Gokapi implements the Okapi BM25 retriever in Go.

go get github.com/raphaelsty/[email protected]

Gokapi is suitable when you want to:

• Implement the search engine on a single computer.
• Store the data on disk and thus be able to search among millions of documents without memory constraints.
• Update the retriever with new documents.
• Avoid the constraints of Elasticsearch and the JVM.

Here are some comparisons between Gokapi and the retrievers of the tool Cherche

Gokapi stores the data necessary for the search on the disk (frequency of terms and metadata on the corpus). The reading (query) and writing (document indexing) times are higher than a retriever which stores data in memory, but Gokapi allows to process more documents without overloading the memory. Writing and reading on the disk are done with the Diskv library. Gokapi does not store the content of the documents on the disk.

package main

import (
	"fmt"

	"github.com/raphaelsty/gokapi"
)

func main() {

	data := make(map[string]string)

	data["document_0"] = "Paris is the capital of France"
	data["document_1"] = "Montreal is the capital of Canada"
	data["document_2"] = "Madrid is the capital of Spain"
	data["document_3"] = "Rome is the capital of Italy"

	retriever := gokapi.BM25("index")

	// Add the documents to the retriever.
	retriever.Add(data)

	// Top five answers.
	answers := retriever.Query("Paris France Canada", 5)

	for _, answer := range answers {
		fmt.Println(answer)
	}

	// Delete the index.
	retriever.Reset()

}

{document_0 2.002704}
{document_1 1.001352}

Gokapi is under construction and may change soon.

Here are some "short-term" goals:

1. To be called by the Cherche library via Python to provide a lighter alternative to Elasticsearch.

2. Provide a command-line client to search for documents locally on your machine with the terminal at lightning speed.

3. Gokapi code needs to be enhanced.

4. Provide benchmarks.