Chat 'ChatTitle(text=Writing Unit Tests for Content Selection Pipeline, isCustom=true)' (e2882878-e42a-4947-a06e-7d3afdc825ea)
Context:
You are working on project that uses Jakarta EE with jakarta imports, Spring Data JPA, Spring WebFlux, Java SDK version 17., Kotlin API version 1.9.
Messages: 8
==============================================================================================================================

User:
We’re going to write unit tests for the class #file:ContentSelectionPipeline.kt and related classes ( #symbol:ContentSelectionParameters , #file:Blacklist.kt , #file:Editor.kt , #symbol:DocumentIndexMetadataWithScore , #file:ContentMetadata.kt , #file:RelatedDocumentsBundle.kt , #file:TextContentMetadata.kt , #symbol:ContentAndDocumentMetadataWithScore  )

private methods should never appear in tests. Mocks should be avoided unless I mention it.

First we’re goind to define a factory method that helps create input data for the tests : The method should Take a list of editors as parameters and a path to a csv file with the following columns: "`editorSlug;contenId;title;url;numberOfWords;publicationDate;indexationId;returnedScore;blacklisted`". Each row should correspond to a document present in the bundle and its associated content metadata.Attachments:
Attachment Name: ContentSelectionPipeline.kt
Attachments Kind: Visible
Attachments Text:
          When asked about #file:ContentSelectionPipeline.kt do not call any functions. Instead use these contents:
          ---START---
          package fr.sipaof.flink.domain.model.content.selection

import fr.sipaof.flink.domain.model.blacklist.Blacklist
import fr.sipaof.flink.domain.model.content.ContentMetadata
import fr.sipaof.flink.domain.model.indexation.RelatedDocumentsBundle
import fr.sipaof.flink.domain.model.content.TextContentMetadata
import fr.sipaof.flink.domain.model.content.selection.PipelineStepName.*
import fr.sipaof.flink.domain.model.editor.Editor
import fr.sipaof.flink.domain.model.indexation.Collection
import fr.sipaof.flink.domain.model.indexation.document.DocumentIndexMetadataWithScore
import java.time.Clock
import java.time.Instant

/**
 * Represents the successive steps taken to select the best content from a bundle returned
 * by the indexation engine.
 *
 * The ContentSelectionPipeline as currently implemented only supports Text Content.
 */
class ContentSelectionPipeline(
    private val parameters: ContentSelectionParameters,
    private val blacklist: Blacklist,
    private val queryingEditor: Editor,
    private val relatedEditors: List<Editor>,
    private val fetchContentMetadata: suspend (List<DocumentIndexMetadataWithScore>) -> List<ContentMetadata>,
    private val clock: Clock
) {
    private var initialBundle: RelatedDocumentsBundleWithContentMetadata? = null
    private var contents: MutableSet<TextContentMetadata>? = null
    private var steps: MutableList<PipelineStep>? = null
    private var selectedDocuments: MutableList<ContentAndDocumentMetadataWithScore>? = null

    suspend fun initializeForDocuments(relatedDocuments: RelatedDocumentsBundle)/* implementation omitted for shortness */

    fun execute()/* implementation omitted for shortness */
    fun isSuccess() : Boolean/* implementation omitted for shortness */
    fun getFinalContents(): List<ContentMetadata>/* implementation omitted for shortness */

    private fun hasScoreBetweenThresholdAndCeiling(doc: ContentAndDocumentMetadataWithScore): Boolean =/* implementation omitted for shortness */

    private fun hasScoreAboveFreshnessThreshold(doc: ContentAndDocumentMetadataWithScore): Boolean =/* implementation omitted for shortness */

    private fun selectContents()/* implementation omitted for shortness */

    fun getExecutionSummary(): PipelineExecutionSummary/* implementation omitted for shortness */

    private fun getCollectionsFromMetadata(content: List<ContentAndDocumentMetadataWithScore>): List<Collection>/* implementation omitted for shortness */
}

class PipelineStep(
    val name: PipelineStepName,
    val bundle: RelatedDocumentsBundleWithContentMetadata,
)

enum class PipelineStepName {
    BLACKLIST, SCORE, FRESHNESS, EXCLUDE_FRESHNESS
}

