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package io.thomasvitale.langchain4j.spring.openai;
import java.util.List;
import dev.langchain4j.agent.tool.ToolExecutionRequest;
import dev.langchain4j.agent.tool.ToolParameters;
import dev.langchain4j.agent.tool.ToolSpecification;
import dev.langchain4j.data.embedding.Embedding;
import dev.langchain4j.data.image.Image;
import dev.langchain4j.data.message.AiMessage;
import dev.langchain4j.data.message.ChatMessage;
import dev.langchain4j.data.message.Content;
import dev.langchain4j.data.message.ContentType;
import dev.langchain4j.data.message.ImageContent;
import dev.langchain4j.data.message.SystemMessage;
import dev.langchain4j.data.message.TextContent;
import dev.langchain4j.data.message.ToolExecutionResultMessage;
import dev.langchain4j.data.message.UserMessage;
import dev.langchain4j.model.output.FinishReason;
import dev.langchain4j.model.output.TokenUsage;
import org.springframework.lang.Nullable;
import org.springframework.util.CollectionUtils;
import io.thomasvitale.langchain4j.spring.openai.api.chat.ChatCompletionFinishReason;
import io.thomasvitale.langchain4j.spring.openai.api.chat.ChatCompletionMessage;
import io.thomasvitale.langchain4j.spring.openai.api.chat.ChatCompletionRequest;
import io.thomasvitale.langchain4j.spring.openai.api.chat.ChatCompletionResponse;
import io.thomasvitale.langchain4j.spring.openai.api.chat.Tool;
import io.thomasvitale.langchain4j.spring.openai.api.embedding.EmbeddingResponse;
import io.thomasvitale.langchain4j.spring.openai.api.image.ImageGenerationResponse;
import io.thomasvitale.langchain4j.spring.openai.api.shared.Usage;
import static java.lang.String.format;
import static java.util.stream.Collectors.toList;
/**
* Utility class to convert between OpenAI and Langchain4j types.
* <p>
* Based on the original LangChain4j implementation.
*/
public final class OpenAiAdapters {
/**
* Converts from a list of LangChain4J ChatMessage to a list of OpenAI ChatCompletionMessage.
*/
public static List<ChatCompletionMessage> toOpenAiMessages(List<ChatMessage> messages) {
return messages.stream()
.map(OpenAiAdapters::toOpenAiMessage)
.toList();
}
/**
* Converts from LangChain4J ChatMessage to ChatCompletionMessage.
*/
private static ChatCompletionMessage toOpenAiMessage(ChatMessage message) {
if (message instanceof SystemMessage systemMessage) {
return ChatCompletionMessage.builder()
.role(ChatCompletionMessage.Role.SYSTEM)
.content(systemMessage.text())
.build();
}
if (message instanceof UserMessage userMessage) {
if (userMessage.hasSingleText()) {
return ChatCompletionMessage.builder()
.role(ChatCompletionMessage.Role.USER)
.content(userMessage.text())
.name(userMessage.name())
.build();
} else {
return ChatCompletionMessage.builder()
.content(userMessage.contents().stream()
.map(OpenAiAdapters::toOpenAiContent)
.collect(toList()))
.name(userMessage.name())
.build();
}
}
if (message instanceof AiMessage aiMessage) {
if (!aiMessage.hasToolExecutionRequests()) {
return ChatCompletionMessage.builder()
.role(ChatCompletionMessage.Role.ASSISTANT)
.content(aiMessage.text())
.build();
}
return ChatCompletionMessage.builder()
.role(ChatCompletionMessage.Role.ASSISTANT)
.toolCalls(toOpenAiToolCalls(aiMessage.toolExecutionRequests()))
.build();
}
if (message instanceof ToolExecutionResultMessage toolExecutionResultMessage) {
return ChatCompletionMessage.builder()
.role(ChatCompletionMessage.Role.TOOL)
.content(toolExecutionResultMessage.text())
.toolCallId(toolExecutionResultMessage.id())
.build();
}
throw new IllegalArgumentException("Unknown message type: " + message.type());
}
/**
* Converts from LangChain4J Content to OpenAI Content.
*/
public static ChatCompletionMessage.Content toOpenAiContent(Content content) {
if (ContentType.TEXT.equals(content.type())) {
var textContent = (TextContent) content;
return ChatCompletionMessage.Content.builder()
.type(ChatCompletionMessage.ContentType.TEXT)
.text(textContent.text())
.build();
} else if (ContentType.IMAGE.equals(content.type())) {
var imageContent = (ImageContent) content;
return ChatCompletionMessage.Content.builder()
.type(ChatCompletionMessage.ContentType.IMAGE_URL)
.imageUrl(new ChatCompletionMessage.ImageUrl(
toOpenAiUrl(imageContent.image()),
toOpenAiDetail(imageContent.detailLevel())))
.build();
} else {
throw new IllegalArgumentException("Unknown content type: " + content.type());
}
}
/**
* Converts from LangChain4J Image to OpenAI Image URL.
*/
private static String toOpenAiUrl(Image image) {
if (image.url() != null) {
return image.url().toString();
}
return format("data:%s;base64,%s", image.mimeType(), image.base64Data());
}
/**
* Converts from LangChain4J DetailLevel to OpenAI DetailLevel.
*/
private static String toOpenAiDetail(ImageContent.DetailLevel detailLevel) {
if (detailLevel == null) {
return null;
}
return detailLevel.name();
}
/**
* Converts from a list of LangChain4J ToolExecutionRequest to a list of OpenAI ToolCall.
*/
private static List<ChatCompletionMessage.ToolCall> toOpenAiToolCalls(List<ToolExecutionRequest> toolExecutionRequests) {
return toolExecutionRequests.stream()
.map(OpenAiAdapters::toOpenAiToolCall)
.toList();
}
/**
* Converts from LangChain4J ToolExecutionRequest to OpenAI ToolCall.
*/
private static ChatCompletionMessage.ToolCall toOpenAiToolCall(ToolExecutionRequest toolExecutionRequest) {
var functionCall = new ChatCompletionMessage.ChatCompletionFunction(
toolExecutionRequest.name(),
toolExecutionRequest.arguments());
return new ChatCompletionMessage.ToolCall(toolExecutionRequest.id(), functionCall);
}
/**
* Converts from LangChain4J ToolSpecification to OpenAI ToolChoice.
*/
public static ChatCompletionRequest.ToolChoice toOpenAiToolChoice(ToolSpecification toolSpecification) {
return new ChatCompletionRequest.ToolChoice(toolSpecification.name());
}
/**
* Converts from a list of LangChain4J ToolSpecification to a list of OpenAI Tool.
*/
public static List<Tool> toOpenAiTools(List<ToolSpecification> toolSpecifications) {
return toolSpecifications.stream()
.map(OpenAiAdapters::toOpenAiTool)
.toList();
}
/**
* Converts from LangChain4J ToolSpecification to OpenAI Tool.
*/
private static Tool toOpenAiTool(ToolSpecification toolSpecification) {
var function = Tool.Function.builder()
.description(toolSpecification.description())
.name(toolSpecification.name())
.parameters(OpenAiAdapters.toOpenAiParameters(toolSpecification.parameters()))
.build();
return new Tool(function);
}
/**
* Converts from LangChain4J ToolParameters to OpenAI Tool.Parameters.
*/
private static Tool.Parameters toOpenAiParameters(@Nullable ToolParameters toolParameters) {
if (toolParameters == null) {
return Tool.Parameters.builder().build();
}
return Tool.Parameters.builder()
.properties(toolParameters.properties())
.required(toolParameters.required())
.build();
}
/**
* Converts from OpenAI Usage to LangChain4J Usage.
*/
public static TokenUsage toTokenUsage(Usage usage) {
return new TokenUsage(usage.promptTokens(), usage.completionTokens(), usage.totalTokens());
}
/**
* Converts from OpenAI ChatCompletionResponse to LangChain4J AiMessage.
*/
public static AiMessage toAiMessage(ChatCompletionResponse response) {
var assistantMessage = response.choices().get(0).message();
var toolCalls = assistantMessage.toolCalls();
if (!(CollectionUtils.isEmpty(toolCalls))) {
List<ToolExecutionRequest> toolExecutionRequests = toolCalls.stream()
.filter(toolCall -> "function".equals(toolCall.type()))
.map(OpenAiAdapters::toToolExecutionRequest)
.toList();
return AiMessage.from(toolExecutionRequests);
}
return AiMessage.from((String) assistantMessage.content());
}
/**
* Converts from OpenAI ToolCall to LangChain4J ToolExecutionRequest.
*/
private static ToolExecutionRequest toToolExecutionRequest(ChatCompletionMessage.ToolCall toolCall) {
return ToolExecutionRequest.builder()
.id(toolCall.id())
.name(toolCall.function().name())
.arguments(toolCall.function().arguments())
.build();
}
/**
* Converts from OpenAI ChatCompletionFinishReason to LangChain4J FinishReason.
*/
public static FinishReason toFinishReason(ChatCompletionFinishReason finishReason) {
return switch (finishReason) {
case STOP -> FinishReason.STOP;
case LENGTH -> FinishReason.LENGTH;
case TOOL_CALLS -> FinishReason.TOOL_EXECUTION;
case CONTENT_FILTER -> FinishReason.CONTENT_FILTER;
};
}
/**
* Converts from OpenAI EmbeddingData to LangChain4J Embedding.
*/
public static Embedding toEmbedding(EmbeddingResponse.EmbeddingData embeddingData) {
var floatVectors = embeddingData.embedding().stream()
.map(Double::floatValue)
.toList();
return Embedding.from(floatVectors);
}
/**
* Converts from OpenAI ImageData to LangChain4J Image.
*/
public static Image toImage(ImageGenerationResponse.ImageData imageData) {
return Image.builder()
.url(imageData.url())
.base64Data(imageData.b64Json())
.revisedPrompt(imageData.revisedPrompt())
.build();
}
}
|
[
"dev.langchain4j.agent.tool.ToolExecutionRequest.builder",
"dev.langchain4j.data.message.ContentType.TEXT.equals",
"dev.langchain4j.data.image.Image.builder",
"dev.langchain4j.data.message.ContentType.IMAGE.equals"
] |
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|
package org.jugph;
import dev.langchain4j.agent.tool.Tool;
import dev.langchain4j.data.document.Document;
import dev.langchain4j.data.document.UrlDocumentLoader;
import dev.langchain4j.data.document.transformer.HtmlTextExtractor;
import dev.langchain4j.memory.chat.MessageWindowChatMemory;
import dev.langchain4j.model.openai.OpenAiChatModel;
import dev.langchain4j.service.AiServices;
import dev.langchain4j.service.UserMessage;
import static java.time.Duration.ofSeconds;
public class JavaNewsRetrieverToolsExample {
static class JavaNewsRetriever {
@Tool("Retrieves the latest java news. Limit to the 3 latest news")
String retrieveJavaNews() {
Document javaNews = UrlDocumentLoader.load("https://dev.java/news/");
Document transformedJavaNews = new HtmlTextExtractor(".container", null, true)
.transform(javaNews);
return transformedJavaNews.text().replaceAll("\n", " ");
}
}
interface Assistant {
String chat(String userMessage);
}
interface NewsPrettierAssistant {
@UserMessage("Given a jumbled java news {{it}}, summarize each, and list down them in numerical format, latest to oldest. " +
"Include details such as url and date announced.")
String prettify(String userMessage);
}
public static void main(String[] args) {
var model = OpenAiChatModel.builder()
.apiKey(System.getenv("OPENAI_API_KEY"))
.timeout(ofSeconds(120))
.build();
var assistant = AiServices.builder(Assistant.class)
.chatLanguageModel(model)
.tools(new JavaNewsRetriever())
.chatMemory(MessageWindowChatMemory.withMaxMessages(10))
.build();
var newsPrettierAssistant = AiServices.builder(NewsPrettierAssistant.class)
.chatLanguageModel(model)
.build();
var question = "What are latest java news?";
var answer = assistant.chat(question);
var prettiedAnswer = newsPrettierAssistant.prettify(answer);
System.out.println("\n=================================\n"+prettiedAnswer);
}
}
|
[
"dev.langchain4j.service.AiServices.builder",
"dev.langchain4j.model.openai.OpenAiChatModel.builder"
] |
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|
package io.quarkiverse.langchain4j.sample.chatbot;
import static dev.langchain4j.data.document.splitter.DocumentSplitters.recursive;
import java.io.File;
import java.util.List;
import jakarta.enterprise.context.ApplicationScoped;
import jakarta.enterprise.event.Observes;
import jakarta.inject.Inject;
import dev.langchain4j.data.document.Document;
import dev.langchain4j.data.document.loader.FileSystemDocumentLoader;
import dev.langchain4j.data.document.parser.TextDocumentParser;
import dev.langchain4j.model.embedding.EmbeddingModel;
import dev.langchain4j.store.embedding.EmbeddingStore;
import dev.langchain4j.store.embedding.EmbeddingStoreIngestor;
import io.quarkus.logging.Log;
import io.quarkus.runtime.StartupEvent;
@ApplicationScoped
public class IngestorExample {
/**
* The embedding store (the database).
* The bean is provided by the quarkus-langchain4j-redis extension.
*/
@Inject
EmbeddingStore store;
/**
* The embedding model (how the vector of a document is computed).
* The bean is provided by the LLM (like openai) extension.
