# Creating a new connector.

Pierrick Roger

#### 15 April 2025

#### Abstract

This vignette shows how to create a new connector class and the corresponding new entry class for accessing a remote database.

#### Package

biodb 1.16.0

# 1 Introduction

biodb is a framework designed to help you implement new connectors for databases. To illustrate this, we will show you a practical example where we create a connector for the ChEBI database. In this example, we will present you a small implementation of a ChEBI connector, and show you how to declare it to your biodb instance.

A more complete and functional connector for accessing ChEBI database is implemented in biodbChEBI library. See 1 for a list of the capabilities of this official biodb connector.

Table 1: Capabilities of the biodbChebi extension package.
Title / method name | Description
--- | ---
Fields parsing | Formula, charge, InChI, InChIKey, molecular mass, monoisotopic mass, KEGG id, entity stars, SMILES.
getEntryPageUrl() | Returns the URL of the website page of an entry.
getEntryImageUrl() | Returns the URL to the molecule image of an entry.
wsWsdl() | Returns the WSDL definition (i.e.: list of available web services and their parameters).
wsGetLiteEntity() | Runs the getLiteEntity web service that returns database entries with their contents.
convIdsToChebiIds() | Converts a list of IDs (InChI, InChI Keys, CAS, …) into a list of ChEBI IDs.
convInchiToChebi() | Converts a list of InChI or InChI KEYs into a list of ChEBI IDs.
convCasToChebi() | Converts a list of CAS IDs into a list of ChEBI IDs.
searchForEntries() | Searches for entries by mass and/or by name.

# 2 Generating a new extension package

When creating a new extension package, biodb can help you generate all the necessary files.

A call to `genNewExtPkg()` will generate the skeletons for the biodb connector class and the biodb entry class, along with the testthat files, the DESCRIPTION file, etc. A simplified call might look like this:

    biodb::genNewExtPkg(path='biodbChebiEx', dbName='chebi.ex', connType='compound',
                        dbTitle='ChEBI connector example', entryType='xml', remote=TRUE)

See 2 for a brief description of the parameters. Other parameters exist for the author’s email, the author’s name, for generating a `Makefile`, or configuring for writing C++ code with `Rcpp`.

Table 2: A brief description of some parameters of `biodb::genNewExtPkg()`.
Parameter | Description
--- | ---
path | The path to the package folder to create.
dbName | The name of the connector to create.
dbTitle | A short description of the database.
connType | The type of connector.
entryType | The type of the entry.
remote | Must be set to if a connection to a web server is needed.

The files generated by the `genNewExtPkg()` function are the following ones:

    list.files('biodbChebiEx', all.files=TRUE, recursive=TRUE)

    ##  [1] ".BBSoptions"
    ##  [2] ".gitignore"
    ##  [3] ".Rbuildignore"
    ##  [4] "biodb_ext.yml"
    ##  [5] "DESCRIPTION"
    ##  [6] "inst/definitions.yml"
    ##  [7] "inst/testref/entry-chebi.ex-0001.json"
    ##  [8] "LICENSE"
    ##  [9] "longtests/testthat.R"
    ## [10] "longtests/testthat/test_long_001_init_logging.R"
    ## [11] "longtests/testthat/test_long_100_generic.R"
    ## [12] "longtests/testthat/test_long_200_example.R"
    ## [13] "R/ChebiExConn.R"
    ## [14] "R/ChebiExEntry.R"
    ## [15] "R/package.R"
    ## [16] "README.md"
    ## [17] "tests/testthat.R"
    ## [18] "tests/testthat/test_001_init_logging.R"
    ## [19] "tests/testthat/test_050_fcts.R"
    ## [20] "tests/testthat/test_100_generic.R"
    ## [21] "tests/testthat/test_200_example.R"
    ## [22] "vignettes/biodbChebiEx.Rmd"

Inside the `biodb_ext.yml` file are stored the values of the parameters used with `biodb::genNewExtPkg()`. This is in case you want to upgrade some the generated files (`.gitignore`, `.travis.yml`, `Makefile`, etc) with newer versions from biodb package. You would then only need to call `biodb::upgradeExtPkg(path='biodbChebiEx')` and the `biodb_ext.yml` file would be read for parameter values.

The `inst/definitions.yml` file defines the new connector, we will fill in some values inside it. Then we need to write implementations for the methods in the connector class `R/ChebiExConn.R`. On the other side, `R/ChebiExEntry.R`, the entry class, needs no modification for our basic usage.

The test files in `tests/testthat` will be executed when running `R CMD check`, they need to be edited first though. Generic tests need to enabled inside `tests/testthat/test_100_generic.R`. The files `tests/testthat/test_050_fcts.R` and `tests/testthat/test_200_example.R` contain only examples, thus they need to be modified or removed.

The test files in `tests/long` will not be executed when running `R CMD check`. They can be run manually after installing the package locally, by calling `R -e "testthat::test_dir('tests/long')"`.

A skeleton vignette has also been generated (`vignettes/intro.Rmd`), and should be completed with specific examples for this package.

# 3 Editing the generated skeleton

Starting from the skeleton files generated by `genNewExtPkg()`, we need now to fill in the blanks.

The first file to take care of is `inst/definitions.yml`, which contains the definition of the new connector.