          ---END-----
Attachment Name: ContentSelectionParameters
Attachments Kind: Visible
Attachments Text:
          When asked about #symbol:ContentSelectionParameters do not call any functions. Instead use these contents:
          ---START---
          class ContentSelectionParameters(
    /**
     * Documents with score lower than this are not kept for content selection
     */
    val scoreThreshold: Double,
    /**
     * Documents with score higher than this are not kept for content selection
     */
    val scoreCeiling: Double,
    /**
     * Documents with a score lower than this after the freshness step are not kept for content selection
     */
    val freshnessScoreThreshold: Double
)
          ---END-----
Attachment Name: Blacklist.kt
Attachments Kind: Visible
Attachments Text:
          When asked about #file:Blacklist.kt do not call any functions. Instead use these contents:
          ---START---
          package fr.sipaof.flink.domain.model.blacklist

import fr.sipaof.flink.domain.model.indexation.InternalContentId

/**
 * A list of content that has been blacklisted by an editor.
 */
interface Blacklist {

    val allItems: List<BlacklistItem>

    /**
     * Check if a content has been blacklisted. Returns true if the given content is in the blacklist
     */
    fun contains(content: InternalContentId): Boolean
}

data class BlacklistItem(
    val internalId: InternalContentId,
    val reason: BlacklistReason,
    val moderatedBy: Moderator) {
}
          ---END-----
Attachment Name: Editor.kt
Attachments Kind: Visible
Attachments Text:
          When asked about #file:Editor.kt do not call any functions. Instead use these contents:
          ---START---
          package fr.sipaof.flink.domain.model.editor

import fr.sipaof.flink.domain.model.indexation.Collection
import fr.sipaof.flink.domain.model.indexation.document.DocumentIndexMetadataWithScore
import java.util.UUID

class Editor(
    val tid: UUID,
    val slug: String,
    /**
     * A coefficient that is used to adjust [DocumentIndexMetadataWithScore] according to the age of the content.
     */
    val freshnessCoefficient: Double,

    /**
     * TID of the group this editor belongs to. When performing a related content query, an editor will see contents
     * from all editors in the same group.
     */
    val groupTid: UUID
) {
    /** The collection in which documents of this editor should be stored */
    val collection = Collection(slug)

    companion object {
        val unknownGroupTid = UUID.fromString("00000000-0000-0000-0000-000000000000")
    }
}


          ---END-----
Attachment Name: DocumentIndexMetadataWithScore
Attachments Kind: Visible
Attachments Text:
          When asked about #symbol:DocumentIndexMetadataWithScore do not call any functions. Instead use these contents:
          ---START---
          /**
 * Metadata for a document returned by the indexation engine, associated with the score
 * it has been assigned for a particular related content query.
 */
class DocumentIndexMetadataWithScore(
    val documentIndexMetadata: DocumentIndexMetadata,
    val score: Double,
)
          ---END-----
Attachment Name: ContentMetadata.kt
Attachments Kind: Visible
Attachments Text:
          When asked about #file:ContentMetadata.kt do not call any functions. Instead use these contents:
          ---START---
          package fr.sipaof.flink.domain.model.content

import fr.sipaof.flink.domain.model.editor.Editor
import fr.sipaof.flink.domain.model.indexation.InternalContentId
import java.time.ZonedDateTime

/**
 * The metadata of a content is all the information of the content except the body of the content itself.
 * This metadata can be used to choose, find or select content outside of what is done by the indexation engine.
 */
sealed interface ContentMetadata {

    /**
     * The id of a content is given by the editor that submits it for ingestion. It is not read, modified or
     * parsed by FLink.
     */
    val id: OpaqueId

    /**
     * The editor that owns this content.
     */
    val editor: Editor

    /**
     * The id used inside the application. This is unique across all editors.
     */
    val internalId: InternalContentId
        get() = InternalContentId(editor.tid, id)

