travels" ##### Query Test: "Gulliver's Travels" @@ -372,20 +372,20 @@ _** Memory Allocation: The amount of memory which was additionally allocated dur _*** The preset "fastest" was used for this test_
_**** The preset "memory" was used for this test_ -Library Comparison: Benchmark "Gulliver's Travels" +Library Comparison: Benchmark "Gulliver's Travels" ## Contextual Search > "TF-IDF and all kinds of variations (like BM25) is a big mistake in searching algorithms today. They don't provide neither: a meaningful relevance of a term nor the importance of it! Like many pseudo-intelligent algorithms this is also just an example of mathematical stupidity." — Thomas Wilkerling, _Contextual-based Scoring_, 2018 -FlexSearch introduce a new scoring mechanism called __Contextual Search__ which was invented by Thomas Wilkerling, the author of this library. A Contextual Search incredibly boost up queries to a complete new level but also requires a lot of additionally memory. +FlexSearch introduce a new scoring mechanism called __Contextual Search__ which was invented by Thomas Wilkerling, the author of this library. A Contextual Search incredibly boost up queries to a complete new level but also requires a lot of additionally memory. The basic idea of this concept is to limit relevance by its context instead of calculating relevance through the whole (unlimited) distance. Imagine you add a text block of some sentences to an index ID. Assuming the query includes a combination of first and last word from this text block, are they really relevant to each other? -In this way contextual search also improves the results of relevance-based queries on large amount of text data. +In this way contextual search also improves the results of relevance-based queries on large amount of text data.

- +

__Note:__ This feature is actually not enabled by default. Read here how to enable. @@ -491,12 +491,12 @@ __Note:__ This feature is actually not enabled by default. Read + ``` Or a specific version: ```html - + ``` AMD: @@ -1537,7 +1537,7 @@ The required memory for the index depends on several options: The book "Gulliver's Travels" (Swift Jonathan 1726) was used for this test.
- + ## Presets @@ -1614,8 +1614,8 @@ __"fastest"__: Absolute fastest profile ``` Compare these presets: -- Relevance Scoring
-- Benchmarks +- Relevance Scoring
+- Benchmarks ## Best Practices