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Updated Ollama Examples, Structured Outputs, ChatRequestAPI, Tools (#135
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LangChain4j committed Dec 22, 2024
1 parent c6f16c0 commit c9379e7
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22 changes: 20 additions & 2 deletions ollama-examples/pom.xml
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Expand Up @@ -16,6 +16,12 @@

<dependencies>

<dependency>
<groupId>dev.langchain4j</groupId>
<artifactId>langchain4j</artifactId>
<version>1.0.0-alpha1</version>
</dependency>

<dependency>
<groupId>dev.langchain4j</groupId>
<artifactId>langchain4j-ollama</artifactId>
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<dependency>
<groupId>org.testcontainers</groupId>
<artifactId>testcontainers</artifactId>
<version>1.19.6</version>
<artifactId>ollama</artifactId>
<version>1.20.4</version>
</dependency>

<dependency>
<groupId>com.fasterxml.jackson.core</groupId>
<artifactId>jackson-databind</artifactId>
<version>2.18.2</version>
</dependency>

<dependency>
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<version>5.10.0</version>
</dependency>

<dependency>
<groupId>org.assertj</groupId>
<artifactId>assertj-core</artifactId>
<version>3.27.0</version>
</dependency>

<dependency>
<groupId>org.tinylog</groupId>
<artifactId>tinylog-impl</artifactId>
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153 changes: 76 additions & 77 deletions ollama-examples/src/main/java/OllamaChatModelTest.java
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import dev.langchain4j.agent.tool.ToolSpecification;
import dev.langchain4j.data.message.AiMessage;
import com.fasterxml.jackson.core.JsonProcessingException;
import com.fasterxml.jackson.databind.ObjectMapper;
import dev.langchain4j.data.message.UserMessage;
import dev.langchain4j.model.chat.ChatLanguageModel;
import dev.langchain4j.model.chat.request.ChatRequest;
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import dev.langchain4j.model.chat.request.json.JsonSchema;
import dev.langchain4j.model.chat.response.ChatResponse;
import dev.langchain4j.model.ollama.OllamaChatModel;
import dev.langchain4j.model.output.Response;
import dev.langchain4j.service.AiServices;
import org.junit.jupiter.api.Test;
import org.testcontainers.containers.GenericContainer;
import org.testcontainers.junit.jupiter.Container;
import org.testcontainers.junit.jupiter.Testcontainers;
import utils.AbstractOllamaInfrastructure;

import java.util.List;
import java.util.Map;

import static dev.langchain4j.model.chat.request.ResponseFormat.JSON;
import static dev.langchain4j.model.chat.Capability.RESPONSE_FORMAT_JSON_SCHEMA;
import static org.assertj.core.api.Assertions.assertThat;

@Testcontainers
class OllamaChatModelTest {
class OllamaChatModelTest extends AbstractOllamaInfrastructure {

/**
* The first time you run this test, it will download a Docker image with Ollama and a model.
* If you have Ollama running locally,
* please set the OLLAMA_BASE_URL environment variable (e.g., http://localhost:11434).
* If you do not set the OLLAMA_BASE_URL environment variable,
* Testcontainers will download and start Ollama Docker container.
* 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())
ChatLanguageModel chatModel = OllamaChatModel.builder()
.baseUrl(ollamaBaseUrl(ollama))
.modelName(MODEL_NAME)
.logRequests(true)
.build();

String answer = model.generate("Provide 3 short bullet points explaining why Java is awesome");

String answer = chatModel.chat("Provide 3 short bullet points explaining why Java is awesome");
System.out.println(answer);

assertThat(answer).isNotBlank();
}

@Test
void json_output_example() {
void json_schema_with_AI_Service_example() {

record Person(String name, int age) {
}

interface PersonExtractor {

ChatLanguageModel model = OllamaChatModel.builder()
.baseUrl(baseUrl())
Person extractPersonFrom(String text);
}

ChatLanguageModel chatModel = OllamaChatModel.builder()
.baseUrl(ollamaBaseUrl(ollama))
.modelName(MODEL_NAME)
.responseFormat(JSON)
.temperature(0.0)
.supportedCapabilities(RESPONSE_FORMAT_JSON_SCHEMA)
.logRequests(true)
.build();

String json = model.generate("Give me a JSON with 2 fields: name and age of a John Doe, 42");
PersonExtractor personExtractor = AiServices.create(PersonExtractor.class, chatModel);