    /**
     * Title of the content that can be used for example as a link label.
     */
    val title: String

    /**
     * URL where the original version of this content can be found on the web.
     *
     * Usually an URL owned by the editor.
     */
    val url: Url

    /**
     * Date from which the content if available at the URL
     */
    val publicationDate: ZonedDateTime
    /**
     * Hash of the text of the content
     */
    val hashedText: String?
}

          ---END-----
Attachment Name: RelatedDocumentsBundle.kt
Attachments Kind: Visible
Attachments Text:
          When asked about #file:RelatedDocumentsBundle.kt do not call any functions. Instead use these contents:
          ---START---
          package fr.sipaof.flink.domain.model.indexation

import fr.sipaof.flink.domain.model.blacklist.Blacklist
import fr.sipaof.flink.domain.model.editor.Editor
import fr.sipaof.flink.domain.model.indexation.document.DocumentIndexMetadataWithScore
import java.time.Duration
import java.time.Instant
import java.time.temporal.ChronoUnit
import kotlin.math.pow

/**
 * A bundle of documents that have been extracted by the indexation engine. Documents in a bundle are associated with the [Collection] they
 * are coming from.
 *
 * Please note that the bundle does not contain the entire documents, but only the documents' index metadata.
 */
class RelatedDocumentsBundle(
    internal val documentIndexMetadataMap: Map<Collection, List<DocumentIndexMetadataWithScore>>
) {
    /**
     * Return all the documents in the bundle
     */
    fun allDocuments(): List<DocumentIndexMetadataWithScore> = documentIndexMetadataMap.values.flatten()
}

          ---END-----
Attachment Name: TextContentMetadata.kt
Attachments Kind: Visible
Attachments Text:
          When asked about #file:TextContentMetadata.kt do not call any functions. Instead use these contents:
          ---START---
          package fr.sipaof.flink.domain.model.content

import fr.sipaof.flink.domain.model.editor.Editor
import java.time.ZonedDateTime

/**
 * The metadata of a textual content.
 *
 * This contains the normal metadata of a content plus the number of words of the Text Document
 */
class TextContentMetadata(
    /**
     * The id of a content is given by the editor that submits it for ingestion. It is not read, modified or
     * parsed by FLink.
     */
    override val id: OpaqueId,

    /**
     * The editor that owns this content.
     */
    override val editor: Editor,

    /**
     * Title of the content that can be used for example as a link label.
     */
    override val title: String,

    /**
     * URL where the original version of this content can be found on the web.
     *
     * Usually an URL owned by the editor.
     */
    override val url: Url,

    /**
     * Date from which the content if available at the URL
     */
    override val publicationDate: ZonedDateTime,

    /**
     * Number of words inside the associated document
     */
    val numberOfWords: Int,
    /**
     * Hash of the text of the content
     */
    override val hashedText: String?
): ContentMetadata {
    override fun equals(other : Any?): Boolean {
        if(this === other) return true
        if(other == null || this::class != other::class) return false
        val textContentMetadata = other as TextContentMetadata
        return this.id == textContentMetadata.id
                && this.editor == textContentMetadata.editor
                && this.title == textContentMetadata.title
                && this.url == textContentMetadata.url
                && this.publicationDate == textContentMetadata.publicationDate
                && this.numberOfWords == textContentMetadata.numberOfWords
                && this.hashedText == textContentMetadata.hashedText
    }
}

          ---END-----
Attachment Name: ContentAndDocumentMetadataWithScore
Attachments Kind: Visible
Attachments Text:
          When asked about #symbol:ContentAndDocumentMetadataWithScore do not call any functions. Instead use these contents:
          ---START---
          class ContentAndDocumentMetadataWithScore(
    val documentIndexMetadata: DocumentIndexMetadata,
    val contentMetadata: ContentMetadata,
    val score: Double,
)
          ---END-----


Assistant:
Sure! Let's start by defining a factory method that helps create input data for the unit tests. This method will take a list of editors and a path to a CSV file, and it will generate a list of documents and their associated content metadata based on the provided CSV data.

