diff --git a/Makefile b/Makefile
index 47a9fd9..2753d0e 100644
--- a/Makefile
+++ b/Makefile
@@ -29,10 +29,10 @@ list-releases:
--compressed \
--silent | jq -r '.components[].version'
-docs:
+docs-build:
npm i --prefix docs && npm run build --prefix docs
-docs-dev:
+docs-serve:
npm i --prefix docs && npm run start --prefix docs
start-cpu:
diff --git a/docs/blog/2023-12-01-welcome/index.md b/docs/blog/2023-12-01-welcome/index.md
deleted file mode 100644
index 5d74320..0000000
--- a/docs/blog/2023-12-01-welcome/index.md
+++ /dev/null
@@ -1,9 +0,0 @@
----
-slug: welcome
-title: Welcome
-authors: [ amith ]
-tags: [ Java, AI, LLM, GenAI, GenerativeAI, Ollama, Ollama4J, OpenSource, Developers
-]
----
-
-Welcome Java Developers!
diff --git a/docs/blog/2023-12-22-release-post.md b/docs/blog/2023-12-22-release-post.md
index aba2a53..070b515 100644
--- a/docs/blog/2023-12-22-release-post.md
+++ b/docs/blog/2023-12-22-release-post.md
@@ -1,6 +1,6 @@
---
slug: release-post
-title: Release
+title: First Release 🚀
authors: [ amith ]
tags: [ Java, AI, LLM, GenAI, GenerativeAI, Ollama, Ollama4j, OpenSource, Developers
]
diff --git a/docs/docs/apis-generate/chat-with-tools.md b/docs/docs/apis-generate/chat-with-tools.md
new file mode 100644
index 0000000..577c26e
--- /dev/null
+++ b/docs/docs/apis-generate/chat-with-tools.md
@@ -0,0 +1,69 @@
+---
+sidebar_position: 8
+---
+
+import CodeEmbed from '@site/src/components/CodeEmbed';
+
+# Chat with Tools
+
+### Using Tools in Chat
+
+If you want to have a natural back-and-forth chat experience with tools, you can directly integrate tools into
+the `chat()` method, instead of using the `generateWithTools()` method. This allows you to register tools that are
+automatically used during the conversation between the user and the assistant, creating a more conversational
+experience.
+
+When the model determines that a tool should be used, the tool is automatically executed. The result is then seamlessly
+incorporated back into the conversation, enhancing the interaction with real-world data and actions.
+
+The following example demonstrates usage of a simple tool, registered with the `OllamaAPI`, and then used within a chat
+session. The tool invocation and response handling are all managed internally by the API.
+
+<CodeEmbed src="https://raw.githubusercontent.com/ollama4j/ollama4j-examples/refs/heads/main/src/main/java/io/github/ollama4j/examples/ChatWithTools.java"/>
+
+::::tip[LLM Response]
+> First answer: 6527fb60-9663-4073-b59e-855526e0a0c2 is the ID of the employee named 'Rahul Kumar'.
+>
+> Second answer: Kumar is the last name of the employee named 'Rahul Kumar'.
+::::
+
+This tool calling can also be done using the streaming API.
+
+### Annotation-Based Tool Registration
+
+Ollama4j provides a declarative and convenient way to define and register tools using Java annotations and reflection.
+This approach offers an alternative to the more verbose, explicit tool registration method.
+
+To use a method as a tool within a chat call, follow these steps:
+
+* **Annotate the Tool Method:**
+ * Use the `@ToolSpec` annotation to mark a method as a tool. This annotation describes the tool's purpose.
+ * Use the `@ToolProperty` annotation to define the input parameters of the tool. The following data types are
+ currently supported:
+ * `java.lang.String`
+ * `java.lang.Integer`
+ * `java.lang.Boolean`
+ * `java.math.BigDecimal`
+* **Annotate the Ollama Service Class:**
+ * Annotate the class that interacts with the `OllamaAPI` client using the `@OllamaToolService` annotation. Reference
+ the provider class(es) containing the `@ToolSpec` annotated methods within this annotation.
+* **Register the Annotated Tools:**
+ * Before making a chat request with the `OllamaAPI`, call the `OllamaAPI.registerAnnotatedTools()` method. This
+ registers the annotated tools, making them available for use during the chat session.
+
+Let's try an example. Consider an `OllamaToolService` class that needs to ask the LLM a question that can only be answered by a specific tool.
+This tool is implemented within a `GlobalConstantGenerator` class. Following is the code that exposes an annotated method as a tool:
+
+<CodeEmbed src="https://raw.githubusercontent.com/ollama4j/ollama4j-examples/refs/heads/main/src/main/java/io/github/ollama4j/examples/toolcalling/annotated/GlobalConstantGenerator.java"/>
+
+The annotated method can then be used as a tool in the chat session:
+
+<CodeEmbed src="https://raw.githubusercontent.com/ollama4j/ollama4j-examples/refs/heads/main/src/main/java/io/github/ollama4j/examples/toolcalling/annotated/AnnotatedToolCallingExample.java"/>
+
+Running the above would produce a response similar to:
+
+::::tip[LLM Response]
+> First answer: 0.0000112061 is the most important constant in the world using 10 digits, according to my function. This constant is known as Planck's constant and plays a fundamental role in quantum mechanics. It relates energy and frequency in electromagnetic radiation and action (the product of momentum and distance) for particles.
+>
+> Second answer: 3-digit constant: 8.001
+::::
diff --git a/docs/docs/apis-generate/chat.md b/docs/docs/apis-generate/chat.md
index bf2e1f5..d5a147c 100644
--- a/docs/docs/apis-generate/chat.md
+++ b/docs/docs/apis-generate/chat.md
@@ -2,267 +2,100 @@
sidebar_position: 7
---
+import CodeEmbed from '@site/src/components/CodeEmbed';
+
# Chat
This API lets you create a conversation with LLMs. Using this API enables you to ask questions to the model including
information using the history of already asked questions and the respective answers.
+### Create a new conversation and use chat history to augment follow up questions
-
-## Create a new conversation and use chat history to augment follow up questions
-
-```java
-import io.github.ollama4j.OllamaAPI;
-import io.github.ollama4j.models.chat.OllamaChatMessageRole;
-import io.github.ollama4j.models.chat.OllamaChatRequestBuilder;
-import io.github.ollama4j.models.chat.OllamaChatRequest;
-import io.github.ollama4j.models.chat.OllamaChatResult;
-import io.github.ollama4j.types.OllamaModelType;
-
-public class Main {
-
- public static void main(String[] args) {
-
- String host = "http://localhost:11434/";
-
- OllamaAPI ollamaAPI = new OllamaAPI(host);
- OllamaChatRequestBuilder builder = OllamaChatRequestBuilder.getInstance(OllamaModelType.LLAMA2);
-
- // create first user question
- OllamaChatRequest requestModel = builder.withMessage(OllamaChatMessageRole.USER, "What is the capital of France?")
- .build();
-
- // start conversation with model
- OllamaChatResult chatResult = ollamaAPI.chat(requestModel);
-
- System.out.println("First answer: " + chatResult.getResponseModel().getMessage().getContent());
-
- // create next userQuestion
- requestModel = builder.withMessages(chatResult.getChatHistory()).withMessage(OllamaChatMessageRole.USER, "And what is the second largest city?").build();
-
- // "continue" conversation with model
- chatResult = ollamaAPI.chat(requestModel);
-
- System.out.println("Second answer: " + chatResult.getResponseModel().getMessage().getContent());
-
- System.out.println("Chat History: " + chatResult.getChatHistory());
- }
-}
-
-```
+<CodeEmbed src="https://raw.githubusercontent.com/ollama4j/ollama4j-examples/refs/heads/main/src/main/java/io/github/ollama4j/examples/ChatExample.java" />
You will get a response similar to:
-> First answer: Should be Paris!
+::::tip[LLM Response]
+
+> First answer: The capital of France is Paris.
>
-> Second answer: Marseille.
+> Second answer: The second-largest city in France is Marseille.
>
> Chat History:
```json
-[
- {
- "role": "user",
- "content": "What is the capital of France?",
- "images": []
- },
- {
- "role": "assistant",
- "content": "Should be Paris!",
- "images": []
- },
- {
- "role": "user",
- "content": "And what is the second largest city?",
- "images": []
- },
- {
- "role": "assistant",
- "content": "Marseille.",
- "images": []
- }
-]
+[{
+ "role" : "user",
+ "content" : "What is the capital of France?",
+ "images" : null,
+ "tool_calls" : [ ]
+}, {
+ "role" : "assistant",
+ "content" : "The capital of France is Paris.",
+ "images" : null,
+ "tool_calls" : null
+}, {
+ "role" : "user",
+ "content" : "And what is the second largest city?",
+ "images" : null,
+ "tool_calls" : [ ]
+}, {
+ "role" : "assistant",
+ "content" : "The second-largest city in France is Marseille.",
+ "images" : null,
+ "tool_calls" : null
+}]
```
+::::
-## Conversational loop
+### Create a conversation where the answer is streamed
-```java
-public class Main {
-
- public static void main(String[] args) {
-
- OllamaAPI ollamaAPI = new OllamaAPI();
- ollamaAPI.setRequestTimeoutSeconds(60);
-
- OllamaChatRequestBuilder builder = OllamaChatRequestBuilder.getInstance("<your-model>");
-
- OllamaChatRequest requestModel = builder.withMessage(OllamaChatMessageRole.USER, "<your-first-message>").build();
- OllamaChatResult initialChatResult = ollamaAPI.chat(requestModel);
- System.out.println(initialChatResult.getResponse());
-
- List<OllamaChatMessage> history = initialChatResult.getChatHistory();
-
- while (true) {
- OllamaChatResult chatResult = ollamaAPI.chat(builder.withMessages(history).withMessage(OllamaChatMessageRole.USER, "<your-new-message").build());
- System.out.println(chatResult.getResponse());
- history = chatResult.getChatHistory();
- }
- }
-}
-```
-
-## Create a conversation where the answer is streamed
-
-```java
-import io.github.ollama4j.OllamaAPI;
-import io.github.ollama4j.models.chat.OllamaChatMessageRole;
-import io.github.ollama4j.models.chat.OllamaChatRequest;
-import io.github.ollama4j.models.chat.OllamaChatRequestBuilder;
-import io.github.ollama4j.models.chat.OllamaChatResult;
-import io.github.ollama4j.models.generate.OllamaStreamHandler;
-
-
-public class Main {
-
- public static void main(String[] args) {
-
- String host = "http://localhost:11434/";
-
- OllamaAPI ollamaAPI = new OllamaAPI(host);
- OllamaChatRequestBuilder builder = OllamaChatRequestBuilder.getInstance(config.getModel());
- OllamaChatRequest requestModel = builder.withMessage(OllamaChatMessageRole.USER,
- "What is the capital of France? And what's France's connection with Mona Lisa?")
