Add Document structure with source to VectorStore

core/src/commonMain/kotlin/com/xebia/functional/xef/conversation/Conversation.kt

@@ -16,18 +16,6 @@ constructor(
   val conversationId: ConversationId? = ConversationId(UUID.generateUUID().toString())
 ) {
 
-  @AiDsl
-  @JvmSynthetic
-  suspend fun addContext(vararg docs: String) {
-    store.addTexts(docs.toList())
-  }
-
-  @AiDsl
-  @JvmSynthetic
-  suspend fun addContext(docs: Iterable<String>): Unit {
-    store.addTexts(docs.toList())
-  }
-
   companion object {
 
     @JvmSynthetic

core/src/commonMain/kotlin/com/xebia/functional/xef/store/CombinedVectorStore.kt

@@ -27,15 +27,18 @@ class CombinedVectorStore(private val top: VectorStore, private val bottom: Vect
       .reversed()
   }
 
-  override suspend fun similaritySearch(query: String, limit: Int): List<String> {
+  override suspend fun similaritySearch(query: String, limit: Int): List<VectorStore.Document> {
     val topResults = top.similaritySearch(query, limit)
     return when {
       topResults.size >= limit -> topResults
       else -> topResults + bottom.similaritySearch(query, limit - topResults.size)
     }
   }
 
-  override suspend fun similaritySearchByVector(embedding: Embedding, limit: Int): List<String> {
+  override suspend fun similaritySearchByVector(
+    embedding: Embedding,
+    limit: Int
+  ): List<VectorStore.Document> {
     val topResults = top.similaritySearchByVector(embedding, limit)
     return when {
       topResults.size >= limit -> topResults

core/src/commonMain/kotlin/com/xebia/functional/xef/store/LocalVectorStore.kt

@@ -15,7 +15,7 @@ import kotlin.math.sqrt
 
 private data class State(
   val orderedMemories: Map<ConversationId, List<Memory>>,
-  val documents: List<String>,
+  val documents: List<VectorStore.Document>,
   val precomputedEmbeddings: Map<String, Embedding>
 ) {
   companion object {
@@ -75,26 +75,30 @@ private constructor(
       .reversed()
   }
 
-  override suspend fun addTexts(texts: List<String>) {
+  override suspend fun addDocuments(texts: List<VectorStore.Document>) {
+    val docsContent = texts.map { it.content }
     val embeddingsList =
-      embeddings.embedDocuments(texts, embeddingRequestModel = embeddingRequestModel)
+      embeddings.embedDocuments(docsContent, embeddingRequestModel = embeddingRequestModel)
     state.getAndUpdate { prevState ->
-      val newEmbeddings = prevState.precomputedEmbeddings + texts.zip(embeddingsList)
+      val newEmbeddings = prevState.precomputedEmbeddings + docsContent.zip(embeddingsList)
       State(prevState.orderedMemories, prevState.documents + texts, newEmbeddings)
     }
   }
 
-  override suspend fun similaritySearch(query: String, limit: Int): List<String> {
+  override suspend fun similaritySearch(query: String, limit: Int): List<VectorStore.Document> {
     val queryEmbedding =
       embeddings.embedQuery(query, embeddingRequestModel = embeddingRequestModel).firstOrNull()
     return queryEmbedding?.let { similaritySearchByVector(it, limit) }.orEmpty()
   }
 
-  override suspend fun similaritySearchByVector(embedding: Embedding, limit: Int): List<String> {
+  override suspend fun similaritySearchByVector(
+    embedding: Embedding,
+    limit: Int
+  ): List<VectorStore.Document> {
     val state0 = state.get()
     return state0.documents
       .asSequence()
-      .mapNotNull { doc -> state0.precomputedEmbeddings[doc]?.let { doc to it } }
+      .mapNotNull { doc -> state0.precomputedEmbeddings[doc.content]?.let { doc to it } }
       .map { (doc, e) -> doc to embedding.cosineSimilarity(e) }
       .sortedByDescending { (_, similarity) -> similarity }
       .take(limit)
@@ -103,9 +107,9 @@ private constructor(
   }
 
   private fun Embedding.cosineSimilarity(other: Embedding): Double {
-    val dotProduct = this.embedding.zip(other.embedding).sumOf { (a, b) -> (a * b).toDouble() }
-    val magnitudeA = sqrt(this.embedding.sumOf { (it * it).toDouble() })
-    val magnitudeB = sqrt(other.embedding.sumOf { (it * it).toDouble() })
+    val dotProduct = this.embedding.zip(other.embedding).sumOf { (a, b) -> (a * b) }
+    val magnitudeA = sqrt(this.embedding.sumOf { (it * it) })
+    val magnitudeB = sqrt(other.embedding.sumOf { (it * it) })
     return dotProduct / (magnitudeA * magnitudeB)
   }
 }