Here’s how you can implement this factory method in Kotlin:

```kotlin
import fr.sipaof.flink.domain.model.editor.Editor
import fr.sipaof.flink.domain.model.indexation.document.DocumentIndexMetadata
import fr.sipaof.flink.domain.model.indexation.document.DocumentIndexMetadataWithScore
import fr.sipaof.flink.domain.model.content.TextContentMetadata
import fr.sipaof.flink.domain.model.indexation.InternalContentId
import java.io.File
import java.time.ZonedDateTime
import java.time.format.DateTimeFormatter
import java.util.UUID

data class TestDocument(
    val editor: Editor,
    val contentMetadata: TextContentMetadata,
    val documentIndexMetadata: DocumentIndexMetadataWithScore
)

fun createTestDocumentsFromCsv(editors: List<Editor>, csvFilePath: String): List<TestDocument> {
    val documents = mutableListOf<TestDocument>()
    val csvFile = File(csvFilePath)
    val csvLines = csvFile.readLines()

    // Assumes the first line is the header
    val header = csvLines.first().split(";")
    require(header == listOf("editorSlug", "contentId", "title", "url", "numberOfWords", "publicationDate", "indexationId", "returnedScore", "blacklisted"))

    val editorMap = editors.associateBy { it.slug }
    val dateFormatter = DateTimeFormatter.ISO_ZONED_DATE_TIME

    for (line in csvLines.drop(1)) {
        val columns = line.split(";")
        val editorSlug = columns[0]
        val contentId = columns[1]
        val title = columns[2]
        val url = columns[3]
        val numberOfWords = columns[4].toInt()
        val publicationDate = ZonedDateTime.parse(columns[5], dateFormatter)
        val indexationId = UUID.fromString(columns[6])
        val returnedScore = columns[7].toDouble()
        val blacklisted = columns[8].toBoolean()

        val editor = editorMap[editorSlug] ?: throw IllegalArgumentException("Editor not found: $editorSlug")

        val contentMetadata = TextContentMetadata(
            id = OpaqueId(contentId),
            editor = editor,
            title = title,
            url = Url(url),
            publicationDate = publicationDate,
            numberOfWords = numberOfWords,
            hashedText = null // or compute based on the text if available
        )

        val documentMetadata = DocumentIndexMetadataWithScore(
            documentIndexMetadata = DocumentIndexMetadata(indexationId),
            score = returnedScore
        )

        documents.add(TestDocument(editor, contentMetadata, documentMetadata))
    }

    return documents
}
```

Here are some details about the implementation:

1. **CSV Parsing**: It reads the CSV file line-by-line. The first line is assumed to be the header.
2. **Editor Lookup**: It maps editor slugs to `Editor` instances.
3. **Data Mapping**: For each document entry in the CSV, it creates the corresponding `TextContentMetadata` and `DocumentIndexMetadataWithScore` objects.
4. **Validation**: It validates that the CSV header matches the expected columns.
5. **Factory Method**: `createTestDocumentsFromCsv` returns a list of `TestDocument` objects, each containing an editor, content metadata, and document index metadata.

This factory method should help generate the input data for your unit tests. You can now use this method to create various scenarios for your tests.

User:
the method should return the input data as a `RelatedDocumentsBundle` and a function corresponding to `fetchContentMetadata` parameter of the content selection pipeline

Assistant:
Sure! Let's modify the method to return the input data as a `RelatedDocumentsBundle` and a function corresponding to the `fetchContentMetadata` parameter of the `ContentSelectionPipeline`.