- .build();
-
- // define a handler (Consumer<String>)
- OllamaStreamHandler streamHandler = (s) -> {
- System.out.println(s);
- };
-
- OllamaChatResult chatResult = ollamaAPI.chat(requestModel, streamHandler);
- }
-}
-```
-
-You will get a response similar to:
+<CodeEmbed src="https://raw.githubusercontent.com/ollama4j/ollama4j-examples/refs/heads/main/src/main/java/io/github/ollama4j/examples/ChatStreamingWithTokenConcatenationExample.java" />
+::::tip[LLM Response]
+>
> 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.
+>
+::::
-## Use a simple Console Output Stream Handler
+### Using a simple Console Output Stream Handler
-```java
-import io.github.ollama4j.OllamaAPI;
-import io.github.ollama4j.impl.ConsoleOutputStreamHandler;
-import io.github.ollama4j.models.chat.OllamaChatMessageRole;
-import io.github.ollama4j.models.chat.OllamaChatRequestBuilder;
-import io.github.ollama4j.models.chat.OllamaChatRequest;
-import io.github.ollama4j.models.generate.OllamaStreamHandler;
-import io.github.ollama4j.types.OllamaModelType;
+<CodeEmbed src="https://raw.githubusercontent.com/ollama4j/ollama4j-examples/refs/heads/main/src/main/java/io/github/ollama4j/examples/ConsoleOutputStreamHandlerExample.java" />
-public class Main {
- public static void main(String[] args) throws Exception {
- String host = "http://localhost:11434/";
- OllamaAPI ollamaAPI = new OllamaAPI(host);
+### With a Stream Handler to receive the tokens as they are generated
- OllamaChatRequestBuilder builder = OllamaChatRequestBuilder.getInstance(OllamaModelType.LLAMA2);
- OllamaChatRequest requestModel = builder.withMessage(OllamaChatMessageRole.USER, "List all cricket world cup teams of 2019. Name the teams!")
- .build();
- OllamaStreamHandler streamHandler = new ConsoleOutputStreamHandler();
- ollamaAPI.chat(requestModel, streamHandler);
- }
-}
-```
+<CodeEmbed src="https://raw.githubusercontent.com/ollama4j/ollama4j-examples/refs/heads/main/src/main/java/io/github/ollama4j/examples/ChatStreamingExample.java" />
-## Create a new conversation with individual system prompt
+### Create a new conversation with custom system prompt
-```java
-import io.github.ollama4j.OllamaAPI;
-import io.github.ollama4j.models.chat.OllamaChatMessageRole;
-import io.github.ollama4j.models.chat.OllamaChatRequestBuilder;
-import io.github.ollama4j.models.chat.OllamaChatRequest;
-import io.github.ollama4j.models.chat.OllamaChatResult;
-import io.github.ollama4j.types.OllamaModelType;
+<CodeEmbed src="https://raw.githubusercontent.com/ollama4j/ollama4j-examples/refs/heads/main/src/main/java/io/github/ollama4j/examples/ChatWithCustomSystemPrompt.java" />
+
+You will get a response as:
+
+::::tip[LLM Response]
+> Shhh!
+::::
-public class Main {
+## Create a conversation about an image (requires a vision model)
- public static void main(String[] args) {
-
- String host = "http://localhost:11434/";
-
- OllamaAPI ollamaAPI = new OllamaAPI(host);
- OllamaChatRequestBuilder builder = OllamaChatRequestBuilder.getInstance(OllamaModelType.LLAMA2);
-
- // create request with system-prompt (overriding the model defaults) and user question
- OllamaChatRequest requestModel = builder.withMessage(OllamaChatMessageRole.SYSTEM, "You are a silent bot that only says 'NI'. Do not say anything else under any circumstances!")
- .withMessage(OllamaChatMessageRole.USER, "What is the capital of France? And what's France's connection with Mona Lisa?")
- .build();
-
- // start conversation with model
- OllamaChatResult chatResult = ollamaAPI.chat(requestModel);
-
- System.out.println(chatResult.getResponseModel());
- }
-}
-
-```
-
-You will get a response similar to:
-
-> NI.
-
-## Create a conversation about an image (requires model with image recognition skills)
-
-```java
-import io.github.ollama4j.OllamaAPI;
-import io.github.ollama4j.models.chat.OllamaChatMessageRole;
-import io.github.ollama4j.models.chat.OllamaChatRequest;
-import io.github.ollama4j.models.chat.OllamaChatRequestBuilder;
-import io.github.ollama4j.models.chat.OllamaChatResult;
-import io.github.ollama4j.types.OllamaModelType;
-
-import java.io.File;
-import java.util.List;
-
-public class Main {
-
- public static void main(String[] args) {
-
- String host = "http://localhost:11434/";
-
- OllamaAPI ollamaAPI = new OllamaAPI(host);
- OllamaChatRequestBuilder builder = OllamaChatRequestBuilder.getInstance(OllamaModelType.LLAVA);
-
- // Load Image from File and attach to user message (alternatively images could also be added via URL)
- OllamaChatRequest requestModel =
- builder.withMessage(OllamaChatMessageRole.USER, "What's in the picture?",
- List.of(
- new File("/path/to/image"))).build();
-
- OllamaChatResult chatResult = ollamaAPI.chat(requestModel);
- System.out.println("First answer: " + chatResult.getResponseModel());
-
- builder.reset();
-
- // Use history to ask further questions about the image or assistant answer
- requestModel =
- builder.withMessages(chatResult.getChatHistory())
- .withMessage(OllamaChatMessageRole.USER, "What's the dogs breed?").build();
-
- chatResult = ollamaAPI.chat(requestModel);
- System.out.println("Second answer: " + chatResult.getResponseModel());
- }
-}
-```
+<CodeEmbed src="https://raw.githubusercontent.com/ollama4j/ollama4j-examples/refs/heads/main/src/main/java/io/github/ollama4j/examples/ChatWithImage.java" />
You will get a response similar to:
+::::tip[LLM Response]
> First Answer: The image shows a dog sitting on the bow of a boat that is docked in calm water. The boat has two
> levels, with the lower level containing seating and what appears to be an engine cover. The dog seems relaxed and
> comfortable on the boat, looking out over the water. The background suggests it might be late afternoon or early
@@ -272,11 +105,4 @@ You will get a response similar to:
> appears to be medium-sized with a short coat and a brown coloration, which might suggest that it is a Golden Retriever
> or a similar breed. Without more details like ear shape and tail length, it's not possible to identify the exact breed
> confidently.
-
-
-[//]: # (Generated using: https://emgithub.com/)
-<iframe style={{ width: '100%', height: '919px', border: 'none' }} allow="clipboard-write" src="https://emgithub.com/iframe.html?target=https%3A%2F%2Fgithub.com%2Follama4j%2Follama4j-examples%2Fblob%2Fmain%2Fsrc%2Fmain%2Fjava%2Fio%2Fgithub%2Follama4j%2Fexamples%2FChatExample.java&style=default&type=code&showBorder=on&showLineNumbers=on&showFileMeta=on&showFullPath=on&showCopy=on" />
-
-<a href="https://github.com/ollama4j/ollama4j-examples/blob/main/src/main/java/io/github/ollama4j/examples/ChatExample.java" target="_blank">
- View ChatExample.java on GitHub
-</a>
\ No newline at end of file
+::::
diff --git a/docs/docs/apis-generate/custom-roles.md b/docs/docs/apis-generate/custom-roles.md
index c735827..44df8b1 100644
--- a/docs/docs/apis-generate/custom-roles.md
+++ b/docs/docs/apis-generate/custom-roles.md
@@ -1,5 +1,5 @@
---
-sidebar_position: 8
+sidebar_position: 9
---
# Custom Roles
diff --git a/docs/docs/apis-generate/generate-async.md b/docs/docs/apis-generate/generate-async.md
index 1b8b47c..1f2ca9e 100644
--- a/docs/docs/apis-generate/generate-async.md
+++ b/docs/docs/apis-generate/generate-async.md
@@ -2,7 +2,9 @@
sidebar_position: 2
---
-# Generate - Async
+import CodeEmbed from '@site/src/components/CodeEmbed';
+
+# Generate (Async)
This API lets you ask questions to the LLMs in a asynchronous way.
This is particularly helpful when you want to issue a generate request to the LLM and collect the response in the
@@ -11,38 +13,18 @@ background (such as threads) without blocking your code until the response arriv
This API corresponds to
the [completion](https://github.com/jmorganca/ollama/blob/main/docs/api.md#generate-a-completion) API.
-```java
-import io.github.ollama4j.OllamaAPI;
-import io.github.ollama4j.models.response.OllamaAsyncResultStreamer;
-import io.github.ollama4j.types.OllamaModelType;
+<CodeEmbed src="https://raw.githubusercontent.com/ollama4j/ollama4j-examples/refs/heads/main/src/main/java/io/github/ollama4j/examples/GenerateAsync.java" />
-public class Main {
+::::tip[LLM Response]
+Here are the participating teams in the 2019 ICC Cricket World Cup:
- public static void main(String[] args) throws Exception {
- String host = "http://localhost:11434/";
- OllamaAPI ollamaAPI = new OllamaAPI(host);
- ollamaAPI.setRequestTimeoutSeconds(60);
- String prompt = "List all cricket world cup teams of 2019.";
- OllamaAsyncResultStreamer streamer = ollamaAPI.generateAsync(OllamaModelType.LLAMA3, prompt, false);
-
- // Set the poll interval according to your needs.
- // Smaller the poll interval, more frequently you receive the tokens.
- int pollIntervalMilliseconds = 1000;
-
- while (true) {
- String tokens = streamer.getStream().poll();
- System.out.print(tokens);
- if (!streamer.isAlive()) {
- break;
- }
- Thread.sleep(pollIntervalMilliseconds);
- }
-
- System.out.println("\n------------------------");
- System.out.println("Complete Response:");
- System.out.println("------------------------");
-
- System.out.println(streamer.getCompleteResponse());
- }
-}
-```
\ No newline at end of file
+1. Australia
+2. Bangladesh
+3. India
+4. New Zealand
+5. Pakistan
+6. England
+7. South Africa
+8. West Indies (as a team)
+9. Afghanistan
+::::
\ No newline at end of file
diff --git a/docs/docs/apis-generate/generate-embeddings.md b/docs/docs/apis-generate/generate-embeddings.md
index ae2aaa1..1adcde9 100644
--- a/docs/docs/apis-generate/generate-embeddings.md
+++ b/docs/docs/apis-generate/generate-embeddings.md
@@ -1,112 +1,49 @@
---
-sidebar_position: 6
+sidebar_position: 5
---
+import CodeEmbed from '@site/src/components/CodeEmbed';
+
# Generate Embeddings
Generate embeddings from a model.