Then we will look quickly at `R/ChebiExEntry.R`, which is rather empty in our case, and `R/ChebiExConn.R`, which requires much more attention, having several methods that need implementation.

The naming of the classes inside the R files is important. They must be named `ChebiExEntry` and `ChebiExConn`, in order to match the name defined inside `inst/definitions.yml` (`chebi.ex`). Hopefully the generator has taken care of this, and no special action is required on this aspect, except not modifying the names.

## 3.1 Editing the YAML definition of the new connector

The content of the generated YAML file `inst/definitions.yml` is as follow:

    # biodb example definitions file for extensions packages, version 1.0.0

    databases:
      chebi.ex:
        name: ChEBI connector example
        description: Write here the description of this database.
        compound.db: true
        entry.content.type: xml
        parsing.expr:
          accession: substring-after(//dbns:return/dbns:accessionId,'ACCESSION:')
          name:
          - //dbns:name
          - //dbns:synonyms/dbns:data
          mass: //dbns:mass
          monoisotopic.mass: //dbns:monoisotopicMass
          smiles: //dbns:return/dbns:smiles
          inchi: //dbns:return/dbns:inchi
          inchikey: //dbns:return/dbns:inchiKey
          formula:
          - //dbns:Formulae/dbns:source[text()='MyDatabase']/../dbns:data
          - (//dbns:Formulae/dbns:data)[1]
        xml.ns:
          dbns: https://my.database.org/webservices/v1
          xsd: http://www.w3.org/2001/XMLSchema
        searchable.fields:
          - name
          - monoisotopic.mass
          - molecular.mass
          - average.mass
          - nominal.mass
        remote: true
        # Length in seconds of the connection sliding window
        scheduler.t: 1
        # Number of connections allowed inside the connection sliding window
        scheduler.n: 3
        urls:
          # Base URL of the database server, where to find entry pages
          base.url: https://my.database.org/mydb/
          # Webservice URL to use to contact web services
          ws.url: https://my.database.org/webservices/mydb/3.2/
          # Add any other URL you need for the development of your connector
          # Inside your code, you can get each of these URLs with a call like the following one:
          #     .self$getPropValSlot('urls', 'ws.url')

    fields:
      chebi.ex.id:
        description: ChEBI connector example ID
        case.insensitive: true
        forbids.duplicates: true
        type: id
        card: many

It is mainly filled with examples.

This YAML file contains two main parts: `databases` and `fields`. The `databases` part is where you list the new connectors you’ve created, and the `fields` part is where you define the new entry fields your new connectors need.

### 3.1.1 Fields definition

We just have one new field to define: `chebi.ex.id`. This is the accession field for our new connector. All connector accession fields are in the form `<connector_class_id>.id`. This accession field is mainly used inside other databases, when they make references to other databases. The field `accession`, which is used in all entries of biodb connectors, contains the same value as the connector accession field (`chebi.ex.id` in our case) and is preferable when accessing an entry. The definition of the new field is quite simple, See 3 for explanations of the different parameters.

Table 3: Field’s parameters
Description of the parameters used when declaring a new entry field.
Parameter | Description
--- | ---
`description` | A free description of your field.
`type` | The type of the field. Here we declare that this is an accession (identifier) field: `id`.
`card` | The cardinality of the field: `one` if field accepts only one value, or `many` if multiple values can be stored inside the field.
`forbids.duplicates` | If `TRUE` then duplicates are forbidden. This supposes that we allow to store multiple values inside this field (i.e.: cardinality is set to `many`).
`case.insensitive` | If `TRUE` then values will be compared in case insensitive mode. This is mostly useful when looking for duplicates.

### 3.1.2 Database definition

The main part is the declaration of the new connector. This is done in the `databases` section, under the key `chebi.id`, which is the database identifier. See 4 for explanations of the different parameters.

Table 4: Connector declaration’s parameters
Description of the parameters used when declaring a new connector.
Parameter | Description
--- | ---
`name` | The full name of your new connector.
`urls` | A list (key/values) of URLs of the remote database. The common URLs to define are `base.url` to access pages of the database website, and `ws.url` for web service URLs. Those URLs are just “prefix” and are used inside the connector class for building real URLs. You can define as much URLs as the remote database requires, like a second base URL (`base2.url`) or a second web service URL (`ws2.url`), or any other URL with the key name you want.
`xml.ns` | This parameter defines namespaces for XML documents returned by the remote database. This is thus only useful for databases that return data in XML format.
`scheduler.n` | The maximum number of queries to send to the remote database, each T (stored as `scheduler.t`) seconds.
`scheduler.t` | The time (in seconds) during which a maximum of N (stored as `scheduler.n`) queries is allowed.
`entry.content.type` | The type of content sent by the database for an entry. Here we have specified `xml`. Allowed values are: `html`, `sdf`, `txt`, `xml`, `csv`, `tsv`, `json`, `list`. This is mainly used to add an extension to the file saved inside biodb cache.
`entry.content.encoding` | The text encoding used inside the entry’s content by the database.
`parsing.expr` | This is the most important part of the declaration. It is lists the different expressions to use in order to parse the values of the entry fields. The format is a key/value list, the key being the biodb field name, and the value the expression to run. Since the entry content type is XML, we have to use XPath expressions here. See this XPath Tutorial, for instance, to get an introduction to XPath. Note that we can define multiple expressions, like for `formula` field, in case of XPath expressions. If the first expression fails, then next expressions will be tried.
`searchable.fields` | A list of biodb entry fields that are searchable when calling a search function like `searchCompound()`.

