added OptionsBuilder and support for specifying extra params for ask API

docs/docs/apis-ask/ask.md

@@ -8,6 +8,11 @@ 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
@@ -19,11 +24,13 @@ public class Main {
 
         OllamaAPI ollamaAPI = new OllamaAPI(host);
 
-        OllamaResult result = ollamaAPI.ask(OllamaModelType.LLAMA2, "Who are you?");
+        OllamaResult result =
+                ollamaAPI.ask(OllamaModelType.LLAMA2, "Who are you?", new OptionsBuilder().build());
 
         System.out.println(result.getResponse());
     }
 }
+
 ```
 
 You will get a response similar to:
@@ -47,11 +54,13 @@ public class Main {
 
         String prompt = "List all cricket world cup teams of 2019.";
 
-        OllamaResult result = ollamaAPI.ask(OllamaModelType.LLAMA2, prompt);
+        OllamaResult result =
+                ollamaAPI.ask(OllamaModelType.LLAMA2, prompt, new OptionsBuilder().build());
 
         System.out.println(result.getResponse());
     }
 }
+
 ```
 
 You'd then get a response from the model:
@@ -84,12 +93,15 @@ public class Main {
         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.ask(OllamaModelType.SQLCODER, prompt);
+        String prompt =
+                SamplePrompts.getSampleDatabasePromptWithQuestion(
+                        "List all customer names who have bought one or more products");
+        OllamaResult result =
+                ollamaAPI.ask(OllamaModelType.SQLCODER, prompt, new OptionsBuilder().build());
         System.out.println(result.getResponse());
     }
 }
+
 ```
 
 _Note: Here I've used

docs/docs/apis-extras/options-builder.md

@@ -0,0 +1,53 @@
+---
+sidebar_position: 1
+---
+
+# Options Builder
+
+This lets you build options for the `ask()` API.
+Check out the supported
+options [here](https://github.com/jmorganca/ollama/blob/main/docs/modelfile.md#valid-parameters-and-values).
+
+## Build an empty Options object
+
+```java
+import io.github.amithkoujalgi.ollama4j.core.utils.Options;
+import io.github.amithkoujalgi.ollama4j.core.utils.OptionsBuilder;
+
+public class Main {
+
+    public static void main(String[] args) {
+
+        String host = "http://localhost:11434/";
+
+        OllamaAPI ollamaAPI = new OllamaAPI(host);
+
+        Options options = new OptionsBuilder().build();
+    }
+}
+```
+
+## Build an empty Options object
+
+```java
+import io.github.amithkoujalgi.ollama4j.core.utils.Options;
+import io.github.amithkoujalgi.ollama4j.core.utils.OptionsBuilder;
+
+public class Main {
+
+    public static void main(String[] args) {
+
+        String host = "http://localhost:11434/";
+
+        OllamaAPI ollamaAPI = new OllamaAPI(host);
+
+        Options options =
+                new OptionsBuilder()
+                        .setMirostat(10)
+                        .setMirostatEta(0.5f)
+                        .setNumGpu(2)
+                        .setTemperature(1.5f)
+                        .build();
+    }
+}
+```

src/main/java/io/github/amithkoujalgi/ollama4j/core/OllamaAPI.java

@@ -6,6 +6,7 @@ import io.github.amithkoujalgi.ollama4j.core.models.request.CustomModelFileConte
 import io.github.amithkoujalgi.ollama4j.core.models.request.CustomModelFilePathRequest;
 import io.github.amithkoujalgi.ollama4j.core.models.request.ModelEmbeddingsRequest;
 import io.github.amithkoujalgi.ollama4j.core.models.request.ModelRequest;
+import io.github.amithkoujalgi.ollama4j.core.utils.Options;
 import io.github.amithkoujalgi.ollama4j.core.utils.Utils;
 import java.io.BufferedReader;
 import java.io.ByteArrayOutputStream;
@@ -332,11 +333,15 @@ public class OllamaAPI {
    *
    * @param model the ollama model to ask the question to
    * @param prompt the prompt/question text
+   * @param options the Options object - <a
+   *     href="https://github.com/jmorganca/ollama/blob/main/docs/modelfile.md#valid-parameters-and-values">More
+   *     details on the options</a>
    * @return OllamaResult that includes response text and time taken for response
    */
-  public OllamaResult ask(String model, String prompt)
+  public OllamaResult ask(String model, String prompt, Options options)
       throws OllamaBaseException, IOException, InterruptedException {
     OllamaRequestModel ollamaRequestModel = new OllamaRequestModel(model, prompt);
+    ollamaRequestModel.setOptions(options.getOptionsMap());
     return askSync(ollamaRequestModel);
   }
 

src/main/java/io/github/amithkoujalgi/ollama4j/core/models/OllamaRequestModel.java

@@ -1,10 +1,10 @@
 package io.github.amithkoujalgi.ollama4j.core.models;
 
-
 import static io.github.amithkoujalgi.ollama4j.core.utils.Utils.getObjectMapper;
 
 import com.fasterxml.jackson.core.JsonProcessingException;