System.out.println(json);
Person person = personExtractor.extractPersonFrom("John Doe is 42 years old");
System.out.println(person);

assertThat(person).isEqualTo(new Person("John Doe", 42));
}

@Test
void json_schema_builder_example() {
void json_schema_with_low_level_chat_api_example() {

ChatLanguageModel model = OllamaChatModel.builder()
.baseUrl(baseUrl())
ChatLanguageModel chatModel = OllamaChatModel.builder()
.baseUrl(ollamaBaseUrl(ollama))
.modelName(MODEL_NAME)
.temperature(0.0)
.logRequests(true)
.build();

ChatRequest chatRequest = ChatRequest.builder()
.messages(UserMessage.from("John Doe is 42 years old"))
.responseFormat(ResponseFormat.builder()
.type(ResponseFormatType.JSON)
.jsonSchema(JsonSchema.builder()
.name("Person")
.rootElement(JsonObjectSchema.builder()
.addStringProperty("fullName")
.addStringProperty("name")
.addIntegerProperty("age")
.build())
.build())
.build())
.build();

String json = model.generate("Extract data: John Doe, 42");
ChatResponse chatResponse = chatModel.chat(chatRequest);
System.out.println(chatResponse);

System.out.println(json);
assertThat(toMap(chatResponse.aiMessage().text())).isEqualTo(Map.of("name", "John Doe", "age", 42));
}

@Test
void json_schema_chat_api_example() {
void json_schema_with_low_level_model_builder_example() {

ChatLanguageModel model = OllamaChatModel.builder()
.baseUrl(baseUrl())
ChatLanguageModel chatModel = OllamaChatModel.builder()
.baseUrl(ollamaBaseUrl(ollama))
.modelName(MODEL_NAME)
.build();


ChatResponse chatResponse = model.chat(ChatRequest.builder()
.messages(UserMessage.from("Extract data: John Doe, 42"))
.temperature(0.0)
.responseFormat(ResponseFormat.builder()
.type(ResponseFormatType.JSON)
.jsonSchema(JsonSchema.builder()
.name("Person")
.rootElement(JsonObjectSchema.builder()
.addStringProperty("fullName")
.addStringProperty("name")
.addIntegerProperty("age")
.build())
.build())
.build())
.build());
.logRequests(true)
.build();

System.out.println(chatResponse.aiMessage().text());
}
String json = chatModel.chat("Extract: John Doe is 42 years old");
System.out.println(json);

assertThat(toMap(json)).isEqualTo(Map.of("name", "John Doe", "age", 42));
}

@Test
void ollama_tools_specification_example() {
void json_mode_with_low_level_model_builder_example() {

ChatLanguageModel model = OllamaChatModel.builder()
.baseUrl(baseUrl())
ChatLanguageModel chatModel = OllamaChatModel.builder()
.baseUrl(ollamaBaseUrl(ollama))
.modelName(MODEL_NAME)
.temperature(0.0)
.responseFormat(ResponseFormat.JSON)
.logRequests(true)
.build();

String json = chatModel.chat("Give me a JSON object with 2 fields: name and age of a John Doe, 42");
System.out.println(json);

List<ToolSpecification> toolSpecificationList = List.of(
ToolSpecification.builder()
.name("get_fav_color")
.description("Gets favorite color of user by ID")
.parameters(JsonObjectSchema.builder()
.addIntegerProperty("user_id")
.required("user_id")
.build())
.build()
);

Response<AiMessage> aiMessageResponse = model.generate(
List.of(UserMessage.from("Find the favorite color of user Jim with ID 21")),
toolSpecificationList
);

System.out.println(aiMessageResponse.content().toolExecutionRequests());
assertThat(toMap(json)).isEqualTo(Map.of("name", "John Doe", "age", 42));
}

static String baseUrl() {
return String.format("http://%s:%d", ollama.getHost(), ollama.getFirstMappedPort());
private static Map<String, Object> toMap(String json) {
try {
return new ObjectMapper().readValue(json, Map.class);
} catch (JsonProcessingException e) {
throw new RuntimeException(e);
}
}
}
31 changes: 8 additions & 23 deletions ollama-examples/src/main/java/OllamaStreamingChatModelTest.java
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import dev.langchain4j.model.ollama.OllamaStreamingChatModel;
import dev.langchain4j.model.output.Response;
import org.junit.jupiter.api.Test;
import org.testcontainers.containers.GenericContainer;
import org.testcontainers.junit.jupiter.Container;
import org.testcontainers.junit.jupiter.Testcontainers;
import utils.AbstractOllamaInfrastructure;

import java.util.concurrent.CompletableFuture;