### 3.1.3 Final version of the YAML file

After setting some parsing expressions, the URLs and the searchable fields, we get a complete definition file, that you can find at:

    defFile <- system.file("extdata", "chebi_ex.yml", package='biodb')

Its content is as follow:

    databases:

      chebi.ex:
        name: ChEBI example connector
        description: An example connector for ChEBI.
        compound.db: true
        entry.content.encoding: UTF-8
        entry.content.type: xml
        parsing.expr:
          accession: substring-after(//chebi:return/chebi:chebiId,'CHEBI:')
          formula:
            - //chebi:Formulae/chebi:source[text()='ChEBI']/../chebi:data
            - (//chebi:Formulae/chebi:data)[1]
          inchi: //chebi:return/chebi:inchi
          inchikey: //chebi:return/chebi:inchiKey
          mass: //chebi:mass
          monoisotopic.mass: //chebi:monoisotopicMass
          name:
            - //chebi:chebiAsciiName
          smiles: //chebi:return/chebi:smiles
        searchable.fields:
          - name
          - monoisotopic.mass
          - molecular.mass
        remote: true
        scheduler.t: 1
        scheduler.n: 3
        urls:
          base.url: https://www.ebi.ac.uk/chebi/
          ws.url: https://www.ebi.ac.uk/webservices/chebi/2.0/
        xml.ns:
          chebi: https://www.ebi.ac.uk/webservices/chebi
          xsd: http://www.w3.org/2001/XMLSchema

    fields:

      chebi.ex.id:
        description: ChEBI ID
        type: id
        card: many
        forbids.duplicates: true
        case.insensitive: true

## 3.2 The entry class

The entry class represents an entry from the database. Each instance of an entry contains the values parsed from the database downloaded content.

The entry class of our example extension package has been generated inside `R/ChebiExEntry.R`. Here is its content:

    #' ChEBI connector example entry class.
    #'
    #' Entry class for ChEBI connector example.
    #'
    #' @seealso
    #' \code{\link{BiodbXmlEntry}}.
    #'
    #' @examples
    #' # Create an instance with default settings:
    #' mybiodb <- biodb::newInst()
    #'
    #' # Get a connector that inherits from ChebiExConn:
    #' conn <- mybiodb$getFactory()$createConn('chebi.ex')
    #'
    #' # Get the first entry
    #' e <- conn$getEntry(conn$getEntryIds(1L))
    #'
    #' # Terminate instance.
    #' mybiodb$terminate()
    #'
    #' @import biodb
    #' @import R6
    #' @export
    ChebiExEntry <- R6::R6Class("ChebiExEntry",
        inherit=
            biodb::BiodbXmlEntry
        ,

    public=list(

    initialize=function(...) {
        super$initialize(...)
    }

    ,doCheckContent=function(content) {

        # You can do some more checks of the content here.

        return(TRUE)
    }

    ,doParseFieldsStep2=function(parsed.content) {

        # TODO Implement your custom parsing processing here.
    }
    ))

The class inherits from `BiodbXmlEntry` since we have set the `entryType` parameter to `"xml"`. An entry class must inherit from the `BiodbEntry` class and define some methods. To simplify this step, several generic entry classes have been defined in biodb (see 5), depending on the type of content downloaded from the database. To use one of these classes for your entry class, you only have to make your class inherit from the desired generic class.

Table 5: Provided abstract entry classes
These are the entry classes already defined inside biodb package that facilitates the parsing of the corresponding content type.
Entry class | Content type handled
--- | ---
`BiodbCsvEntry` | CSV file.
`BiodbHtmlEntry` | HTML, the parsing will be done using XPath expressions.
`BiodbJsonEntry` | JSON.
`BiodbListEntry` | R list.
`BiodbSdfEntry` | SDF file (chemical data file format).
`BiodbTxtEntry` | Text file, the parsing will be done using regular expressions.
`BiodbXmlEntry` | XML file, the parsing will be done using XPath expressions.

Two methods are defined that can be used to enhance our implementation. The method `doCheckContent()` can be used to further check the parsed content of an entry, for instance for some incoherence between fields. The method `doParseFieldsStep2()` allows to run some custom code for complex parsing of the entry’s content. This method is run after `doParseFieldsStep1()`, which is defined inside the mother class (here `BiodbXmlEntry`) and executes the parsing expression defined inside `inst/definitions.yml`.

Note: biodb uses R6 as OOP (Object Oriented Programming) model. Please see vignette Details on biodb , for more explanations.

## 3.3 The connector class

The generator has generated the full class, and thus has taken care of the inheritance part, as well as the declaration of the required methods. See 6 for a description of these methods. What is left to us is the implementation of those methods.