*/
@Inject
EmbeddingModel embeddingModel;
public void ingest(@Observes StartupEvent event) {
Log.infof("Ingesting documents...");
List<Document> documents = FileSystemDocumentLoader.loadDocuments(new File("src/main/resources/catalog").toPath(),
new TextDocumentParser());
var ingestor = EmbeddingStoreIngestor.builder()
.embeddingStore(store)
.embeddingModel(embeddingModel)
.documentSplitter(recursive(500, 0))
.build();
ingestor.ingest(documents);
Log.infof("Ingested %d documents.%n", documents.size());
}
}
|
[
"dev.langchain4j.store.embedding.EmbeddingStoreIngestor.builder"
] |
[((1441, 1638), 'dev.langchain4j.store.embedding.EmbeddingStoreIngestor.builder'), ((1441, 1613), 'dev.langchain4j.store.embedding.EmbeddingStoreIngestor.builder'), ((1441, 1560), 'dev.langchain4j.store.embedding.EmbeddingStoreIngestor.builder'), ((1441, 1512), 'dev.langchain4j.store.embedding.EmbeddingStoreIngestor.builder')]
|
package com.honvay.flychat.langchain.llama.model;
import dev.ai4j.openai4j.chat.*;
import dev.langchain4j.agent.tool.ToolExecutionRequest;
import dev.langchain4j.agent.tool.ToolParameters;
import dev.langchain4j.agent.tool.ToolSpecification;
import dev.langchain4j.data.message.AiMessage;
import dev.langchain4j.data.message.ChatMessage;
import dev.langchain4j.data.message.SystemMessage;
import dev.langchain4j.data.message.ToolExecutionResultMessage;
import java.util.Collection;
import java.util.List;
import java.util.stream.Collectors;
import static dev.ai4j.openai4j.chat.Role.*;
import static dev.langchain4j.data.message.AiMessage.aiMessage;
public class OpenAiConverters {
static List<Message> toOpenAiMessages(List<ChatMessage> messages) {
return messages.stream()
.map(OpenAiConverters::toOpenAiMessage)
.collect(Collectors.toList());
}
static Message toOpenAiMessage(ChatMessage message) {
return Message.builder()
.role(roleFrom(message))
.name(nameFrom(message))
.content(message.text())
.functionCall(functionCallFrom(message))
.build();
}
private static String nameFrom(ChatMessage message) {
if (message instanceof ToolExecutionResultMessage) {
return ((ToolExecutionResultMessage) message).toolName();
}
return null;
}
private static FunctionCall functionCallFrom(ChatMessage message) {
if (message instanceof AiMessage) {
AiMessage aiMessage = (AiMessage) message;
if (aiMessage.toolExecutionRequest() != null) {
return FunctionCall.builder()
.name(aiMessage.toolExecutionRequest().name())
.arguments(aiMessage.toolExecutionRequest().arguments())
.build();
}
}
return null;
}
static Role roleFrom(ChatMessage message) {
if (message instanceof AiMessage) {
return Role.ASSISTANT;
} else if (message instanceof ToolExecutionResultMessage) {
return Role.FUNCTION;
} else if (message instanceof SystemMessage) {
return Role.SYSTEM;
} else {
return Role.USER;
}
}
static List<Function> toFunctions(Collection<ToolSpecification> toolSpecifications) {
if (toolSpecifications == null) {
return null;
}
return toolSpecifications.stream()
.map(OpenAiConverters::toFunction)
.collect(Collectors.toList());
}
private static Function toFunction(ToolSpecification toolSpecification) {
return Function.builder()
.name(toolSpecification.name())
.description(toolSpecification.description())
.parameters(toOpenAiParameters(toolSpecification.parameters()))
.build();
}
private static dev.ai4j.openai4j.chat.Parameters toOpenAiParameters(ToolParameters toolParameters) {
if (toolParameters == null) {
return dev.ai4j.openai4j.chat.Parameters.builder().build();
}
return dev.ai4j.openai4j.chat.Parameters.builder()
.properties(toolParameters.properties())
.required(toolParameters.required())
.build();
}
static AiMessage aiMessageFrom(ChatCompletionResponse response) {
return AiMessage.aiMessage(response.choices().get(0).delta().content());
}
}
|
[
"dev.langchain4j.data.message.AiMessage.aiMessage.toolExecutionRequest"
] |
[((1743, 1782), 'dev.langchain4j.data.message.AiMessage.aiMessage.toolExecutionRequest'), ((1819, 1863), 'dev.langchain4j.data.message.AiMessage.aiMessage.toolExecutionRequest')]
|
package ma.enset.projet.service;
import dev.langchain4j.chain.ConversationalRetrievalChain;
import dev.langchain4j.data.document.Document;
import dev.langchain4j.data.document.DocumentSplitter;
import dev.langchain4j.memory.ChatMemory;
import dev.langchain4j.memory.chat.MessageWindowChatMemory;
import dev.langchain4j.model.embedding.AllMiniLmL6V2EmbeddingModel;
import dev.langchain4j.model.embedding.EmbeddingModel;
import dev.langchain4j.model.openai.OpenAiChatModel;
import dev.langchain4j.retriever.EmbeddingStoreRetriever;
import dev.langchain4j.store.embedding.EmbeddingStoreIngestor;
import dev.langchain4j.store.embedding.chroma.ChromaEmbeddingStore;
import ma.enset.projet.Dao.DocumentImp;
import ma.enset.projet.Dao.VectorBd;
import java.io.IOException;
import java.nio.file.Files;
import java.nio.file.Path;
public class ConversationBuild {
private final ChatMemory customChatMemory = MessageWindowChatMemory.withMaxMessages(20);
private DocumentImp document;
private VectorBd vectorBd = new VectorBd() ;
public void setVectorBd(VectorBd vectorBd) {
this.vectorBd = vectorBd;
}
public ConversationBuild() {
}
public ChatMemory getCustomChatMemory() {
return customChatMemory;
}
public DocumentImp getDocument() {
return document;
}
public void setDocument(DocumentImp document) {
this.document = document;
}
public VectorBd getVectorBd() {
return vectorBd;
}
public int getMaxResults() {
return maxResults;
}
public Double getMinScore() {
return minScore;
}
private final int maxResults = 4;
private final Double minScore = 0.7;
public EmbeddingStoreRetriever retriever(){
ChromaEmbeddingStore chromaStore = vectorBd.getConnection();
EmbeddingModel embeddingModel = new AllMiniLmL6V2EmbeddingModel();
return new EmbeddingStoreRetriever(chromaStore, embeddingModel, maxResults, minScore);
}
public void build(String path) throws IOException {
document = new DocumentImp(path);
String txtFilePath = document.setTxtFilePath(path);
String txtContent = Files.readString(Path.of(txtFilePath));
DocumentSplitter lineSplitter = document.SplitDocument();
Document doc = document.Document(txtContent);
// EmbeddingStoreIngestor.builder()
// .documentSplitter(lineSplitter)
// .embeddingModel(new AllMiniLmL6V2EmbeddingModel())
// .embeddingStore(vectorBd.getConnection())
// .build()
// .ingest(doc);
}
public ConversationalRetrievalChain chain(String API){
return ConversationalRetrievalChain.builder()
.chatLanguageModel(OpenAiChatModel.withApiKey(API))
.chatMemory(customChatMemory)
.promptTemplate(document.template())
.retriever(retriever())
.build();
}
}
|
[
"dev.langchain4j.chain.ConversationalRetrievalChain.builder"
] |
[((2701, 2971), 'dev.langchain4j.chain.ConversationalRetrievalChain.builder'), ((2701, 2946), 'dev.langchain4j.chain.ConversationalRetrievalChain.builder'), ((2701, 2906), 'dev.langchain4j.chain.ConversationalRetrievalChain.builder'), ((2701, 2853), 'dev.langchain4j.chain.ConversationalRetrievalChain.builder'), ((2701, 2807), 'dev.langchain4j.chain.ConversationalRetrievalChain.builder')]
|
package com.datawise.bertdocqa.config;
import dev.langchain4j.store.embedding.EmbeddingStore;
import dev.langchain4j.store.embedding.elasticsearch.ElasticsearchEmbeddingStore;
import dev.langchain4j.data.segment.TextSegment;
import org.springframework.beans.factory.annotation.Value;
import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;
@Configuration
public class ElasticsearchConfig {
@Value("${elasticsearch.server.url}")
private String serverUrl;
@Value("${elasticsearch.index.name}")
private String indexName;
@Value("${elasticsearch.dimension}")
private int dimension;
@Bean
public EmbeddingStore<TextSegment> embeddingStore() {
return ElasticsearchEmbeddingStore.builder()
.serverUrl(serverUrl)
.indexName(indexName)
.dimension(dimension)
.build();
}
}
|
[
"dev.langchain4j.store.embedding.elasticsearch.ElasticsearchEmbeddingStore.builder"
] |
[((747, 923), 'dev.langchain4j.store.embedding.elasticsearch.ElasticsearchEmbeddingStore.builder'), ((747, 898), 'dev.langchain4j.store.embedding.elasticsearch.ElasticsearchEmbeddingStore.builder'), ((747, 860), 'dev.langchain4j.store.embedding.elasticsearch.ElasticsearchEmbeddingStore.builder'), ((747, 822), 'dev.langchain4j.store.embedding.elasticsearch.ElasticsearchEmbeddingStore.builder')]
|
import dev.langchain4j.chain.ConversationalRetrievalChain;
import dev.langchain4j.data.document.Document;
import dev.langchain4j.data.document.DocumentParser;
import dev.langchain4j.data.document.DocumentSplitter;
import dev.langchain4j.data.document.loader.FileSystemDocumentLoader;
import dev.langchain4j.data.document.parser.TextDocumentParser;
import dev.langchain4j.data.document.splitter.DocumentSplitters;
import dev.langchain4j.data.embedding.Embedding;
import dev.langchain4j.data.segment.TextSegment;
import dev.langchain4j.memory.chat.MessageWindowChatMemory;
import dev.langchain4j.model.chat.ChatLanguageModel;
import dev.langchain4j.model.embedding.AllMiniLmL6V2EmbeddingModel;
import dev.langchain4j.model.embedding.EmbeddingModel;
import dev.langchain4j.model.openai.OpenAiChatModel;
import dev.langchain4j.rag.DefaultRetrievalAugmentor;
import dev.langchain4j.rag.RetrievalAugmentor;
import dev.langchain4j.rag.content.retriever.ContentRetriever;
import dev.langchain4j.rag.content.retriever.EmbeddingStoreContentRetriever;
import dev.langchain4j.rag.query.transformer.CompressingQueryTransformer;
import dev.langchain4j.rag.query.transformer.QueryTransformer;
import dev.langchain4j.service.AiServices;
import dev.langchain4j.store.embedding.EmbeddingStore;
import dev.langchain4j.store.embedding.inmemory.InMemoryEmbeddingStore;
import java.net.URISyntaxException;
import java.net.URL;
import java.nio.file.Path;
import java.nio.file.Paths;
import java.util.List;
import java.util.Scanner;
public class _02_Advanced_RAG_with_Query_Compression {
/**
* Please refer to previous examples for basic context.
* <p>
* Advanced RAG in LangChain4j is described here: https://github.com/langchain4j/langchain4j/pull/538
* <p>
* This example illustrates the implementation of a more sophisticated RAG application
* using a technique known as "query compression".
* Often, a query from a user is a follow-up question that refers back to earlier parts of the conversation
* and lacks all the necessary details for effective retrieval.
* For example, consider this conversation:
* User: What is the legacy of John Doe?
* AI: John Doe was a...
* User: When was he born?
* <p>
* In such scenarios, using a basic RAG approach with a query like "When was he born?"
* would likely fail to find articles about John Doe, as it doesn't contain "John Doe" in the query.
* Query compression involves taking the user's query and the preceding conversation, then asking the LLM
* to "compress" this into a single, self-contained query.
* The LLM should generate a query like "When was John Doe born?".
* This method adds a bit of latency and cost but significantly enhances the quality of the RAG process.
* It's worth noting that the LLM used for compression doesn't have to be the same as the one
* used for conversation. For instance, you might use a smaller local model trained for summarization.
* <p>
* In this example, we will continue using {@link AiServices},
* but the same principles apply to {@link ConversationalRetrievalChain}, or you can develop your custom RAG flow.
*/
public static void main(String[] args) {
Biographer biographer = createBiographer();
// First, ask "What is the legacy of John Doe?"
// Then, ask "When was he born?"
// Now, review the logs:
// The first query was not compressed as there was no preceding context to compress.
// The second query, however, was compressed into something like "When was John Doe born?"
try (Scanner scanner = new Scanner(System.in)) {
while (true) {
System.out.println("==================================================");
System.out.print("User: ");
String userQuery = scanner.nextLine();
System.out.println("==================================================");
if ("exit".equalsIgnoreCase(userQuery)) {
break;
}
String biographerAnswer = biographer.answer(userQuery);
System.out.println("==================================================");
System.out.println("Biographer: " + biographerAnswer);
}
}
}
private static Biographer createBiographer() {
// Check _01_Naive_RAG if you need more details on what is going on here
ChatLanguageModel chatModel = OpenAiChatModel.builder()
.apiKey("demo")
.build();
EmbeddingModel embeddingModel = new AllMiniLmL6V2EmbeddingModel();
Path documentPath = toPath("biography-of-john-doe.txt");
EmbeddingStore<TextSegment> embeddingStore = embed(documentPath, embeddingModel);
// We will create a CompressingQueryTransformer, which is responsible for compressing
// the user's query and the preceding conversation into a single, stand-alone query.
// This should significantly improve the quality of the retrieval process.
QueryTransformer queryTransformer = new CompressingQueryTransformer(chatModel);
ContentRetriever contentRetriever = EmbeddingStoreContentRetriever.builder()
.embeddingStore(embeddingStore)
.embeddingModel(embeddingModel)
.maxResults(2)
.minScore(0.6)
.build();
// The RetrievalAugmentor serves as the entry point into the RAG flow in LangChain4j.
// It can be configured to customize the RAG behavior according to your requirements.
// In subsequent examples, we will explore more customizations.
RetrievalAugmentor retrievalAugmentor = DefaultRetrievalAugmentor.builder()
.queryTransformer(queryTransformer)
.contentRetriever(contentRetriever)
.build();
return AiServices.builder(Biographer.class)
.chatLanguageModel(chatModel)
.retrievalAugmentor(retrievalAugmentor)
.chatMemory(MessageWindowChatMemory.withMaxMessages(10))
.build();
}
private static EmbeddingStore<TextSegment> embed(Path documentPath, EmbeddingModel embeddingModel) {
DocumentParser documentParser = new TextDocumentParser();
Document document = FileSystemDocumentLoader.loadDocument(documentPath, documentParser);
DocumentSplitter splitter = DocumentSplitters.recursive(300, 0);
List<TextSegment> segments = splitter.split(document);
List<Embedding> embeddings = embeddingModel.embedAll(segments).content();
EmbeddingStore<TextSegment> embeddingStore = new InMemoryEmbeddingStore<>();
embeddingStore.addAll(embeddings, segments);
return embeddingStore;
}
interface Biographer {
String answer(String query);
}
private static Path toPath(String fileName) {
try {
URL fileUrl = _02_Advanced_RAG_with_Query_Compression.class.getResource(fileName);
return Paths.get(fileUrl.toURI());
} catch (URISyntaxException e) {
throw new RuntimeException(e);
}
}
}
|
[
"dev.langchain4j.service.AiServices.builder",
"dev.langchain4j.rag.DefaultRetrievalAugmentor.builder",
"dev.langchain4j.rag.content.retriever.EmbeddingStoreContentRetriever.builder",
"dev.langchain4j.model.openai.OpenAiChatModel.builder"
] |
[((4539, 4621), 'dev.langchain4j.model.openai.OpenAiChatModel.builder'), ((4539, 4596), 'dev.langchain4j.model.openai.OpenAiChatModel.builder'), ((5259, 5482), 'dev.langchain4j.rag.content.retriever.EmbeddingStoreContentRetriever.builder'), ((5259, 5457), 'dev.langchain4j.rag.content.retriever.EmbeddingStoreContentRetriever.builder'), ((5259, 5426), 'dev.langchain4j.rag.content.retriever.EmbeddingStoreContentRetriever.builder'), ((5259, 5395), 'dev.langchain4j.rag.content.retriever.EmbeddingStoreContentRetriever.builder'), ((5259, 5347), 'dev.langchain4j.rag.content.retriever.EmbeddingStoreContentRetriever.builder'), ((5793, 5957), 'dev.langchain4j.rag.DefaultRetrievalAugmentor.builder'), ((5793, 5932), 'dev.langchain4j.rag.DefaultRetrievalAugmentor.builder'), ((5793, 5880), 'dev.langchain4j.rag.DefaultRetrievalAugmentor.builder'), ((5975, 6211), 'dev.langchain4j.service.AiServices.builder'), ((5975, 6186), 'dev.langchain4j.service.AiServices.builder'), ((5975, 6113), 'dev.langchain4j.service.AiServices.builder'), ((5975, 6057), 'dev.langchain4j.service.AiServices.builder')]
|
package _Engenharia;
import dev.langchain4j.chain.ConversationalRetrievalChain;
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.memory.ChatMemory;
import dev.langchain4j.memory.chat.TokenWindowChatMemory;
import dev.langchain4j.model.chat.ChatLanguageModel;
//import dev.langchain4j.model.embedding.AllMiniLmL6V2EmbeddingModel;
import dev.langchain4j.model.embedding.EmbeddingModel;
import dev.langchain4j.model.huggingface.HuggingFaceEmbeddingModel;
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.retriever.EmbeddingStoreRetriever;
import dev.langchain4j.store.embedding.EmbeddingMatch;
import dev.langchain4j.store.embedding.EmbeddingStore;
import dev.langchain4j.store.embedding.EmbeddingStoreIngestor;
import dev.langchain4j.store.embedding.inmemory.InMemoryEmbeddingStore;
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 java.util.Scanner;
import static dev.langchain4j.data.document.FileSystemDocumentLoader.loadDocument;
import static dev.langchain4j.data.message.UserMessage.userMessage;
import static dev.langchain4j.model.openai.OpenAiModelName.GPT_3_5_TURBO;
import static java.util.stream.Collectors.joining;
import static java.time.Duration.ofSeconds;
import assistente.ApiKeys;
public class AssistenteMemoryDocument {
private static final String vetModel = "sentence-transformers/all-MiniLM-L6-v2";
private static final ChatMemory chatMemory = TokenWindowChatMemory.withMaxTokens(300, new OpenAiTokenizer(GPT_3_5_TURBO));
public String fazerPergunta(String pergunta) throws Exception {
//ChatMemoria
//ChatLanguageModel model = OpenAiChatModel.withApiKey(ApiKeys.OPENAI_API_KEY);
//Chat Language Model Builder OpenAi
ChatLanguageModel chatLanguageModel = OpenAiChatModel.builder()
.apiKey(ApiKeys.OPENAI_API_KEY)
.temperature(0.0)
.timeout(ofSeconds(900))
.build();
//EmbeddingModel Builder HuggingFace
EmbeddingModel embeddingModel = HuggingFaceEmbeddingModel.builder()
.accessToken(ApiKeys.HF_API_KEY)
.modelId(vetModel)
.waitForModel(true)
.timeout(ofSeconds(60))
.build();
EmbeddingStore<TextSegment> embeddingStore = new InMemoryEmbeddingStore<>();
//"Cosumo" do texto do arquivo
EmbeddingStoreIngestor ingestor = EmbeddingStoreIngestor.builder()
.documentSplitter(DocumentSplitters.recursive(500, 0))
.embeddingModel(embeddingModel)
.embeddingStore(embeddingStore)
.build();
Document document = loadDocument(toPath("template.txt"));
ingestor.ingest(document);
ConversationalRetrievalChain chain = ConversationalRetrievalChain.builder()
.chatLanguageModel(chatLanguageModel)
.retriever(EmbeddingStoreRetriever.from(embeddingStore, embeddingModel))
.chatMemory(chatMemory) // you can override default chat memory
// .promptTemplate() // you can override default prompt template
.build();
chatMemory.add(userMessage(pergunta));
return chain.execute(pergunta);
}
//Procura arquivo
private static Path toPath(String fileName) {
try {
URL fileUrl = AssistenteMemoryDocument.class.getResource(fileName);
return Paths.get(fileUrl.toURI());
} catch (URISyntaxException e) {
throw new RuntimeException(e);
}
}
}
|
[
"dev.langchain4j.chain.ConversationalRetrievalChain.builder",
"dev.langchain4j.model.huggingface.HuggingFaceEmbeddingModel.builder",
"dev.langchain4j.store.embedding.EmbeddingStoreIngestor.builder",
"dev.langchain4j.model.openai.OpenAiChatModel.builder"
] |
[((2335, 2508), 'dev.langchain4j.model.openai.OpenAiChatModel.builder'), ((2335, 2483), 'dev.langchain4j.model.openai.OpenAiChatModel.builder'), ((2335, 2442), 'dev.langchain4j.model.openai.OpenAiChatModel.builder'), ((2335, 2408), 'dev.langchain4j.model.openai.OpenAiChatModel.builder'), ((2615, 2835), 'dev.langchain4j.model.huggingface.HuggingFaceEmbeddingModel.builder'), ((2615, 2810), 'dev.langchain4j.model.huggingface.HuggingFaceEmbeddingModel.builder'), ((2615, 2770), 'dev.langchain4j.model.huggingface.HuggingFaceEmbeddingModel.builder'), ((2615, 2734), 'dev.langchain4j.model.huggingface.HuggingFaceEmbeddingModel.builder'), ((2615, 2699), 'dev.langchain4j.model.huggingface.HuggingFaceEmbeddingModel.builder'), ((3022, 3246), 'dev.langchain4j.store.embedding.EmbeddingStoreIngestor.builder'), ((3022, 3221), 'dev.langchain4j.store.embedding.EmbeddingStoreIngestor.builder'), ((3022, 3173), 'dev.langchain4j.store.embedding.EmbeddingStoreIngestor.builder'), ((3022, 3125), 'dev.langchain4j.store.embedding.EmbeddingStoreIngestor.builder'), ((3396, 3763), 'dev.langchain4j.chain.ConversationalRetrievalChain.builder'), ((3396, 3617), 'dev.langchain4j.chain.ConversationalRetrievalChain.builder'), ((3396, 3577), 'dev.langchain4j.chain.ConversationalRetrievalChain.builder'), ((3396, 3488), 'dev.langchain4j.chain.ConversationalRetrievalChain.builder')]
|
package io.quarkiverse.langchain4j.sample.chatbot;
import static dev.langchain4j.data.document.splitter.DocumentSplitters.recursive;
import java.io.File;
import java.io.FileReader;
import java.io.IOException;
import java.io.Reader;
import java.util.ArrayList;
import java.util.HashMap;
import java.util.List;
import java.util.Map;
import jakarta.enterprise.context.ApplicationScoped;
import jakarta.enterprise.event.Observes;
import jakarta.inject.Inject;
import org.apache.commons.csv.CSVFormat;
import org.apache.commons.csv.CSVRecord;
import org.eclipse.microprofile.config.inject.ConfigProperty;
import dev.langchain4j.data.document.Document;
import dev.langchain4j.data.document.Metadata;
import dev.langchain4j.model.embedding.EmbeddingModel;
import dev.langchain4j.store.embedding.EmbeddingStoreIngestor;
import io.quarkiverse.langchain4j.redis.RedisEmbeddingStore;
import io.quarkus.runtime.StartupEvent;
@ApplicationScoped
public class CsvIngestorExample {
/**
* The embedding store (the database).
