Create JVM compatible versions of suspend functions. Add annotations …

…to replace names in JVM compiled libraries.
theam · Aug 2, 2023 · 536885a · 536885a
1 parent 243bb3b
commit 536885a
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diff --git a/modules/semanticsearch/build.gradle.kts b/modules/semanticsearch/build.gradle.kts
@@ -10,4 +10,5 @@ dependencies {
     implementation(projects.core)
     implementation(projects.modules.embeddingsmodel)
     implementation(projects.modules.vectorstore)
+    implementation("org.jetbrains.kotlinx:kotlinx-coroutines-jdk8:1.7.3")
 }
diff --git a/...nticsearch/src/main/kotlin/com/theagilemonkeys/ellmental/semanticsearch/SemanticSearch.kt b/...nticsearch/src/main/kotlin/com/theagilemonkeys/ellmental/semanticsearch/SemanticSearch.kt
@@ -3,6 +3,9 @@ package com.theagilemonkeys.ellmental.semanticsearch
 import com.theagilemonkeys.ellmental.core.schema.SemanticEntry
 import com.theagilemonkeys.ellmental.embeddingsmodel.EmbeddingsModel
 import com.theagilemonkeys.ellmental.vectorstore.VectorStore
+import kotlinx.coroutines.DelicateCoroutinesApi
+import kotlinx.coroutines.GlobalScope
+import kotlinx.coroutines.future.future
 import kotlinx.serialization.Serializable
 
 context(EmbeddingsModel<Any>, VectorStore)
@@ -21,6 +24,7 @@ class SemanticSearch {
      *
      * @param input A list of texts to be learned.
      */
+    @JvmName("learn\$Kotlin")
     suspend fun learn(input: SearchInput) =
         input.texts.forEach { text ->
             check(text.isNotBlank()) { "Text cannot be blank" }
@@ -29,6 +33,21 @@ class SemanticSearch {
             upsert(semanticEntry)
         }
 
+    /**
+     *
+     * "Learns" a list of texts to make them available in future semantic searches. It uses the
+     * [EmbeddingsModel] to calculate text embeddings for each piece of text. Then it uses the
+     * [VectorStore] to persist them.
+     *
+     * @param input A list of texts to be learned.
+     *
+     * (JVM compatible version)
+     */
+    @OptIn(DelicateCoroutinesApi::class)
+    @JvmName("learn")
+    suspend fun learnCompletableFuture(input: SearchInput) =
+        GlobalScope.future { learn(input) }
+
     /**
      * Performs a semantic search operation using the provided text as reference. It will look for
      * texts stored in the [VectorStore] that are "semantically close" to the provided one and return
@@ -37,12 +56,28 @@ class SemanticSearch {
      * @param text The text to be used as reference for semantic search.
      * @return A list of semantically similar texts ranked by semantic distance (the closest first).
      */
+    @JvmName("search\$Kotlin")
     suspend fun search(text: String): SearchOutput {
         check(text.isNotBlank()) { "Text cannot be blank" }
         val embedding = embed(text)
         val semanticEntry = SemanticEntry(content = text, embedding = embedding)
         return SearchOutput(query(semanticEntry).entries)
     }
+
+    /**
+     * Performs a semantic search operation using the provided text as reference. It will look for
+     * texts stored in the [VectorStore] that are "semantically close" to the provided one and return
+     * a ranked list of results.
+     *
+     * @param text The text to be used as reference for semantic search.
+     * @return A list of semantically similar texts ranked by semantic distance (the closest first).
+     *
+     * (JVM compatible version)
+     */
+    @OptIn(DelicateCoroutinesApi::class)
+    @JvmName("search")
+    suspend fun searchCompletableFuture(text: String) =
+        GlobalScope.future { search(text) }
 }
 
 @Serializable