Here is the generated skeleton:

    #' ChEBI connector example connector class.
    #'
    #' Connector class for ChEBI connector example.
    #'
    #' @seealso \code{\link{BiodbConn}}.
    #'
    #' @examples
    #' # Create an instance with default settings:
    #' mybiodb <- biodb::newInst()
    #'
    #' # Get a connector:
    #' conn <- mybiodb$getFactory()$createConn('chebi.ex')
    #'
    #' # Get the first entry
    #' e <- conn$getEntry(conn$getEntryIds(1L))
    #'
    #' # Terminate instance.
    #' mybiodb$terminate()
    #'
    #' @import biodb
    #' @import R6
    #' @export
    ChebiExConn <- R6::R6Class("ChebiExConn",
    inherit=biodb::BiodbConn,

    public=list(

    initialize=function(...) {
        super$initialize(...)
    }

    ,wsFind=function(name="", retfmt=c('plain', 'parsed', 'ids', 'request')) {
        # This is the implementation of a fictive web service called "find" that
        # search for entries by name.
        # Use it as an example for implementing your own web services.

        retfmt <- match.arg(retfmt)

        # Build request
        params <- list(name=name)
        url <- BiodbUrl$new(url=c(self$getPropValSlot('urls', 'ws.url'), 'find'),
                        params=params)
        request <- self$makeRequest(method='get', url=url)

        # Return request
        if (retfmt == 'request')
            return(request)

        # Send request
        # This the line that should be run for sending the request and getting the
        # results:
        #results <- self$getBiodb()$getRequestScheduler()$sendRequest(request)
        # Instead, for this example, we just generate the results of this fictive
        # web service:
        results <- paste('{"0001": {"name": "name1"},',
                         ' "0198": {"name": "name2"},',
                         ' "9834": {"name": "name3"}}')

        # Parse
        if (retfmt != 'plain') {

            # Parse JSON
            results <- jsonlite::fromJSON(results, simplifyDataFrame=FALSE)

            # Get IDs
            if (retfmt == 'ids')
                results <- names(results)
        }

        return(results)
    }
    ),

    private=list(

    doGetNbEntries=function(count=FALSE) {

        # Replace the call below if you have a direct way (specific web service for
        # a remote database, provided method or information for a local database)
        # to count entries for your database.
        return(callSuper(count=count))
    }

    ,doGetEntryIds=function(max.results=NA_integer_) {
        # Overrides super class' method.

        ids <- NA_character_

        # TODO Implement retrieval of accession numbers.

        return(ids)
    }

    ,doSearchForEntries=function(fields=NULL, max.results=NA_integer_) {
        # Overrides super class' method.

        ids <- character()

        # TODO Implement search of entries by filtering on values of fields.

        return(ids)
    }

    ,doGetEntryContentRequest=function(id, concatenate=TRUE) {

        # TODO Modify the code below to build the URLs to get the contents of the
        # entries.
        # Depending on the database, you may have to build one URL for each
        # individual entry or may be able to write just one or a few URL for all
        # entries to retrieve.
        u <- c(self$getPropValSlot('urls', 'base.url'), 'entries',
               paste(id, 'xml', sep='.'))
        url <- BiodbUrl$new(url=u)$toString()

        return(url)
    }

    ,doGetEntryPageUrl=function(id) {

        # TODO Modify this code to build the individual URLs to the entry web pages
        fct <- function(x) {
            u <- c(self$getPropValSlot('urls', 'base.url'), 'entries', x)
            BiodbUrl$new(url=u)$toString()
        }

        return(vapply(id, fct, FUN.VALUE=''))
    }

    ,doGetEntryImageUrl=function(id) {

        # TODO Modify this code to build the individual URLs to the entry images
        fct <- function(x) {
            u <- c(self$getPropValSlot('urls', 'base.url'), 'images', x,
                   'image.png')
            BiodbUrl$new(url=u)$toString()
        }

        return(vapply(id, fct, FUN.VALUE=''))
    }
    ))

### 3.3.1 Inheritance

The connector class is responsible for the connection to the database. In our case, the database is a compound database.

### 3.3.2 Methods to implement

Table 6: Methods to implement inside the chebi.ex connector.
Method | Description
--- | ---
`doGetEntryPageUrl()` | This method returns the official URL of the entry page on the database website, for each each accession number passed. The return type is thus a list. If no entry pages are available for the database, the method must return a list of `NULL` values, the same length as the input vector.
`doGetEntryImageUrl()` | This method returns the official URL of the entry picture on the database website, for each each accession number passed. The picture returned must be visual representation of the entry (a molecule 3D model, a mass spectrum, …). The return type is thus a list. If no entry pages are available for the database, the method must return a list of `NULL` values, the same length as the input vector.
`doGetEntryContentRequest()` | This method is called by `getEntryContentRequest()`, and must return a list of URLs used to retrieve entry contents. If `concatenate` parameter is `FALSE`, the list returned must be the same length as the vector `id` and each URL must point to one entry content only. If `concatenate` parameter is `TRUE`, then it is permitted (but not compulsory) to return URLs that get more than one entry at a time.
`doGetEntryIds()` | This method, called by `getEntryIds()`, should return the full list of accession numbers of the entries contained in the database, or a subset if `max.results` is set. This method is used for testing, in order to get a sample of existing entries, but may also be useful for users when developing.
`doSearchForEntries()` | This method implements the search of entries by filtering on some field values. For our example, we have kept it simple by implementing only the search by name (field `"name"`), because a full implementation with mass search would require much more code with complex calls to ChEBI API. You can however see a real implementation inside biodbChebi, the package that implements the ChEBI connector.