* The bean is provided by the quarkus-langchain4j-redis extension.
*/
@Inject
RedisEmbeddingStore store;
/**
* The embedding model (how the vector of a document is computed).
* The bean is provided by the LLM (like openai) extension.
*/
@Inject
EmbeddingModel embeddingModel;
@ConfigProperty(name = "csv.file")
File file;
@ConfigProperty(name = "csv.headers")
List<String> headers;
public void ingest(@Observes StartupEvent event) throws IOException {
CSVFormat csvFormat = CSVFormat.DEFAULT.builder()
.setHeader(headers.toArray(new String[0]))
.setSkipHeaderRecord(true)
.build();
List<Document> documents = new ArrayList<>();
try (Reader reader = new FileReader(file)) {
// Generate on document per row, the document is using the following syntax:
// key1: value1
// key2: value2
Iterable<CSVRecord> records = csvFormat.parse(reader);
int i = 1;
for (CSVRecord record : records) {
Map<String, String> metadata = new HashMap<>();
metadata.put("source", file.getAbsolutePath());
metadata.put("row", String.valueOf(i++));
StringBuilder content = new StringBuilder();
for (String header : headers) {
metadata.put(header, record.get(header)); // Include all headers in the metadata.
content.append(header).append(": ").append(record.get(header)).append("\n");
}
documents.add(new Document(content.toString(), Metadata.from(metadata)));
}
var ingestor = EmbeddingStoreIngestor.builder()
.embeddingStore(store)
.embeddingModel(embeddingModel)
.documentSplitter(recursive(300, 0))
.build();
ingestor.ingest(documents);
System.out.printf("Ingested %d documents.%n", documents.size());
}
}
}
|
[
"dev.langchain4j.store.embedding.EmbeddingStoreIngestor.builder"
] |
[((1575, 1729), 'org.apache.commons.csv.CSVFormat.DEFAULT.builder'), ((1575, 1704), 'org.apache.commons.csv.CSVFormat.DEFAULT.builder'), ((1575, 1661), 'org.apache.commons.csv.CSVFormat.DEFAULT.builder'), ((1575, 1602), 'org.apache.commons.csv.CSVFormat.DEFAULT.builder'), ((2766, 2979), 'dev.langchain4j.store.embedding.EmbeddingStoreIngestor.builder'), ((2766, 2950), 'dev.langchain4j.store.embedding.EmbeddingStoreIngestor.builder'), ((2766, 2893), 'dev.langchain4j.store.embedding.EmbeddingStoreIngestor.builder'), ((2766, 2841), 'dev.langchain4j.store.embedding.EmbeddingStoreIngestor.builder')]
|
package org.goafabric.dbagent.ai;
import dev.langchain4j.memory.chat.MessageWindowChatMemory;
import dev.langchain4j.model.chat.ChatLanguageModel;
import dev.langchain4j.model.openai.OpenAiChatModel;
import dev.langchain4j.service.AiServices;
import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;
import org.springframework.context.annotation.Profile;
import static java.time.Duration.ofSeconds;
@Configuration
@Profile("openai")
public class OpenAiConfiguration {
@Bean
ChatLanguageModel chatModelOpenAi(DatabaseTool databaseTool) {
return OpenAiChatModel.builder().apiKey("demo")
.modelName("gpt-3.5-turbo")
.timeout(ofSeconds(30)).temperature(0.0)
.build();
}
@Bean
DatabaseAgent databaseAgent(ChatLanguageModel chatLanguageModel, DatabaseTool databaseTool) {
return AiServices.builder(DatabaseAgent.class)
.chatLanguageModel(chatLanguageModel)
.chatMemory(MessageWindowChatMemory.withMaxMessages(20))
.tools(databaseTool)
.build();
}
}
|
[
"dev.langchain4j.service.AiServices.builder",
"dev.langchain4j.model.openai.OpenAiChatModel.builder"
] |
[((620, 786), 'dev.langchain4j.model.openai.OpenAiChatModel.builder'), ((620, 761), 'dev.langchain4j.model.openai.OpenAiChatModel.builder'), ((620, 744), 'dev.langchain4j.model.openai.OpenAiChatModel.builder'), ((620, 704), 'dev.langchain4j.model.openai.OpenAiChatModel.builder'), ((620, 660), 'dev.langchain4j.model.openai.OpenAiChatModel.builder'), ((918, 1146), 'dev.langchain4j.service.AiServices.builder'), ((918, 1121), 'dev.langchain4j.service.AiServices.builder'), ((918, 1084), 'dev.langchain4j.service.AiServices.builder'), ((918, 1011), 'dev.langchain4j.service.AiServices.builder')]
|
package com.elec5620.studyhelper.api.llm;
import io.github.cdimascio.dotenv.Dotenv;
import java.net.URL;
import java.util.Scanner;
import com.elec5620.studyhelper.core.HelperSystem;
import dev.langchain4j.chain.ConversationalRetrievalChain;
import dev.langchain4j.data.document.Document;
import dev.langchain4j.data.document.splitter.DocumentSplitters;
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.openai.OpenAiChatModel;
import dev.langchain4j.retriever.EmbeddingStoreRetriever;
import dev.langchain4j.store.embedding.EmbeddingStore;
import dev.langchain4j.store.embedding.EmbeddingStoreIngestor;
import dev.langchain4j.store.embedding.inmemory.InMemoryEmbeddingStore;
import java.io.File;
import java.io.IOException;
import java.net.MalformedURLException;
import java.net.URISyntaxException;
import java.nio.file.Path;
import java.nio.file.Paths;
import java.time.Duration;
import static dev.langchain4j.data.document.FileSystemDocumentLoader.loadDocument;
public class ChatBot {
public static String response(String filePath, String request) {
Dotenv dotenv = Dotenv.load();
String token = dotenv.get("OPENAI_API_KEY");
System.out.println("file path: " + filePath);
try {
EmbeddingModel embeddingModel = new AllMiniLmL6V2EmbeddingModel();
OpenAiChatModel model = OpenAiChatModel.builder().apiKey(token).timeout(Duration.ofMinutes(1)).build();
EmbeddingStore<TextSegment> embeddingStore = new InMemoryEmbeddingStore<>();
EmbeddingStoreIngestor ingestor = EmbeddingStoreIngestor.builder()
.documentSplitter(DocumentSplitters.recursive(500, 0))
.embeddingModel(embeddingModel)
.embeddingStore(embeddingStore)
.build();
Document document = loadDocument(toPath(filePath));
ingestor.ingest(document);
ConversationalRetrievalChain chain = ConversationalRetrievalChain.builder()
.chatLanguageModel(model)
.retriever(EmbeddingStoreRetriever.from(embeddingStore, embeddingModel))
// .chatMemory() // you can override default chat memory
// .promptTemplate() // you can override default prompt template
.build();
String answer = chain.execute(request);
System.out.println(answer); // answer based on given information
answer = answer.replace("\\n", System.lineSeparator());
return answer;
} catch (Exception e) {
return e.getMessage();
}
}
public static void main(String[] args) {
Dotenv dotenv = Dotenv.load();
String token = dotenv.get("OPENAI_API_KEY");
try (Scanner myObj = new Scanner(System.in)) {
System.out.println("Enter question to ask:");
String question = myObj.nextLine();
EmbeddingModel embeddingModel = new AllMiniLmL6V2EmbeddingModel();
EmbeddingStore<TextSegment> embeddingStore = new InMemoryEmbeddingStore<>();
EmbeddingStoreIngestor ingestor = EmbeddingStoreIngestor.builder()
.documentSplitter(DocumentSplitters.recursive(500, 0))
.embeddingModel(embeddingModel)
.embeddingStore(embeddingStore)
.build();
Document document = loadDocument(toPath("example.txt"));
ingestor.ingest(document);
ConversationalRetrievalChain chain = ConversationalRetrievalChain.builder()
.chatLanguageModel(OpenAiChatModel.withApiKey(token))
.retriever(EmbeddingStoreRetriever.from(embeddingStore, embeddingModel))
// .chatMemory() // you can override default chat memory
// .promptTemplate() // you can override default prompt template
.build();
String answer = chain.execute(question);
System.out.println(answer); // answer based on given information
}
}
private static Path toPath(String fileName) {
try {
Path filePath = Paths.get(fileName);
URL fileURL = filePath.toUri().toURL();
return Paths.get(fileURL.toURI());
} catch (URISyntaxException e) {
throw new RuntimeException(e);
} catch (MalformedURLException e) {
// TODO Auto-generated catch block
e.printStackTrace();
}
return null;
}
}
|
[
"dev.langchain4j.chain.ConversationalRetrievalChain.builder",
"dev.langchain4j.store.embedding.EmbeddingStoreIngestor.builder",
"dev.langchain4j.model.openai.OpenAiChatModel.builder"
] |
[((1554, 1632), 'dev.langchain4j.model.openai.OpenAiChatModel.builder'), ((1554, 1624), 'dev.langchain4j.model.openai.OpenAiChatModel.builder'), ((1554, 1593), 'dev.langchain4j.model.openai.OpenAiChatModel.builder'), ((1788, 2060), 'dev.langchain4j.store.embedding.EmbeddingStoreIngestor.builder'), ((1788, 2023), 'dev.langchain4j.store.embedding.EmbeddingStoreIngestor.builder'), ((1788, 1963), 'dev.langchain4j.store.embedding.EmbeddingStoreIngestor.builder'), ((1788, 1903), 'dev.langchain4j.store.embedding.EmbeddingStoreIngestor.builder'), ((2248, 2656), 'dev.langchain4j.chain.ConversationalRetrievalChain.builder'), ((2248, 2441), 'dev.langchain4j.chain.ConversationalRetrievalChain.builder'), ((2248, 2340), 'dev.langchain4j.chain.ConversationalRetrievalChain.builder'), ((3503, 3759), 'dev.langchain4j.store.embedding.EmbeddingStoreIngestor.builder'), ((3503, 3726), 'dev.langchain4j.store.embedding.EmbeddingStoreIngestor.builder'), ((3503, 3670), 'dev.langchain4j.store.embedding.EmbeddingStoreIngestor.builder'), ((3503, 3614), 'dev.langchain4j.store.embedding.EmbeddingStoreIngestor.builder'), ((3940, 4356), 'dev.langchain4j.chain.ConversationalRetrievalChain.builder'), ((3940, 4153), 'dev.langchain4j.chain.ConversationalRetrievalChain.builder'), ((3940, 4056), 'dev.langchain4j.chain.ConversationalRetrievalChain.builder')]
|
package org.agoncal.fascicle.langchain4j.vectordb.cassandra;
import dev.langchain4j.data.embedding.Embedding;
import dev.langchain4j.data.segment.TextSegment;
import dev.langchain4j.model.embedding.AllMiniLmL6V2EmbeddingModel;
import dev.langchain4j.model.embedding.EmbeddingModel;
import dev.langchain4j.store.embedding.EmbeddingMatch;
import dev.langchain4j.store.embedding.EmbeddingStore;
import dev.langchain4j.store.embedding.cassandra.CassandraEmbeddingStore;
import java.util.List;
// tag::adocSkip[]
/**
* @author Antonio Goncalves
* http://www.antoniogoncalves.org
* --
*/
// end::adocSkip[]
public class MusicianService {
public static void main(String[] args) {
MusicianService musicianService = new MusicianService();
musicianService.useQdrantToStoreEmbeddings();
}
public void useQdrantToStoreEmbeddings() {
System.out.println("### useQdrantToStoreEmbeddings");
// tag::adocSnippet[]
EmbeddingStore<TextSegment> embeddingStore =
CassandraEmbeddingStore.builder()
.port(6334)
.build();
EmbeddingModel embeddingModel = new AllMiniLmL6V2EmbeddingModel();
TextSegment segment1 = TextSegment.from("I've been to France twice.");
Embedding embedding1 = embeddingModel.embed(segment1).content();
embeddingStore.add(embedding1, segment1);
TextSegment segment2 = TextSegment.from("New Delhi is the capital of India.");
Embedding embedding2 = embeddingModel.embed(segment2).content();
embeddingStore.add(embedding2, segment2);
Embedding queryEmbedding = embeddingModel.embed("Did you ever travel abroad?").content();
List<EmbeddingMatch<TextSegment>> relevant = embeddingStore.findRelevant(queryEmbedding, 1);
EmbeddingMatch<TextSegment> embeddingMatch = relevant.get(0);
System.out.println(embeddingMatch.score());
System.out.println(embeddingMatch.embedded().text());
// end::adocSnippet[]
}
}
|
[
"dev.langchain4j.store.embedding.cassandra.CassandraEmbeddingStore.builder"
] |
[((986, 1056), 'dev.langchain4j.store.embedding.cassandra.CassandraEmbeddingStore.builder'), ((986, 1039), 'dev.langchain4j.store.embedding.cassandra.CassandraEmbeddingStore.builder')]
|
package embedding.model;
import dev.langchain4j.data.embedding.Embedding;
import dev.langchain4j.model.embedding.EmbeddingModel;
import dev.langchain4j.model.output.Response;
import dev.langchain4j.model.vertexai.VertexAiEmbeddingModel;
public class VertexAiEmbeddingModelExample {
public static void main(String[] args) {
EmbeddingModel embeddingModel = VertexAiEmbeddingModel.builder()
.endpoint("us-central1-aiplatform.googleapis.com:443")
.project("langchain4j")
.location("us-central1")
.publisher("google")
.modelName("textembedding-gecko@001")
.build();
Response<Embedding> response = embeddingModel.embed("Hello, how are you?");
System.out.println(response);
}
}
|
[
"dev.langchain4j.model.vertexai.VertexAiEmbeddingModel.builder"
] |
[((371, 671), 'dev.langchain4j.model.vertexai.VertexAiEmbeddingModel.builder'), ((371, 646), 'dev.langchain4j.model.vertexai.VertexAiEmbeddingModel.builder'), ((371, 592), 'dev.langchain4j.model.vertexai.VertexAiEmbeddingModel.builder'), ((371, 555), 'dev.langchain4j.model.vertexai.VertexAiEmbeddingModel.builder'), ((371, 514), 'dev.langchain4j.model.vertexai.VertexAiEmbeddingModel.builder'), ((371, 474), 'dev.langchain4j.model.vertexai.VertexAiEmbeddingModel.builder')]
|
package com.example;
import dev.langchain4j.data.segment.TextSegment;
import dev.langchain4j.model.embedding.EmbeddingModel;
import dev.langchain4j.model.openai.OpenAiEmbeddingModel;
import dev.langchain4j.store.embedding.EmbeddingStore;
import dev.langchain4j.store.embedding.chroma.ChromaEmbeddingStore;
public class Chroma {
public static final EmbeddingStore<TextSegment> embeddingStore =
ChromaEmbeddingStore.builder()
.baseUrl("http://localhost:8000/")
.collectionName("my-collection")
.build();
public static final EmbeddingModel embeddingModel =
OpenAiEmbeddingModel.builder()
.apiKey("API_KEY")
.modelName("text-embedding-ada-002")
.build();
}
|
[
"dev.langchain4j.store.embedding.chroma.ChromaEmbeddingStore.builder",
"dev.langchain4j.model.openai.OpenAiEmbeddingModel.builder"
] |
[((412, 579), 'dev.langchain4j.store.embedding.chroma.ChromaEmbeddingStore.builder'), ((412, 550), 'dev.langchain4j.store.embedding.chroma.ChromaEmbeddingStore.builder'), ((412, 497), 'dev.langchain4j.store.embedding.chroma.ChromaEmbeddingStore.builder'), ((650, 805), 'dev.langchain4j.model.openai.OpenAiEmbeddingModel.builder'), ((650, 776), 'dev.langchain4j.model.openai.OpenAiEmbeddingModel.builder'), ((650, 719), 'dev.langchain4j.model.openai.OpenAiEmbeddingModel.builder')]
|
package io.quarkiverse.langchain4j.deployment;
import static dev.langchain4j.agent.tool.JsonSchemaProperty.ARRAY;
import static dev.langchain4j.agent.tool.JsonSchemaProperty.BOOLEAN;
import static dev.langchain4j.agent.tool.JsonSchemaProperty.INTEGER;
import static dev.langchain4j.agent.tool.JsonSchemaProperty.NUMBER;
import static dev.langchain4j.agent.tool.JsonSchemaProperty.OBJECT;
import static dev.langchain4j.agent.tool.JsonSchemaProperty.STRING;
import static dev.langchain4j.agent.tool.JsonSchemaProperty.description;
import static dev.langchain4j.agent.tool.JsonSchemaProperty.enums;
import static java.util.Arrays.stream;
import static java.util.stream.Collectors.toList;
import java.lang.reflect.Modifier;
import java.util.ArrayList;
import java.util.Collection;
import java.util.HashMap;
import java.util.List;
import java.util.Map;
import java.util.Objects;