-Parameters:
+### Using `embed()`
-- `model`: name of model to generate embeddings from
-- `input`: text/s to generate embeddings for
+<CodeEmbed src="https://raw.githubusercontent.com/ollama4j/ollama4j-examples/refs/heads/main/src/main/java/io/github/ollama4j/examples/GenerateEmbeddings.java" />
-```java
-import io.github.ollama4j.OllamaAPI;
-import io.github.ollama4j.types.OllamaModelType;
-import io.github.ollama4j.models.embeddings.OllamaEmbedRequestModel;
-import io.github.ollama4j.models.embeddings.OllamaEmbedResponseModel;
-import java.util.Arrays;
-import java.util.Collections;
-import java.util.List;
-
-public class Main {
-
- public static void main(String[] args) {
-
- String host = "http://localhost:11434/";
-
- OllamaAPI ollamaAPI = new OllamaAPI(host);
-
- OllamaEmbedResponseModel embeddings = ollamaAPI.embed("all-minilm", Arrays.asList("Why is the sky blue?", "Why is the grass green?"));
-
- System.out.println(embeddings);
- }
-}
+::::tip[LLM Response]
+```json
+[[0.010000081, -0.0017487297, 0.050126992, 0.04694895, 0.055186987, 0.008570699, 0.10545243, -0.02591801, 0.1296789, 0.031844463, -0.044533115, -0.009081162, -4.7557743E-4, -0.06383077, -0.016083026, 0.04666039, -0.022107942, -0.15835331, -0.07281923, -0.061205965, -0.06593526, 0.054214016, -0.062174935, 0.038974375, -0.04570855, 0.05495598, -0.035383187, 0.012725615, 0.04252694, -0.00806814, -0.019041134, 0.061063103, 0.036943648, 0.013529706, -0.025880618, -0.04349401, 0.07276639, -0.048439376, 0.004148429, -0.029453786, -0.029147545, -0.03282039, -0.018276647, 0.0155515345, -0.011599436, 0.015321048, -0.009423502, 0.02589781, 0.095306225, -0.015580891, -0.024553236, 0.009203469, -0.07652067, 0.01593109, 0.049590923, 0.11590031, 9.564879E-4, -0.020308463, 0.09234688, 0.008461708, -0.057039093, 0.06883451, -0.07657848, 0.06934012, 0.09227977, -0.055535857, -0.05371766, 0.008418901, -0.06311155, -0.066414595, -0.025158273, 0.018782357, 0.061182138, -0.028296644, 0.0362281, 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```
+::::
-Or, using the `OllamaEmbedRequestModel`:
+You could also use the `OllamaEmbedRequestModel` to specify the options such as `seed`, `temperature`, etc., to apply for generating embeddings.
-```java
-import io.github.ollama4j.OllamaAPI;
-import io.github.ollama4j.types.OllamaModelType;
-import io.github.ollama4j.models.embeddings.OllamaEmbedRequestModel;
-import io.github.ollama4j.models.embeddings.OllamaEmbedResponseModel;import java.util.Arrays;
-import java.util.Collections;
-import java.util.List;
-
-public class Main {
-
- public static void main(String[] args) {
-
- String host = "http://localhost:11434/";
-
- OllamaAPI ollamaAPI = new OllamaAPI(host);
-
- OllamaEmbedResponseModel embeddings = ollamaAPI.embed(new OllamaEmbedRequestModel("all-minilm", Arrays.asList("Why is the sky blue?", "Why is the grass green?")));
-
- System.out.println(embeddings);
- }
-}
-```
+<CodeEmbed src="https://raw.githubusercontent.com/ollama4j/ollama4j-examples/refs/heads/main/src/main/java/io/github/ollama4j/examples/GenerateEmbeddingsWithRequestModel.java" />
You will get a response similar to:
+::::tip[LLM Response]
```json
-{
- "model": "all-minilm",
- "embeddings": [[-0.034674067, 0.030984823, 0.0067988685]],
- "total_duration": 14173700,
- "load_duration": 1198800,
- "prompt_eval_count": 2
-}
-````
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-0.086338826, -0.087730855, 0.0071089785, -0.0075092614, -0.016718967, 0.045219034, 0.067585975, -0.042870898, 0.0863409, 0.045438178, 0.06795051, 0.009950505, -0.0029959748, 0.058502916, -0.035608236, 0.036216073, 0.066100344, -0.03785426, -0.062264763, -0.04450461, 0.07720427, 0.043325383, -0.021296863, -0.0217195, 0.062334213, -0.0391459, 0.028083341, -0.013057875, 0.051180184, -0.036750164, 0.054655746, -0.066471115, 0.022967137, 0.0047796182, 0.09052008, 0.005167651, -0.0830967, -0.055065937, 0.07320647, -0.11054101, -0.020116722, 0.11247867, -0.053230446, -0.057548687, -0.023836475, 0.056587286, 0.12725416, 0.036107734, -0.043944683, 0.017000921, -0.024768567, 0.07276523, 0.043141358, 0.08048159, -0.019533968, -0.03447826, 0.096703045, 0.051834024, 0.010554283, 0.04019631, 0.0020927596, -0.007590705, 0.0016895492, 0.014211798, 0.02047324, -0.023020415, 0.021562004, -0.00928612, -0.050591297, -0.01619291, -0.08997802, -0.060895078, 0.08100429, 0.0022806204, 0.041729365, 0.043731183, -0.025113516, -0.09526692, 0.08865304, -0.09853454, -0.0048426827, 0.035341848, 0.0143458955, -0.064700805, -0.07586452, 0.012436738, -0.05000536, -0.05567604, -0.056878153, -0.018541014, -0.0021473207, -0.0022214772, 0.035333972, -0.05470233, -1.4311088E-8, -0.00807994, 0.026629517, 0.002253397, 0.009933685, -0.02166608, -0.021526324, 0.11142737, 0.0047573056, 0.03775078, 0.0039694835, -0.066885866, -0.028193833, -0.044485897, 0.071223155, 0.018815499, -0.049034107, -0.10390887, -0.043636143, 0.010163606, 0.0418435, -0.013363032, -0.033802148, -0.025276663, -0.013619332, 0.0033778746, 0.033192083, -0.021926358, 0.022021232, 0.071396865, 0.020565767, 0.024445849, 0.035862394, -0.001007896, -0.061173376, -0.08546204, 0.0073751807, -0.038680665, 0.07989097, -0.025537722, -0.060529694, 0.060663767, 0.082347505, -0.056607824, 0.004820212, 0.045103956, 0.023633804, 0.043377202, 0.09108467, -0.051370483, -0.011107505, -0.06897708, 0.007159519, 0.072742105, -0.04338072, 0.025991833, -0.11408352, -0.009605889, 0.022043642, 0.02668666, 0.0038960192, 0.015961006, 0.0036130184, -0.020764133, 0.03348443], [-0.009868476, 0.060335685, 0.025288988, -0.0062160683, 0.07281043, 0.017217565, 0.090314455, -0.051715206, 0.09947815, 0.090584196, 0.0071719657, -0.019594174, -0.075078875, -0.017466826, 0.019347396, 0.040821016, -0.011118273, -0.05854725, -0.12543073, -0.048901077, -0.044018935, 0.031114545, 0.037799157, -0.031743918, -0.0910083, 0.06356124, -0.07640408, 0.08509329, 0.035593998, -0.07126983, 0.021175714, 0.11015013, 0.03325966, -0.02586855, -0.061687328, -0.026381517, 0.020523364, -0.054436196, 0.056599274, 0.032927252, -0.08997798, -0.057034135, 0.026899701, 0.07513233, -0.071349114, -0.004237693, 0.054284442, 0.026307901, 0.078129396, -0.048960682, 0.056613132, -0.04913771, -0.07579886, 0.0069060107, 0.0063763415, 0.036972668, 0.025060013, 0.02181742, 0.01020716, -0.040446986, -0.012050511, -0.0333741, -0.07564401, 0.07132756, -0.02063755, -0.06318399, -0.0013259775, -0.05526086, 0.009020493, -0.08710191, 0.020652942, 0.05299636, -0.009691467, -0.052739665, -0.06480449, 0.042018816, 0.044661146, 0.03273904, -0.01949503, 2.4002639E-4, 0.038351417, 0.050931647, 0.0046273004, 0.057359487, 0.046486866, 0.042649552, -0.017132936, 0.011823174, -0.056949086, -0.035009447, -0.019008413, -0.0074347625, -0.07384615, 0.04393877, -0.09906426, 0.041409962, -0.023977123, -0.12038461, 1.2660562E-4, -0.003843579, 0.05607605, -4.8949895E-4, 0.07111408, -0.036223337, -0.06402696, -0.009451222, -0.042215906, 0.0780337, -0.02371912, 0.007851906, -0.023734692, -0.018583676, -0.033396255, 0.077651344, -0.06062816, 0.053625435, 0.033917766, 0.012533888, -0.032672815, 0.029700326, -0.016981928, 0.0281869, -0.018111452, 0.06656012, -0.06950031, -0.017839137, -0.037340533, -6.835867E-33, -0.0055358508, -0.031647824, 0.048153512, -0.09928821, 0.093719974, -0.051801622, -0.036978923, -0.026406623, -0.037407376, -0.030371925, 0.0061302963, -0.0982039, -0.017523993, -0.07252802, 0.03850968, 0.008900545, -0.13075978, 0.021422677, -0.075673044, -0.01029629, -0.017209169, 0.05839448, 0.021881068, 0.0015664459, 0.009339571, -0.0314211, 0.080039345, 0.030477382, 0.056920107, -0.012233044, 0.11584086, 0.0040954757, 0.017554974, 0.04336152, 0.029307723, -0.0068410966, -0.025106648, -0.026154919, 0.013292939, 0.121521436, -0.0045275204, -0.045257635, 0.04338726, -0.017685922, 0.06218186, -0.03933278, 0.01735304, 0.008501952, -0.026153479, 0.010192709, 0.023137128, 0.053122364, 0.0690913, 0.05310471, -2.4858408E-4, 0.0096703665, 0.08885624, -0.030429514, -0.09079217, -0.05130283, -0.07371132, 0.08618689, 0.0033454685, 0.010966452, -0.008318914, -0.011407435, -0.029051095, 0.06551204, 0.0054419613, 0.06888426, 0.04281082, -0.018798476, -0.016944146, -0.036808517, -0.006201687, -0.08704525, -0.008700292, -0.013294283, -0.0046790023, 0.042446434, -0.03576464, 0.017382197, -0.08722518, -0.051042743, -0.14942876, -0.0020754328, -0.026410934, 0.018064674, 0.021632154, -0.015141376, 0.003725257, -0.01553318, -0.01327707, -0.098052144, -0.011788692, 2.8246808E-33, -0.022846783, 0.008986764, -0.005797001, 0.066113144, 0.042615797, -0.039735403, 0.027211439, 0.032038726, -0.028711542, 0.04562696, -0.05573173, 0.09247299, -0.046873886, 0.080259465, 0.1185751, 0.048753165, -0.06736519, 0.10693395, 0.009406613, -0.051305998, -0.06782429, 0.015892766, -0.010205388, 0.044131745, -0.030017989, 0.022811258, -0.031549934, -0.022227649, -0.0023231048, -0.00993543, 0.00321092, -0.036961444, -0.112086535, 0.028652154, 0.030226015, 0.031845823, -0.017391989, -0.018336339, -0.036368545, 0.08388451, 0.0079623535, -0.023648826, 0.15845971, 0.03212625, -0.07040057, -0.03407339, -0.0154629275, 0.07972614, -0.062146895, 0.046385817, 0.045025956, 0.10424084, 0.029551638, 0.04803619, 0.09714898, -0.015759284, -0.06423742, 0.019712195, -0.09390805, -0.0027662653, 0.019718736, -0.027150705, -0.07877321, 0.068945184, 0.10764347, -0.033046924, -0.06445677, 0.016665269, 0.01923717, -0.022991046, -0.01895388, -0.018677948, -0.064167276, -0.032140236, -0.00278987, 0.05908604, 0.05976127, -0.044849344, -0.066649735, 0.043921255, -0.01992474, -0.0067276787, 0.047306404, -0.046795446, 0.034644924, -0.015249516, -0.039549574, 0.047282677, 0.052053083, 8.0526056E-4, 0.052853532, 0.0245618, -0.02940274, 0.034503356, 0.0131183695, -1.3674445E-8, -0.034011222, 0.0074544777, -0.010649118, 0.043190703, 0.014776448, -0.041673005, 0.10555256, -0.012789219, -0.0024177078, 0.04042407, -0.0813939, 0.033735543, -0.019764481, 0.07754665, 0.060485274, -0.01606053, -0.11340549, -0.04295919, 0.023188036, 0.074550346, -0.06057766, -0.045957167, -0.08755505, 0.053412285, -0.043960545, 0.029779192, 0.038598273, 0.016920617, 0.027635917, 0.0780365, 0.055601224, 0.052999303, -0.010710994, -0.029806744, -0.08503494, -0.01641341, 0.030428788, 0.06950053, -0.062027372, -0.12210805, 0.011878054, 0.072504126, -0.01782069, 0.0031250992, 0.014375631, -0.037944797, -0.052864, 0.060364977, -0.053158067, 0.018045388, -0.1042992, 0.010478333, 0.042992577, 0.04256209, -0.0030047095, -0.08532544, -0.03959884, -0.004523487, 0.013003125, -0.008466674, -0.029106006, -0.035763003, 0.059537675, -1.000059E-4]]
