[maven-release-plugin] prepare release v1.0.42

README.md

@@ -126,13 +126,14 @@ Actions CI workflow.
 - [ ] Async APIs for images
 - [ ] Add custom headers to requests
 - [ ] Add additional params for `ask` APIs such as:
-    - `options`: additional model parameters for the Modelfile such as `temperature`
+    - [x] `options`: additional model parameters for the Modelfile such as `temperature` -
-    - `system`: system prompt to (overrides what is defined in the Modelfile)
+      Supported [params](https://github.com/jmorganca/ollama/blob/main/docs/modelfile.md#valid-parameters-and-values).
-    - `template`: the full prompt or prompt template (overrides what is defined in the Modelfile)
+    - [ ] `system`: system prompt to (overrides what is defined in the Modelfile)
-    - `context`: the context parameter returned from a previous request, which can be used to keep a
+    - [ ] `template`: the full prompt or prompt template (overrides what is defined in the Modelfile)
+    - [ ] `context`: the context parameter returned from a previous request, which can be used to keep a
       short
       conversational memory
-    - `stream`: Add support for streaming responses from the model
+    - [ ] `stream`: Add support for streaming responses from the model
 - [ ] Add test cases
 - [ ] Handle exceptions better (maybe throw more appropriate exceptions)
 

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(
+        String prompt =
-                "List all customer names who have bought one or more products");
+                SamplePrompts.getSampleDatabasePromptWithQuestion(
-        OllamaResult result = ollamaAPI.ask(OllamaModelType.SQLCODER, prompt);
+                        "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 +1,78 @@
+---
+sidebar_position: 1
+---
+
+# Options Builder
+
+This lets you build options for the `ask()` API.
+
+Following are the parameters supported by Ollama:
+
+| Parameter      | Description                                                                                                                                                                                                                                             | Value Type | Example Usage        |
+|----------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|------------|----------------------|
+| mirostat       | Enable Mirostat sampling for controlling perplexity. (default: 0, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0)                                                                                                                                         | int        | mirostat 0           |
+| mirostat_eta   | 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)                        | float      | mirostat_eta 0.1     |
+| mirostat_tau   | Controls the balance between coherence and diversity of the output. A lower value will result in more focused and coherent text. (Default: 5.0)                                                                                                         | float      | mirostat_tau 5.0     |
+| num_ctx        | Sets the size of the context window used to generate the next token. (Default: 2048)                                                                                                                                                                    | int        | num_ctx 4096         |
+| num_gqa        | The number of GQA groups in the transformer layer. Required for some models, for example it is 8 for llama2:70b                                                                                                                                         | int        | num_gqa 1            |
+| num_gpu        | The number of layers to send to the GPU(s). On macOS it defaults to 1 to enable metal support, 0 to disable.                                                                                                                                            | int        | num_gpu 50           |
+| num_thread     | 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). | int        | num_thread 8         |
+| repeat_last_n  | Sets how far back for the model to look back to prevent repetition. (Default: 64, 0 = disabled, -1 = num_ctx)                                                                                                                                           | int        | repeat_last_n 64     |
+| repeat_penalty | 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)                                                                     | float      | repeat_penalty 1.1   |
+| temperature    | The temperature of the model. Increasing the temperature will make the model answer more creatively. (Default: 0.8)                                                                                                                                     | float      | temperature 0.7      |
+| seed           | 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)                                                                                       | int        | seed 42              |
+| stop           | 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.                                      | string     | stop "AI assistant:" |
+| tfs_z          | 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)                                               | float      | tfs_z 1              |
+| num_predict    | Maximum number of tokens to predict when generating text. (Default: 128, -1 = infinite generation, -2 = fill context)                                                                                                                                   | int        | num_predict 42       |
+| top_k          | 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)                                                                        | int        | top_k 40             |
+| top_p          | 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)                                                                 | float      | top_p 0.9            |
+
+Link to [source](https://github.com/jmorganca/ollama/blob/main/docs/modelfile.md#valid-parameters-and-values).
+
+Also, see how to set those Ollama parameters using
+the `OptionsBuilder`
+from [javadoc](https://amithkoujalgi.github.io/ollama4j/apidocs/io/github/amithkoujalgi/ollama4j/core/utils/OptionsBuilder.html).
+
+## 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 the `Options` object with values
+
+```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();
+    }
+}
+```

docs/docusaurus.config.js

@@ -76,7 +76,7 @@ const config = {
                         type: 'docSidebar',
                         sidebarId: 'tutorialSidebar',
                         position: 'left',
-                        label: 'Usage',
+                        label: 'Docs',
                     },
                     {to: 'https://amithkoujalgi.github.io/ollama4j/apidocs/', label: 'Javadoc', position: 'left'},
                     {to: '/blog', label: 'Blog', position: 'left'},

pom.xml

@@ -4,7 +4,7 @@
 
     <groupId>io.github.amithkoujalgi</groupId>
     <artifactId>ollama4j</artifactId>
-    <version>1.0.40</version>
+    <version>1.0.42</version>
 
   <name>Ollama4j</name>
   <description>Java library for interacting with Ollama API.</description>
@@ -39,7 +39,7 @@
     <connection>scm:git:git@github.com:amithkoujalgi/ollama4j.git</connection>
     <developerConnection>scm:git:https://github.com/amithkoujalgi/ollama4j.git</developerConnection>
     <url>https://github.com/amithkoujalgi/ollama4j</url>
-    <tag>v1.0.40</tag>
+    <tag>v1.0.42</tag>
   </scm>
 
   <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));
+      when(ollamaAPI.ask(model, prompt, optionsBuilder.build()))
-      ollamaAPI.ask(model, prompt);
+          .thenReturn(new OllamaResult("", 0, 200));
-      verify(ollamaAPI, times(1)).ask(model, prompt);
+      ollamaAPI.ask(model, prompt, optionsBuilder.build());
+      verify(ollamaAPI, times(1)).ask(model, prompt, optionsBuilder.build());
     } catch (IOException | OllamaBaseException | InterruptedException e) {
       throw new RuntimeException(e);
     }