import java.util.function.BiFunction;
import java.util.stream.Collectors;
import org.jboss.jandex.AnnotationInstance;
import org.jboss.jandex.AnnotationTarget;
import org.jboss.jandex.AnnotationValue;
import org.jboss.jandex.ClassInfo;
import org.jboss.jandex.DotName;
import org.jboss.jandex.IndexView;
import org.jboss.jandex.MethodInfo;
import org.jboss.jandex.MethodParameterInfo;
import org.jboss.jandex.Type;
import org.jboss.logging.Logger;
import org.objectweb.asm.ClassVisitor;
import org.objectweb.asm.Opcodes;
import dev.langchain4j.agent.tool.JsonSchemaProperty;
import dev.langchain4j.agent.tool.Tool;
import dev.langchain4j.agent.tool.ToolMemoryId;
import dev.langchain4j.agent.tool.ToolParameters;
import dev.langchain4j.agent.tool.ToolSpecification;
import io.quarkiverse.langchain4j.runtime.ToolsRecorder;
import io.quarkiverse.langchain4j.runtime.prompt.Mappable;
import io.quarkiverse.langchain4j.runtime.tool.ToolInvoker;
import io.quarkiverse.langchain4j.runtime.tool.ToolMethodCreateInfo;
import io.quarkiverse.langchain4j.runtime.tool.ToolParametersObjectSubstitution;
import io.quarkiverse.langchain4j.runtime.tool.ToolSpanWrapper;
import io.quarkiverse.langchain4j.runtime.tool.ToolSpecificationObjectSubstitution;
import io.quarkus.arc.deployment.AdditionalBeanBuildItem;
import io.quarkus.arc.deployment.ValidationPhaseBuildItem;
import io.quarkus.deployment.Capabilities;
import io.quarkus.deployment.Capability;
import io.quarkus.deployment.GeneratedClassGizmoAdaptor;
import io.quarkus.deployment.annotations.BuildProducer;
import io.quarkus.deployment.annotations.BuildStep;
import io.quarkus.deployment.annotations.ExecutionTime;
import io.quarkus.deployment.annotations.Record;
import io.quarkus.deployment.builditem.BytecodeTransformerBuildItem;
import io.quarkus.deployment.builditem.CombinedIndexBuildItem;
import io.quarkus.deployment.builditem.GeneratedClassBuildItem;
import io.quarkus.deployment.builditem.nativeimage.ReflectiveClassBuildItem;
import io.quarkus.deployment.recording.RecorderContext;
import io.quarkus.gizmo.ClassCreator;
import io.quarkus.gizmo.ClassOutput;
import io.quarkus.gizmo.ClassTransformer;
import io.quarkus.gizmo.FieldDescriptor;
import io.quarkus.gizmo.MethodCreator;
import io.quarkus.gizmo.MethodDescriptor;
import io.quarkus.gizmo.ResultHandle;
public class ToolProcessor {
private static final Logger log = Logger.getLogger(AiServicesProcessor.class);
private static final DotName TOOL = DotName.createSimple(Tool.class);
private static final DotName TOOL_MEMORY_ID = DotName.createSimple(ToolMemoryId.class);
private static final DotName P = DotName.createSimple(dev.langchain4j.agent.tool.P.class);
private static final MethodDescriptor METHOD_METADATA_CTOR = MethodDescriptor
.ofConstructor(ToolInvoker.MethodMetadata.class, boolean.class, Map.class, Integer.class);
private static final MethodDescriptor HASHMAP_CTOR = MethodDescriptor.ofConstructor(HashMap.class);
public static final MethodDescriptor MAP_PUT = MethodDescriptor.ofMethod(Map.class, "put", Object.class, Object.class,
Object.class);
@BuildStep
public void telemetry(Capabilities capabilities, BuildProducer<AdditionalBeanBuildItem> additionalBeanProducer) {
var addOpenTelemetrySpan = capabilities.isPresent(Capability.OPENTELEMETRY_TRACER);
if (addOpenTelemetrySpan) {
additionalBeanProducer.produce(AdditionalBeanBuildItem.builder().addBeanClass(ToolSpanWrapper.class).build());
}
}
@BuildStep
@Record(ExecutionTime.STATIC_INIT)
public void handleTools(CombinedIndexBuildItem indexBuildItem,
ToolsRecorder recorder,
RecorderContext recorderContext,
BuildProducer<BytecodeTransformerBuildItem> transformerProducer,
BuildProducer<GeneratedClassBuildItem> generatedClassProducer,
BuildProducer<ReflectiveClassBuildItem> reflectiveClassProducer,
BuildProducer<ValidationPhaseBuildItem.ValidationErrorBuildItem> validation,
BuildProducer<ToolsMetadataBuildItem> toolsMetadataProducer) {
recorderContext.registerSubstitution(ToolSpecification.class, ToolSpecificationObjectSubstitution.Serialized.class,
ToolSpecificationObjectSubstitution.class);
recorderContext.registerSubstitution(ToolParameters.class, ToolParametersObjectSubstitution.Serialized.class,
ToolParametersObjectSubstitution.class);
IndexView index = indexBuildItem.getIndex();
Collection<AnnotationInstance> instances = index.getAnnotations(TOOL);
Map<String, List<ToolMethodCreateInfo>> metadata = new HashMap<>();
List<String> generatedInvokerClasses = new ArrayList<>();
List<String> generatedArgumentMapperClasses = new ArrayList<>();
if (!instances.isEmpty()) {
ClassOutput classOutput = new GeneratedClassGizmoAdaptor(generatedClassProducer, true);
Map<DotName, List<MethodInfo>> methodsPerClass = new HashMap<>();
for (AnnotationInstance instance : instances) {
if (instance.target().kind() != AnnotationTarget.Kind.METHOD) {
continue;
}
MethodInfo methodInfo = instance.target().asMethod();
ClassInfo classInfo = methodInfo.declaringClass();
if (classInfo.isInterface() || Modifier.isAbstract(classInfo.flags())) {
validation.produce(
new ValidationPhaseBuildItem.ValidationErrorBuildItem(new IllegalStateException(
"@Tool is only supported on non-abstract classes, all other usages are ignored. Offending method is '"
+ methodInfo.declaringClass().name().toString() + "#" + methodInfo.name() + "'")));
continue;
}
DotName declaringClassName = classInfo.name();
methodsPerClass.computeIfAbsent(declaringClassName, (n -> new ArrayList<>())).add(methodInfo);
}
boolean validationErrorFound = false;
Map<String, ClassInfo> discoveredTools = new HashMap<>();
for (var entry : methodsPerClass.entrySet()) {
DotName className = entry.getKey();
List<MethodInfo> toolMethods = entry.getValue();
List<MethodInfo> privateMethods = new ArrayList<>();
for (MethodInfo toolMethod : toolMethods) {
// Validation
// - Must not have another tool with the same method name
// - Must have at least one parameter
if (discoveredTools.containsKey(toolMethod.name())) {
validation.produce(
new ValidationPhaseBuildItem.ValidationErrorBuildItem(new IllegalStateException(
"A tool with the name '" + toolMethod.name() + "' from class '"
+ className + "' is already declared in class '"
+ discoveredTools.get(toolMethod.name())
+ "'. Tools method name must be unique.")));
validationErrorFound = true;
continue;
}
discoveredTools.put(toolMethod.name(), toolMethod.declaringClass());
if (Modifier.isPrivate(toolMethod.flags())) {
privateMethods.add(toolMethod);
}
}
if (!privateMethods.isEmpty()) {
transformerProducer.produce(new BytecodeTransformerBuildItem(className.toString(),
new RemovePrivateFromMethodsVisitor(privateMethods)));
}
if (validationErrorFound) {
return;
}
for (MethodInfo toolMethod : toolMethods) {
AnnotationInstance instance = toolMethod.annotation(TOOL);
AnnotationValue nameValue = instance.value("name");
AnnotationValue descriptionValue = instance.value();
String toolName = getToolName(nameValue, toolMethod);
String toolDescription = getToolDescription(descriptionValue);
ToolSpecification.Builder builder = ToolSpecification.builder()
.name(toolName)
.description(toolDescription);
MethodParameterInfo memoryIdParameter = null;
for (MethodParameterInfo parameter : toolMethod.parameters()) {
if (parameter.hasAnnotation(TOOL_MEMORY_ID)) {
memoryIdParameter = parameter;
continue;
}
builder.addParameter(parameter.name(), toJsonSchemaProperties(parameter, index));
}
Map<String, Integer> nameToParamPosition = toolMethod.parameters().stream().collect(
Collectors.toMap(MethodParameterInfo::name, i -> Integer.valueOf(i.position())));
String methodSignature = createUniqueSignature(toolMethod);
String invokerClassName = generateInvoker(toolMethod, classOutput, nameToParamPosition,
memoryIdParameter != null ? memoryIdParameter.position() : null, methodSignature);
generatedInvokerClasses.add(invokerClassName);
String argumentMapperClassName = generateArgumentMapper(toolMethod, classOutput,
methodSignature);
generatedArgumentMapperClasses.add(argumentMapperClassName);
ToolSpecification toolSpecification = builder.build();
ToolMethodCreateInfo methodCreateInfo = new ToolMethodCreateInfo(
toolMethod.name(), invokerClassName,
toolSpecification, argumentMapperClassName);
metadata.computeIfAbsent(className.toString(), (c) -> new ArrayList<>()).add(methodCreateInfo);
}
}
}
if (!generatedInvokerClasses.isEmpty()) {
reflectiveClassProducer.produce(ReflectiveClassBuildItem
.builder(generatedInvokerClasses.toArray(String[]::new))
.constructors(true)
.build());
}
if (!generatedArgumentMapperClasses.isEmpty()) {
reflectiveClassProducer.produce(ReflectiveClassBuildItem
.builder(generatedArgumentMapperClasses.toArray(String[]::new))
.fields(true)
.constructors(true)
.build());
}
toolsMetadataProducer.produce(new ToolsMetadataBuildItem(metadata));
recorder.setMetadata(metadata);
}
private static String createUniqueSignature(MethodInfo toolMethod) {
StringBuilder sigBuilder = new StringBuilder();
sigBuilder.append(toolMethod.name())
.append(toolMethod.returnType().name().toString());
for (MethodParameterInfo t : toolMethod.parameters()) {
sigBuilder.append(t.type().name().toString());
}
return sigBuilder.toString();
}
private static String getToolName(AnnotationValue nameValue, MethodInfo methodInfo) {
if (nameValue == null) {
return methodInfo.name();
}
String annotationValue = nameValue.asString();
if (annotationValue.isEmpty()) {
return methodInfo.name();
}
return annotationValue;
}
private String getToolDescription(AnnotationValue descriptionValue) {
if (descriptionValue == null) {
return "";
}
return String.join("\n", descriptionValue.asStringArray());
}
private static String generateInvoker(MethodInfo methodInfo, ClassOutput classOutput,
Map<String, Integer> nameToParamPosition, Short memoryIdParamPosition, String methodSignature) {
String implClassName = methodInfo.declaringClass().name() + "$$QuarkusInvoker$" + methodInfo.name() + "_"
+ HashUtil.sha1(methodSignature);
try (ClassCreator classCreator = ClassCreator.builder()
.classOutput(classOutput)
.className(implClassName)
.interfaces(ToolInvoker.class)
.build()) {
MethodCreator invokeMc = classCreator.getMethodCreator(
MethodDescriptor.ofMethod(implClassName, "invoke", Object.class, Object.class, Object[].class));
ResultHandle result;
if (methodInfo.parametersCount() > 0) {
List<ResultHandle> argumentHandles = new ArrayList<>(methodInfo.parametersCount());
for (int i = 0; i < methodInfo.parametersCount(); i++) {
argumentHandles.add(invokeMc.readArrayValue(invokeMc.getMethodParam(1), i));
}
ResultHandle[] targetMethodHandles = argumentHandles.toArray(new ResultHandle[0]);
result = invokeMc.invokeVirtualMethod(MethodDescriptor.of(methodInfo), invokeMc.getMethodParam(0),
targetMethodHandles);
} else {
result = invokeMc.invokeVirtualMethod(MethodDescriptor.of(methodInfo), invokeMc.getMethodParam(0));
}
boolean toolReturnsVoid = methodInfo.returnType().kind() == Type.Kind.VOID;
if (toolReturnsVoid) {
invokeMc.returnValue(invokeMc.load("Success"));
} else {
invokeMc.returnValue(result);
}
MethodCreator methodMetadataMc = classCreator
.getMethodCreator(MethodDescriptor.ofMethod(implClassName, "methodMetadata",
ToolInvoker.MethodMetadata.class));
ResultHandle nameToParamPositionHandle = methodMetadataMc.newInstance(HASHMAP_CTOR);
for (var entry : nameToParamPosition.entrySet()) {
methodMetadataMc.invokeInterfaceMethod(MAP_PUT, nameToParamPositionHandle,
methodMetadataMc.load(entry.getKey()),
methodMetadataMc.load(entry.getValue()));
}
ResultHandle resultHandle = methodMetadataMc.newInstance(METHOD_METADATA_CTOR,
methodMetadataMc.load(toolReturnsVoid),
nameToParamPositionHandle,
memoryIdParamPosition != null ? methodMetadataMc.load(Integer.valueOf(memoryIdParamPosition))
: methodMetadataMc.loadNull());
methodMetadataMc.returnValue(resultHandle);
}
return implClassName;
}
private String generateArgumentMapper(MethodInfo methodInfo, ClassOutput classOutput,
String methodSignature) {
String implClassName = methodInfo.declaringClass().name() + "$$QuarkusToolArgumentMapper$" + methodInfo.name() + "_"
+ HashUtil.sha1(methodSignature);
try (ClassCreator classCreator = ClassCreator.builder()
.classOutput(classOutput)
.className(implClassName)
.interfaces(Mappable.class)
.build()) {
List<FieldDescriptor> fieldDescriptors = new ArrayList<>();
for (MethodParameterInfo parameter : methodInfo.parameters()) {
FieldDescriptor fieldDescriptor = FieldDescriptor.of(implClassName, parameter.name(),
parameter.type().name().toString());
fieldDescriptors.add(fieldDescriptor);
classCreator.getFieldCreator(fieldDescriptor).setModifiers(Modifier.PUBLIC);
}
MethodCreator mc = classCreator
.getMethodCreator(MethodDescriptor.ofMethod(implClassName, "obtainFieldValuesMap", Map.class));
ResultHandle mapHandle = mc.newInstance(MethodDescriptor.ofConstructor(HashMap.class));
for (FieldDescriptor field : fieldDescriptors) {
ResultHandle fieldValue = mc.readInstanceField(field, mc.getThis());
mc.invokeInterfaceMethod(MAP_PUT, mapHandle, mc.load(field.getName()), fieldValue);
}
mc.returnValue(mapHandle);
}
return implClassName;
}
private Iterable<JsonSchemaProperty> toJsonSchemaProperties(MethodParameterInfo parameter, IndexView index) {
Type type = parameter.type();
DotName typeName = parameter.type().name();
AnnotationInstance pInstance = parameter.annotation(P);
JsonSchemaProperty description = pInstance == null ? null : description(pInstance.value().asString());
if (DotNames.STRING.equals(typeName) || DotNames.CHARACTER.equals(typeName)
|| DotNames.PRIMITIVE_CHAR.equals(typeName)) {
return removeNulls(STRING, description);
}
if (DotNames.BOOLEAN.equals(typeName) || DotNames.PRIMITIVE_BOOLEAN.equals(typeName)) {
return removeNulls(BOOLEAN, description);
}
if (DotNames.BYTE.equals(typeName) || DotNames.PRIMITIVE_BYTE.equals(typeName)
|| DotNames.SHORT.equals(typeName) || DotNames.PRIMITIVE_SHORT.equals(typeName)
|| DotNames.INTEGER.equals(typeName) || DotNames.PRIMITIVE_INT.equals(typeName)
|| DotNames.LONG.equals(typeName) || DotNames.PRIMITIVE_LONG.equals(typeName)
|| DotNames.BIG_INTEGER.equals(typeName)) {
return removeNulls(INTEGER, description);
}
// TODO put constraints on min and max?
if (DotNames.FLOAT.equals(typeName) || DotNames.PRIMITIVE_FLOAT.equals(typeName)
|| DotNames.DOUBLE.equals(typeName) || DotNames.PRIMITIVE_DOUBLE.equals(typeName)
|| DotNames.BIG_DECIMAL.equals(typeName)) {
return removeNulls(NUMBER, description);
}
if ((type.kind() == Type.Kind.ARRAY)
|| DotNames.LIST.equals(typeName)
|| DotNames.SET.equals(typeName)) { // TODO something else?
return removeNulls(ARRAY, description); // TODO provide type of array?