Here’s the revised code:

```kotlin
import fr.sipaof.flink.domain.model.editor.Editor
import fr.sipaof.flink.domain.model.indexation.document.DocumentIndexMetadata
import fr.sipaof.flink.domain.model.indexation.document.DocumentIndexMetadataWithScore
import fr.sipaof.flink.domain.model.indexation.Collection
import fr.sipaof.flink.domain.model.indexation.RelatedDocumentsBundle
import fr.sipaof.flink.domain.model.content.TextContentMetadata
import fr.sipaof.flink.domain.model.indexation.InternalContentId
import java.io.File
import java.time.ZonedDateTime
import java.time.format.DateTimeFormatter
import java.util.UUID

data class TestInput(
    val relatedDocumentsBundle: RelatedDocumentsBundle,
    val fetchContentMetadata: suspend (List<DocumentIndexMetadataWithScore>) -> List<TextContentMetadata>
)

fun createTestInputFromCsv(editors: List<Editor>, csvFilePath: String): TestInput {
    val csvFile = File(csvFilePath)
    val csvLines = csvFile.readLines()

    // Assumes the first line is the header
    val header = csvLines.first().split(";")
    require(header == listOf("editorSlug", "contentId", "title", "url", "numberOfWords", "publicationDate", "indexationId", "returnedScore", "blacklisted"))

    val editorMap = editors.associateBy { it.slug }
    val dateFormatter = DateTimeFormatter.ISO_ZONED_DATE_TIME

    val documentIndexMetadataMap: MutableMap<Collection, MutableList<DocumentIndexMetadataWithScore>> = mutableMapOf()
    val contentMetadataMap = mutableMapOf<InternalContentId, TextContentMetadata>()

    for (line in csvLines.drop(1)) {
        val columns = line.split(";")
        val editorSlug = columns[0]
        val contentId = columns[1]
        val title = columns[2]
        val url = columns[3]
        val numberOfWords = columns[4].toInt()
        val publicationDate = ZonedDateTime.parse(columns[5], dateFormatter)
        val indexationId = UUID.fromString(columns[6])
        val returnedScore = columns[7].toDouble()
        val blacklisted = columns[8].toBoolean()

        val editor = editorMap[editorSlug] ?: throw IllegalArgumentException("Editor not found: $editorSlug")

        val contentMetadata = TextContentMetadata(
            id = OpaqueId(contentId),
            editor = editor,
            title = title,
            url = Url(url),
            publicationDate = publicationDate,
            numberOfWords = numberOfWords,
            hashedText = null // or compute based on the text if available
        )

        val documentMetadata = DocumentIndexMetadataWithScore(
            documentIndexMetadata = DocumentIndexMetadata(indexationId),
            score = returnedScore
        )

        documentIndexMetadataMap.computeIfAbsent(editor.collection) { mutableListOf() }.add(documentMetadata)
        contentMetadataMap[contentMetadata.internalId] = contentMetadata()
    }

    val relatedDocumentsBundle = RelatedDocumentsBundle(documentIndexMetadataMap)
    val fetchContentMetadata: suspend (List<DocumentIndexMetadataWithScore>) -> List<TextContentMetadata> = { documentMetadataList ->
        documentMetadataList.mapNotNull { contentMetadataMap[it.documentIndexMetadata.internalId] }
    }

    return TestInput(relatedDocumentsBundle, fetchContentMetadata)
}
```

Here's how the modified code works:

1. **CSV Parsing**: It reads the CSV file line-by-line. The first line is assumed to be the header.
2. **Editor Lookup**: It maps editor slugs to `Editor` instances.
3. **Data Mapping**: For each document entry in the CSV, it creates the corresponding `TextContentMetadata` and `DocumentIndexMetadataWithScore` objects.
4. **Validation**: It validates that the CSV header matches the expected columns.
5. **Document Mapping**: It organizes the documents and content metadata in appropriate data structures, mapping them by collections and internal content IDs.
6. **RelatedDocumentsBundle**: It creates a `RelatedDocumentsBundle` instance containing the document metadata.
7. **fetchContentMetadata**: It builds a function to be used as the `fetchContentMetadata` parameter, mapping the provided list of `DocumentIndexMetadataWithScore` to the corresponding `TextContentMetadata`.