+```
-:::note
+::::
+
+### Using `generateEmbeddings()`
+
+::::danger[NOTE]
This is a deprecated API
-:::
+::::
-Parameters:
-
-- `model`: name of model to generate embeddings from
-- `prompt`: text to generate embeddings for
-
-```java
-import io.github.ollama4j.OllamaAPI;
-import io.github.ollama4j.types.OllamaModelType;
-
-import java.util.List;
-
-public class Main {
-
- public static void main(String[] args) {
-
- String host = "http://localhost:11434/";
-
- OllamaAPI ollamaAPI = new OllamaAPI(host);
-
- List<Double> embeddings = ollamaAPI.generateEmbeddings(OllamaModelType.LLAMA2,
- "Here is an article about llamas...");
-
- embeddings.forEach(System.out::println);
- }
-}
-```
+<CodeEmbed src="https://raw.githubusercontent.com/ollama4j/ollama4j-examples/refs/heads/main/src/main/java/io/github/ollama4j/examples/GenerateEmbeddingsOld.java" />
You will get a response similar to:
+::::tip[LLM Response]
```javascript
[
0.5670403838157654,
@@ -115,4 +52,5 @@ You will get a response similar to:
-0.2916173040866852,
-0.8924556970596313
]
-```
\ No newline at end of file
+```
+::::
\ No newline at end of file
diff --git a/docs/docs/apis-generate/generate-with-image-files.md b/docs/docs/apis-generate/generate-with-image-files.md
index b26f9ba..5f2e8ea 100644
--- a/docs/docs/apis-generate/generate-with-image-files.md
+++ b/docs/docs/apis-generate/generate-with-image-files.md
@@ -1,8 +1,10 @@
---
-sidebar_position: 4
+sidebar_position: 3
---
-# Generate - With Image Files
+import CodeEmbed from '@site/src/components/CodeEmbed';
+
+# Generate with Image Files
This API lets you ask questions along with the image files to the LLMs.
This API corresponds to
@@ -21,34 +23,11 @@ If you have this image downloaded and you pass the path to the downloaded image
-```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 java.io.File;
-import java.util.List;
-
-public class Main {
-
- public static void main(String[] args) {
- String host = "http://localhost:11434/";
- OllamaAPI ollamaAPI = new OllamaAPI(host);
- ollamaAPI.setRequestTimeoutSeconds(10);
-
- OllamaResult result = ollamaAPI.generateWithImageFiles(OllamaModelType.LLAVA,
- "What's in this image?",
- List.of(
- new File("/path/to/image")),
- new OptionsBuilder().build()
- );
- System.out.println(result.getResponse());
- }
-}
-```
+<CodeEmbed src="https://raw.githubusercontent.com/ollama4j/ollama4j-examples/refs/heads/main/src/main/java/io/github/ollama4j/examples/GenerateWithImageFile.java" />
You will get a response similar to:
+::::tip[LLM Response]
> This image features a white boat with brown cushions, where a dog is sitting on the back of the boat. The dog seems to
-> be enjoying its time outdoors, perhaps on a lake.
\ No newline at end of file
+> be enjoying its time outdoors, perhaps on a lake.
+::::
\ No newline at end of file
diff --git a/docs/docs/apis-generate/generate-with-image-urls.md b/docs/docs/apis-generate/generate-with-image-urls.md
index cf9e755..f047032 100644
--- a/docs/docs/apis-generate/generate-with-image-urls.md
+++ b/docs/docs/apis-generate/generate-with-image-urls.md
@@ -1,8 +1,10 @@
---
-sidebar_position: 5
+sidebar_position: 4
---
-# Generate - With Image URLs
+import CodeEmbed from '@site/src/components/CodeEmbed';
+
+# Generate with Image URLs
This API lets you ask questions along with the image files to the LLMs.
This API corresponds to
@@ -21,33 +23,11 @@ Passing the link of this image the following code:
-```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 java.util.List;
-
-public class Main {
-
- public static void main(String[] args) {
- String host = "http://localhost:11434/";
- OllamaAPI ollamaAPI = new OllamaAPI(host);
- ollamaAPI.setRequestTimeoutSeconds(10);
-
- OllamaResult result = ollamaAPI.generateWithImageURLs(OllamaModelType.LLAVA,
- "What's in this image?",
- List.of(
- "https://t3.ftcdn.net/jpg/02/96/63/80/360_F_296638053_0gUVA4WVBKceGsIr7LNqRWSnkusi07dq.jpg"),
- new OptionsBuilder().build()
- );
- System.out.println(result.getResponse());
- }
-}
-```
+<CodeEmbed src="https://raw.githubusercontent.com/ollama4j/ollama4j-examples/refs/heads/main/src/main/java/io/github/ollama4j/examples/GenerateWithImageURL.java" />
You will get a response similar to:
+::::tip[LLM Response]
> This image features a white boat with brown cushions, where a dog is sitting on the back of the boat. The dog seems to
-> be enjoying its time outdoors, perhaps on a lake.
\ No newline at end of file
+> be enjoying its time outdoors, perhaps on a lake.
+::::
\ No newline at end of file
diff --git a/docs/docs/apis-generate/generate-with-tools.md b/docs/docs/apis-generate/generate-with-tools.md
index f0722e3..d25a5fc 100644
--- a/docs/docs/apis-generate/generate-with-tools.md
+++ b/docs/docs/apis-generate/generate-with-tools.md
@@ -1,10 +1,12 @@
---
-sidebar_position: 3
+sidebar_position: 6
---
-# Generate - With Tools
+import CodeEmbed from '@site/src/components/CodeEmbed';
-This API lets you perform [function calling](https://docs.mistral.ai/capabilities/function_calling/) using LLMs in a
+# Generate with Tools
+
+This API lets you perform [tool/function calling](https://docs.mistral.ai/capabilities/function_calling/) using LLMs in a
synchronous way.
This API corresponds to
the [generate](https://github.com/ollama/ollama/blob/main/docs/api.md#request-raw-mode) API with `raw` mode.
@@ -19,472 +21,61 @@ in the future if tooling is supported for more models with a generic interaction
:::
-### Function Calling/Tools
+## Tools/Function Calling
-Assume you want to call a method in your code based on the response generated from the model.
+Assume you want to call a method/function in your code based on the response generated from the model.
For instance, let's say that based on a user's question, you'd want to identify a transaction and get the details of the
transaction from your database and respond to the user with the transaction details.
You could do that with ease with the `function calling` capabilities of the models by registering your `tools`.
-### Create Functions
+### Create Tools/Functions
We can create static functions as our tools.
This function takes the arguments `location` and `fuelType` and performs an operation with these arguments and returns
fuel price value.
-```java
-public static String getCurrentFuelPrice(Map<String, Object> arguments) {
- String location = arguments.get("location").toString();
- String fuelType = arguments.get("fuelType").toString();
- return "Current price of " + fuelType + " in " + location + " is Rs.103/L";
-}
-```
+<CodeEmbed src="https://raw.githubusercontent.com/ollama4j/ollama4j-examples/refs/heads/main/src/main/java/io/github/ollama4j/examples/toolcalling/tools/FuelPriceTool.java"/ >
This function takes the argument `city` and performs an operation with the argument and returns the weather for a
location.
-```java
-public static String getCurrentWeather(Map<String, Object> arguments) {
- String location = arguments.get("city").toString();
- return "Currently " + location + "'s weather is nice.";
-}
-```
+<CodeEmbed src="https://raw.githubusercontent.com/ollama4j/ollama4j-examples/refs/heads/main/src/main/java/io/github/ollama4j/examples/toolcalling/tools/WeatherTool.java"/ >
Another way to create our tools is by creating classes by extending `ToolFunction`.
This function takes the argument `employee-name` and performs an operation with the argument and returns employee
details.
-```java
-class DBQueryFunction implements ToolFunction {
- @Override
- public Object apply(Map<String, Object> arguments) {
- if (arguments == null || arguments.isEmpty() || arguments.get("employee-name") == null || arguments.get("employee-address") == null || arguments.get("employee-phone") == null) {
- throw new RuntimeException("Tool was called but the model failed to provide all the required arguments.");
- }
- // perform DB operations here
- return String.format("Employee Details {ID: %s, Name: %s, Address: %s, Phone: %s}", UUID.randomUUID(), arguments.get("employee-name").toString(), arguments.get("employee-address").toString(), arguments.get("employee-phone").toString());
- }
-}
-```
+<CodeEmbed src="https://raw.githubusercontent.com/ollama4j/ollama4j-examples/refs/heads/main/src/main/java/io/github/ollama4j/examples/toolcalling/tools/DBQueryFunction.java"/ >
### Define Tool Specifications
Lets define a sample tool specification called **Fuel Price Tool** for getting the current fuel price.
- Specify the function `name`, `description`, and `required` properties (`location` and `fuelType`).
-- Associate the `getCurrentFuelPrice` function you defined earlier with `SampleTools::getCurrentFuelPrice`.
+- Associate the `getCurrentFuelPrice` function you defined earlier.