}
if (isEnum(type, index)) {
return removeNulls(STRING, enums(enumConstants(type)), description);
}
return removeNulls(OBJECT, description); // TODO provide internals
}
private Iterable<JsonSchemaProperty> removeNulls(JsonSchemaProperty... properties) {
return stream(properties)
.filter(Objects::nonNull)
.collect(toList());
}
private boolean isEnum(Type returnType, IndexView index) {
if (returnType.kind() != Type.Kind.CLASS) {
return false;
}
ClassInfo maybeEnum = index.getClassByName(returnType.name());
return maybeEnum != null && maybeEnum.isEnum();
}
private static Object[] enumConstants(Type type) {
return JandexUtil.load(type, Thread.currentThread().getContextClassLoader()).getEnumConstants();
}
/**
* Simply removes the {@code private} modifier from tool methods
*/
private static class RemovePrivateFromMethodsVisitor implements
BiFunction<String, ClassVisitor, ClassVisitor> {
private final List<MethodInfo> privateMethods;
private RemovePrivateFromMethodsVisitor(List<MethodInfo> privateMethods) {
this.privateMethods = privateMethods;
}
@Override
public ClassVisitor apply(String className, ClassVisitor classVisitor) {
ClassTransformer transformer = new ClassTransformer(className);
for (MethodInfo method : privateMethods) {
transformer.modifyMethod(MethodDescriptor.of(method)).removeModifiers(Opcodes.ACC_PRIVATE);
}
return transformer.applyTo(classVisitor);
}
}
}
|
[
"dev.langchain4j.agent.tool.ToolSpecification.builder"
] |
[((4315, 4392), 'io.quarkus.arc.deployment.AdditionalBeanBuildItem.builder'), ((4315, 4384), 'io.quarkus.arc.deployment.AdditionalBeanBuildItem.builder'), ((9418, 9547), 'dev.langchain4j.agent.tool.ToolSpecification.builder'), ((9418, 9489), 'dev.langchain4j.agent.tool.ToolSpecification.builder'), ((13423, 13601), 'io.quarkus.gizmo.ClassCreator.builder'), ((13423, 13576), 'io.quarkus.gizmo.ClassCreator.builder'), ((13423, 13529), 'io.quarkus.gizmo.ClassCreator.builder'), ((13423, 13487), 'io.quarkus.gizmo.ClassCreator.builder'), ((16279, 16454), 'io.quarkus.gizmo.ClassCreator.builder'), ((16279, 16429), 'io.quarkus.gizmo.ClassCreator.builder'), ((16279, 16385), 'io.quarkus.gizmo.ClassCreator.builder'), ((16279, 16343), 'io.quarkus.gizmo.ClassCreator.builder')]
|
import dev.langchain4j.model.chat.ChatLanguageModel;
import dev.langchain4j.model.ollama.OllamaChatModel;
import org.junit.jupiter.api.Test;
import org.testcontainers.containers.GenericContainer;
import org.testcontainers.junit.jupiter.Container;
import org.testcontainers.junit.jupiter.Testcontainers;
@Testcontainers
class OllamaChatModelTest {
/**
* The first time you run this test, it will download a Docker image with Ollama and a model.
* It might take a few minutes.
* <p>
* This test uses modified Ollama Docker images, which already contain models inside them.
* All images with pre-packaged models are available here: https://hub.docker.com/repositories/langchain4j
* <p>
* However, you are not restricted to these images.
* You can run any model from https://ollama.ai/library by following these steps:
* 1. Run "docker run -d -v ollama:/root/.ollama -p 11434:11434 --name ollama ollama/ollama"
* 2. Run "docker exec -it ollama ollama run mistral" <- specify the desired model here
*/
static String MODEL_NAME = "orca-mini"; // try "mistral", "llama2", "codellama", "phi" or "tinyllama"
@Container
static GenericContainer<?> ollama = new GenericContainer<>("langchain4j/ollama-" + MODEL_NAME + ":latest")
.withExposedPorts(11434);
@Test
void simple_example() {
ChatLanguageModel model = OllamaChatModel.builder()
.baseUrl(baseUrl())
.modelName(MODEL_NAME)
.build();
String answer = model.generate("Provide 3 short bullet points explaining why Java is awesome");
System.out.println(answer);
}
@Test
void json_output_example() {
ChatLanguageModel model = OllamaChatModel.builder()
.baseUrl(baseUrl())
.modelName(MODEL_NAME)
.format("json")
.build();
String json = model.generate("Give me a JSON with 2 fields: name and age of a John Doe, 42");
System.out.println(json);
}
static String baseUrl() {
return String.format("http://%s:%d", ollama.getHost(), ollama.getFirstMappedPort());
}
}
|
[
"dev.langchain4j.model.ollama.OllamaChatModel.builder"
] |
[((1404, 1529), 'dev.langchain4j.model.ollama.OllamaChatModel.builder'), ((1404, 1504), 'dev.langchain4j.model.ollama.OllamaChatModel.builder'), ((1404, 1465), 'dev.langchain4j.model.ollama.OllamaChatModel.builder'), ((1758, 1915), 'dev.langchain4j.model.ollama.OllamaChatModel.builder'), ((1758, 1890), 'dev.langchain4j.model.ollama.OllamaChatModel.builder'), ((1758, 1858), 'dev.langchain4j.model.ollama.OllamaChatModel.builder'), ((1758, 1819), 'dev.langchain4j.model.ollama.OllamaChatModel.builder')]
|
package com.honvay.flychat.langchain.llama.embedding;
import dev.langchain4j.data.embedding.Embedding;
import dev.langchain4j.data.segment.TextSegment;
import dev.langchain4j.model.embedding.EmbeddingModel;
import dev.langchain4j.model.openai.OpenAiEmbeddingModel;
import dev.langchain4j.model.openai.OpenAiModelName;
import org.springframework.beans.factory.annotation.Value;
import org.springframework.stereotype.Service;
import java.time.Duration;
import java.util.List;
import java.util.stream.Collectors;
@Service
public class OpenAiEmbeddingService implements EmbeddingService {
private final String apiKey;
public OpenAiEmbeddingService(@Value("${openai.apiKey:}") String apiKey) {
this.apiKey = apiKey;
}
@Override
public List<float[]> embed(List<String> texts){
List<TextSegment> segments = texts.stream()
.map(TextSegment::from)
.collect(Collectors.toList());
EmbeddingModel embeddingModel = OpenAiEmbeddingModel.builder()
.apiKey(apiKey) // https://platform.openai.com/account/api-keys
.modelName(OpenAiModelName.TEXT_EMBEDDING_ADA_002)
.timeout(Duration.ofSeconds(15))
.build();
List<Embedding> embeddings = embeddingModel.embedAll(segments);
return embeddings
.stream()
.map(Embedding::vector)
.collect(Collectors.toList());
}
@Override
public float[] embed(String text) {
EmbeddingModel embeddingModel = OpenAiEmbeddingModel.builder()
.apiKey(apiKey) // https://platform.openai.com/account/api-keys
.modelName(OpenAiModelName.TEXT_EMBEDDING_ADA_002)
.timeout(Duration.ofSeconds(15))
.build();
Embedding embedding = embeddingModel.embed(text);
return embedding.vector();
}
}
|
[
"dev.langchain4j.model.openai.OpenAiEmbeddingModel.builder"
] |
[((981, 1228), 'dev.langchain4j.model.openai.OpenAiEmbeddingModel.builder'), ((981, 1204), 'dev.langchain4j.model.openai.OpenAiEmbeddingModel.builder'), ((981, 1156), 'dev.langchain4j.model.openai.OpenAiEmbeddingModel.builder'), ((981, 1042), 'dev.langchain4j.model.openai.OpenAiEmbeddingModel.builder'), ((1539, 1790), 'dev.langchain4j.model.openai.OpenAiEmbeddingModel.builder'), ((1539, 1765), 'dev.langchain4j.model.openai.OpenAiEmbeddingModel.builder'), ((1539, 1716), 'dev.langchain4j.model.openai.OpenAiEmbeddingModel.builder'), ((1539, 1601), 'dev.langchain4j.model.openai.OpenAiEmbeddingModel.builder')]
|
package com.wxm158.promptgeneration.service;
import com.fasterxml.jackson.annotation.JsonAutoDetect;
import com.fasterxml.jackson.annotation.PropertyAccessor;
import com.fasterxml.jackson.core.JsonProcessingException;
import com.fasterxml.jackson.databind.ObjectMapper;
import com.fasterxml.jackson.databind.ObjectWriter;
import com.google.common.reflect.TypeToken;
import com.google.gson.Gson;
import com.google.gson.GsonBuilder;
import com.wxm158.promptgeneration.OpenAI.OpenAiEmbeddingModel;
import com.wxm158.promptgeneration.mapper.QuestionMapper;
import com.wxm158.promptgeneration.model.dto.ChatRequest;
import com.wxm158.promptgeneration.model.dto.QuestionGeneration;
import com.wxm158.promptgeneration.model.dto.TopicResponse;
import com.wxm158.promptgeneration.model.entity.Question;
import com.wxm158.promptgeneration.repository.QuestionRepository;
import com.wxm158.promptgeneration.weviate.WeaviateEmbeddingStore;
import dev.langchain4j.chain.ConversationalRetrievalChain;
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.message.ChatMessage;
import dev.langchain4j.data.message.SystemMessage;
import dev.langchain4j.data.message.UserMessage;
import dev.langchain4j.data.segment.TextSegment;
import dev.langchain4j.memory.chat.MessageWindowChatMemory;
import dev.langchain4j.model.embedding.EmbeddingModel;
import dev.langchain4j.model.input.PromptTemplate;
import dev.langchain4j.model.openai.OpenAiChatModel;
import dev.langchain4j.model.openai.OpenAiTokenizer;
import dev.langchain4j.model.output.Response;
import dev.langchain4j.retriever.EmbeddingStoreRetriever;
import dev.langchain4j.retriever.Retriever;
import dev.langchain4j.store.embedding.EmbeddingStore;
import lombok.RequiredArgsConstructor;
import lombok.extern.slf4j.Slf4j;
import org.apache.commons.lang3.StringEscapeUtils;
import org.springframework.beans.factory.annotation.Value;
import org.springframework.data.domain.PageRequest;
import org.springframework.data.domain.Pageable;
import org.springframework.http.ResponseEntity;
import org.springframework.stereotype.Service;
import java.io.FileWriter;
import java.io.IOException;
import java.lang.reflect.Type;
import java.net.Proxy;
import java.nio.file.Paths;
import java.time.Duration;
import java.time.LocalDateTime;
import java.util.ArrayList;
import java.util.Arrays;
import java.util.List;
import static dev.langchain4j.data.document.FileSystemDocumentLoader.loadDocument;
import static dev.langchain4j.internal.Utils.getOrDefault;
import static dev.langchain4j.model.openai.OpenAiModelName.GPT_3_5_TURBO;
import static java.util.stream.Collectors.joining;
@Slf4j
@Service
@RequiredArgsConstructor
public class EmbeddingService {
@Value("${OPENAI_API_KEY}")
private String API_KEY;
@Value("${WEAVIATE_API_KEY}")
private String WEAVIATE_API_KEY;
private static final String DEFAULT_NAMESPACE = "default"; // do not change, will break backward compatibility!
private static final String DEFAULT_METADATA_TEXT_KEY = "text_segment"; // do not change, will break backward compatibility!
String baseUrl = "https://api.openai.com/v1";
String modelName = "text-embedding-ada-002"; // You can change this if needed
Duration timeout = Duration.ofSeconds(120); // You can change this if needed
Integer maxRetries = 3; // You can change this if needed
Proxy proxy = null; // You can provide a proxy if needed
Boolean logRequests = true; // Set to true if you want to log requests
Boolean logResponses = true; // Set to true if you want to log responses
private final QuestionRepository questionRepository;
private final QuestionMapper questionMapper;
// Create embedding model
private EmbeddingModel createEmbeddingModel() {
return OpenAiEmbeddingModel.builder()
.baseUrl(baseUrl)
.apiKey(API_KEY)
.modelName(modelName)
.timeout(timeout)
.maxRetries(maxRetries)
.proxy(proxy)
.logRequests(logRequests)
.logResponses(logResponses)
.build();
}
// Create embedding store
private EmbeddingStore<TextSegment> createEmbeddingStore() {
return WeaviateEmbeddingStore.builder()
.apiKey(WEAVIATE_API_KEY)
.scheme("https")
.host("question-gen-wwxbinax.weaviate.network")
.avoidDups(true)
.consistencyLevel("ALL")
.build();
}
// Create Chat Model
private OpenAiChatModel createChatModel() {
return OpenAiChatModel.builder()
.apiKey(API_KEY)
// old key 8T6eTtmk
.modelName("ft:gpt-3.5-turbo-1106:personal::8VzKieWR")
.timeout(timeout)
.temperature(0.3)
.build();
}
private String format(List<TextSegment> relevantSegments) {
return relevantSegments.stream()
.map(TextSegment::text)
.map(segment -> "..." + segment + "...")
.collect(joining("\n\n"));
}
// Create question from embedding and fine tuning
public List<QuestionGeneration> createQuestions(ChatRequest chatRequest) {
String message = chatRequest.getTopic();
String questionType = chatRequest.getQuestionType();
String questionAmount = chatRequest.getQuestionAmount();
// Initialise fine tuned chat model, embedding model, embedding store.
OpenAiChatModel chatModel = createChatModel();
EmbeddingModel embeddingModel = createEmbeddingModel();
EmbeddingStore<TextSegment> embeddingStore = createEmbeddingStore();
// retrieve relevant text from embedding store. max three text segments.
Retriever<TextSegment> retriever = EmbeddingStoreRetriever.from(embeddingStore, embeddingModel, 1);
String information = format(retriever.findRelevant(message)).replace("\n", " ");
// Create the prompt in format used in training fine-tuned model.
