forked from Mirror/ollama4j
118 lines
3.3 KiB
Markdown
118 lines
3.3 KiB
Markdown
---
|
|
sidebar_position: 1
|
|
---
|
|
|
|
# Generate - Sync
|
|
|
|
This API lets you ask questions to the LLMs in a synchronous way.
|
|
These APIs correlate to
|
|
the [completion](https://github.com/jmorganca/ollama/blob/main/docs/api.md#generate-a-completion) APIs.
|
|
|
|
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](/docs/apis-extras/options-builder).
|
|
|
|
## Try asking a question about the model.
|
|
|
|
```java
|
|
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 from general topics.
|
|
|
|
```java
|
|
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
|
|
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/amithkoujalgi/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;
|
|
``` |