See the help inside R about `BiodbConn` for details about the parameters of those functions.

### 3.3.3 Remote connection methods

The remote methods are used for three different goals. First to build URLs that access the web site, to get the URL of an entry page (`doGetEntryPageUrl()`) or to get the URL of an entry picture (`doGetEntryImageUrl()`) like a molecule representation. Second to get a list of database entry identifiers (`doGetEntryIds()`). Third to Get the content of an entry (`doGetEntryContentRequest()`).

In our implementations of `doGetEntryPageUrl()`, `doGetEntryImageUrl()` and `doGetEntryContentRequest()` (see below), you may notice the use of the `getPropValSlot()` method to get some base URLs (`"base.url"`, `"ws.url"`). These values are defined inside the connector YAML definition file that we will detail below. Also, in those methods, we use the `BiobdUrl` class to build the URLs. `BiodbUrl` handles the building of the URL parameters, as well as the encoding of special characters.

### 3.3.4 Method for searching for entries

The implemented method (`doSearchForEntries()`) is a generic method used to search for entries inside the database by name, mass, or any other field. For our example we have decided to implement only the search by name in order to keep the code as simple and short as possible. To see a full implementation of this method, look at the official biodb ChEBI connector at biodbChebi. Inside the method’s code you will see that the implementation of the call to the ChEBI web service API has been left to the dedicated method `wsGetLiteEntity()`.

### 3.3.5 Prototype to respect for web service methods

In biodb official implementations of remote connectors, the implementations of calls to web services are done in separate dedicated methods having in common some principles.

These principles are important, because they assure a uniformity between biodb extension packages, allowing users to identify immediately a web service method and recognize the biodb generic parameters inside it.

Example of a web service method, taken from official biodb ChEBI extension package:

    wsGetLiteEntity=function(search=NULL, search.category='ALL', stars='ALL',
                             max.results=10,
                             retfmt=c('plain', 'parsed', 'request', 'ids')) {
    }

A web service method name must start with the prefix `ws`, which stands for web service, and be followed by the database API name of the web service written in Java style (i.e.: an uppercase letter for the start of each word and lowercase letters for the rest).

The first parameters of the method are the database web service parameters.

The last parameters (`max.results` and `retfmt`) are biodb specific.

`max.results` controls the maximum number of results wanted, and must have a default value (usually `10`).

`retfmt`, which stands for return format, controls the format of the method’s returned value. The default value of `retfmt` is set to a vector and then processed inside the method with the `match.arg()` method. Thus the “real” default value is the first value of the vector, which must always be `"plain"`. The set of possible values for `retfmt` is variable from one web service method to another. However some of the values are compulsory. See 7 for a full list of `retfmt` possible values officially accepted by biodb.

Table 7: `retfmt` accepted values
The list of values of `retfmt` officially accepted by biodb.
Value | Compulsory | Description
--- | --- | ---
`plain` | yes | Results are returned verbatim, without any change on the data returned by the server.
`parsed` | yes | Results are parsed according to the data format expected from the server (JSON, CSV, …) before being returned.
`request` | yes | Instead of returning the results of the query, the query is returned as a `BiodbRequest` object. The query is only built, and is never sent to the server.
`ids` | no | Results are returned as a character vector of entry identifiers.
`queryid` | no | This value is used when dealing with an asynchronous web service. The value returned is the ID of the asynchronous query extracted from the parsed results returned by the server. This query ID is then used to query the query status and to query the query results, usually with two other web services.
`status` | no | When dealing with an asynchronous web service query, this value asks for the current status of the query.
`data.frame` | no | Results are formatted into a data frame.

You may want to look into some of biodb implementations of connectors to official remote databases, and see how the calls to web services have been implemented in dedicated web service methods. See 8.

Table 8: biodb connectors to remote databases
Some of the biodb packages implementing connectors to official remote databases.
Package | Official database site
--- | ---
biodbChebi | ChEBI
biodbHmdb | HMDB
biodbKegg | KEGG
biodbUniprot | UniProt

### 3.3.6 Implementation

Here is our implementation of the connector class:

    ChebiExConn <- R6::R6Class("ChebiExConn",
    inherit=biodb::BiodbConn,

    public=list(

    initialize=function(...) {
        super$initialize(...)
    },

    wsGetLiteEntity=function(search=NULL, search.category='ALL', stars='ALL',
                             max.results=10,
                             retfmt=c('plain', 'parsed', 'request', 'ids')) {

        # Check parameters
        chk::chk_string(search)
        chk::chk_in(search.category, self$getSearchCategories())
        chk::chk_number(max.results)
        chk::chk_gte(max.results, 0)
        chk::chk_in(stars, self$getStarsCategories())
        retfmt <- match.arg(retfmt)

        # Build request
        params <- c(search=search,
                    searchCategory=search.category,
                    maximumResults=max.results,
                    starsCategory=stars)
        url <- c(self$getPropValSlot('urls', 'ws.url'), 'test/getLiteEntity')
        request <- self$makeRequest(method='get', url=BiodbUrl$new(url=url,
                                                                    params=params),
                                     encoding='UTF-8')
        if (retfmt == 'request')
            return(request)