 import java.util.List;
+import java.util.Map;
 import lombok.Data;
 
 @Data
@@ -13,6 +13,7 @@ public class OllamaRequestModel {
   private String model;
   private String prompt;
   private List<String> images;
+  private Map<String, Object> options;
 
   public OllamaRequestModel(String model, String prompt) {
     this.model = model;

src/main/java/io/github/amithkoujalgi/ollama4j/core/utils/Options.java

@@ -0,0 +1,11 @@
+package io.github.amithkoujalgi.ollama4j.core.utils;
+
+import java.util.Map;
+import lombok.Data;
+
+/** Class for options for Ollama model. */
+@Data
+public class Options {
+
+  private final Map<String, Object> optionsMap;
+}

src/main/java/io/github/amithkoujalgi/ollama4j/core/utils/OptionsBuilder.java

@@ -0,0 +1,218 @@
+package io.github.amithkoujalgi.ollama4j.core.utils;
+
+import java.util.HashMap;
+
+/** Builder class for creating options for Ollama model. */
+public class OptionsBuilder {
+
+  private final Options options;
+
+  /** Constructs a new OptionsBuilder with an empty options map. */
+  public OptionsBuilder() {
+    this.options = new Options(new HashMap<>());
+  }
+
+  /**
+   * Enable Mirostat sampling for controlling perplexity. (default: 0, 0 = disabled, 1 = Mirostat, 2
+   * = Mirostat 2.0)
+   *
+   * @param value The value for the "mirostat" parameter.
+   * @return The updated OptionsBuilder.
+   */
+  public OptionsBuilder setMirostat(int value) {
+    options.getOptionsMap().put("mirostat", value);
+    return this;
+  }
+
+  /**
+   * Influences how quickly the algorithm responds to feedback from the generated text. A lower
+   * learning rate will result in slower adjustments, while a higher learning rate will make the
+   * algorithm more responsive. (Default: 0.1)
+   *
+   * @param value The value for the "mirostat_eta" parameter.
+   * @return The updated OptionsBuilder.
+   */
+  public OptionsBuilder setMirostatEta(float value) {
+    options.getOptionsMap().put("mirostat_eta", value);
+    return this;
+  }
+
+  /**
+   * Controls the balance between coherence and diversity of the output. A lower value will result
+   * in more focused and coherent text. (Default: 5.0)
+   *
+   * @param value The value for the "mirostat_tau" parameter.
+   * @return The updated OptionsBuilder.
+   */
+  public OptionsBuilder setMirostatTau(float value) {
+    options.getOptionsMap().put("mirostat_tau", value);
+    return this;
+  }
+
+  /**
+   * Sets the size of the context window used to generate the next token. (Default: 2048)
+   *
+   * @param value The value for the "num_ctx" parameter.
+   * @return The updated OptionsBuilder.
+   */
+  public OptionsBuilder setNumCtx(int value) {
+    options.getOptionsMap().put("num_ctx", value);
+    return this;
+  }
+
+  /**
+   * The number of GQA groups in the transformer layer. Required for some models, for example, it is
+   * 8 for llama2:70b.
+   *
+   * @param value The value for the "num_gqa" parameter.
+   * @return The updated OptionsBuilder.
+   */
+  public OptionsBuilder setNumGqa(int value) {
+    options.getOptionsMap().put("num_gqa", value);
+    return this;
+  }
+
+  /**
+   * The number of layers to send to the GPU(s). On macOS it defaults to 1 to enable metal support,
+   * 0 to disable.
+   *
+   * @param value The value for the "num_gpu" parameter.
+   * @return The updated OptionsBuilder.
+   */
+  public OptionsBuilder setNumGpu(int value) {
+    options.getOptionsMap().put("num_gpu", value);
+    return this;
+  }
+
+  /**
+   * Sets the number of threads to use during computation. By default, Ollama will detect this for
+   * optimal performance. It is recommended to set this value to the number of physical CPU cores
+   * your system has (as opposed to the logical number of cores).
+   *
+   * @param value The value for the "num_thread" parameter.
+   * @return The updated OptionsBuilder.
+   */
+  public OptionsBuilder setNumThread(int value) {
+    options.getOptionsMap().put("num_thread", value);
+    return this;
+  }
+
+  /**
+   * Sets how far back for the model to look back to prevent repetition. (Default: 64, 0 = disabled,
+   * -1 = num_ctx)
+   *
+   * @param value The value for the "repeat_last_n" parameter.
+   * @return The updated OptionsBuilder.
+   */
+  public OptionsBuilder setRepeatLastN(int value) {
+    options.getOptionsMap().put("repeat_last_n", value);
+    return this;
+  }
+
+  /**
+   * Sets how strongly to penalize repetitions. A higher value (e.g., 1.5) will penalize repetitions
+   * more strongly, while a lower value (e.g., 0.9) will be more lenient. (Default: 1.1)
+   *
+   * @param value The value for the "repeat_penalty" parameter.