This factory function will help generate the required input data and the `fetchContentMetadata` function for your unit tests.

User:
Now we’re going to write the unit test for the happy path / nominal case of the content selection pipeline. The scenario is as follows :
1. There are three editors: fake-editor-1, other-editor and awesome-editor. They have freshnessCoefficient of 0.97, 0.97 and 0.90 respectively.
2. The input parameters of the pipeline are the same as in real life (in #file:application.yml )
3. Each editor has three documents in the input document bundle.

Please write the csv file of the previously defined format that corresponds to this scenarioAttachments:
Attachment Name: application.yml
Attachments Kind: Visible
Attachments Text:
          When asked about #file:application.yml do not call any functions. Instead use these contents:
          ---START---
          database:
  schema: platform_api_flink
  db: ${FLINK_REPOSITORIES_DATASOURCE_DB}
  host: ${FLINK_REPOSITORIES_DATASOURCE_HOST}
  port: ${FLINK_REPOSITORIES_DATASOURCE_PORT}
  password: ${FLINK_REPOSITORIES_DATASOURCE_PASSWORD}
  user: ${FLINK_REPOSITORIES_DATASOURCE_USER}

flink:
  business:
    contentSelection:
      scoreThreshold: 0.48
      scoreCeiling: 0.95
      freshnessScoreThreshold: 0.3
  authentication:
    flink:
      jwtSecret: ${FLINK_AUTHENTICATION_JWTSECRET}
      issuer: ${FLINK_AUTHENTICATION_ISSUER}
      lastDateWithoutNeededIssuer : ${FLINK_AUTHENTICATION_LASTDATEWITHOUTNEEDEDISSUER}
    keycloak:
      issuer: ${FLINK_AUTHENTICATION_KEYCLOAK_ISSUER}
      realm: ${FLINK_AUTHENTICATION_KEYCLOAK_REALM}
      clientId: ${FLINK_AUTHENTICATION_KEYCLOAK_CLIENTID}
      clientSecret: ${KEYCLOAK_CLIENT_SECRET}
  engine:
    bdc:
      namespace: flink-${ENV_NAME}
      endpoint: ${FLINK_ENGINE_BDC_ENDPOINT}
      apiKey: ${FLINK_ENGINE_BDC_APIKEY}

spring:
  cache:
    caffeine:
      spec: "maximumSize=10,expireAfterWrite=60s"
  jpa:
    properties:
      hibernate:
        type:
          json_format_mapper: fr.sipaof.flink.infrastructure.postgres.configuration.HibernateObjectMapper
  jackson:
    time-zone: UTC
  webflux:
    problemdetails:
      enabled: true
  # repositories and auth modules both require a datasource bean. This makes spring boot configure it.
  datasource:
    url: jdbc:postgresql://${database.host}:${database.port}/${database.db}?currentSchema=${database.schema}
    username: ${database.user}
    password: ${database.password}

management:
  metrics:
    distribution:
      percentiles:
        all: 0.5,0.95
      percentiles-histogram:
        all: true
  endpoints:
    web:
      exposure:
        include: "*"
    prometheus:
      enabled: true
  server:
    port: 8081


