ollama4j/docs/docs/apis-generate/generate.md

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# Generate - Sync

This API lets you ask questions to the LLMs in a synchronous way.
This API corresponds to
the [completion](https://github.com/jmorganca/ollama/blob/main/docs/api.md#generate-a-completion) API.

Use the `OptionBuilder` to build the `Options` object
with [extra parameters](https://github.com/jmorganca/ollama/blob/main/docs/modelfile.md#valid-parameters-and-values).
Refer
to [this](/apis-extras/options-builder).

## Try asking a question about the model.

```java
import io.github.ollama4j.OllamaAPI;
import io.github.ollama4j.models.response.OllamaResult;
import io.github.ollama4j.types.OllamaModelType;
import io.github.ollama4j.utils.OptionsBuilder;

public class Main {

    public static void main(String[] args) {

        String host = "http://localhost:11434/";

        OllamaAPI ollamaAPI = new OllamaAPI(host);

        OllamaResult result =
                ollamaAPI.generate(OllamaModelType.LLAMA2, "Who are you?", new OptionsBuilder().build());

        System.out.println(result.getResponse());
    }
}

```

You will get a response similar to:

> 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.

## Try asking a question, receiving the answer streamed

```java
import io.github.ollama4j.OllamaAPI;
import io.github.ollama4j.models.response.OllamaResult;
import io.github.ollama4j.models.generate.OllamaStreamHandler;
import io.github.ollama4j.utils.OptionsBuilder;

public class Main {

    public static void main(String[] args) {

        String host = "http://localhost:11434/";

        OllamaAPI ollamaAPI = new OllamaAPI(host);
        // define a stream handler (Consumer<String>)
        OllamaStreamHandler streamHandler = (s) -> {
            System.out.println(s);
        };

        // Should be called using seperate thread to gain non blocking streaming effect.
        OllamaResult result = ollamaAPI.generate(config.getModel(),
                "What is the capital of France? And what's France's connection with Mona Lisa?",
                new OptionsBuilder().build(), streamHandler);

        System.out.println("Full response: " + result.getResponse());
    }
}
```

You will get a response similar to:

> The
> The capital
> The capital of
> The capital of France
> The capital of France is
> The capital of France is Paris
> The capital of France is Paris.
> Full response: The capital of France is Paris.

## Try asking a question from general topics.

```java
import io.github.ollama4j.OllamaAPI;
import io.github.ollama4j.models.response.OllamaResult;
import io.github.ollama4j.types.OllamaModelType;
import io.github.ollama4j.utils.OptionsBuilder;

public class Main {

    public static void main(String[] args) {

        String host = "http://localhost:11434/";

        OllamaAPI ollamaAPI = new OllamaAPI(host);

        String prompt = "List all cricket world cup teams of 2019.";

        OllamaResult result =
                ollamaAPI.generate(OllamaModelType.LLAMA2, prompt, new OptionsBuilder().build());

        System.out.println(result.getResponse());
    }
}

```

You'd then get a response from the model:

> The 2019 ICC Cricket World Cup was held in England and Wales from May 30 to July 14, 2019. The
> following teams
> participated in the tournament:
>
> 1. Afghanistan
> 2. Australia
> 3. Bangladesh
> 4. England
> 5. India
> 6. New Zealand
> 7. Pakistan
> 8. South Africa
> 9. Sri Lanka
> 10. West Indies
>
> These teams competed in a round-robin format, with the top four teams advancing to the
> semi-finals. The tournament was
> won by the England cricket team, who defeated New Zealand in the final.

## Try asking for a Database query for your data schema.

```java
import io.github.ollama4j.OllamaAPI;
import io.github.ollama4j.models.response.OllamaResult;
import io.github.ollama4j.types.OllamaModelType;
import io.github.ollama4j.utils.OptionsBuilder;
import io.github.ollama4j.utils.SamplePrompts;

public class Main {

    public static void main(String[] args) {
        String host = "http://localhost:11434/";
        OllamaAPI ollamaAPI = new OllamaAPI(host);

        String prompt =
                SamplePrompts.getSampleDatabasePromptWithQuestion(
                        "List all customer names who have bought one or more products");
        OllamaResult result =
                ollamaAPI.generate(OllamaModelType.SQLCODER, prompt, new OptionsBuilder().build());
        System.out.println(result.getResponse());
    }
}

```

_Note: Here I've used
a [sample prompt](https://github.com/ollama4j/ollama4j/blob/main/src/main/resources/sample-db-prompt-template.txt)
containing a database schema from within this library for demonstration purposes._

You'd then get a response from the model:

```sql
SELECT customers.name
FROM sales
         JOIN customers ON sales.customer_id = customers.customer_id
GROUP BY customers.name;
```