        # Send request
        results <- self$getBiodb()$getRequestScheduler()$sendRequest(request)

        # Parse
        if (retfmt != 'plain') {

            # Parse XML
            results <-  XML::xmlInternalTreeParse(results, asText=TRUE)

            if (retfmt == 'ids') {
                ns <- self$getPropertyValue('xml.ns')
                results <- XML::xpathSApply(results, "//chebi:chebiId",
                                            XML::xmlValue, namespaces=ns)
                results <- sub('CHEBI:', '', results)
                if (length(grep("^[0-9]+$", results)) != length(results))
                    self$error("Impossible to parse XML to get entry IDs.")
            }
        }

        return(results)
    }
    ),

    private=list(
    doSearchForEntries=function(fields=NULL, max.results=0) {

        ids <- character()

        if ( ! is.null(fields)) {

            # Search by name
            if ('name' %in% names(fields))
                ids <- self$wsGetLiteEntity(search=fields$name,
                                             search.category="ALL NAMES",
                                             max.results=0, retfmt='ids')
        }

        # Cut
        if (max.results > 0 && max.results < length(ids))
            ids <- ids[seq_len(max.results)]

        return(ids)
    },

    doGetEntryContentRequest=function(id, concatenate=TRUE) {

        url <- c(self$getPropValSlot('urls', 'ws.url'), 'test',
                 'getCompleteEntity')

        urls <- vapply(id, function(x) BiodbUrl$new(url=url,
                                                params=list(chebiId=x))$toString(),
                       FUN.VALUE='')

        return(urls)
    },

    doGetEntryIds=function(max.results=NA_integer_) {
        return(NULL)
    },

    doGetEntryPageUrl=function(id) {
        # Overrides super class' method

        url <- c(self$getPropValSlot('urls', 'base.url'), 'searchId.do')

        fct <- function(x) {
            BiodbUrl$new(url=url, params=list(chebiId=x))$toString()
        }

        urls <- vapply(id, fct, FUN.VALUE='')

        return(urls)
    },

    doGetEntryImageUrl=function(id) {
        # Overrides super class' method

        url <- c(self$getPropValSlot('urls', 'base.url'), 'displayImage.do')

        fct <- function(x) {
            BiodbUrl$new(url=url, params=list(defaultImage='true', imageIndex=0,
                                          chebiId=x, dimensions=400))$toString()
        }

        urls <- vapply(id, fct, FUN.VALUE='')

        return(urls)
    }
    ))

Here is our implementation of the entry class:

    ChebiExEntry <- R6::R6Class("ChebiExEntry",
    inherit=BiodbXmlEntry,

    public=list(

    initialize=function(...) {
        super$initialize(...)
    }
    ),

    private=list(
    doCheck=function(content) {

        # You can do some more checks of the content here.

        return(TRUE)
    }

    ,doParseFieldsStep2=function(parsed.content) {

        # TODO Implement your custom parsing processing here.
    }

    ))

## 3.4 Using the new connector

To use the new connector, we first need to load the YAML definition file inside our biodb instance.

To start we create an instance of the `BiodbMain` class:

    mybiodb <- biodb::newInst()

    ## INFO  [19:22:38.175] Loading definitions from package biodb version 1.16.0.

The loading of the definitions is done with a call to `loadDefinitions()`:

    mybiodb$loadDefinitions(defFile)

Now our biodb instance is aware of our new connector, and is ready to create instances of it.

To create an instance of our new connector class, we proceeds as usual in biodb, by calling `createConn()` on the factory instance, using our connector identifier:

    conn <- mybiodb$getFactory()$createConn('chebi.ex')

Now we can retrieve a ChEBI entry from the remote database:

    entry <- conn$getEntry('17001')

    ## INFO  [19:22:38.637] Create cache folder "C:\Users\biocbuild\AppData\Local/R/cache/R/biodb/chebi.ex-0c5076ac2a43d16dbce503a44b09f649" for "chebi.ex-0c5076ac2a43d16dbce503a44b09f649".

    entry$getFieldsAsDataframe()

    ##   accession   formula
    ## 1     17001 C9H13N5O4
    ##                                                                                                                     inchi
    ## 1 InChI=1S/C9H13N5O4/c10-9-13-7-5(8(18)14-9)12-3(1-11-7)6(17)4(16)2-15/h4,6,15-17H,1-2H2,(H4,10,11,13,14,18)/t4-,6+/m1/s1
    ##                      inchikey molecular.mass monoisotopic.mass
    ## 1 YQIFAMYNGGOTFB-XINAWCOVSA-N       255.2308          255.0967
    ##                   name                                      smiles chebi.ex.id
    ## 1 7,8-dihydroneopterin Nc1nc2NCC(=Nc2c(=O)[nH]1)[C@H](O)[C@H](O)CO       17001

Do not forget to terminate your biodb instance once you are done with it:

    mybiodb$terminate()

    ## INFO  [19:22:40.088] Closing BiodbMain instance...
    ## INFO  [19:22:40.091] Connector "chebi.ex" deleted.

## 3.5 Other types of connectors and entries

We describe here the other types of connectors and entries that biodb provide. The generator that we have used to generate the package skeleton for `chebi.ex` can also be used to generate skeleton for all the types described here.