-```java
-Tools.ToolSpecification fuelPriceToolSpecification = Tools.ToolSpecification.builder()
- .functionName("current-fuel-price")
- .functionDescription("Get current fuel price")
- .toolFunction(SampleTools::getCurrentFuelPrice)
- .toolPrompt(
- Tools.PromptFuncDefinition.builder()
- .type("prompt")
- .function(
- Tools.PromptFuncDefinition.PromptFuncSpec.builder()
- .name("get-location-fuel-info")
- .description("Get location and fuel type details")
- .parameters(
- Tools.PromptFuncDefinition.Parameters.builder()
- .type("object")
- .properties(
- Map.of(
- "location", Tools.PromptFuncDefinition.Property.builder()
- .type("string")
- .description("The city, e.g. New Delhi, India")
- .required(true)
- .build(),
- "fuelType", Tools.PromptFuncDefinition.Property.builder()
- .type("string")
- .description("The fuel type.")
- .enumValues(Arrays.asList("petrol", "diesel"))
- .required(true)
- .build()
- )
- )
- .required(java.util.List.of("location", "fuelType"))
- .build()
- )
- .build()
- )
- .build()
- ).build();
-```
+<CodeEmbed src="https://raw.githubusercontent.com/ollama4j/ollama4j-examples/refs/heads/main/src/main/java/io/github/ollama4j/examples/toolcalling/toolspecs/FuelPriceToolSpec.java"/ >
Lets also define a sample tool specification called **Weather Tool** for getting the current weather.
- Specify the function `name`, `description`, and `required` property (`city`).
-- Associate the `getCurrentWeather` function you defined earlier with `SampleTools::getCurrentWeather`.
+- Associate the `getCurrentWeather` function you defined earlier.
-```java
-Tools.ToolSpecification weatherToolSpecification = Tools.ToolSpecification.builder()
- .functionName("current-weather")
- .functionDescription("Get current weather")
- .toolFunction(SampleTools::getCurrentWeather)
- .toolPrompt(
- Tools.PromptFuncDefinition.builder()
- .type("prompt")
- .function(
- Tools.PromptFuncDefinition.PromptFuncSpec.builder()
- .name("get-location-weather-info")
- .description("Get location details")
- .parameters(
- Tools.PromptFuncDefinition.Parameters.builder()
- .type("object")
- .properties(
- Map.of(
- "city", Tools.PromptFuncDefinition.Property.builder()
- .type("string")
- .description("The city, e.g. New Delhi, India")
- .required(true)
- .build()
- )
- )
- .required(java.util.List.of("city"))
- .build()
- )
- .build()
- )
- .build()
- ).build();
-```
+<CodeEmbed src="https://raw.githubusercontent.com/ollama4j/ollama4j-examples/refs/heads/main/src/main/java/io/github/ollama4j/examples/toolcalling/toolspecs/WeatherToolSpec.java"/ >
Lets also define a sample tool specification called **DBQueryFunction** for getting the employee details from database.
- Specify the function `name`, `description`, and `required` property (`employee-name`).
- Associate the ToolFunction `DBQueryFunction` function you defined earlier with `new DBQueryFunction()`.
-```java
-Tools.ToolSpecification databaseQueryToolSpecification = Tools.ToolSpecification.builder()
- .functionName("get-employee-details")
- .functionDescription("Get employee details from the database")
- .toolFunction(new DBQueryFunction())
- .toolPrompt(
- Tools.PromptFuncDefinition.builder()
- .type("prompt")
- .function(
- Tools.PromptFuncDefinition.PromptFuncSpec.builder()
- .name("get-employee-details")
- .description("Get employee details from the database")
- .parameters(
- Tools.PromptFuncDefinition.Parameters.builder()
- .type("object")
- .properties(
- Map.of(
- "employee-name", Tools.PromptFuncDefinition.Property.builder()
- .type("string")
- .description("The name of the employee, e.g. John Doe")
- .required(true)
- .build(),
- "employee-address", Tools.PromptFuncDefinition.Property.builder()
- .type("string")
- .description("The address of the employee, Always return a random value. e.g. Roy St, Bengaluru, India")
- .required(true)
- .build(),
- "employee-phone", Tools.PromptFuncDefinition.Property.builder()
- .type("string")
- .description("The phone number of the employee. Always return a random value. e.g. 9911002233")
- .required(true)
- .build()
- )
- )
- .required(java.util.List.of("employee-name", "employee-address", "employee-phone"))
- .build()
- )
- .build()
- )
- .build()
- )
- .build();
-```
+<CodeEmbed src="https://raw.githubusercontent.com/ollama4j/ollama4j-examples/refs/heads/main/src/main/java/io/github/ollama4j/examples/toolcalling/toolspecs/DatabaseQueryToolSpec.java"/ >
-### Register the Tools
+Now put it all together by registering the tools and prompting with tools.
-Register the defined tools (`fuel price` and `weather`) with the OllamaAPI.
-
-```shell
-ollamaAPI.registerTool(fuelPriceToolSpecification);
-ollamaAPI.registerTool(weatherToolSpecification);
-ollamaAPI.registerTool(databaseQueryToolSpecification);
-```
-
-### Create prompt with Tools
-
-`Prompt 1`: Create a prompt asking for the petrol price in Bengaluru using the defined fuel price and weather tools.
-
-```shell
-String prompt1 = new Tools.PromptBuilder()
- .withToolSpecification(fuelPriceToolSpecification)
- .withToolSpecification(weatherToolSpecification)
- .withPrompt("What is the petrol price in Bengaluru?")
- .build();
-OllamaToolsResult toolsResult = ollamaAPI.generateWithTools(model, prompt1, new OptionsBuilder().build());
-for (OllamaToolsResult.ToolResult r : toolsResult.getToolResults()) {
- System.out.printf("[Result of executing tool '%s']: %s%n", r.getFunctionName(), r.getResult().toString());
-}
-```
-
-Now, fire away your question to the model.
-
-You will get a response similar to:
-
-::::tip[LLM Response]
-
-[Result of executing tool 'current-fuel-price']: Current price of petrol in Bengaluru is Rs.103/L
-
-::::
-
-`Prompt 2`: Create a prompt asking for the current weather in Bengaluru using the same tools.
-
-```shell
-String prompt2 = new Tools.PromptBuilder()
- .withToolSpecification(fuelPriceToolSpecification)
- .withToolSpecification(weatherToolSpecification)
- .withPrompt("What is the current weather in Bengaluru?")
- .build();
-OllamaToolsResult toolsResult = ollamaAPI.generateWithTools(model, prompt2, new OptionsBuilder().build());
-for (OllamaToolsResult.ToolResult r : toolsResult.getToolResults()) {
- System.out.printf("[Result of executing tool '%s']: %s%n", r.getFunctionName(), r.getResult().toString());
-}
-```
-
-Again, fire away your question to the model.
-
-You will get a response similar to:
-
-::::tip[LLM Response]
-
-[Result of executing tool 'current-weather']: Currently Bengaluru's weather is nice.
-
-::::
-
-`Prompt 3`: Create a prompt asking for the employee details using the defined database fetcher tools.
-
-```shell
-String prompt3 = new Tools.PromptBuilder()
- .withToolSpecification(fuelPriceToolSpecification)
- .withToolSpecification(weatherToolSpecification)
- .withToolSpecification(databaseQueryToolSpecification)
- .withPrompt("Give me the details of the employee named 'Rahul Kumar'?")
- .build();
-OllamaToolsResult toolsResult = ollamaAPI.generateWithTools(model, prompt3, new OptionsBuilder().build());
-for (OllamaToolsResult.ToolResult r : toolsResult.getToolResults()) {
- System.out.printf("[Result of executing tool '%s']: %s%n", r.getFunctionName(), r.getResult().toString());
-}
-```
-
-Again, fire away your question to the model.
-
-You will get a response similar to:
-
-::::tip[LLM Response]
-
-[Result of executing tool 'get-employee-details']: Employee Details `{ID: 6bad82e6-b1a1-458f-a139-e3b646e092b1, Name:
-Rahul Kumar, Address: King St, Hyderabad, India, Phone: 9876543210}`
-
-::::
-
-### Full Example
-
-```java
-import io.github.ollama4j.OllamaAPI;
-import io.github.ollama4j.exceptions.OllamaBaseException;
-import io.github.ollama4j.exceptions.ToolInvocationException;
-import io.github.ollama4j.tools.OllamaToolsResult;
-import io.github.ollama4j.tools.ToolFunction;
-import io.github.ollama4j.tools.Tools;
-import io.github.ollama4j.utils.OptionsBuilder;
-
-import java.io.IOException;
-import java.util.Arrays;
-import java.util.Map;
-import java.util.UUID;
-
-public class FunctionCallingWithMistralExample {
- public static void main(String[] args) throws Exception {
- String host = "http://localhost:11434/";
- OllamaAPI ollamaAPI = new OllamaAPI(host);
- ollamaAPI.setRequestTimeoutSeconds(60);
-
- String model = "mistral";
-
- Tools.ToolSpecification fuelPriceToolSpecification = Tools.ToolSpecification.builder()
- .functionName("current-fuel-price")
- .functionDescription("Get current fuel price")
- .toolFunction(SampleTools::getCurrentFuelPrice)
- .toolPrompt(
- Tools.PromptFuncDefinition.builder()
- .type("prompt")
- .function(
- Tools.PromptFuncDefinition.PromptFuncSpec.builder()
- .name("get-location-fuel-info")
- .description("Get location and fuel type details")
- .parameters(
- Tools.PromptFuncDefinition.Parameters.builder()
- .type("object")
- .properties(
- Map.of(
- "location", Tools.PromptFuncDefinition.Property.builder()
- .type("string")
- .description("The city, e.g. New Delhi, India")
- .required(true)
- .build(),
- "fuelType", Tools.PromptFuncDefinition.Property.builder()
- .type("string")
- .description("The fuel type.")