core/src/commonMain/kotlin/com/xebia/functional/xef/store/VectorStore.kt

@@ -3,10 +3,22 @@ package com.xebia.functional.xef.store
 import arrow.atomic.AtomicInt
 import com.xebia.functional.openai.generated.model.CreateChatCompletionRequestModel
 import com.xebia.functional.openai.generated.model.Embedding
+import com.xebia.functional.xef.Config
 import kotlin.jvm.JvmStatic
+import kotlinx.serialization.Serializable
 
 interface VectorStore {
 
+  @Serializable
+  data class Document(val content: String, val source: String) {
+    fun toJson(): String = Config.DEFAULT.json.encodeToString(serializer(), this)
+
+    companion object {
+      fun fromJson(json: String): Document =
+        Config.DEFAULT.json.decodeFromString(serializer(), json)
+    }
+  }
+
   val indexValue: AtomicInt
 
   fun incrementIndexAndGet(): Int = indexValue.addAndGet(1)
@@ -27,9 +39,9 @@ interface VectorStore {
    * @param texts list of text to add to the vector store
    * @return a list of IDs from adding the texts to the vector store
    */
-  suspend fun addTexts(texts: List<String>)
+  suspend fun addDocuments(texts: List<Document>)
 
-  suspend fun addText(texts: String) = addTexts(listOf(texts))
+  suspend fun addDocument(texts: Document) = addDocuments(listOf(texts))
 
   /**
    * Return the docs most similar to the query
@@ -38,7 +50,7 @@ interface VectorStore {
    * @param limit number of documents to return
    * @return a list of Documents most similar to query
    */
-  suspend fun similaritySearch(query: String, limit: Int): List<String>
+  suspend fun similaritySearch(query: String, limit: Int): List<Document>
 
   /**
    * Return the docs most similar to the embedding
@@ -47,7 +59,7 @@ interface VectorStore {
    * @param limit number of documents to return
    * @return list of Documents most similar to the embedding
    */
-  suspend fun similaritySearchByVector(embedding: Embedding, limit: Int): List<String>
+  suspend fun similaritySearchByVector(embedding: Embedding, limit: Int): List<Document>
 
   companion object {
     @JvmStatic
@@ -65,14 +77,15 @@ interface VectorStore {
           limitTokens: Int
         ): List<Memory> = emptyList()
 
-        override suspend fun addTexts(texts: List<String>) {}
+        override suspend fun addDocuments(texts: List<Document>) {}
 
-        override suspend fun similaritySearch(query: String, limit: Int): List<String> = emptyList()
+        override suspend fun similaritySearch(query: String, limit: Int): List<Document> =
+          emptyList()
 
         override suspend fun similaritySearchByVector(
           embedding: Embedding,
           limit: Int
-        ): List<String> = emptyList()
+        ): List<Document> = emptyList()
       }
   }
 }

examples/src/main/kotlin/com/xebia/functional/xef/vectorstore/VectorStoreExample.kt

@@ -0,0 +1,18 @@
+package com.xebia.functional.xef.vectorstore
+
+import com.xebia.functional.xef.OpenAI
+import com.xebia.functional.xef.store.LocalVectorStore
+import com.xebia.functional.xef.store.VectorStore.Document
+
+suspend fun main() {
+  val embeddings = OpenAI().embeddings
+  val vectorStore = LocalVectorStore(embeddings)
+  val helloDoc = Document("Hello, how are you?", "source1")
+  val unrelatedDoc = Document("Unrelated text", "source2")
+  vectorStore.addDocuments(listOf(helloDoc, unrelatedDoc))
+  val maybeHelloDoc = vectorStore.similaritySearch("Hello", 1).first()
+  assert(maybeHelloDoc == helloDoc) { "Expected $helloDoc but got $maybeHelloDoc" }
+  val maybeUnrelatedDoc = vectorStore.similaritySearch("Unrelated", 1).first()
+  assert(maybeUnrelatedDoc == unrelatedDoc) { "Expected $unrelatedDoc but got $maybeUnrelatedDoc" }
+  println("All expected documents found!")
+}

integrations/postgresql/src/main/kotlin/com/xebia/functional/xef/store/PostgreSQLVectorStore.kt

