To build and maintain inverted indexes for full-text search in Go, you can follow these steps:
Tokenization: Break the input text into individual words or tokens. This can be done using a tokenizer or by splitting the text on whitespace or punctuation. For example, you can use the strings.Fields or regexp packages to split the text into words.
Stopword Removal: Remove common words (also known as stopwords) like "and", "the", "is", etc., as they do not add much value to the search. You can maintain a predefined list of stopwords or use a library like the github.com/blevesearch/snowball package to remove stopwords.
Stemming: Reduce words to their base form (stem) to improve search results. For example, reducing "running" and "ran" to "run". The github.com/blevesearch/snowball package mentioned earlier can help with stemming.
Inverted Index Construction: Create a mapping of each word to the documents or locations where it occurs. This inverted index can be implemented as a map or a trie data structure. For every token, maintain a list of documents where it appears. You can use the built-in map data structure in Go for this purpose.
Posting List: For each token, you need to record the positions where it occurs in the document. This allows for proximity searches later. Create a list of positions for each token in a document. You can use a slice of integers or a custom data structure to store the positions.
Document ID Assignment: Assign a unique document ID to each document being indexed. This ID will be used to identify and retrieve documents during search operations.
Index Persistence: Store the built inverted index in a persistent storage medium like a file or a database. You can use Go's built-in file I/O or a database package like github.com/boltdb/bolt to store the index.
Index Updates: As new documents are added or existing documents are updated or deleted, update the inverted index accordingly. When adding a new document, tokenize it, remove stopwords, stem the words, and update the inverted index with the document ID and word positions. For deleted documents, remove the respective entries from the inverted index.
Search: To perform a search, tokenize the search query, remove stopwords, and stem the words in a way similar to the indexing process. Retrieve the posting lists for each token and apply Boolean operations like AND, OR, and NOT to get the final result set.
Ranking: If required, apply ranking algorithms to the search results based on relevance. You can use various ranking algorithms like TF-IDF, BM25, or PageRank depending on your requirements.
By following these steps, you can build and maintain inverted indexes for full-text search in Go. There are also open-source libraries like github.com/blevesearch/bleve that provide advanced indexing and search functionalities in Go, which you can consider if you require more comprehensive features.