- .enumValues(Arrays.asList("petrol", "diesel"))
- .required(true)
- .build()
- )
- )
- .required(java.util.List.of("location", "fuelType"))
- .build()
- )
- .build()
- )
- .build()
- ).build();
-
- Tools.ToolSpecification weatherToolSpecification = Tools.ToolSpecification.builder()
- .functionName("current-weather")
- .functionDescription("Get current weather")
- .toolFunction(SampleTools::getCurrentWeather)
- .toolPrompt(
- Tools.PromptFuncDefinition.builder()
- .type("prompt")
- .function(
- Tools.PromptFuncDefinition.PromptFuncSpec.builder()
- .name("get-location-weather-info")
- .description("Get location details")
- .parameters(
- Tools.PromptFuncDefinition.Parameters.builder()
- .type("object")
- .properties(
- Map.of(
- "city", Tools.PromptFuncDefinition.Property.builder()
- .type("string")
- .description("The city, e.g. New Delhi, India")
- .required(true)
- .build()
- )
- )
- .required(java.util.List.of("city"))
- .build()
- )
- .build()
- )
- .build()
- ).build();
-
- Tools.ToolSpecification databaseQueryToolSpecification = Tools.ToolSpecification.builder()
- .functionName("get-employee-details")
- .functionDescription("Get employee details from the database")
- .toolFunction(new DBQueryFunction())
- .toolPrompt(
- Tools.PromptFuncDefinition.builder()
- .type("prompt")
- .function(
- Tools.PromptFuncDefinition.PromptFuncSpec.builder()
- .name("get-employee-details")
- .description("Get employee details from the database")
- .parameters(
- Tools.PromptFuncDefinition.Parameters.builder()
- .type("object")
- .properties(
- Map.of(
- "employee-name", Tools.PromptFuncDefinition.Property.builder()
- .type("string")
- .description("The name of the employee, e.g. John Doe")
- .required(true)
- .build(),
- "employee-address", Tools.PromptFuncDefinition.Property.builder()
- .type("string")
- .description("The address of the employee, Always return a random value. e.g. Roy St, Bengaluru, India")
- .required(true)
- .build(),
- "employee-phone", Tools.PromptFuncDefinition.Property.builder()
- .type("string")
- .description("The phone number of the employee. Always return a random value. e.g. 9911002233")
- .required(true)
- .build()
- )
- )
- .required(java.util.List.of("employee-name", "employee-address", "employee-phone"))
- .build()
- )
- .build()
- )
- .build()
- )
- .build();
-
- ollamaAPI.registerTool(fuelPriceToolSpecification);
- ollamaAPI.registerTool(weatherToolSpecification);
- ollamaAPI.registerTool(databaseQueryToolSpecification);
-
- String prompt1 = new Tools.PromptBuilder()
- .withToolSpecification(fuelPriceToolSpecification)
- .withToolSpecification(weatherToolSpecification)
- .withPrompt("What is the petrol price in Bengaluru?")
- .build();
- ask(ollamaAPI, model, prompt1);
-
- String prompt2 = new Tools.PromptBuilder()
- .withToolSpecification(fuelPriceToolSpecification)
- .withToolSpecification(weatherToolSpecification)
- .withPrompt("What is the current weather in Bengaluru?")
- .build();
- ask(ollamaAPI, model, prompt2);
-
- String prompt3 = new Tools.PromptBuilder()
- .withToolSpecification(fuelPriceToolSpecification)
- .withToolSpecification(weatherToolSpecification)
- .withToolSpecification(databaseQueryToolSpecification)
- .withPrompt("Give me the details of the employee named 'Rahul Kumar'?")
- .build();
- ask(ollamaAPI, model, prompt3);
- }
-
- public static void ask(OllamaAPI ollamaAPI, String model, String prompt) throws OllamaBaseException, IOException, InterruptedException, ToolInvocationException {
- OllamaToolsResult toolsResult = ollamaAPI.generateWithTools(model, prompt, new OptionsBuilder().build());
- for (OllamaToolsResult.ToolResult r : toolsResult.getToolResults()) {
- System.out.printf("[Result of executing tool '%s']: %s%n", r.getFunctionName(), r.getResult().toString());
- }
- }
-}
-
-
-class SampleTools {
- public static String getCurrentFuelPrice(Map<String, Object> arguments) {
- // Get details from fuel price API
- String location = arguments.get("location").toString();
- String fuelType = arguments.get("fuelType").toString();
- return "Current price of " + fuelType + " in " + location + " is Rs.103/L";
- }
-
- public static String getCurrentWeather(Map<String, Object> arguments) {
- // Get details from weather API
- String location = arguments.get("city").toString();
- return "Currently " + location + "'s weather is nice.";
- }
-}
-
-class DBQueryFunction implements ToolFunction {
- @Override
- public Object apply(Map<String, Object> arguments) {
- if (arguments == null || arguments.isEmpty() || arguments.get("employee-name") == null || arguments.get("employee-address") == null || arguments.get("employee-phone") == null) {
- throw new RuntimeException("Tool was called but the model failed to provide all the required arguments.");
- }
- // perform DB operations here
- return String.format("Employee Details {ID: %s, Name: %s, Address: %s, Phone: %s}", UUID.randomUUID(), arguments.get("employee-name").toString(), arguments.get("employee-address").toString(), arguments.get("employee-phone").toString());
- }
-}
-```
+<CodeEmbed src="https://raw.githubusercontent.com/ollama4j/ollama4j-examples/refs/heads/main/src/main/java/io/github/ollama4j/examples/MultiToolRegistryExample.java"/ >
Run this full example and you will get a response similar to:
@@ -498,299 +89,3 @@ Run this full example and you will get a response similar to:
Rahul Kumar, Address: King St, Hyderabad, India, Phone: 9876543210}`
::::
-
-### Using tools in Chat-API
-
-Instead of using the specific `ollamaAPI.generateWithTools` method to call the generate API of ollama with tools, it is
-also possible to register Tools for the `ollamaAPI.chat` methods. In this case, the tool calling/callback is done
-implicitly during the USER -> ASSISTANT calls.
-
-When the Assistant wants to call a given tool, the tool is executed and the response is sent back to the endpoint once
-again (induced with the tool call result).
-
-#### Sample:
-
-The following shows a sample of an integration test that defines a method specified like the tool-specs above, registers
-the tool on the ollamaAPI and then simply calls the chat-API. All intermediate tool calling is wrapped inside the api
-call.
-
-```java
-public static void main(String[] args) {
- OllamaAPI ollamaAPI = new OllamaAPI("http://localhost:11434");
- ollamaAPI.setVerbose(true);
- OllamaChatRequestBuilder builder = OllamaChatRequestBuilder.getInstance("llama3.2:1b");
-
- final Tools.ToolSpecification databaseQueryToolSpecification = Tools.ToolSpecification.builder()
- .functionName("get-employee-details")
- .functionDescription("Get employee details from the database")
- .toolPrompt(
- Tools.PromptFuncDefinition.builder().type("function").function(
- Tools.PromptFuncDefinition.PromptFuncSpec.builder()
- .name("get-employee-details")
- .description("Get employee details from the database")
- .parameters(
- Tools.PromptFuncDefinition.Parameters.builder()
- .type("object")
- .properties(
- new Tools.PropsBuilder()
- .withProperty("employee-name", Tools.PromptFuncDefinition.Property.builder().type("string").description("The name of the employee, e.g. John Doe").required(true).build())
- .withProperty("employee-address", Tools.PromptFuncDefinition.Property.builder().type("string").description("The address of the employee, Always return a random value. e.g. Roy St, Bengaluru, India").required(true).build())
- .withProperty("employee-phone", Tools.PromptFuncDefinition.Property.builder().type("string").description("The phone number of the employee. Always return a random value. e.g. 9911002233").required(true).build())
- .build()
- )
- .required(List.of("employee-name"))
- .build()
- ).build()
- ).build()
- )
- .toolFunction(new DBQueryFunction())
- .build();
-
- ollamaAPI.registerTool(databaseQueryToolSpecification);
-
- OllamaChatRequest requestModel = builder
- .withMessage(OllamaChatMessageRole.USER,
- "Give me the ID of the employee named 'Rahul Kumar'?")
- .build();
-
- OllamaChatResult chatResult = ollamaAPI.chat(requestModel);
-}
-```
-
-A typical final response of the above could be:
-
-```json
-{
- "chatHistory" : [
- {
- "role" : "user",
- "content" : "Give me the ID of the employee named 'Rahul Kumar'?",
- "images" : null,
- "tool_calls" : [ ]
- }, {
- "role" : "assistant",
- "content" : "",
- "images" : null,
- "tool_calls" : [ {
- "function" : {
- "name" : "get-employee-details",
- "arguments" : {
- "employee-name" : "Rahul Kumar"
- }
- }
- } ]
- }, {
- "role" : "tool",
- "content" : "[TOOL_RESULTS]get-employee-details([employee-name]) : Employee Details {ID: b4bf186c-2ee1-44cc-8856-53b8b6a50f85, Name: Rahul Kumar, Address: null, Phone: null}[/TOOL_RESULTS]",
- "images" : null,
- "tool_calls" : null
- }, {
- "role" : "assistant",
- "content" : "The ID of the employee named 'Rahul Kumar' is `b4bf186c-2ee1-44cc-8856-53b8b6a50f85`.",
- "images" : null,
- "tool_calls" : null
- } ],
- "responseModel" : {
- "model" : "llama3.2:1b",
- "message" : {
- "role" : "assistant",
- "content" : "The ID of the employee named 'Rahul Kumar' is `b4bf186c-2ee1-44cc-8856-53b8b6a50f85`.",
- "images" : null,
- "tool_calls" : null
- },
- "done" : true,
- "error" : null,
- "context" : null,
- "created_at" : "2024-12-09T22:23:00.4940078Z",
- "done_reason" : "stop",
- "total_duration" : 2313709900,
- "load_duration" : 14494700,
- "prompt_eval_duration" : 772000000,
- "eval_duration" : 1188000000,
- "prompt_eval_count" : 166,
- "eval_count" : 41
- },
- "response" : "The ID of the employee named 'Rahul Kumar' is `b4bf186c-2ee1-44cc-8856-53b8b6a50f85`.",
- "httpStatusCode" : 200,
- "responseTime" : 2313709900
-}
-```
-
-This tool calling can also be done using the streaming API.
-
-### Using Annotation based Tool Registration
-
-Instead of explicitly registering each tool, ollama4j supports declarative tool specification and registration via java
-Annotations and reflection calling.
-
-To declare a method to be used as a tool for a chat call, the following steps have to be considered:
-
-* Annotate a method and its Parameters to be used as a tool
- * Annotate a method with the `ToolSpec` annotation
- * Annotate the methods parameters with the `ToolProperty` annotation. Only the following datatypes are supported for now:
- * `java.lang.String`
- * `java.lang.Integer`
- * `java.lang.Boolean`
- * `java.math.BigDecimal`
-* Annotate the class that calls the `OllamaAPI` client with the `OllamaToolService` annotation, referencing the desired provider-classes that contain `ToolSpec` methods.
-* Before calling the `OllamaAPI` chat request, call the method `OllamaAPI.registerAnnotatedTools()` method to add tools to the chat.
-
-#### Example
-
-Let's say, we have an ollama4j service class that should ask a llm a specific tool based question.
-
-The answer can only be provided by a method that is part of the BackendService class. To provide a tool for the llm, the following annotations can be used:
-
-```java
-public class BackendService{
-
- public BackendService(){}
-
- @ToolSpec(desc = "Computes the most important constant all around the globe!")