@@ -84,22 +84,23 @@ class PGVectorStore(
       }
     }
 
-  override suspend fun addTexts(texts: List<String>): Unit =
+  override suspend fun addDocuments(texts: List<VectorStore.Document>): Unit =
     dataSource.connection {
-      val embeddings = embeddings.embedDocuments(texts, chunkSize, embeddingRequestModel)
+      val docsContent = texts.map { it.content }
+      val embeddings = embeddings.embedDocuments(docsContent, chunkSize, embeddingRequestModel)
       val collection = getCollection(collectionName)
       texts.zip(embeddings) { text, embedding ->
         val uuid = UUID.generateUUID()
         update(addNewText) {
           bind(uuid.toString())
           bind(collection.uuid.toString())
           bind(embedding.embedding.toString())
-          bind(text)
+          bind(text.toJson())
         }
       }
     }
 
-  override suspend fun similaritySearch(query: String, limit: Int): List<String> =
+  override suspend fun similaritySearch(query: String, limit: Int): List<VectorStore.Document> =
     dataSource.connection {
       val collection = getCollection(collectionName)
 
@@ -123,10 +124,12 @@ class PGVectorStore(
         }
       ) {
         string()
+      }.map { json ->
+        VectorStore.Document.fromJson(json)
       }
     }
 
-  override suspend fun similaritySearchByVector(embedding: Embedding, limit: Int): List<String> =
+  override suspend fun similaritySearchByVector(embedding: Embedding, limit: Int): List<VectorStore.Document> =
     dataSource.connection {
       val collection = getCollection(collectionName)
       queryAsList(
@@ -138,6 +141,8 @@ class PGVectorStore(
         }
       ) {
         string()
+      }.map { json ->
+        VectorStore.Document.fromJson(json)
       }
     }
 

integrations/postgresql/src/test/kotlin/xef/PGVectorStoreSpec.kt

@@ -4,6 +4,7 @@ import com.xebia.functional.openai.generated.model.CreateChatCompletionRequestMo
 import com.xebia.functional.openai.generated.model.CreateEmbeddingRequestModel
 import com.xebia.functional.openai.generated.model.Embedding
 import com.xebia.functional.xef.store.PGVectorStore
+import com.xebia.functional.xef.store.VectorStore
 import com.xebia.functional.xef.store.migrations.runDatabaseMigrations
 import com.xebia.functional.xef.store.postgresql.PGDistanceStrategy
 import com.zaxxer.hikari.HikariConfig
@@ -17,7 +18,6 @@ import io.kotest.matchers.shouldBe
 import org.junit.jupiter.api.assertThrows
 import org.testcontainers.containers.PostgreSQLContainer
 import org.testcontainers.utility.DockerImageName
-import kotlin.coroutines.coroutineContext
 
 val postgres: PostgreSQLContainer<Nothing> =
   PostgreSQLContainer(
@@ -67,10 +67,12 @@ class PGVectorStoreSpec :
       postgresVector.createCollection()
     }
 
+    val docs = listOf(VectorStore.Document(content = "foo", source = "tests"), VectorStore.Document(content = "bar", source = "tests"))
+
     "initialDbSetup should configure the DB properly" { pg().initialDbSetup() }
 
     "addTexts should fail with a CollectionNotFoundError if collection isn't present in the DB" {
-      assertThrows<IllegalStateException> { pg().addTexts(listOf("foo", "bar")) }.message shouldBe
+      assertThrows<IllegalStateException> { pg().addDocuments(docs) }.message shouldBe
         "Collection 'test_collection' not found"
     }
 
@@ -82,13 +84,13 @@ class PGVectorStoreSpec :
     "createCollection should create collection" { pg().createCollection() }
 
     "addTexts should not fail now that we created the collection" {
-      pg().addTexts(listOf("foo", "bar"))
+      pg().addDocuments(docs)
     }
 
     "similaritySearchByVector should return both documents" {
-      pg().addTexts(listOf("bar", "foo"))
+      pg().addDocuments(docs.reversed())
       pg().similaritySearchByVector(Embedding(0, listOf(4.0, 5.0, 6.0), Embedding.Object.embedding), 2) shouldBe
-        listOf("bar", "foo")
+        docs.reversed()
     }
 
     "similaritySearch should return 2 documents" {
@@ -104,7 +106,7 @@ class PGVectorStoreSpec :
       pg().similaritySearchByVector(
         Embedding(0, listOf(1.0, 2.0, 3.0), Embedding.Object.embedding),
         1
-      ) shouldBe listOf("foo")
+      ) shouldBe listOf(docs[0])
     }
 
     "the added memories sorted by index should be obtained in the same order" {