### 3.5.1 Connector for a local database

With biodb we can also write a connector for a local database. As a matter of fact, all the connectors included in biodb base package are local connectors only: `mass.csv.file`, `comp.csv.file` and `mass.sqlite`. See 9 for a list of methods to implement when writing a local connector.

Table 9: `BiodbConn` methods to implement
The list of methods to implement when inheriting from the `BiodbConn` class.
Method | Description
--- | ---
`doGetNbEntries()` | Must return the number of entries contained in the database.
`doGetEntryContentFromDb()` | Return the content(s), as strings, of one or more entries from the database.
`doDefineParsingExpressions()` | May be overriden in order to define parsing expressions dynamically (see `CsvFileConn` class for an example).
`doGetEntryIds()` | This method, called by `getEntryIds()`, should return the full list of accession numbers of the entries contained in the database, or a subset if `max.results` is set. This method is used for testing, in order to get a sample of existing entries, but may also be useful for users when developing.

### 3.5.2 Connector for a mass spectra database

In the example above, we have implemented a compound database. Another type of database is a mass spectra database. The following connectors included in biodb package are mass spectra database connectors: `mass.csv.file` and `mass.sqlite`. See 10 for a list of methods to implement when writing a mass spectra database connector.

Table 10: Methods to implement when defining a connector to a mass spectra database.
Method | Description
--- | ---
`doGetChromCol()` | Returns a data frame containing the description of the chromatographic columns.
`doGetNbPeaks()` | Returns the total number of MS peaks contained in the database.
`doGetMzValues()` | Returns a list of M/Z values contained inside the database, with the possibility of filtering on MS mode, MS level, and some other variables.
`doSearchMzRange()` | Searches for spectra using an M/Z range and optional filtering on some other variables.

### 3.5.3 Connector for a downloadable database

Some database servers do not propose web services, or other connection to the database, but propose to download the whole database for local processing.

biodb offers the possibility to handle the connection to such database servers, by setting `downloadable` to `TRUE` inside the definition of the database connector.

See 11 for a list of methods to implement inside your connector when writing a downloadable database connector.

Table 11: Methods to implement when defining a downloadable connector class.
Method | Description
--- | ---
`doesRequireDownload()` | This method must return TRUE if the connector requires to download files locally with the `BiodbDownloadable` interface.
`doDownload()` | This method must implement the download of the database file.
`doExtractDownload()` | This method must implement the extraction of the database files (e.g.: from a zip).

### 3.5.4 How to implement other types of entry classes

We have seen in the example how to parse XML entries by writing an entry class that inherits from the `BiodbXmlEntry` class. As stated before, biodb provides other types of abstract entry classes, that facilitate the parsing of diverse entry content formats. Here is a review of those formats.

#### 3.5.4.1 HTML content

To parse HTML content, your entry class should inherit from `BiodbHtmlEntry`. The parsing expressions must be written in XPath language, as for XML content, but it uses a special parsing algorithm since HTML is less strict than XML and allows some “illegal” constructs.

Example of a parsing expression:

    path: //input[@id='DATA']

#### 3.5.4.2 JSON content

To parse JSON content, your entry class should inherit from `BiodbJsonEntry`. The parsing expressions are written in the form of lists of keys to follow as a path inside the JSON tree. Here is an example:

    chrom.col.id:
    - liquidChromatography
    - columnCode

#### 3.5.4.3 List content

If your connector gets entry contents directly as an R list object, like in the case of `MassSqliteConn`, you have interest in making your entry class inherit from `BiodbListEntry` abstract class. With this class, the entry content is provided as a flat named R list object, although it is also possible to pass a JSON string containing flat key/value pairs instead. The parsing expressions are the names used inside the list object. Here is an example:

    accession: id
    compound.id: comp_id
    formula: chem_form

#### 3.5.4.4 CSV content

The `BiodbCsvEntry` class helps you handle entry content in CSV (using comma separator or any other character) format. When declaring the constructor for your own entry class, do not forget to call the mother class constructor to pass it your separator and/or the string values that have to be converted to `NA`:

    MyEntryClass <- R6::R6Class("MyEntryClass", inherit=biodb::BiodbCsvEntry,
        public=list(
            initialize=function() {
                super$initialize(sep=';', na.strings=c('', 'NA'))
            }
    ))

The parsing expressions are the column names of the CSV file:

    accession: id
    name: fullname

#### 3.5.4.5 SDF content

If your entry content is in SDF (Structure Data File) chemical file format, make you entry class inherit from `BiodbSdfEntry` abstract class. Since the SDF format is an official standard format, the parsing expressions are useless in this case, your class only has to inherit from `BiodbSdfEntry`.

#### 3.5.4.6 Text content

The `BiodbTxtEntry` abstract class allows you to handle any text file content for entries. Parsing expressions are defined as regular expressions, using the stringr package, hence in ICU Regular Expressions format.

Here is an example:

    accession: ^ENTRY\s+(\S+)\s+Compound
    exact.mass: ^EXACT_MASS\s+(\S+)$
    formula: ^FORMULA\s+(\S+)$

#### 3.5.4.7 Implementing your own parsing

If none of the predefined formats fits your needs, your class have to inherit directly from `BiodbEntry`.

