![]() Var doc = new IndexDocument("ExternalId") ĭoc.Add("longValue".GetFilterField(20l)) ĭoc.Add("dateValue".GetFilterField(DateTime. Var searchService = new SearchServiceEngine(documentIndex) Var content = "one two one two second try to welcome" Therefore, we strongly recommend upgrading to. From this release onwards, we no longer ship or support versions compatible with. Var documentIndex = new MemoryDocumentIndex() I438158 - Unicode text is now rendered properly while converting a Word document to PDFImage. ShardDocumentIndex - stores large indexes on disk of more than 3Įxample of using Memory index var field = "*".In order to search text from the whole document, you need to call the Accept method of Pages collection. DiskDocumentIndex stores the index on disk Search and Get Text from All the Pages of PDF Document TextFragmentAbsorber class allows you to find text, matching a particular phrase, from all the pages of a PDF document.MemoryDocumentIndex - fast memory index.has 50+ other features not listed here, refer to API and configuration manual! supports ODBC compliant databases (MS SQL, Oracle, etc) natively.supports MySQL natively (all types of tables, including MyISAM, InnoDB, NDB, Archive, etc are supported).Also, you can find hidden text using TextFragmentAbsorber. You can add hidden text during document generation. ShardDocumentIndex - stores large indexes on disk of more than 3 million documents. Sometimes we want to add hidden text in a PDF document and then search hidden text and use its position for post-processing. DiskDocumentIndex stores the index on disk. How to apply I have the need to develop a system that turns an image into a searchable PDF. supports stemming (stemmers for English, Russian and Czech are built-in and stemmers for French, Spanish, Portuguese, Italian, Romanian, German, Dutch, Swedish, Norwegian, Danish, Finnish, Hungarian, are available by building third party libstemmer library) The library contains 4 index types: MemoryDocumentIndex - fast memory index.supports both single-byte encodings and UTF-8.supports morphological word forms dictionaries.supports multiple additional attributes per document (ie. Our PDF API allows you to search for words or phrases in a PDF document, specify search parameters such as case sensitivity and search direction.supports multiple full-text fields per document (upto 32 by default).supports boolean, phrase, word proximity and other types of queries.provides searching from within application with SphinxAPI or SphinxQL interfaces, and from within MySQL with pluggable SphinxSE storage engine.provides document excerpts (snippets) generation.provides distributed searching capabilities.provides good relevance ranking through combination of phrase proximity ranking and statistical (BM25) ranking.has high scalability (biggest known cluster indexes over 3,000,000,000 documents, and busiest one peaks over 50,000,000 queries/day).has high search speed (upto 150-250 queries/sec per core against 1,000,000 documents, 1.2 GB of data on an internal benchmark).has high indexing speed (upto 10-15 MB/sec per core on an internal benchmark).easy scaling with distributed searches.easy integration with SQL and XML data sources, and SphinxAPI, SphinxQL, or SphinxSE search interfaces. ![]()
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