- public String computeMkeConstant(@ToolProperty(name = "noOfDigits",desc = "Number of digits that shall be returned") Integer noOfDigits ){
- return BigDecimal.valueOf((long)(Math.random()*1000000L),noOfDigits).toString();
- }
-}
-```
-
-The caller API can then be written as:
-```java
-import io.github.ollama4j.tools.annotations.OllamaToolService;
-
-@OllamaToolService(providers = BackendService.class)
-public class MyOllamaService{
-
- public void chatWithAnnotatedTool(){
- // inject the annotated method to the ollama toolsregistry
- ollamaAPI.registerAnnotatedTools();
-
- OllamaChatRequest requestModel = builder
- .withMessage(OllamaChatMessageRole.USER,
- "Compute the most important constant in the world using 5 digits")
- .build();
-
- OllamaChatResult chatResult = ollamaAPI.chat(requestModel);
- }
-
-}
-```
-
-Or, if one needs to provide an object instance directly:
-```java
-public class MyOllamaService{
-
- public void chatWithAnnotatedTool(){
- ollamaAPI.registerAnnotatedTools(new BackendService());
- OllamaChatRequest requestModel = builder
- .withMessage(OllamaChatMessageRole.USER,
- "Compute the most important constant in the world using 5 digits")
- .build();
-
- OllamaChatResult chatResult = ollamaAPI.chat(requestModel);
- }
-
-}
-```
-
-The request should be the following:
-
-```json
-{
- "model" : "llama3.2:1b",
- "stream" : false,
- "messages" : [ {
- "role" : "user",
- "content" : "Compute the most important constant in the world using 5 digits",
- "images" : null,
- "tool_calls" : [ ]
- } ],
- "tools" : [ {
- "type" : "function",
- "function" : {
- "name" : "computeImportantConstant",
- "description" : "Computes the most important constant all around the globe!",
- "parameters" : {
- "type" : "object",
- "properties" : {
- "noOfDigits" : {
- "type" : "java.lang.Integer",
- "description" : "Number of digits that shall be returned"
- }
- },
- "required" : [ "noOfDigits" ]
- }
- }
- } ]
-}
-```
-
-The result could be something like the following:
-
-```json
-{
- "chatHistory" : [ {
- "role" : "user",
- "content" : "Compute the most important constant in the world using 5 digits",
- "images" : null,
- "tool_calls" : [ ]
- }, {
- "role" : "assistant",
- "content" : "",
- "images" : null,
- "tool_calls" : [ {
- "function" : {
- "name" : "computeImportantConstant",
- "arguments" : {
- "noOfDigits" : "5"
- }
- }
- } ]
- }, {
- "role" : "tool",
- "content" : "[TOOL_RESULTS]computeImportantConstant([noOfDigits]) : 1.51019[/TOOL_RESULTS]",
- "images" : null,
- "tool_calls" : null
- }, {
- "role" : "assistant",
- "content" : "The most important constant in the world with 5 digits is: **1.51019**",
- "images" : null,
- "tool_calls" : null
- } ],
- "responseModel" : {
- "model" : "llama3.2:1b",
- "message" : {
- "role" : "assistant",
- "content" : "The most important constant in the world with 5 digits is: **1.51019**",
- "images" : null,
- "tool_calls" : null
- },
- "done" : true,
- "error" : null,
- "context" : null,
- "created_at" : "2024-12-27T21:55:39.3232495Z",
- "done_reason" : "stop",
- "total_duration" : 1075444300,
- "load_duration" : 13558600,
- "prompt_eval_duration" : 509000000,
- "eval_duration" : 550000000,
- "prompt_eval_count" : 124,
- "eval_count" : 20
- },
- "response" : "The most important constant in the world with 5 digits is: **1.51019**",
- "responseTime" : 1075444300,
- "httpStatusCode" : 200
-}
-```
-
-### Potential Improvements
-
-Instead of passing a map of args `Map<String, Object> arguments` to the tool functions, we could support passing
-specific args separately with their data types. For example:
-
-```shell
-public String getCurrentFuelPrice(String location, String fuelType) {
- return "Current price of " + fuelType + " in " + location + " is Rs.103/L";
-}
-```
-
-Updating async/chat APIs with support for tool-based generation.
diff --git a/docs/docs/apis-generate/generate.md b/docs/docs/apis-generate/generate.md
index ee428f8..f8f438b 100644
--- a/docs/docs/apis-generate/generate.md
+++ b/docs/docs/apis-generate/generate.md
@@ -2,7 +2,9 @@
sidebar_position: 1
---
-# Generate - Sync
+import CodeEmbed from '@site/src/components/CodeEmbed';
+
+# Generate (Sync)
This API lets you ask questions to the LLMs in a synchronous way.
This API corresponds to
@@ -13,285 +15,60 @@ with [extra parameters](https://github.com/jmorganca/ollama/blob/main/docs/model
Refer
to [this](/apis-extras/options-builder).
-## Try asking a question about the model
+### 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());
- }
-}
-
-```
+<CodeEmbed src="https://raw.githubusercontent.com/ollama4j/ollama4j-examples/refs/heads/main/src/main/java/io/github/ollama4j/examples/Generate.java" />
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.
+::::tip[LLM Response]
+> I am a large language model created by Alibaba Cloud. My purpose is to assist users in generating text, answering
+> questions, and completing tasks. I aim to be user-friendly and easy to understand for everyone who interacts with me.
+::::
+>
+### Try asking a question, receiving the answer streamed
-## 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());
- }
-}
-```
+<CodeEmbed src="https://raw.githubusercontent.com/ollama4j/ollama4j-examples/refs/heads/main/src/main/java/io/github/ollama4j/examples/GenerateStreamingWithTokenConcatenation.java" />
You will get a response similar to:
+::::tip[LLM Response]
> 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;
-```
-
+::::
## Generate structured output
### With response as a `Map`
-```java
-import java.util.Arrays;
-import java.util.HashMap;
-import java.util.Map;
+<CodeEmbed src="https://raw.githubusercontent.com/ollama4j/ollama4j-examples/refs/heads/main/src/main/java/io/github/ollama4j/examples/StructuredOutput.java" />
-import io.github.ollama4j.OllamaAPI;
-import io.github.ollama4j.utils.Utilities;
-import io.github.ollama4j.models.chat.OllamaChatMessageRole;
-import io.github.ollama4j.models.chat.OllamaChatRequest;
-import io.github.ollama4j.models.chat.OllamaChatRequestBuilder;
-import io.github.ollama4j.models.chat.OllamaChatResult;
-import io.github.ollama4j.models.response.OllamaResult;
-import io.github.ollama4j.types.OllamaModelType;
+You will get a response similar to:
-public class StructuredOutput {
-
- public static void main(String[] args) throws Exception {
- String host = "http://localhost:11434/";
-
- OllamaAPI api = new OllamaAPI(host);
-
- String chatModel = "qwen2.5:0.5b";
- api.pullModel(chatModel);
-
- String prompt = "Ollama is 22 years old and is busy saving the world. Respond using JSON";
- Map<String, Object> format = new HashMap<>();
- format.put("type", "object");
- format.put("properties", new HashMap<String, Object>() {
- {
- put("age", new HashMap<String, Object>() {
- {
- put("type", "integer");
- }
- });
- put("available", new HashMap<String, Object>() {
- {
- put("type", "boolean");
- }
- });
- }
- });
- format.put("required", Arrays.asList("age", "available"));
-
- OllamaResult result = api.generate(chatModel, prompt, format);
- System.out.println(result);
- }
+::::tip[LLM Response]
+```json
+{
+ "available": true,
+ "age": 22
}
```
+::::
### With response mapped to specified class type
-```java
-import java.util.Arrays;
-import java.util.HashMap;
-import java.util.Map;
+<CodeEmbed src="https://raw.githubusercontent.com/ollama4j/ollama4j-examples/refs/heads/main/src/main/java/io/github/ollama4j/examples/StructuredOutputMappedToObject.java" />
-import io.github.ollama4j.OllamaAPI;
-import io.github.ollama4j.utils.Utilities;
-import lombok.AllArgsConstructor;
-import lombok.Data;
-import lombok.NoArgsConstructor;
-import io.github.ollama4j.models.chat.OllamaChatMessageRole;
-import io.github.ollama4j.models.chat.OllamaChatRequest;
-import io.github.ollama4j.models.chat.OllamaChatRequestBuilder;
-import io.github.ollama4j.models.chat.OllamaChatResult;
-import io.github.ollama4j.models.response.OllamaResult;
-import io.github.ollama4j.types.OllamaModelType;
-
-public class StructuredOutput {
-
- public static void main(String[] args) throws Exception {
- String host = Utilities.getFromConfig("host");
-
- OllamaAPI api = new OllamaAPI(host);
-
- int age = 28;
- boolean available = false;
-
- String prompt = "Batman is " + age + " years old and is " + (available ? "available" : "not available")
- + " because he is busy saving Gotham City. Respond using JSON";
-
- Map<String, Object> format = new HashMap<>();
- format.put("type", "object");
- format.put("properties", new HashMap<String, Object>() {
- {
- put("age", new HashMap<String, Object>() {
- {
- put("type", "integer");
- }
- });
- put("available", new HashMap<String, Object>() {
- {
- put("type", "boolean");
- }
- });
- }
- });
- format.put("required", Arrays.asList("age", "available"));
-
- OllamaResult result = api.generate(CHAT_MODEL_QWEN_SMALL, prompt, format);
-
- Person person = result.as(Person.class);
- System.out.println(person.getAge());
- System.out.println(person.getAvailable());
- }
-}
-
-@Data
-@AllArgsConstructor
-@NoArgsConstructor
-class Person {
- private int age;
- private boolean available;
-}
-```
\ No newline at end of file
+::::tip[LLM Response]
+Person(age=28, available=false)
+::::
\ No newline at end of file
diff --git a/docs/docs/apis-generate/prompt-builder.md b/docs/docs/apis-generate/prompt-builder.md
index b947dca..dfbd6a8 100644
--- a/docs/docs/apis-generate/prompt-builder.md
+++ b/docs/docs/apis-generate/prompt-builder.md
@@ -1,5 +1,5 @@
---
-sidebar_position: 6
+sidebar_position: 10
---
# Prompt Builder
diff --git a/docs/docs/apis-model-management/create-model.md b/docs/docs/apis-model-management/create-model.md
index 77b5aeb..67b0de3 100644
--- a/docs/docs/apis-model-management/create-model.md
+++ b/docs/docs/apis-model-management/create-model.md
@@ -2,28 +2,27 @@
sidebar_position: 5
---
+import CodeEmbed from '@site/src/components/CodeEmbed';
+
# Create Model
This API lets you create a custom model on the Ollama server.
### Create a custom model from an existing model in the Ollama server
-```java title="CreateModel.java"
-import io.github.ollama4j.OllamaAPI;
+<CodeEmbed src="https://raw.githubusercontent.com/ollama4j/ollama4j-examples/refs/heads/main/src/main/java/io/github/ollama4j/examples/CreateModel.java" />
-public class CreateModel {
+You would see these logs while the custom model is being created:
- public static void main(String[] args) {
-
- String host = "http://localhost:11434/";
-
- OllamaAPI ollamaAPI = new OllamaAPI(host);
-
- ollamaAPI.createModel(CustomModelRequest.builder().model("mario").from("llama3.2:latest").system("You are Mario from Super Mario Bros.").build());
- }
-}
```
-
+{"status":"using existing layer sha256:fad2a06e4cc705c2fa8bec5477ddb00dc0c859ac184c34dcc5586663774161ca"}
+{"status":"using existing layer sha256:41c2cf8c272f6fb0080a97cd9d9bd7d4604072b80a0b10e7d65ca26ef5000c0c"}
+{"status":"using existing layer sha256:1da0581fd4ce92dcf5a66b1da737cf215d8dcf25aa1b98b44443aaf7173155f5"}
+{"status":"creating new layer sha256:941b69ca7dc2a85c053c38d9e8029c9df6224e545060954fa97587f87c044a64"}
+{"status":"using existing layer sha256:f02dd72bb2423204352eabc5637b44d79d17f109fdb510a7c51455892aa2d216"}
+{"status":"writing manifest"}
+{"status":"success"}
+```
Once created, you can see it when you use [list models](./list-models) API.