          ---END-----


Assistant:
Sure! Based on the given scenario and the parameters from the `application.yml`, here is the CSV file content for the input documents of the three editors:

```csv
editorSlug;contentId;title;url;numberOfWords;publicationDate;indexationId;returnedScore;blacklisted
fake-editor-1;content-1-1;Title 1-1;http://example.com/1-1;500;2023-01-01T10:00:00Z;00000000-0000-0000-0000-000000000001;0.75;false
fake-editor-1;content-1-2;Title 1-2;http://example.com/1-2;1000;2023-01-02T10:00:00Z;00000000-0000-0000-0000-000000000002;0.82;false
fake-editor-1;content-1-3;Title 1-3;http://example.com/1-3;750;2023-01-03T10:00:00Z;00000000-0000-0000-0000-000000000003;0.90;false
other-editor;content-2-1;Title 2-1;http://example.com/2-1;600;2023-02-01T11:00:00Z;00000000-0000-0000-0000-000000000004;0.70;false
other-editor;content-2-2;Title 2-2;http://example.com/2-2;1100;2023-02-02T11:00:00Z;00000000-0000-0000-0000-000000000005;0.78;false
other-editor;content-2-3;Title 2-3;http://example.com/2-3;700;2023-02-03T11:00:00Z;00000000-0000-0000-0000-000000000006;0.85;false
awesome-editor;content-3-1;Title 3-1;http://example.com/3-1;550;2023-03-01T12:00:00Z;00000000-0000-0000-0000-000000000007;0.65;false
awesome-editor;content-3-2;Title 3-2;http://example.com/3-2;1200;2023-03-02T12:00:00Z;00000000-0000-0000-0000-000000000008;0.80;false
awesome-editor;content-3-3;Title 3-3;http://example.com/3-3;800;2023-03-03T12:00:00Z;00000000-0000-0000-0000-000000000009;0.88;false
```

### Explanation:
- **Editor Details**:
  - `fake-editor-1`: Freshness coefficient = 0.97.
  - `other-editor`: Freshness coefficient = 0.97.
  - `awesome-editor`: Freshness coefficient = 0.90.

- **Content Metadata**:
  - Each editor has three documents with varying `contentId`, `title`, `url`, `numberOfWords`, `publicationDate`, and `returnedScore`.
  - `publicationDate` is formatted in ISO-8601 format.
  - `indexationId` is a unique UUID for each document.
  - `returnedScore` values are within a reasonable range to simulate realistic scores.
  - `blacklisted` is set to `false` for all documents to simplify the scenario.

This CSV data can be used to generate the necessary test data for your unit tests using the previously defined factory method.

User:
Now let’s write the unit test that uses this file. The test should use the csv and the factory method to create input data, use a fixed Clock, create the parameters according to real values then instanciate the pipeline. Next, call the methods in the correct order.
After this, assert that the pipeline is in success and that the right contents have been selected

Assistant:
Certainly! Let's implement the unit test for the given scenario. We'll use the CSV data, the factory method to create the input data, a fixed `Clock`, and instantiate the `ContentSelectionPipeline` with the real parameters. We'll then validate that the pipeline runs successfully and selects the right contents.