+   * @return The updated OptionsBuilder.
+   */
+  public OptionsBuilder setRepeatPenalty(float value) {
+    options.getOptionsMap().put("repeat_penalty", value);
+    return this;
+  }
+
+  /**
+   * The temperature of the model. Increasing the temperature will make the model answer more
+   * creatively. (Default: 0.8)
+   *
+   * @param value The value for the "temperature" parameter.
+   * @return The updated OptionsBuilder.
+   */
+  public OptionsBuilder setTemperature(float value) {
+    options.getOptionsMap().put("temperature", value);
+    return this;
+  }
+
+  /**
+   * Sets the random number seed to use for generation. Setting this to a specific number will make
+   * the model generate the same text for the same prompt. (Default: 0)
+   *
+   * @param value The value for the "seed" parameter.
+   * @return The updated OptionsBuilder.
+   */
+  public OptionsBuilder setSeed(int value) {
+    options.getOptionsMap().put("seed", value);
+    return this;
+  }
+
+  /**
+   * Sets the stop sequences to use. When this pattern is encountered the LLM will stop generating
+   * text and return. Multiple stop patterns may be set by specifying multiple separate `stop`
+   * parameters in a modelfile.
+   *
+   * @param value The value for the "stop" parameter.
+   * @return The updated OptionsBuilder.
+   */
+  public OptionsBuilder setStop(String value) {
+    options.getOptionsMap().put("stop", value);
+    return this;
+  }
+
+  /**
+   * Tail free sampling is used to reduce the impact of less probable tokens from the output. A
+   * higher value (e.g., 2.0) will reduce the impact more, while a value of 1.0 disables this
+   * setting. (default: 1)
+   *
+   * @param value The value for the "tfs_z" parameter.
+   * @return The updated OptionsBuilder.
+   */
+  public OptionsBuilder setTfsZ(float value) {
+    options.getOptionsMap().put("tfs_z", value);
+    return this;
+  }
+
+  /**
+   * Maximum number of tokens to predict when generating text. (Default: 128, -1 = infinite
+   * generation, -2 = fill context)
+   *
+   * @param value The value for the "num_predict" parameter.
+   * @return The updated OptionsBuilder.
+   */
+  public OptionsBuilder setNumPredict(int value) {
+    options.getOptionsMap().put("num_predict", value);
+    return this;
+  }
+
+  /**
+   * Reduces the probability of generating nonsense. A higher value (e.g. 100) will give more
+   * diverse answers, while a lower value (e.g. 10) will be more conservative. (Default: 40)
+   *
+   * @param value The value for the "top_k" parameter.
+   * @return The updated OptionsBuilder.
+   */
+  public OptionsBuilder setTopK(int value) {
+    options.getOptionsMap().put("top_k", value);
+    return this;
+  }
+
+  /**
+   * Works together with top-k. A higher value (e.g., 0.95) will lead to more diverse text, while a
+   * lower value (e.g., 0.5) will generate more focused and conservative text. (Default: 0.9)
+   *
+   * @param value The value for the "top_p" parameter.
+   * @return The updated OptionsBuilder.
+   */
+  public OptionsBuilder setTopP(float value) {
+    options.getOptionsMap().put("top_p", value);
+    return this;
+  }
+
+  /**
+   * Builds the options map.
+   *
+   * @return The populated options map.
+   */
+  public Options build() {
+    return options;
+  }
+}

src/test/java/io/github/amithkoujalgi/ollama4j/unittests/TestMockedAPIs.java

@@ -8,6 +8,7 @@ import io.github.amithkoujalgi.ollama4j.core.models.ModelDetail;
 import io.github.amithkoujalgi.ollama4j.core.models.OllamaAsyncResultCallback;
 import io.github.amithkoujalgi.ollama4j.core.models.OllamaResult;
 import io.github.amithkoujalgi.ollama4j.core.types.OllamaModelType;
+import io.github.amithkoujalgi.ollama4j.core.utils.OptionsBuilder;
 import java.io.IOException;
 import java.net.URISyntaxException;
 import java.util.ArrayList;
@@ -100,10 +101,12 @@ class TestMockedAPIs {
     OllamaAPI ollamaAPI = Mockito.mock(OllamaAPI.class);
     String model = OllamaModelType.LLAMA2;
     String prompt = "some prompt text";
+    OptionsBuilder optionsBuilder = new OptionsBuilder();
     try {
-      when(ollamaAPI.ask(model, prompt)).thenReturn(new OllamaResult("", 0, 200));
-      ollamaAPI.ask(model, prompt);
-      verify(ollamaAPI, times(1)).ask(model, prompt);
+      when(ollamaAPI.ask(model, prompt, optionsBuilder.build()))
+          .thenReturn(new OllamaResult("", 0, 200));
+      ollamaAPI.ask(model, prompt, optionsBuilder.build());
+      verify(ollamaAPI, times(1)).ask(model, prompt, optionsBuilder.build());
     } catch (IOException | OllamaBaseException | InterruptedException e) {
       throw new RuntimeException(e);
     }