@Testcontainers
class OllamaStreamingChatModelTest {
class OllamaStreamingChatModelTest extends AbstractOllamaInfrastructure {

/**
* The first time you run this test, it will download a Docker image with Ollama and a model.
* If you have Ollama running locally,
* please set the OLLAMA_BASE_URL environment variable (e.g., http://localhost:11434).
* If you do not set the OLLAMA_BASE_URL environment variable,
* Testcontainers will download and start Ollama Docker container.
* 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 llama2" <- specify the desired model here
*/

static String MODEL_NAME = "orca-mini"; // try "mistral", "llama2", "codellama" or "phi"
static String DOCKER_IMAGE_NAME = "langchain4j/ollama-" + MODEL_NAME + ":latest";
static Integer PORT = 11434;

@Container
static GenericContainer<?> ollama = new GenericContainer<>(DOCKER_IMAGE_NAME)
.withExposedPorts(PORT);

@Test
void streaming_example() {

StreamingChatLanguageModel model = OllamaStreamingChatModel.builder()
.baseUrl(String.format("http://%s:%d", ollama.getHost(), ollama.getMappedPort(PORT)))
.baseUrl(ollamaBaseUrl(ollama))
.modelName(MODEL_NAME)
.temperature(0.0)
.build();

String userMessage = "Write a 100-word poem about Java and AI";

CompletableFuture<Response<AiMessage>> futureResponse = new CompletableFuture<>();
model.generate(userMessage, new StreamingResponseHandler<AiMessage>() {
model.generate(userMessage, new StreamingResponseHandler<>() {

@Override
public void onNext(String token) {
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31 changes: 31 additions & 0 deletions ollama-examples/src/main/java/utils/AbstractOllamaInfrastructure.java
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package utils;

import static dev.langchain4j.internal.Utils.isNullOrEmpty;
import static utils.OllamaImage.LLAMA_3_1;
import static utils.OllamaImage.localOllamaImage;

public class AbstractOllamaInfrastructure {

public static final String OLLAMA_BASE_URL = System.getenv("OLLAMA_BASE_URL");
public static final String MODEL_NAME = LLAMA_3_1;

public static LangChain4jOllamaContainer ollama;

static {
if (isNullOrEmpty(OLLAMA_BASE_URL)) {
String localOllamaImage = localOllamaImage(MODEL_NAME);
ollama = new LangChain4jOllamaContainer(OllamaImage.resolve(OllamaImage.OLLAMA_IMAGE, localOllamaImage))
.withModel(MODEL_NAME);
ollama.start();
ollama.commitToImage(localOllamaImage);
}
}

public static String ollamaBaseUrl(LangChain4jOllamaContainer ollama) {
if (isNullOrEmpty(OLLAMA_BASE_URL)) {
return ollama.getEndpoint();
} else {
return OLLAMA_BASE_URL;
}
}
}
38 changes: 38 additions & 0 deletions ollama-examples/src/main/java/utils/LangChain4jOllamaContainer.java
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package utils;

import com.github.dockerjava.api.command.InspectContainerResponse;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import org.testcontainers.ollama.OllamaContainer;
import org.testcontainers.utility.DockerImageName;

import java.io.IOException;

public class LangChain4jOllamaContainer extends OllamaContainer {

private static final Logger log = LoggerFactory.getLogger(LangChain4jOllamaContainer.class);

private String model;

public LangChain4jOllamaContainer(DockerImageName dockerImageName) {
super(dockerImageName);
}

public LangChain4jOllamaContainer withModel(String model) {
this.model = model;
return this;
}

@Override
protected void containerIsStarted(InspectContainerResponse containerInfo) {
if (this.model != null) {
try {
log.info("Start pulling the '{}' model ... would take several minutes ...", this.model);
ExecResult r = execInContainer("ollama", "pull", this.model);
log.info("Model pulling competed! {}", r);
} catch (IOException | InterruptedException e) {
throw new RuntimeException("Error pulling model", e);
}
}
}
}
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