ChatMessage[] messagesArray = {
new SystemMessage("You are an A-level Computer Science teacher. You aim to generate various questions for your students."),
new UserMessage("SCOPE: " + message + ", QUESTION_TYPE: " + questionType + ", QUESTION_AMOUNT: " + questionAmount +
", TEXT: " + information)
};
List<ChatMessage> messages = new ArrayList<>(List.of(messagesArray));
System.out.println(messages.toString());
// Get response from model (json list of questions and answers)
Response<AiMessage> response = chatModel.generate(messages);
String stringResponse = response.content().text();
System.out.println(stringResponse);
// Map response to List of QuestionGeneration object for the frontend.
int startIndex = stringResponse.indexOf("[");
int endIndex = stringResponse.lastIndexOf("]");
Gson gson = new Gson();
Type type = new TypeToken<List<QuestionGeneration>>(){}.getType();
List<QuestionGeneration> questions = gson.fromJson(stringResponse.substring(startIndex, endIndex + 1), type);
for (QuestionGeneration question : questions) {
question.setId((long) questions.indexOf(question));
question.setQuestionType(questionType);
}
return questions;
}
// Save Questions in question-generation-db
public List<Question> saveQuestions(List<QuestionGeneration> questions, String userId) {
List<Question> questionList = questionMapper.mapQuestionGenerationsToQuestions(questions, userId);
List<Question> savedQuestions = new ArrayList<>();
for (Question question: questionList) {
if (!questionRepository.existsByQuestionAndQuestionType(question.getQuestion(), question.getQuestionType())) {
savedQuestions.add(questionRepository.save(question));
}
}
return savedQuestions;
}
public List<Question> getAllQuestions(String userId) {
return questionRepository.findAllByUserId(Long.valueOf(userId));
// return questionMapper.mapQuestionsToQuestionGenerations(questionList);
}
}
|
[
"dev.langchain4j.model.openai.OpenAiChatModel.builder"
] |
[((3990, 4340), 'com.wxm158.promptgeneration.OpenAI.OpenAiEmbeddingModel.builder'), ((3990, 4315), 'com.wxm158.promptgeneration.OpenAI.OpenAiEmbeddingModel.builder'), ((3990, 4271), 'com.wxm158.promptgeneration.OpenAI.OpenAiEmbeddingModel.builder'), ((3990, 4229), 'com.wxm158.promptgeneration.OpenAI.OpenAiEmbeddingModel.builder'), ((3990, 4199), 'com.wxm158.promptgeneration.OpenAI.OpenAiEmbeddingModel.builder'), ((3990, 4159), 'com.wxm158.promptgeneration.OpenAI.OpenAiEmbeddingModel.builder'), ((3990, 4125), 'com.wxm158.promptgeneration.OpenAI.OpenAiEmbeddingModel.builder'), ((3990, 4087), 'com.wxm158.promptgeneration.OpenAI.OpenAiEmbeddingModel.builder'), ((3990, 4054), 'com.wxm158.promptgeneration.OpenAI.OpenAiEmbeddingModel.builder'), ((4457, 4727), 'com.wxm158.promptgeneration.weviate.WeaviateEmbeddingStore.builder'), ((4457, 4702), 'com.wxm158.promptgeneration.weviate.WeaviateEmbeddingStore.builder'), ((4457, 4661), 'com.wxm158.promptgeneration.weviate.WeaviateEmbeddingStore.builder'), ((4457, 4628), 'com.wxm158.promptgeneration.weviate.WeaviateEmbeddingStore.builder'), ((4457, 4564), 'com.wxm158.promptgeneration.weviate.WeaviateEmbeddingStore.builder'), ((4457, 4531), 'com.wxm158.promptgeneration.weviate.WeaviateEmbeddingStore.builder'), ((4822, 5080), 'dev.langchain4j.model.openai.OpenAiChatModel.builder'), ((4822, 5055), 'dev.langchain4j.model.openai.OpenAiChatModel.builder'), ((4822, 5021), 'dev.langchain4j.model.openai.OpenAiChatModel.builder'), ((4822, 4987), 'dev.langchain4j.model.openai.OpenAiChatModel.builder'), ((4822, 4880), 'dev.langchain4j.model.openai.OpenAiChatModel.builder')]
|
package com.gonnect.helpme.config;
import com.gonnect.helpme.agent.ReservationSupportAgent;
import com.gonnect.helpme.service.ReservationToolService;
import dev.langchain4j.data.document.Document;
import dev.langchain4j.data.document.DocumentSplitter;
import dev.langchain4j.data.document.parser.TextDocumentParser;
import dev.langchain4j.data.document.splitter.DocumentSplitters;
import dev.langchain4j.data.segment.TextSegment;
import dev.langchain4j.memory.chat.MessageWindowChatMemory;
import dev.langchain4j.model.chat.ChatLanguageModel;
import dev.langchain4j.model.embedding.AllMiniLmL6V2EmbeddingModel;
import dev.langchain4j.model.embedding.EmbeddingModel;
import dev.langchain4j.model.openai.OpenAiTokenizer;
import dev.langchain4j.retriever.EmbeddingStoreRetriever;
import dev.langchain4j.retriever.Retriever;
import dev.langchain4j.service.AiServices;
import dev.langchain4j.store.embedding.EmbeddingStore;
import dev.langchain4j.store.embedding.EmbeddingStoreIngestor;
import dev.langchain4j.store.embedding.inmemory.InMemoryEmbeddingStore;
import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;
import org.springframework.core.io.Resource;
import org.springframework.core.io.ResourceLoader;
import java.io.IOException;
import static dev.langchain4j.data.document.loader.FileSystemDocumentLoader.loadDocument;
import static dev.langchain4j.model.openai.OpenAiModelName.GPT_3_5_TURBO;
@Configuration
public class ReservationHelpMeApplicationConfigurer {
/**
* Run ReservationSupportApplicationTest to see simulated conversation with customer support agent
*/
@Bean
ReservationSupportAgent reservationSupportAgent(ChatLanguageModel chatLanguageModel,
ReservationToolService reservationToolService,
Retriever<TextSegment> retriever) {
return AiServices.builder(ReservationSupportAgent.class)
.chatLanguageModel(chatLanguageModel)
.chatMemory(MessageWindowChatMemory.withMaxMessages(20))
.tools(reservationToolService)
.retriever(retriever)
.build();
}
@Bean
Retriever<TextSegment> fetch(EmbeddingStore<TextSegment> embeddingStore, EmbeddingModel embeddingModel) {
// You will need to adjust these parameters to find the optimal setting, which will depend on two main factors:
// - The nature of your data
// - The embedding model you are using
int maxResultsRetrieved = 1;
double minScore = 0.6;
return EmbeddingStoreRetriever.from(embeddingStore, embeddingModel, maxResultsRetrieved, minScore);
}
@Bean
EmbeddingModel embeddingModel() {
return new AllMiniLmL6V2EmbeddingModel();
}
@Bean
EmbeddingStore<TextSegment> embeddingStore(EmbeddingModel embeddingModel, ResourceLoader resourceLoader) throws IOException {
// Embedding Store Setup
// --------------------
// For demonstration purposes, the embedding store is populated
// dynamically instead of being pre-filled with application data.
// This allows the code to run self-sufficiently for demos.
// The first step is initializing an embedding store.
// For this example we use an in-memory implementation.
// This stores the vector representations of text for similarity lookups.
EmbeddingStore<TextSegment> embeddingStore = new InMemoryEmbeddingStore<>();
// Load "Gonnect Support Bot" training guidelines as sample
Resource resource = resourceLoader.getResource("classpath:gonnect-miles-terms-and-condition.txt");
Document document = loadDocument(resource.getFile().toPath(), new TextDocumentParser());
// Ingest Sample Document
// ---------------------
// 1. Split document into 100-token segments
// 2. Convert text segments into vector embeddings
// 3. Save embeddings in the store
// The EmbeddingStoreIngestor automates this process of
// analyzing text and populating the embedding store
DocumentSplitter documentSplitter = DocumentSplitters.recursive(100, 0, new OpenAiTokenizer(GPT_3_5_TURBO));
EmbeddingStoreIngestor ingestor = EmbeddingStoreIngestor.builder()
.documentSplitter(documentSplitter)
.embeddingModel(embeddingModel)
.embeddingStore(embeddingStore)
.build();
ingestor.ingest(document);
return embeddingStore;
}
}
|
[
"dev.langchain4j.service.AiServices.builder",
"dev.langchain4j.store.embedding.EmbeddingStoreIngestor.builder"
] |
[((1951, 2237), 'dev.langchain4j.service.AiServices.builder'), ((1951, 2212), 'dev.langchain4j.service.AiServices.builder'), ((1951, 2174), 'dev.langchain4j.service.AiServices.builder'), ((1951, 2127), 'dev.langchain4j.service.AiServices.builder'), ((1951, 2054), 'dev.langchain4j.service.AiServices.builder'), ((4356, 4561), 'dev.langchain4j.store.embedding.EmbeddingStoreIngestor.builder'), ((4356, 4536), 'dev.langchain4j.store.embedding.EmbeddingStoreIngestor.builder'), ((4356, 4488), 'dev.langchain4j.store.embedding.EmbeddingStoreIngestor.builder'), ((4356, 4440), 'dev.langchain4j.store.embedding.EmbeddingStoreIngestor.builder')]
|
package com.anthonyquere.companionapi.completion.langchain;
import com.anthonyquere.companionapi.completion.langchain.services.Summary;
import com.anthonyquere.companionapi.completion.langchain.services.TalkWithCompanion;
import com.anthonyquere.companionapi.crud.companions.Companion;
import com.anthonyquere.companionapi.crud.message.MessageRepository;
import dev.langchain4j.model.chat.ChatLanguageModel;
import dev.langchain4j.service.AiServices;
import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;
@Configuration
public class CompanionAiService {
@Bean
public TalkWithCompanion buildAiCompanionService(
ChatLanguageModel model,
MessageRepository messageRepository
) {
return AiServices.builder(TalkWithCompanion.class)
.chatLanguageModel(model)
.chatMemoryProvider(companion -> new CompanionChatMemory((Companion) companion, messageRepository))
.build();
}
@Bean
public Summary buildAiSummaryService(
ChatLanguageModel model
) {
return AiServices.builder(Summary.class)
.chatLanguageModel(model)
.build();
}
}
|
[
"dev.langchain4j.service.AiServices.builder"
] |
[((789, 1015), 'dev.langchain4j.service.AiServices.builder'), ((789, 990), 'dev.langchain4j.service.AiServices.builder'), ((789, 874), 'dev.langchain4j.service.AiServices.builder'), ((1135, 1235), 'dev.langchain4j.service.AiServices.builder'), ((1135, 1210), 'dev.langchain4j.service.AiServices.builder')]
|
package my.samples;
import dev.langchain4j.data.document.Document;
import dev.langchain4j.data.document.loader.FileSystemDocumentLoader;
import dev.langchain4j.data.document.parser.apache.pdfbox.ApachePdfBoxDocumentParser;
import dev.langchain4j.data.document.splitter.DocumentSplitters;
import dev.langchain4j.data.embedding.Embedding;
import dev.langchain4j.data.segment.TextSegment;
import dev.langchain4j.model.embedding.AllMiniLmL6V2EmbeddingModel;
import dev.langchain4j.model.embedding.EmbeddingModel;
import dev.langchain4j.store.embedding.EmbeddingMatch;
import dev.langchain4j.store.embedding.EmbeddingStore;
import dev.langchain4j.store.embedding.EmbeddingStoreIngestor;
import dev.langchain4j.store.embedding.inmemory.InMemoryEmbeddingStore;
import java.io.IOException;
import java.net.URISyntaxException;
import java.net.URL;
import java.nio.file.Path;
import java.nio.file.Paths;
import java.util.ArrayList;
import java.util.List;
import java.util.Scanner;
public class InMemoryEmbeddingManualExample {
public static final String ANSI_GREEN = "\u001B[32m";
public static final String ANSI_RESET = "\u001B[0m";
public static final String ANSI_YELLOW = "\u001B[33m";
public static void main(String[] args) {
EmbeddingStore<TextSegment> embeddingStore = new InMemoryEmbeddingStore<>();
EmbeddingModel embeddingModel = new AllMiniLmL6V2EmbeddingModel();
EmbeddingStoreIngestor ingestor = EmbeddingStoreIngestor.builder()
.documentSplitter(DocumentSplitters.recursive(300, 0))
.embeddingModel(embeddingModel)
.embeddingStore(embeddingStore)
.build();
Path filePath = toPath("example-files/2025_US_F150_Warranty_Guide_ENG_V1.pdf");
Document document = FileSystemDocumentLoader.loadDocument(filePath, new ApachePdfBoxDocumentParser());
document.metadata().add("fileName", filePath.getFileName().toString());
document.metadata().add("filePath", filePath.toString());
document.metadata().add("company", "FORD");
document.metadata().add("product", "F150");
document.metadata().add("language", "ENG");
document.metadata().add("version", "V1");
document.metadata().add("year", "2025");
document.metadata().add("type", "Warranty Guide");
document.metadata().add("country", "US");
document.metadata().add("category", "Automotive");
ingestor.ingest(document);
Scanner scanner = new Scanner(System.in);
while (true) {
System.out.println("Enter your query (or type 'exit' to quit):");
// Wait for the user to input a query
String query = scanner.nextLine();
// Check if the user wants to exit the program
if ("exit".equalsIgnoreCase(query)) {
System.out.println("Exiting program.");
break;
}
// Who Pays For Warranty Repairs?
// What is the warranty period?
// What is the warranty period for the powertrain?
// What is the warranty period for the powertrain?
// Process the query and get an answer
Embedding queryEmbedding = embeddingModel.embed(query).content();
List<EmbeddingMatch<TextSegment>> relevant = embeddingStore.findRelevant(queryEmbedding,5 );
System.out.println("Start --------- Matching Context from Document: 2025_US_F150_Warranty_Guide_ENG_V1.pdf");
List<String> answers = new ArrayList<>();
for (EmbeddingMatch<TextSegment> match : relevant) {
System.out.println(match.score());
answers.add(match.embedded().text());
System.out.println(ANSI_GREEN+match.embedded().text()+ANSI_RESET);
System.out.println("");
}
System.out.println("End --------- Matching Context from Document: 2025_US_F150_Warranty_Guide_ENG_V1.pdf");
if(!answers.isEmpty()){
try {
System.out.println(ANSI_YELLOW+ RestClient.getAnswer(query, answers) + ANSI_RESET);
} catch (IOException e) {
e.printStackTrace();
}
}
}
// Close the scanner
scanner.close();
// In-memory embedding store can be serialized and deserialized to/from JSON
String serializedStore = ((InMemoryEmbeddingStore)embeddingStore).serializeToJson();
System.out.println(serializedStore);
// InMemoryEmbeddingStore<TextSegment> deserializedStore = InMemoryEmbeddingStore.fromJson(serializedStore);
// In-memory embedding store can be serialized and deserialized to/from file
// String filePath = "/home/me/embedding.store";
// embeddingStore.serializeToFile(filePath);
// InMemoryEmbeddingStore<TextSegment> deserializedStore = InMemoryEmbeddingStore.fromFile(filePath);
}
private static Path toPath(String fileName) {
try {
// Corrected path assuming files are in src/main/resources/example-files
URL fileUrl = InMemoryEmbeddingManualExample.class.getClassLoader().getResource( fileName);
if (fileUrl == null) {
throw new RuntimeException("Resource not found: " + fileName);
}
return Paths.get(fileUrl.toURI());
} catch (URISyntaxException e) {
throw new RuntimeException("Failed to resolve URI for: " + fileName, e);
}
}
}
|
[
"dev.langchain4j.store.embedding.EmbeddingStoreIngestor.builder"
] |
[((1444, 1668), 'dev.langchain4j.store.embedding.EmbeddingStoreIngestor.builder'), ((1444, 1643), 'dev.langchain4j.store.embedding.EmbeddingStoreIngestor.builder'), ((1444, 1595), 'dev.langchain4j.store.embedding.EmbeddingStoreIngestor.builder'), ((1444, 1547), 'dev.langchain4j.store.embedding.EmbeddingStoreIngestor.builder')]
|
package org.agoncal.fascicle.langchain4j.accessing.bedrock;
import dev.langchain4j.model.bedrock.BedrockAnthropicChatModel;
import software.amazon.awssdk.regions.Region;
// tag::adocSkip[]
/**
* @author Antonio Goncalves
* http://www.antoniogoncalves.org
* --
*/
// end::adocSkip[]
public class MusicianService {
public static void main(String[] args) {
MusicianService musicianService = new MusicianService();
musicianService.useBedrockLanguageModelBuilder();
}
private static final String AZURE_OPENAI_KEY = System.getenv("AZURE_OPENAI_KEY");
private static final String AZURE_OPENAI_ENDPOINT = System.getenv("AZURE_OPENAI_ENDPOINT");
private static final String AZURE_OPENAI_DEPLOYMENT_NAME = System.getenv("AZURE_OPENAI_DEPLOYMENT_NAME");
private static final String PROMPT = "When was the first Beatles album released?";
// ##############################
// ### BEDROCK LANGUAGE MODEL ###
// ##############################
public void useBedrockLanguageModelBuilder() {
System.out.println("### useBedrockLanguageModelBuilder");
// tag::adocSnippet[]
BedrockAnthropicChatModel model = BedrockAnthropicChatModel.builder()
.build();
// end::adocSnippet[]
String completion = model.generate(PROMPT);
}
// ##########################
// ### BEDROCK CHAT MODEL ###
// ##########################
public void useBedrockChatModelRequest() {
System.out.println("### useBedrockChatModelRequest");
// tag::adocRequest[]
BedrockAnthropicChatModel model = BedrockAnthropicChatModel.builder()
.maxRetries(3)
.topP(1.0f)
.temperature(0.9f)
.model(BedrockAnthropicChatModel.Types.AnthropicClaudeV1)
.maxTokens(100)
.anthropicVersion("v2")
.assistantPrompt("assistant prompt")
.humanPrompt("human prompt")
.region(Region.AF_SOUTH_1)
.build();
// end::adocRequest[]
String completion = model.generate("When was the first Rolling Stones album released?");
System.out.println(completion);
}
}
|
[
"dev.langchain4j.model.bedrock.BedrockAnthropicChatModel.builder"
] |
[((1142, 1192), 'dev.langchain4j.model.bedrock.BedrockAnthropicChatModel.builder'), ((1539, 1880), 'dev.langchain4j.model.bedrock.BedrockAnthropicChatModel.builder'), ((1539, 1865), 'dev.langchain4j.model.bedrock.BedrockAnthropicChatModel.builder'), ((1539, 1832), 'dev.langchain4j.model.bedrock.BedrockAnthropicChatModel.builder'), ((1539, 1797), 'dev.langchain4j.model.bedrock.BedrockAnthropicChatModel.builder'), ((1539, 1754), 'dev.langchain4j.model.bedrock.BedrockAnthropicChatModel.builder'), ((1539, 1724), 'dev.langchain4j.model.bedrock.BedrockAnthropicChatModel.builder'), ((1539, 1702), 'dev.langchain4j.model.bedrock.BedrockAnthropicChatModel.builder'), ((1539, 1638), 'dev.langchain4j.model.bedrock.BedrockAnthropicChatModel.builder'), ((1539, 1613), 'dev.langchain4j.model.bedrock.BedrockAnthropicChatModel.builder'), ((1539, 1595), 'dev.langchain4j.model.bedrock.BedrockAnthropicChatModel.builder')]