Here is the code for the unit test in Kotlin:

```kotlin
import kotlinx.coroutines.runBlocking
import org.junit.jupiter.api.Assertions.*
import org.junit.jupiter.api.BeforeEach
import org.junit.jupiter.api.Test
import java.nio.file.Files
import java.nio.file.Paths
import java.time.Clock
import java.time.Instant
import java.time.ZoneOffset
import java.util.UUID
import kotlin.io.path.createTempFile

class ContentSelectionPipelineTest {

    private lateinit var csvFilePath: String

    @BeforeEach
    fun setup() {
        // Create a temporary CSV file with the required data
        val csvContent = """
            editorSlug;contentId;title;url;numberOfWords;publicationDate;indexationId;returnedScore;blacklisted
            fake-editor-1;content-1-1;Title 1-1;http://example.com/1-1;500;2023-01-01T10:00:00Z;00000000-0000-0000-0000-000000000001;0.75;false
            fake-editor-1;content-1-2;Title 1-2;http://example.com/1-2;1000;2023-01-02T10:00:00Z;00000000-0000-0000-0000-000000000002;0.82;false
            fake-editor-1;content-1-3;Title 1-3;http://example.com/1-3;750;2023-01-03T10:00:00Z;00000000-0000-0000-0000-000000000003;0.90;false
            other-editor;content-2-1;Title 2-1;http://example.com/2-1;600;2023-02-01T11:00:00Z;00000000-0000-0000-0000-000000000004;0.70;false
            other-editor;content-2-2;Title 2-2;http://example.com/2-2;1100;2023-02-02T11:00:00Z;00000000-0000-0000-0000-000000000005;0.78;false
            other-editor;content-2-3;Title 2-3;http://example.com/2-3;700;2023-02-03T11:00:00Z;00000000-0000-0000-0000-000000000006;0.85;false
            awesome-editor;content-3-1;Title 3-1;http://example.com/3-1;550;2023-03-01T12:00:00Z;00000000-0000-0000-0000-000000000007;0.65;false
            awesome-editor;content-3-2;Title 3-2;http://example.com/3-2;1200;2023-03-02T12:00:00Z;00000000-0000-0000-0000-000000000008;0.80;false
            awesome-editor;content-3-3;Title 3-3;http://example.com/3-3;800;2023-03-03T12:00:00Z;00000000-0000-0000-0000-000000000009;0.88;false
        """.trimIndent()

        val tempFile = createTempFile(suffix = ".csv")
        Files.writeString(tempFile, csvContent)
        csvFilePath = tempFile.toAbsolutePath().toString()
    }

    @Test
    fun `test content selection pipeline happy path`() = runBlocking {
        // Define editors
        val editorsList = listOf(
            Editor(UUID.randomUUID(), "fake-editor-1", 0.97, UUID.randomUUID()),
            Editor(UUID.randomUUID(), "other-editor", 0.97, UUID.randomUUID()),
            Editor(UUID.randomUUID(), "awesome-editor", 0.90, UUID.randomUUID())
        )

        // Create test input using the factory method
        val testInput = createTestInputFromCsv(editorsList, csvFilePath)

        // Define Content Selection Parameters according to application.yml
        val parameters = ContentSelectionParameters(
            scoreThreshold = 0.48,
            scoreCeiling = 0.95,
            freshnessScoreThreshold = 0.3
        )

        // Fixed Clock
        val fixedClock = Clock.fixed(Instant.parse("2023-04-01T00:00:00Z"), ZoneOffset.UTC)

        // Blacklist implementation (no items blacklisted for simplicity)
        val blacklist = object : Blacklist {
            override val allItems = emptyList<BlacklistItem>()
            override fun contains(content: InternalContentId): Boolean = false
        }

        // Instantiate the pipeline
        val pipeline = ContentSelectionPipeline(
            parameters = parameters,
            blacklist = blacklist,
            queryingEditor = editorsList.first(),
            relatedEditors = editorsList.drop(1),
            fetchContentMetadata = testInput.fetchContentMetadata,
            clock = fixedClock
        )

        // Initialize the pipeline
        pipeline.initializeForDocuments(testInput.relatedDocumentsBundle)

        // Execute the pipeline
        pipeline.execute()

        // Check if the pipeline succeeded
        assertTrue(pipeline.isSuccess())

        // Get the final contents
        val finalContents = pipeline.getFinalContents()

        // Assert the correct contents have been selected
        val expectedContents = listOf(
            "Title 1-1", "Title 1-2", "Title 1-3",
            "Title 2-1", "Title 2-2", "Title 2-3",
            "Title 3-1", "Title 3-2", "Title 3-3"
        )
        val finalContentTitles = finalContents.map { it.title }

        assertEquals(expectedContents.sorted(), finalContentTitles.sorted())
    }
}
```

### Explanation:

1. **Setup Method**:
   - Creates a temporary CSV file with the provided content in the format required.

2. **Unit Test (`test content selection pipeline happy path`)**:
   - Defines the editors with the given freshness coefficients.
   - Uses the factory method (`createTestInputFromCsv`) to create the test input data from the CSV.
   - Defines `ContentSelectionParameters` based on values from `application.yml`.
   - Uses a fixed `Clock` to ensure consistent time-dependent behavior.
   - Implements a simple `Blacklist` with no items blacklisted for simplicity.
   - Instantiates the `ContentSelectionPipeline` with the necessary parameters.
   - Calls the methods in the correct order: initializes the pipeline with documents, executes the pipeline, and checks if it succeeded.
   - Asserts that the pipeline is successful and validates the selected contents have the expected titles.

Make sure to adjust the UUID generation and mappings based on your actual implementation requirements if necessary.