{"slug": "build-a-rag-based-ai-assistant-in-kotlin-with-a-vector-database", "title": "Build a RAG-Based AI Assistant in Kotlin with a Vector Database", "summary": "A developer demonstrates how to build a Retrieval-Augmented Generation (RAG) based AI assistant in Kotlin, using a vector database to enhance LLM responses with private or frequently changing documents. The tutorial outlines the architecture, including document chunking, embeddings, vector search, and a clean Android UI with ViewModel and Repository patterns. It emphasizes keeping the vector database and LLM on the backend and returning sources for verification.", "body_md": "A normal LLM answers questions from information contained in its model. A Retrieval-Augmented Generation (RAG) system adds an external knowledge layer so an application can answer questions using private or frequently changing documents.\n\nIn this tutorial, we will build the architecture for a Kotlin client that communicates with a backend RAG service.\n\nSuppose an application contains company documentation.\n\nInstead of sending the entire documentation to an LLM for every question, the system can:\n\n```\nDocuments\n   ↓\nChunking\n   ↓\nEmbeddings\n   ↓\nVector Database\n```\n\nAt query time:\n\n```\nUser Question\n   ↓\nEmbedding\n   ↓\nVector Search\n   ↓\nRelevant Chunks\n   ↓\nLLM\n   ↓\nAnswer\n```\n\nA clean Android architecture can look like:\n\n```\nCompose UI\n   |\nViewModel\n   |\nRagRepository\n   |\nAPI Client\n   |\nRAG Backend\n```\n\nThe vector database and LLM should normally remain on the backend rather than being exposed directly to the mobile application.\n\nDefine a request:\n\n```\ndata class AskRequest(\n    val question: String,\n    val conversationId: String?\n)\n```\n\nAnd a response:\n\n```\ndata class AskResponse(\n    val answer: String,\n    val sources: List<Source>\n)\n\ndata class Source(\n    val title: String,\n    val chunk: String\n)\n```\n\nA Retrofit interface can expose the backend:\n\n```\ninterface RagApi {\n\n    @POST(\"api/ask\")\n    suspend fun ask(\n        @Body request: AskRequest\n    ): AskResponse\n}\n```\n\nThe backend endpoint is responsible for retrieval and generation.\n\nKeep network details outside the ViewModel:\n\n```\nclass RagRepository(\n    private val api: RagApi\n) {\n    suspend fun ask(\n        question: String,\n        conversationId: String?\n    ): AskResponse {\n        return api.ask(\n            AskRequest(question, conversationId)\n        )\n    }\n}\n```\n\nUse a UI state model:\n\n```\nsealed interface ChatState {\n    data object Idle : ChatState\n    data object Loading : ChatState\n    data class Success(val response: AskResponse) : ChatState\n    data class Error(val message: String) : ChatState\n}\n```\n\nThen:\n\n```\nfun ask(question: String) {\n    viewModelScope.launch {\n        state.value = ChatState.Loading\n\n        try {\n            val response = repository.ask(\n                question,\n                conversationId\n            )\n\n            state.value = ChatState.Success(response)\n        } catch (e: Exception) {\n            state.value = ChatState.Error(\n                e.message ?: \"Request failed\"\n            )\n        }\n    }\n}\n```\n\nThe backend ingestion pipeline might look like:\n\n```\nPDF / Markdown / HTML\n       ↓\nText Extraction\n       ↓\nChunking\n       ↓\nEmbedding Model\n       ↓\nVector Database\n```\n\nA chunk might contain:\n\n```\n{\n  \"text\": \"Kotlin coroutines provide structured concurrency...\",\n  \"document\": \"kotlin-guide.md\",\n  \"section\": \"Coroutines\"\n}\n```\n\nThe metadata is useful when displaying citations to the user.\n\nAn embedding model converts text into a vector.\n\nConceptually:\n\n```\n\"Kotlin coroutines\"\n        ↓\n[0.021, -0.41, 0.73, ...]\n```\n\nThe vector database stores this representation and supports similarity search.\n\nWhen the user asks:\n\n```\nHow does structured concurrency work?\n```\n\nThe backend generates an embedding for the question and searches for similar chunks.\n\nThe top results are then added to the LLM prompt.\n\nA simplified prompt could be:\n\n```\nUse the following context to answer the question.\n\nContext:\n{retrieved_chunks}\n\nQuestion:\n{user_question}\n```\n\nA useful RAG assistant should return sources rather than only an answer.\n\nFor example:\n\n``` php\nLazyColumn {\n    items(response.sources) { source ->\n        Text(source.title)\n        Text(source.chunk)\n    }\n}\n```\n\nThis gives users a way to verify the generated answer.\n\nFor a better chat experience, the backend can stream generated tokens.\n\nDepending on your backend protocol, Kotlin can consume Server-Sent Events or another streaming protocol.\n\nThe UI can then append chunks as they arrive:\n\n``` php\n_response.update { current ->\n    current + token\n}\n```\n\nA RAG system should not blindly answer every question.\n\nIf retrieval produces weak matches, the backend can return:\n\n```\nI could not find enough information in the provided documents.\n```\n\nThis is often safer than allowing the model to invent an answer.\n\nNever put LLM API keys or vector database credentials directly into an Android application.\n\nUse:\n\n```\nAndroid App\n    ↓ HTTPS\nBackend API\n    ↓\nVector Database\n    ↓\nLLM Provider\n```\n\nAuthenticate mobile users at the API layer and enforce authorization when retrieving private documents.\n\nOnce the basic pipeline works, add:\n\nA Kotlin RAG assistant is more than an AI chat screen. The Android application should focus on user experience and secure API communication, while the backend manages embeddings, retrieval, document permissions, and LLM orchestration.\n\nThis architecture scales much better than embedding AI credentials and vector database logic directly inside the mobile application.\n\nSDK Flutter: [https://github.com/v-modal/vmodal_sdk_flutter](https://github.com/v-modal/vmodal_sdk_flutter)\n\nSDK Android: [https://github.com/v-modal/vmodal_sdk_android](https://github.com/v-modal/vmodal_sdk_android)\n\nDiscord: [https://discord.gg/K72z28KUx](https://discord.gg/K72z28KUx)", "url": "https://wpnews.pro/news/build-a-rag-based-ai-assistant-in-kotlin-with-a-vector-database", "canonical_source": "https://dev.to/vmodal_ai/build-a-rag-based-ai-assistant-in-kotlin-with-a-vector-database-4eab", "published_at": "2026-08-14 19:08:20+00:00", "updated_at": "2026-08-14 19:35:34.935925+00:00", "lang": "en", "topics": ["artificial-intelligence", "large-language-models", "developer-tools"], "entities": ["Kotlin", "Retrofit", "Android", "RAG"], "alternates": {"html": "https://wpnews.pro/news/build-a-rag-based-ai-assistant-in-kotlin-with-a-vector-database", "markdown": "https://wpnews.pro/news/build-a-rag-based-ai-assistant-in-kotlin-with-a-vector-database.md", "text": "https://wpnews.pro/news/build-a-rag-based-ai-assistant-in-kotlin-with-a-vector-database.txt", "jsonld": "https://wpnews.pro/news/build-a-rag-based-ai-assistant-in-kotlin-with-a-vector-database.jsonld"}}