|
package dev.langchain4j.model.qianfan;
import dev.langchain4j.agent.tool.ToolExecutionRequest;
import dev.langchain4j.agent.tool.ToolParameters;
import dev.langchain4j.agent.tool.ToolSpecification;
import dev.langchain4j.data.message.*;
import dev.langchain4j.data.message.UserMessage;
import dev.langchain4j.internal.Utils;
import dev.langchain4j.model.qianfan.client.embedding.EmbeddingResponse;
import dev.langchain4j.model.qianfan.client.chat.Parameters;
import dev.langchain4j.model.output.FinishReason;
import dev.langchain4j.model.output.TokenUsage;
import dev.langchain4j.model.qianfan.client.chat.ChatCompletionResponse;
import dev.langchain4j.model.qianfan.client.chat.Message;
import dev.langchain4j.model.qianfan.client.chat.Role;
import dev.langchain4j.model.qianfan.client.chat.FunctionCall;
import dev.langchain4j.model.qianfan.client.chat.Function;
import dev.langchain4j.model.qianfan.client.completion.CompletionResponse;
import java.util.Collection;
import java.util.List;
import java.util.Optional;
import static dev.langchain4j.data.message.AiMessage.aiMessage;
import static dev.langchain4j.internal.Exceptions.illegalArgument;
import static dev.langchain4j.model.output.FinishReason.*;
import static java.util.stream.Collectors.toList;
public class InternalQianfanHelper {
public static List<Function> toFunctions(Collection<ToolSpecification> toolSpecifications) {
return toolSpecifications.stream()
.map(InternalQianfanHelper::toFunction)
.collect(toList());
}
private static Function toFunction(ToolSpecification toolSpecification) {
return Function.builder()
.name(toolSpecification.name())
.description(toolSpecification.description())
.parameters(toOpenAiParameters(toolSpecification.parameters()))
.build();
}
private static Parameters toOpenAiParameters(ToolParameters toolParameters) {
if (toolParameters == null) {
return Parameters.builder().build();
}
return Parameters.builder()
.properties(toolParameters.properties())
.required(toolParameters.required())
.build();
}
public static Message toQianfanMessage(ChatMessage message) {
if (message instanceof UserMessage) {
UserMessage userMessage = (UserMessage) message;
return Message.builder()
.role(Role.USER)
.content(userMessage.text())
.name(userMessage.name())
.build();
}
if (message instanceof AiMessage) {
AiMessage aiMessage = (AiMessage) message;
if (!aiMessage.hasToolExecutionRequests()) {
return Message.builder()
.content(message.text())
.role(Role.ASSISTANT)
.build();
}
ToolExecutionRequest toolExecutionRequest = aiMessage.toolExecutionRequests().get(0);
if (toolExecutionRequest.id() == null) {
FunctionCall functionCall = FunctionCall.builder()
.name(toolExecutionRequest.name())
.arguments(toolExecutionRequest.arguments())
.build();
return Message.builder()
.content(message.text())
.role(Role.ASSISTANT)
.functionCall(functionCall)
.build();
}
}
if (message instanceof ToolExecutionResultMessage) {
ToolExecutionResultMessage toolExecutionResultMessage = (ToolExecutionResultMessage) message;
FunctionCall functionCall = FunctionCall.builder()
.name(toolExecutionResultMessage.toolName())
.arguments(toolExecutionResultMessage.text())
.build();
return Message.builder()
.content(message.text())
.role(Role.FUNCTION)
.name(functionCall.name())
.build();
}
throw illegalArgument("Unknown message type: " + message.type());
}
static TokenUsage tokenUsageFrom(ChatCompletionResponse response) {
return Optional.of(response)
.map(ChatCompletionResponse::getUsage)
.map(usage -> new TokenUsage(usage.promptTokens(), usage.completionTokens(), usage.totalTokens()))
.orElse(null);
}
static TokenUsage tokenUsageFrom(CompletionResponse response) {
return Optional.of(response)
.map(CompletionResponse::getUsage)
.map(usage -> new TokenUsage(usage.promptTokens(), usage.completionTokens(), usage.totalTokens()))
.orElse(null);
}
static TokenUsage tokenUsageFrom(EmbeddingResponse response) {
return Optional.of(response)
.map(EmbeddingResponse::getUsage)
.map(usage -> new TokenUsage(usage.promptTokens(), usage.completionTokens(), usage.totalTokens()))
.orElse(null);
}
public static FinishReason finishReasonFrom(String finishReason) {
if(Utils.isNullOrBlank(finishReason)){
return null;
}
switch (finishReason) {
case "normal":
return STOP;
case "stop":
return STOP;
case "length":
return LENGTH;
case "content_filter":
return CONTENT_FILTER;
case "function_call":
return TOOL_EXECUTION;
default:
return null;
}
}
public static AiMessage aiMessageFrom(ChatCompletionResponse response) {
FunctionCall functionCall = response.getFunctionCall();
if (functionCall != null) {
ToolExecutionRequest toolExecutionRequest = ToolExecutionRequest.builder()
.name(functionCall.name())
.arguments(functionCall.arguments())
.build();
return aiMessage(toolExecutionRequest);
}
return aiMessage(response.getResult());
}
static String getSystemMessage(List<ChatMessage> messages) {
List<ChatMessage> systemMessages = messages.stream().filter(message -> message instanceof SystemMessage).collect(toList());
if (systemMessages.size() > 1) {
throw new RuntimeException("Multiple system messages are not supported");
}
if(Utils.isNullOrEmpty(systemMessages)){
return null;
}
return ((SystemMessage) systemMessages.get(0)).text();
}
public static List<Message> toOpenAiMessages(List<ChatMessage> messages) {
return messages.stream()
.filter(chatMessage -> !(chatMessage instanceof SystemMessage))
.map(InternalQianfanHelper::toQianfanMessage)
.collect(toList());
}
}
|
[
"dev.langchain4j.model.qianfan.client.chat.FunctionCall.builder",
"dev.langchain4j.model.qianfan.client.chat.Message.builder",
"dev.langchain4j.agent.tool.ToolExecutionRequest.builder",
"dev.langchain4j.model.qianfan.client.chat.Parameters.builder",
"dev.langchain4j.model.qianfan.client.chat.Function.builder",
"dev.langchain4j.data.message.AiMessage.aiMessage.toolExecutionRequests"
] |
[((1632, 1865), 'dev.langchain4j.model.qianfan.client.chat.Function.builder'), ((1632, 1840), 'dev.langchain4j.model.qianfan.client.chat.Function.builder'), ((1632, 1760), 'dev.langchain4j.model.qianfan.client.chat.Function.builder'), ((1632, 1698), 'dev.langchain4j.model.qianfan.client.chat.Function.builder'), ((2013, 2041), 'dev.langchain4j.model.qianfan.client.chat.Parameters.builder'), ((2068, 2223), 'dev.langchain4j.model.qianfan.client.chat.Parameters.builder'), ((2068, 2198), 'dev.langchain4j.model.qianfan.client.chat.Parameters.builder'), ((2068, 2145), 'dev.langchain4j.model.qianfan.client.chat.Parameters.builder'), ((2424, 2602), 'dev.langchain4j.model.qianfan.client.chat.Message.builder'), ((2424, 2573), 'dev.langchain4j.model.qianfan.client.chat.Message.builder'), ((2424, 2527), 'dev.langchain4j.model.qianfan.client.chat.Message.builder'), ((2424, 2478), 'dev.langchain4j.model.qianfan.client.chat.Message.builder'), ((2797, 2942), 'dev.langchain4j.model.qianfan.client.chat.Message.builder'), ((2797, 2909), 'dev.langchain4j.model.qianfan.client.chat.Message.builder'), ((2797, 2863), 'dev.langchain4j.model.qianfan.client.chat.Message.builder'), ((3015, 3055), 'dev.langchain4j.data.message.AiMessage.aiMessage.toolExecutionRequests'), ((3154, 3337), 'dev.langchain4j.model.qianfan.client.chat.FunctionCall.builder'), ((3154, 3304), 'dev.langchain4j.model.qianfan.client.chat.FunctionCall.builder'), ((3154, 3235), 'dev.langchain4j.model.qianfan.client.chat.FunctionCall.builder'), ((3363, 3560), 'dev.langchain4j.model.qianfan.client.chat.Message.builder'), ((3363, 3527), 'dev.langchain4j.model.qianfan.client.chat.Message.builder'), ((3363, 3475), 'dev.langchain4j.model.qianfan.client.chat.Message.builder'), ((3363, 3429), 'dev.langchain4j.model.qianfan.client.chat.Message.builder'), ((3799, 3993), 'dev.langchain4j.model.qianfan.client.chat.FunctionCall.builder'), ((3799, 3960), 'dev.langchain4j.model.qianfan.client.chat.FunctionCall.builder'), ((3799, 3890), 'dev.langchain4j.model.qianfan.client.chat.FunctionCall.builder'), ((4019, 4214), 'dev.langchain4j.model.qianfan.client.chat.Message.builder'), ((4019, 4181), 'dev.langchain4j.model.qianfan.client.chat.Message.builder'), ((4019, 4130), 'dev.langchain4j.model.qianfan.client.chat.Message.builder'), ((4019, 4085), 'dev.langchain4j.model.qianfan.client.chat.Message.builder'), ((4394, 4615), 'java.util.Optional.of'), ((4394, 4585), 'java.util.Optional.of'), ((4394, 4470), 'java.util.Optional.of'), ((4707, 4924), 'java.util.Optional.of'), ((4707, 4894), 'java.util.Optional.of'), ((4707, 4779), 'java.util.Optional.of'), ((5016, 5232), 'java.util.Optional.of'), ((5016, 5202), 'java.util.Optional.of'), ((5016, 5087), 'java.util.Optional.of'), ((6045, 6208), 'dev.langchain4j.agent.tool.ToolExecutionRequest.builder'), ((6045, 6179), 'dev.langchain4j.agent.tool.ToolExecutionRequest.builder'), ((6045, 6122), 'dev.langchain4j.agent.tool.ToolExecutionRequest.builder')]
|
package io.quarkiverse.langchain4j.runtime.tool;
import java.util.List;
import java.util.Map;
import dev.langchain4j.agent.tool.ToolParameters;
import io.quarkus.runtime.ObjectSubstitution;
import io.quarkus.runtime.annotations.RecordableConstructor;
public class ToolParametersObjectSubstitution
implements ObjectSubstitution<ToolParameters, ToolParametersObjectSubstitution.Serialized> {
@Override
public Serialized serialize(ToolParameters obj) {
return new Serialized(obj.type(), obj.properties(), obj.required());
}
@Override
public ToolParameters deserialize(Serialized obj) {
return ToolParameters.builder()
.type(obj.type)
.required(obj.required)
.properties(obj.properties).build();
}
public static class Serialized {
private final String type;
private final Map<String, Map<String, Object>> properties;
private final List<String> required;
@RecordableConstructor
public Serialized(String type, Map<String, Map<String, Object>> properties, List<String> required) {
this.type = type;
this.properties = properties;
this.required = required;
}
public String getType() {
return type;
}
public Map<String, Map<String, Object>> getProperties() {
return properties;
}
public List<String> getRequired() {
return required;
}
}
}
|
[
"dev.langchain4j.agent.tool.ToolParameters.builder"
] |
[((639, 787), 'dev.langchain4j.agent.tool.ToolParameters.builder'), ((639, 779), 'dev.langchain4j.agent.tool.ToolParameters.builder'), ((639, 735), 'dev.langchain4j.agent.tool.ToolParameters.builder'), ((639, 695), 'dev.langchain4j.agent.tool.ToolParameters.builder')]
|
package me.nzuguem.something.story.configurations.langchain;
import dev.langchain4j.rag.DefaultRetrievalAugmentor;
import dev.langchain4j.rag.RetrievalAugmentor;
import jakarta.enterprise.context.ApplicationScoped;
import java.util.function.Supplier;
@ApplicationScoped
public class StoryRetrievalAugmentor implements Supplier<RetrievalAugmentor> {
private final StoryContentRetriever retriever;
public StoryRetrievalAugmentor(StoryContentRetriever retriever) {
this.retriever = retriever;
}
@Override
public RetrievalAugmentor get() {
return DefaultRetrievalAugmentor.builder()
.contentRetriever(this.retriever)
.build();
}
}
|
[
"dev.langchain4j.rag.DefaultRetrievalAugmentor.builder"
] |
[((585, 695), 'dev.langchain4j.rag.DefaultRetrievalAugmentor.builder'), ((585, 670), 'dev.langchain4j.rag.DefaultRetrievalAugmentor.builder')]
|
package org.example;
import dev.langchain4j.memory.chat.MessageWindowChatMemory;
import dev.langchain4j.model.openai.OpenAiChatModel;
import dev.langchain4j.service.AiServices;
import dev.langchain4j.service.MemoryId;
import dev.langchain4j.service.UserMessage;
import dev.langchain4j.store.memory.chat.ChatMemoryStore;
public class _09_AIServices_05_ChatMemory {
public static void main(String[] args) {
OpenAiChatModel model = OpenAiChatModel.withApiKey(ApiKeys.OPENAI_DEMO);
ChatAssistant assistant = AiServices.builder(ChatAssistant.class)
.chatLanguageModel(model)
.chatMemoryProvider(memoryId -> MessageWindowChatMemory.withMaxMessages(10))
.build();
System.out.println(assistant.chat(1, "Hello, my name is Michael"));
System.out.println(assistant.chat(2, "Hello, my name is Karl"));
System.out.println(assistant.chat(2, "What is my name?"));
System.out.println(assistant.chat(1, "What is my name?"));
}
}
interface ChatAssistant {
String chat(@MemoryId int memoryId, @UserMessage String message);
}
|
[
"dev.langchain4j.service.AiServices.builder"
] |
[((529, 728), 'dev.langchain4j.service.AiServices.builder'), ((529, 703), 'dev.langchain4j.service.AiServices.builder'), ((529, 610), 'dev.langchain4j.service.AiServices.builder')]
|
package embedding.model;
import dev.langchain4j.data.embedding.Embedding;
import dev.langchain4j.model.embedding.EmbeddingModel;
import dev.langchain4j.model.output.Response;
import dev.langchain4j.model.vertexai.VertexAiEmbeddingModel;
public class VertexAiEmbeddingModelExample {
public static void main(String[] args) {
EmbeddingModel embeddingModel = VertexAiEmbeddingModel.builder()
.endpoint("us-central1-aiplatform.googleapis.com:443")
.project("neat-vent-381323")
.location("us-central1")
.publisher("google")
.modelName("textembedding-gecko@002")
.build();
Response<Embedding> response = embeddingModel.embed("Hello, how are you?");
System.out.println(response);
}
}
|
[
"dev.langchain4j.model.vertexai.VertexAiEmbeddingModel.builder"
] |
[((371, 676), 'dev.langchain4j.model.vertexai.VertexAiEmbeddingModel.builder'), ((371, 651), 'dev.langchain4j.model.vertexai.VertexAiEmbeddingModel.builder'), ((371, 597), 'dev.langchain4j.model.vertexai.VertexAiEmbeddingModel.builder'), ((371, 560), 'dev.langchain4j.model.vertexai.VertexAiEmbeddingModel.builder'), ((371, 519), 'dev.langchain4j.model.vertexai.VertexAiEmbeddingModel.builder'), ((371, 474), 'dev.langchain4j.model.vertexai.VertexAiEmbeddingModel.builder')]
|
package com.magicrepokit.chat;
import cn.hutool.core.util.StrUtil;
import cn.hutool.json.JSONArray;
import cn.hutool.json.ObjectMapper;
import com.magicrepokit.chat.agent.CustomerSupportAgent;
import com.magicrepokit.chat.component.GoogleSearch;
import com.magicrepokit.chat.component.LangchainComponent;
import com.magicrepokit.chat.service.tool.CalculatorService;
import com.magicrepokit.langchain.ElasticOperation;
import com.magicrepokit.langchain.config.ConfigProperties;
import com.magicrepokit.oss.OssTemplate;
import dev.langchain4j.data.document.Document;
import dev.langchain4j.data.document.DocumentSplitter;
import dev.langchain4j.data.document.loader.UrlDocumentLoader;
import dev.langchain4j.data.document.parser.TextDocumentParser;
import dev.langchain4j.data.document.splitter.DocumentSplitters;
import dev.langchain4j.data.embedding.Embedding;
import dev.langchain4j.data.message.AiMessage;
import dev.langchain4j.data.message.ChatMessage;
import dev.langchain4j.data.message.UserMessage;
import dev.langchain4j.data.segment.TextSegment;
import dev.langchain4j.memory.chat.MessageWindowChatMemory;
import dev.langchain4j.model.StreamingResponseHandler;
import dev.langchain4j.model.chat.ChatLanguageModel;
import dev.langchain4j.model.chat.StreamingChatLanguageModel;
import dev.langchain4j.model.embedding.EmbeddingModel;
import dev.langchain4j.model.input.Prompt;
import dev.langchain4j.model.input.PromptTemplate;
import dev.langchain4j.model.openai.OpenAiStreamingChatModel;
import dev.langchain4j.model.openai.OpenAiTokenizer;
import dev.langchain4j.model.output.Response;
import dev.langchain4j.service.AiServices;
import dev.langchain4j.service.SystemMessage;
import dev.langchain4j.store.embedding.EmbeddingMatch;
import dev.langchain4j.store.embedding.elasticsearch.ElasticsearchEmbeddingStore;
import lombok.extern.slf4j.Slf4j;
import org.junit.Test;
import org.junit.runner.RunWith;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.boot.test.context.SpringBootTest;
import org.springframework.context.annotation.ComponentScan;
import org.springframework.test.context.ActiveProfiles;
import org.springframework.test.context.junit4.SpringRunner;
import java.util.ArrayList;
import java.util.HashMap;
import java.util.List;
import java.util.Map;
import static dev.langchain4j.model.openai.OpenAiModelName.GPT_3_5_TURBO;
import static java.util.stream.Collectors.joining;
@SpringBootTest(webEnvironment = SpringBootTest.WebEnvironment.RANDOM_PORT)
@RunWith(SpringRunner.class)
@Slf4j
@ActiveProfiles("test")
@ComponentScan(basePackages = {"com.magicrepokit"})
public class ChatTest {
@Autowired
private ChatLanguageModel model;
@Autowired
CustomerSupportAgent agent;
@Autowired
EmbeddingModel embeddingModel;
@Autowired
ElasticOperation elasticOperation;
@Autowired
OssTemplate ossTemplate;
@Autowired
ConfigProperties langchainConfigProperties;
@Autowired
LangchainComponent langchainComponent;
@Autowired
GoogleSearch googleSearch;
@Test
public void testOssTemplate(){
ossTemplate.makeBucket("test");
}
@Test
public void simpleChat() {
String response = model.generate("你好");
System.out.println(response);
}
interface Assistant {
@SystemMessage({"1.你是是一个数学专家，你只能回答数学方面的知识，如果用户内容与数学无关，你会只能回答：不知道!",
"2.你可以使用工具类，使用之前你需要确定工具类的描述与用户问题相关，如果不相关，你会只能回答：不知道!"})
String chat(String userMessage);
}
@Test
public void chatWithTool() {
Assistant build = AiServices.builder(Assistant.class)
.chatLanguageModel(model)
.tools(new CalculatorService())
.chatMemory(MessageWindowChatMemory.withMaxMessages(10)).build();
String question = "5/4=?";
String answer = build.chat(question);
System.out.println(answer);
}
@Test
public void should_provide_booking_details_and_explain_why_cancellation_is_not_possible() {
// Please define API keys in application.properties before running this test.
