package com.kchandrakant;

import dev.langchain4j.data.document.Document;
import dev.langchain4j.data.document.DocumentSplitter;
import dev.langchain4j.data.document.splitter.DocumentSplitters;
import dev.langchain4j.data.embedding.Embedding;
import dev.langchain4j.data.message.AiMessage;
import dev.langchain4j.data.segment.TextSegment;
import dev.langchain4j.model.chat.ChatLanguageModel;
import dev.langchain4j.model.embedding.AllMiniLmL6V2EmbeddingModel;
import dev.langchain4j.model.embedding.EmbeddingModel;
import dev.langchain4j.model.input.Prompt;
import dev.langchain4j.model.input.PromptTemplate;
import dev.langchain4j.model.openai.OpenAiChatModel;
import dev.langchain4j.model.openai.OpenAiTokenizer;
import dev.langchain4j.store.embedding.EmbeddingMatch;
import dev.langchain4j.store.embedding.EmbeddingStore;
import dev.langchain4j.store.embedding.inmemory.InMemoryEmbeddingStore;

import java.io.File;
import java.net.MalformedURLException;
import java.net.URISyntaxException;
import java.net.URL;
import java.nio.file.Path;
import java.nio.file.Paths;
import java.util.HashMap;
import java.util.List;
import java.util.Map;

import static dev.langchain4j.data.document.FileSystemDocumentLoader.loadDocument;
import static dev.langchain4j.model.openai.OpenAiModelName.GPT_3_5_TURBO;
import static java.time.Duration.ofSeconds;
import static java.util.stream.Collectors.joining;

public class ChatWithDocuments {

    public static void main(String[] args) {

        // Load the document that includes the information you'd like to "chat" about with the model.
        Document document = loadDocument(toPath("src/main/resources/example-files/simpson's_adventures.txt"));

        // Split document into segments 100 tokens each
        DocumentSplitter splitter = DocumentSplitters.recursive(
                100,
                0,
                new OpenAiTokenizer(GPT_3_5_TURBO)
        );
        List<TextSegment> segments = splitter.split(document);

        // Embed segments (convert them into vectors that represent the meaning) using embedding model
        EmbeddingModel embeddingModel = new AllMiniLmL6V2EmbeddingModel();
        List<Embedding> embeddings = embeddingModel.embedAll(segments).content();

        // Store embeddings into embedding store for further search / retrieval
        EmbeddingStore<TextSegment> embeddingStore = new InMemoryEmbeddingStore<>();
        embeddingStore.addAll(embeddings, segments);

        // Specify the question you want to ask the model
        String question = "Who is Simpson?";

        // Embed the question
        Embedding questionEmbedding = embeddingModel.embed(question).content();

        // Find relevant embeddings in embedding store by semantic similarity
        // You can play with parameters below to find a sweet spot for your specific use case
        int maxResults = 3;
        double minScore = 0.7;
        List<EmbeddingMatch<TextSegment>> relevantEmbeddings
                = embeddingStore.findRelevant(questionEmbedding, maxResults, minScore);

        // Create a prompt for the model that includes question and relevant embeddings
        PromptTemplate promptTemplate = PromptTemplate.from(
                "Answer the following question to the best of your ability:\n"
                        + "\n"
                        + "Question:\n"
                        + "{{question}}\n"
                        + "\n"
                        + "Base your answer on the following information:\n"
                        + "{{information}}");

        String information = relevantEmbeddings.stream()
                .map(match -> match.embedded().text())
                .collect(joining("\n\n"));

        Map<String, Object> variables = new HashMap<>();
        variables.put("question", question);
        variables.put("information", information);

        Prompt prompt = promptTemplate.apply(variables);

        // Send the prompt to the OpenAI chat model
        ChatLanguageModel chatModel = OpenAiChatModel.builder()
                .apiKey(ApiKeys.OPENAI_API_KEY)
                .timeout(ofSeconds(60))
                .build();
        AiMessage aiMessage = chatModel.generate(prompt.toUserMessage()).content();

        // See an answer from the model
        String answer = aiMessage.text();
        System.out.println(answer); // Charlie is a cheerful carrot living in VeggieVille...
    }

    private static Path toPath(String fileName) {
        try {
            //URL fileUrl = ChatWithDocuments.class.getResource(fileName);
            URL fileUrl = new File(fileName).toURI().toURL();
            System.out.println(new File(fileName).toURI().toURL());
            return Paths.get(fileUrl.toURI());
        } catch (URISyntaxException | MalformedURLException e) {
            throw new RuntimeException(e);
        }
    }
}
dev.langchain4j.model.openai.OpenAiChatModel.builder
[((4007, 4145), 'dev.langchain4j.model.openai.OpenAiChatModel.builder'), ((4007, 4120), 'dev.langchain4j.model.openai.OpenAiChatModel.builder'), ((4007, 4080), 'dev.langchain4j.model.openai.OpenAiChatModel.builder')]