forked from Mirror/ollama4j
100 lines
2.9 KiB
Markdown
100 lines
2.9 KiB
Markdown
### Ollama4j
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<img src='https://raw.githubusercontent.com/amithkoujalgi/ollama4j/163e88bc82b4beb4a52e4d99f9b5d9ef1255ec06/ollama4j.png' width='100' alt="ollama4j-icon">
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A Java wrapper for [Ollama](https://github.com/jmorganca/ollama/blob/main/docs/api.md) APIs.
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Install:
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From [Maven Central](https://s01.oss.sonatype.org/#nexus-search;quick~ollama4j):
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```xml
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<dependency>
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<groupId>io.github.amithkoujalgi</groupId>
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<artifactId>ollama4j</artifactId>
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<version>1.0-SNAPSHOT</version>
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</dependency>
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```
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You might want to include the Maven repository to pull the ollama4j library from. Include this in your `pom.xml`:
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```xml
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<repositories>
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<repository>
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<id>ollama4j-from-ossrh</id>
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<url>https://s01.oss.sonatype.org/content/repositories/snapshots</url>
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</repository>
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</repositories>
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```
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Verify if the ollama4j dependencies have been resolved by running:
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```bash
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mvn clean install
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```
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Start Ollama Container:
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```
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docker run -v ~/ollama:/root/.ollama -p 11434:11434 ollama/ollama
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```
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Find the full `Javadoc` (API specifications) [here](https://amithkoujalgi.github.io/ollama4j/).
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Pull a model:
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```java
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public class Main {
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public static void main(String[] args) {
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String host = "http://localhost:11434/";
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OllamaAPI ollamaAPI = new OllamaAPI(host);
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ollamaAPI.pullModel(OllamaModel.LLAMA2);
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}
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}
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```
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Post a question to Ollama using Ollama4j:
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Using sync API:
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```java
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public class Main {
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public static void main(String[] args) {
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String host = "http://localhost:11434/";
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OllamaAPI ollamaAPI = new OllamaAPI(host);
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String response = ollamaAPI.runSync(OllamaModel.LLAMA2, "Who are you?");
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System.out.println(response);
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}
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}
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```
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Using async API:
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```java
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public class Main {
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public static void main(String[] args) {
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String host = "http://localhost:11434/";
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OllamaAPI ollamaAPI = new OllamaAPI(host);
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OllamaAsyncResultCallback ollamaAsyncResultCallback = ollamaAPI.runAsync(OllamaModel.LLAMA2, "Who are you?");
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while (true) {
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if (ollamaAsyncResultCallback.isComplete()) {
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System.out.println(ollamaAsyncResultCallback.getResponse());
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break;
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}
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// introduce sleep to check for status with a time interval
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// Thread.sleep(1000);
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}
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}
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}
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```
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You'd then get a response from Ollama:
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```
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I am LLaMA, an AI assistant developed by Meta AI that can understand and respond to human input in a conversational manner. I am trained on a massive dataset of text from the internet and can generate human-like responses to a wide range of topics and questions. I can be used to create chatbots, virtual assistants, and other applications that require natural language understanding and generation capabilities.
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```
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