// Tip: Use gpt-4 for this example, as gpt-3.5-turbo tends to hallucinate often and invent name and surname.
interact(agent, "你好，我忘记我的预订信息");
interact(agent, "123-457");
// interact(agent, "I'm sorry I'm so inattentive today. Klaus Heisler.");
// interact(agent, "My bad, it's 123-456");
//
// // Here, information about the cancellation policy is automatically retrieved and injected into the prompt.
// // Although the LLM sometimes attempts to cancel the booking, it fails to do so and will explain
// // the reason why the booking cannot be cancelled, based on the injected cancellation policy.
// interact(agent, "My plans have changed, can I cancel my booking?");
}
private static void interact(CustomerSupportAgent agent, String userMessage) {
System.out.println("==========================================================================================");
System.out.println("[User]: " + userMessage);
System.out.println("==========================================================================================");
String agentAnswer = agent.chat(userMessage);
System.out.println("==========================================================================================");
System.out.println("[Agent]: " + agentAnswer);
System.out.println("==========================================================================================");
}
private ElasticsearchEmbeddingStore getElasticsearchEmbeddingStore(String indexName){
if(langchainConfigProperties.getEnabled()){
log.error("未开启elasticsearch");
return null;
}
String elasticHost = langchainConfigProperties.getElasticHost();
int elasticPort = langchainConfigProperties.getElasticPort();
String url = StrUtil.format("{}:{}", elasticHost, elasticPort);
return ElasticsearchEmbeddingStore.builder()
.serverUrl(url)
.userName(langchainConfigProperties.getElasticUsername())
.password(langchainConfigProperties.getElasticPassword())
.indexName(indexName)
.dimension(1536)
.build();
}
/**
* 创建EmbeddingStore with Elasticsearch
*/
@Test
public void createEmbeddingStoreWithElasticsearch() {
//1.elsaticstore
ElasticsearchEmbeddingStore embeddingStore = getElasticsearchEmbeddingStore("c2267fb9-7539-46b7-8aab-c1c8c532cbd5");
Embedding content = embeddingModel.embed("这里石昊是谁").content();
List<EmbeddingMatch<TextSegment>> relevant = embeddingStore.findRelevant(content, 1, 0.9);
System.out.println(relevant.get(0).score());
System.out.println(relevant.get(0).embedded());
}
/**
* 创建elastic客户端
*/
@Test
public void createElasticClient() {
//1.判断索引是否存在
System.out.println(elasticOperation.isIndexExist("mrk_gpt_knowledge2"));
//2.根据id查询document
System.out.println(elasticOperation.getDocumentById("mrk_gpt_knowledge2", "62c3470d-6f38-4b52-959e-988dc0721b01"));
//3.删除索引
System.out.println(elasticOperation.deleteIndex("mrk_gpt_knowledge2"));
}
@Test
public void loadFromURL() {
Document document = UrlDocumentLoader.load("http://s6ie5kuog.hd-bkt.clouddn.com/raipiot_user.txt", new TextDocumentParser());
System.out.println(document.text());
DocumentSplitter documentSplitter = DocumentSplitters.recursive(500, 100, new OpenAiTokenizer(GPT_3_5_TURBO));
List<TextSegment> split = documentSplitter.split(document);
ObjectMapper objectMapper = ObjectMapper.of(split);
JSONArray jsonArray = new JSONArray();
objectMapper.map(jsonArray,null);
System.out.println(jsonArray.toString());
}
interface Chef {
String answer(String question);
}
@Test
public void testPoJO() throws InterruptedException {
StreamingChatLanguageModel model =OpenAiStreamingChatModel.builder().apiKey("sk-gRbZ9FJz2E7c7mwO5JOvp2u2rtoWoAbg12CxDy3Y25eLeDvd").baseUrl("https://api.chatanywhere.tech/").build();
List<ChatMessage> chatMessages = new ArrayList<>();
chatMessages.add(new dev.langchain4j.data.message.SystemMessage("你是一个代码专家，你只能回答代码方面的知识，如果用户内容与代码无关，你会只能回答：我是一个代码专家,只能帮你回答有关代码的问题!"));
chatMessages.add(new UserMessage("帮我写一个简单的计算器的js代码？"));
model.generate(chatMessages, new StreamingResponseHandler<AiMessage>() {
@Override
public void onNext(String token) {
System.out.println("==========================================================================================");
System.out.println("[answer]: " + token);
System.out.println("==========================================================================================");
}
@Override
public void onError(Throwable error) {
System.out.println("==========================================================================================");
System.out.println("[error]: " + error);
System.out.println("==========================================================================================");
}
@Override
public void onComplete(Response<AiMessage> response) {
System.out.println("==========================================================================================");
System.out.println("[complate]: " + response);
System.out.println("==========================================================================================");
}
});
Thread.sleep(10000);
}
@Test
public void testTemplate(){
PromptTemplate promptTemplate = new PromptTemplate("你可以根据知识库内容回答用户相关问题\n" +
"知识库：\n"+
"{{knowledge}} \n"+
"用户问题：\n" +
"{{question}} \n"
);
//3.检索知识库
List<TextSegment> relevant = langchainComponent.findRelevant("mrk_gpt_knowledge2","你好，我忘记我的预订信息?");
String relevantContext = relevant.stream().map(TextSegment::text).collect(joining("\n\n"));
Map<String,Object> promtMap = new HashMap<>();
promtMap.put("knowledge",relevantContext);
promtMap.put("question","H你好，我忘记我的预订信息?");
Prompt apply = promptTemplate.apply(promtMap);
System.out.println(apply.text());
}
@Test
public void testCostToken(){
OpenAiTokenizer openAiTokenizer = new OpenAiTokenizer("gpt-3.5-turbo");
int i = openAiTokenizer.estimateTokenCountInText("你好，我忘记我的预订信息?");
String generate = model.generate("你好，我忘记我的预订信息?");
System.out.println(i);
System.out.println(generate);
}
@Test
public void searchTest(){
String s = googleSearch.searchGoogle("完美世界");
System.out.println(s);
}
}
|
[
"dev.langchain4j.service.AiServices.builder",
"dev.langchain4j.model.openai.OpenAiStreamingChatModel.builder",
"dev.langchain4j.store.embedding.elasticsearch.ElasticsearchEmbeddingStore.builder"
] |
[((3786, 3992), 'dev.langchain4j.service.AiServices.builder'), ((3786, 3984), 'dev.langchain4j.service.AiServices.builder'), ((3786, 3911), 'dev.langchain4j.service.AiServices.builder'), ((3786, 3863), 'dev.langchain4j.service.AiServices.builder'), ((6278, 6591), 'dev.langchain4j.store.embedding.elasticsearch.ElasticsearchEmbeddingStore.builder'), ((6278, 6566), 'dev.langchain4j.store.embedding.elasticsearch.ElasticsearchEmbeddingStore.builder'), ((6278, 6533), 'dev.langchain4j.store.embedding.elasticsearch.ElasticsearchEmbeddingStore.builder'), ((6278, 6495), 'dev.langchain4j.store.embedding.elasticsearch.ElasticsearchEmbeddingStore.builder'), ((6278, 6421), 'dev.langchain4j.store.embedding.elasticsearch.ElasticsearchEmbeddingStore.builder'), ((6278, 6347), 'dev.langchain4j.store.embedding.elasticsearch.ElasticsearchEmbeddingStore.builder'), ((8457, 8603), 'dev.langchain4j.model.openai.OpenAiStreamingChatModel.builder'), ((8457, 8595), 'dev.langchain4j.model.openai.OpenAiStreamingChatModel.builder'), ((8457, 8553), 'dev.langchain4j.model.openai.OpenAiStreamingChatModel.builder')]
|
package com.magicrepokit.chat;
import com.magicrepokit.chat.agent.CustomerSupportAgent;
import com.magicrepokit.chat.service.tool.BookingTools;
import com.magicrepokit.chat.vo.knowledge.KnowledgeFileListVO;
import com.magicrepokit.oss.OssTemplate;
import dev.langchain4j.data.document.Document;
import dev.langchain4j.data.document.DocumentSplitter;
import dev.langchain4j.data.document.parser.TextDocumentParser;
import dev.langchain4j.data.document.splitter.DocumentSplitters;
import dev.langchain4j.data.embedding.Embedding;
import dev.langchain4j.data.segment.TextSegment;
import dev.langchain4j.memory.chat.MessageWindowChatMemory;
import dev.langchain4j.model.chat.ChatLanguageModel;
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.OpenAiEmbeddingModel;
import dev.langchain4j.model.openai.OpenAiTokenizer;
import dev.langchain4j.retriever.EmbeddingStoreRetriever;
import dev.langchain4j.retriever.Retriever;
import dev.langchain4j.service.AiServices;
import dev.langchain4j.store.embedding.EmbeddingStore;
import dev.langchain4j.store.embedding.EmbeddingStoreIngestor;
import dev.langchain4j.store.embedding.elasticsearch.ElasticsearchEmbeddingStore;
import dev.langchain4j.store.embedding.inmemory.InMemoryEmbeddingStore;
import org.junit.Test;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;
import org.springframework.core.io.Resource;
import org.springframework.core.io.ResourceLoader;
import java.io.IOException;
import java.util.HashMap;
import java.util.List;
import java.util.Map;
import static dev.langchain4j.data.document.loader.FileSystemDocumentLoader.loadDocument;
import static dev.langchain4j.model.openai.OpenAiModelName.GPT_3_5_TURBO;
import static java.util.stream.Collectors.joining;
@Configuration
public class TestConfiguration {
/**
* 创建openai chat model
* @return
*/
@Bean
ChatLanguageModel chatLanguageModel(){
return OpenAiChatModel.builder().apiKey("sk-gRbZ9FJz2E7c7mwO5JOvp2u2rtoWoAbg12CxDy3Y25eLeDvd").baseUrl("https://api.chatanywhere.tech/").build();
}
/**
* 创建客服代理
*/
@Bean
CustomerSupportAgent customerSupportAgent(ChatLanguageModel chatLanguageModel, BookingTools bookingTools, Retriever<TextSegment> retriever) {
return AiServices.builder(CustomerSupportAgent.class)
.chatLanguageModel(chatLanguageModel)
.chatMemory(MessageWindowChatMemory.withMaxMessages(20))
.tools(bookingTools)
.retriever(retriever)
.build();
}
/**
* 创建retriever
* @param embeddingStore
* @param embeddingModel
* @return
*/
@Bean
Retriever<TextSegment> retriever(EmbeddingStore<TextSegment> embeddingStore, EmbeddingModel embeddingModel) {
// You will need to adjust these parameters to find the optimal setting, which will depend on two main factors:
// - The nature of your data
// - The embedding model you are using
int maxResultsRetrieved = 5;
double minScore = 0.6;
return EmbeddingStoreRetriever.from(embeddingStore, embeddingModel, maxResultsRetrieved, minScore);
}
/**
* 创建embedding model
* @return
*/
@Bean
EmbeddingModel embeddingModel() {
return OpenAiEmbeddingModel.builder().apiKey("sk-gRbZ9FJz2E7c7mwO5JOvp2u2rtoWoAbg12CxDy3Y25eLeDvd").baseUrl("https://api.chatanywhere.tech/v1").build();
}
/**
* 创建embedding store
* @param embeddingModel
* @param resourceLoader
* @return
* @throws IOException
*/
@Bean
EmbeddingStore<TextSegment> embeddingStore(EmbeddingModel embeddingModel, ResourceLoader resourceLoader) throws IOException {
// Normally, you would already have your embedding store filled with your data.
// However, for the purpose of this demonstration, we will:
// 1. Create an in-memory embedding store
//EmbeddingStore<TextSegment> embeddingStore = new InMemoryEmbeddingStore<>();
ElasticsearchEmbeddingStore embeddingStore = ElasticsearchEmbeddingStore.builder()
.serverUrl("154.204.60.125:9200")
.userName("elastic")
.password("123456")
.indexName("mrk_gpt_knowledge2")
.dimension(1536)
.build();
// 2. Load an example document ("Miles of Smiles" terms of use)
Resource resource = resourceLoader.getResource("classpath:miles-of-smiles-terms-of-use.txt");
Document document = loadDocument(resource.getFile().toPath(), new TextDocumentParser());
// 3. Split the document into segments 100 tokens each
// 4. Convert segments into embeddings
// 5. Store embeddings into embedding store
// All this can be done manually, but we will use EmbeddingStoreIngestor to automate this:
DocumentSplitter documentSplitter = DocumentSplitters.recursive(100, 0, new OpenAiTokenizer(GPT_3_5_TURBO));
EmbeddingStoreIngestor ingestor = EmbeddingStoreIngestor.builder()
.documentSplitter(documentSplitter)
.embeddingModel(embeddingModel)
.embeddingStore(embeddingStore)
.build();
ingestor.ingest(document);
return embeddingStore;
}
}
|
[
"dev.langchain4j.service.AiServices.builder",
"dev.langchain4j.store.embedding.elasticsearch.ElasticsearchEmbeddingStore.builder",
"dev.langchain4j.model.openai.OpenAiEmbeddingModel.builder",
"dev.langchain4j.store.embedding.EmbeddingStoreIngestor.builder",
"dev.langchain4j.model.openai.OpenAiChatModel.builder"
] |
[((2216, 2353), 'dev.langchain4j.model.openai.OpenAiChatModel.builder'), ((2216, 2345), 'dev.langchain4j.model.openai.OpenAiChatModel.builder'), ((2216, 2303), 'dev.langchain4j.model.openai.OpenAiChatModel.builder'), ((2577, 2850), 'dev.langchain4j.service.AiServices.builder'), ((2577, 2825), 'dev.langchain4j.service.AiServices.builder'), ((2577, 2787), 'dev.langchain4j.service.AiServices.builder'), ((2577, 2750), 'dev.langchain4j.service.AiServices.builder'), ((2577, 2677), 'dev.langchain4j.service.AiServices.builder'), ((3607, 3751), 'dev.langchain4j.model.openai.OpenAiEmbeddingModel.builder'), ((3607, 3743), 'dev.langchain4j.model.openai.OpenAiEmbeddingModel.builder'), ((3607, 3699), 'dev.langchain4j.model.openai.OpenAiEmbeddingModel.builder'), ((4393, 4660), 'dev.langchain4j.store.embedding.elasticsearch.ElasticsearchEmbeddingStore.builder'), ((4393, 4635), 'dev.langchain4j.store.embedding.elasticsearch.ElasticsearchEmbeddingStore.builder'), ((4393, 4602), 'dev.langchain4j.store.embedding.elasticsearch.ElasticsearchEmbeddingStore.builder'), ((4393, 4553), 'dev.langchain4j.store.embedding.elasticsearch.ElasticsearchEmbeddingStore.builder'), ((4393, 4517), 'dev.langchain4j.store.embedding.elasticsearch.ElasticsearchEmbeddingStore.builder'), ((4393, 4480), 'dev.langchain4j.store.embedding.elasticsearch.ElasticsearchEmbeddingStore.builder'), ((5355, 5560), 'dev.langchain4j.store.embedding.EmbeddingStoreIngestor.builder'), ((5355, 5535), 'dev.langchain4j.store.embedding.EmbeddingStoreIngestor.builder'), ((5355, 5487), 'dev.langchain4j.store.embedding.EmbeddingStoreIngestor.builder'), ((5355, 5439), 'dev.langchain4j.store.embedding.EmbeddingStoreIngestor.builder')]
|
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