Two methods have to be implemented in this case. The first is `doParseContent()`, which parses a string into the acceptable format for the second function, `doParseFieldsStep1()`.

Look for instance at the code of `BiodbTxtEntry` class for a good example. Here is an excerpt:

    doParseContent=function(content) {

        # Get lines of content
        lines <- strsplit(content, "\r?\n")[[1]]

        return(lines)
    },

    doParseFieldsStep1=function(parsed.content) {

        # Get parsing expressions
        parsing.expr <- .self$getParent()$getPropertyValue('parsing.expr')

        .self$.assertNotNull(parsed.content)
        .self$.assertNotNa(parsed.content)
        .self$.assertNotNull(parsing.expr)
        .self$.assertNotNa(parsing.expr)
        .self$.assertNotNull(names(parsing.expr))

        # Loop on all parsing expressions
        for (field in names(parsing.expr)) {

            # Match whole content
            g <- stringr::str_match(parsed.content, parsing.expr[[field]])

            # Get positive results
            results <- g[ ! is.na(g[, 1]), , drop=FALSE]

            # Any match ?
            if (nrow(results) > 0)
                .self$setFieldValue(field, results[, 2])
        }
    }

#### 3.5.4.8 Extending the parsing of an existing class

When inheriting from one of the abstract class listed above (`BiodbTxtEntry`, `BiodbJsonEntry`, `BiodbXmlEntry`, …), you also have the opportunity to write some custom parsing code by implementing `doParseFieldsStep2()`.

This method will be called just after `doParseFieldsStep1()`, which is implemented by the abstract class.

See `HmdbMetabolitesEntry` class inside biodbHmdb extension package for an example. Here is an extract:

    doParseFieldsStep2=function(parsed.content) {

        # Remove fields with empty string
        for (f in .self$getFieldNames()) {
            v <- .self$getFieldValue(f)
            if (is.character(v) && ! is.na(v) && v == '')
                .self$removeField(f)
        }

        # Correct InChIKey
        if (.self$hasField('INCHIKEY')) {
            v <- sub('^InChIKey=', '', .self$getFieldValue('INCHIKEY'), perl=TRUE)
            .self$setFieldValue('INCHIKEY', v)
        }

        # Synonyms
        synonyms <- XML::xpathSApply(parsed.content, "//synonym", XML::xmlValue)
        if (length(synonyms) > 0)
            .self$appendFieldValue('name', synonyms)
    }

# 4 Session information

    sessionInfo()

    ## R version 4.5.0 RC (2025-04-04 r88126 ucrt)
    ## Platform: x86_64-w64-mingw32/x64
    ## Running under: Windows Server 2022 x64 (build 20348)
    ##
    ## Matrix products: default
    ##   LAPACK version 3.12.1
    ##
    ## locale:
    ## [1] LC_COLLATE=en_US.UTF-8  LC_CTYPE=en_US.UTF-8    LC_MONETARY=en_US.UTF-8
    ## [4] LC_NUMERIC=C            LC_TIME=en_US.UTF-8
    ##
    ## time zone: America/New_York
    ## tzcode source: internal
    ##
    ## attached base packages:
    ## [1] stats     graphics  grDevices utils     datasets  methods   base
    ##
    ## other attached packages:
    ## [1] biodb_1.16.0     BiocStyle_2.36.0
    ##
    ## loaded via a namespace (and not attached):
    ##  [1] rappdirs_0.3.3       sass_0.4.10          generics_0.1.3
    ##  [4] bitops_1.0-9         stringi_1.8.7        RSQLite_2.3.9
    ##  [7] hms_1.1.3            digest_0.6.37        magrittr_2.0.3
    ## [10] evaluate_1.0.3       bookdown_0.43        fastmap_1.2.0
    ## [13] blob_1.2.4           plyr_1.8.9           jsonlite_2.0.0
    ## [16] progress_1.2.3       DBI_1.2.3            BiocManager_1.30.25
    ## [19] httr_1.4.7           XML_3.99-0.18        jquerylib_0.1.4
    ## [22] cli_3.6.4            rlang_1.1.6          chk_0.10.0
    ## [25] crayon_1.5.3         dbplyr_2.5.0         bit64_4.6.0-1
    ## [28] withr_3.0.2          cachem_1.1.0         yaml_2.3.10
    ## [31] tools_4.5.0          memoise_2.0.1        dplyr_1.1.4
    ## [34] filelock_1.0.3       curl_6.2.2           vctrs_0.6.5
    ## [37] R6_2.6.1             BiocFileCache_2.16.0 lifecycle_1.0.4
    ## [40] stringr_1.5.1        bit_4.6.0            pkgconfig_2.0.3
    ## [43] pillar_1.10.2        bslib_0.9.0          glue_1.8.0
    ## [46] Rcpp_1.0.14          lgr_0.4.4            xfun_0.52
    ## [49] tibble_3.2.1         tidyselect_1.2.1     knitr_1.50
    ## [52] htmltools_0.5.8.1    rmarkdown_2.29       compiler_4.5.0
    ## [55] prettyunits_1.2.0    askpass_1.2.1        RCurl_1.98-1.17
    ## [58] openssl_2.3.2