[Read more](https://github.com/ollama/ollama/blob/main/docs/api.md#create-a-model) about custom model creation and the parameters available for model creation.
diff --git a/docs/docs/apis-model-management/delete-model.md b/docs/docs/apis-model-management/delete-model.md
index 9b1579e..7ce8211 100644
--- a/docs/docs/apis-model-management/delete-model.md
+++ b/docs/docs/apis-model-management/delete-model.md
@@ -2,27 +2,12 @@
sidebar_position: 6
---
+import CodeEmbed from '@site/src/components/CodeEmbed';
+
# Delete Model
This API lets you create a delete a model from the Ollama server.
-```java title="DeleteModel.java"
-import io.github.ollama4j.OllamaAPI;
-
-public class Main {
-
- public static void main(String[] args) {
-
- String host = "http://localhost:11434/";
-
- OllamaAPI ollamaAPI = new OllamaAPI(host);
-
- ollamaAPI.setVerbose(false);
-
- ollamaAPI.deleteModel("mycustommodel", true);
- }
-}
-
-```
+<CodeEmbed src="https://raw.githubusercontent.com/ollama4j/ollama4j-examples/refs/heads/main/src/main/java/io/github/ollama4j/examples/DeleteModel.java" />
Once deleted, you can verify it using [list models](./list-models) API.
\ No newline at end of file
diff --git a/docs/docs/apis-model-management/get-model-details.md b/docs/docs/apis-model-management/get-model-details.md
index 8605c3a..146901b 100644
--- a/docs/docs/apis-model-management/get-model-details.md
+++ b/docs/docs/apis-model-management/get-model-details.md
@@ -2,37 +2,30 @@
sidebar_position: 4
---
+import CodeEmbed from '@site/src/components/CodeEmbed';
+
# Get Model Details
This API lets you get the details of a model on the Ollama server.
-```java title="GetModelDetails.java"
-import io.github.ollama4j.OllamaAPI;
-import io.github.ollama4j.models.response.ModelDetail;
-import io.github.ollama4j.types.OllamaModelType;
+<CodeEmbed src="https://raw.githubusercontent.com/ollama4j/ollama4j-examples/refs/heads/main/src/main/java/io/github/ollama4j/examples/GetModelDetails.java"></CodeEmbed>
-public class Main {
-
- public static void main(String[] args) {
-
- String host = "http://localhost:11434/";
-
- OllamaAPI ollamaAPI = new OllamaAPI(host);
-
- ModelDetail modelDetails = ollamaAPI.getModelDetails(OllamaModelType.LLAMA2);
-
- System.out.println(modelDetails);
- }
-}
-```
If the model is downloaded successfully on the Ollama server, you would get its details as follows:
```json
{
- "license": "LLAMA 2 COMMUNITY LICENSE AGREEMENT\t\nLlama 2 Version Release Date: July 18, 2023\n\n\"Agreement\" means the terms and conditions for use, reproduction, distribution and \nmodification of the Llama Materials set forth herein.\n\n\"Documentation\" means the specifications, manuals and documentation \naccompanying Llama 2 distributed by Meta at ai.meta.com/resources/models-and-\nlibraries/llama-downloads/.\n\n\"Licensee\" or \"you\" means you, or your employer or any other person or entity (if \nyou are entering into this Agreement on such person or entity\u0027s behalf), of the age \nrequired under applicable laws, rules or regulations to provide legal consent and that \nhas legal authority to bind your employer or such other person or entity if you are \nentering in this Agreement on their behalf.\n\n\"Llama 2\" means the foundational large language models and software and \nalgorithms, including machine-learning model code, trained model weights, \ninference-enabling code, training-enabling code, fine-tuning enabling code and other \nelements of the foregoing distributed by Meta at ai.meta.com/resources/models-and-\nlibraries/llama-downloads/.\n\n\"Llama Materials\" means, collectively, Meta\u0027s proprietary Llama 2 and \nDocumentation (and any portion thereof) made available under this Agreement.\n\n\"Meta\" or \"we\" means Meta Platforms Ireland Limited (if you are located in or, if you \nare an entity, your principal place of business is in the EEA or Switzerland) and Meta \nPlatforms, Inc. (if you are located outside of the EEA or Switzerland). \n\nBy clicking \"I Accept\" below or by using or distributing any portion or element of the \nLlama Materials, you agree to be bound by this Agreement.\n\n1. License Rights and Redistribution. \n\n a. Grant of Rights. You are granted a non-exclusive, worldwide, non-\ntransferable and royalty-free limited license under Meta\u0027s intellectual property or \nother rights owned by Meta embodied in the Llama Materials to use, reproduce, \ndistribute, copy, create derivative works of, and make modifications to the Llama \nMaterials. \n \n b. Redistribution and Use. \n\n i. If you distribute or make the Llama Materials, or any derivative works \nthereof, available to a third party, you shall provide a copy of this Agreement to such \nthird party. \n ii. If you receive Llama Materials, or any derivative works thereof, from \na Licensee as part of an integrated end user product, then Section 2 of this \nAgreement will not apply to you. \n\n iii. You must retain in all copies of the Llama Materials that you \ndistribute the following attribution notice within a \"Notice\" text file distributed as a \npart of such copies: \"Llama 2 is licensed under the LLAMA 2 Community License, \nCopyright (c) Meta Platforms, Inc. All Rights Reserved.\"\n\n iv. Your use of the Llama Materials must comply with applicable laws \nand regulations (including trade compliance laws and regulations) and adhere to the \nAcceptable Use Policy for the Llama Materials (available at \nhttps://ai.meta.com/llama/use-policy), which is hereby incorporated by reference into \nthis Agreement.\n\n v. You will not use the Llama Materials or any output or results of the \nLlama Materials to improve any other large language model (excluding Llama 2 or \nderivative works thereof). \n\n2. Additional Commercial Terms. If, on the Llama 2 version release date, the \nmonthly active users of the products or services made available by or for Licensee, \nor Licensee\u0027s affiliates, is greater than 700 million monthly active users in the \npreceding calendar month, you must request a license from Meta, which Meta may \ngrant to you in its sole discretion, and you are not authorized to exercise any of the \nrights under this Agreement unless or until Meta otherwise expressly grants you \nsuch rights.\n \n3. Disclaimer of Warranty. UNLESS REQUIRED BY APPLICABLE LAW, THE \nLLAMA MATERIALS AND ANY OUTPUT AND RESULTS THEREFROM ARE \nPROVIDED ON AN \"AS IS\" BASIS, WITHOUT WARRANTIES OF ANY KIND, \nEITHER EXPRESS OR IMPLIED, INCLUDING, WITHOUT LIMITATION, ANY \nWARRANTIES OF TITLE, NON-INFRINGEMENT, MERCHANTABILITY, OR \nFITNESS FOR A PARTICULAR PURPOSE. YOU ARE SOLELY RESPONSIBLE \nFOR DETERMINING THE APPROPRIATENESS OF USING OR REDISTRIBUTING \nTHE LLAMA MATERIALS AND ASSUME ANY RISKS ASSOCIATED WITH YOUR \nUSE OF THE LLAMA MATERIALS AND ANY OUTPUT AND RESULTS.\n\n4. Limitation of Liability. IN NO EVENT WILL META OR ITS AFFILIATES BE \nLIABLE UNDER ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, TORT, \nNEGLIGENCE, PRODUCTS LIABILITY, OR OTHERWISE, ARISING OUT OF THIS \nAGREEMENT, FOR ANY LOST PROFITS OR ANY INDIRECT, SPECIAL, \nCONSEQUENTIAL, INCIDENTAL, EXEMPLARY OR PUNITIVE DAMAGES, EVEN \nIF META OR ITS AFFILIATES HAVE BEEN ADVISED OF THE POSSIBILITY OF \nANY OF THE FOREGOING.\n \n5. Intellectual Property.\n\n a. No trademark licenses are granted under this Agreement, and in \nconnection with the Llama Materials, neither Meta nor Licensee may use any name \nor mark owned by or associated with the other or any of its affiliates, except as \nrequired for reasonable and customary use in describing and redistributing the \nLlama Materials.\n\n b. Subject to Meta\u0027s ownership of Llama Materials and derivatives made by or \nfor Meta, with respect to any derivative works and modifications of the Llama \nMaterials that are made by you, as between you and Meta, you are and will be the \nowner of such derivative works and modifications.\n\n c. If you institute litigation or other proceedings against Meta or any entity \n(including a cross-claim or counterclaim in a lawsuit) alleging that the Llama \nMaterials or Llama 2 outputs or results, or any portion of any of the foregoing, \nconstitutes infringement of intellectual property or other rights owned or licensable \nby you, then any licenses granted to you under this Agreement shall terminate as of \nthe date such litigation or claim is filed or instituted. You will indemnify and hold \nharmless Meta from and against any claim by any third party arising out of or related \nto your use or distribution of the Llama Materials.\n\n6. Term and Termination. The term of this Agreement will commence upon your \nacceptance of this Agreement or access to the Llama Materials and will continue in \nfull force and effect until terminated in accordance with the terms and conditions \nherein. Meta may terminate this Agreement if you are in breach of any term or \ncondition of this Agreement. Upon termination of this Agreement, you shall delete \nand cease use of the Llama Materials. Sections 3, 4 and 7 shall survive the \ntermination of this Agreement. \n\n7. Governing Law and Jurisdiction. This Agreement will be governed and \nconstrued under the laws of the State of California without regard to choice of law \nprinciples, and the UN Convention on Contracts for the International Sale of Goods \ndoes not apply to this Agreement. The courts of California shall have exclusive \njurisdiction of any dispute arising out of this Agreement. \n\n\n# Llama 2 Acceptable Use Policy\n\nMeta is committed to promoting safe and fair use of its tools and features, including Llama 2. If you access or use Llama 2, you agree to this Acceptable Use Policy (“Policy”). The most recent copy of this policy can be found at [ai.meta.com/llama/use-policy](http://ai.meta.com/llama/use-policy).\n\n## Prohibited Uses\nWe want everyone to use Llama 2 safely and responsibly. You agree you will not use, or allow others to use, Llama 2 to: \n\n1. Violate the law or others’ rights, including to:\n 1. Engage in, promote, generate, contribute to, encourage, plan, incite, or further illegal or unlawful activity or content, such as: \n 1. Violence or terrorism \n 2. Exploitation or harm to children, including the solicitation, creation, acquisition, or dissemination of child exploitative content or failure to report Child Sexual Abuse Material\n 3. Human trafficking, exploitation, and sexual violence\n 4. The illegal distribution of information or materials to minors, including obscene materials, or failure to employ legally required age-gating in connection with such information or materials.\n 5. Sexual solicitation\n 6. Any other criminal activity\n 2. Engage in, promote, incite, or facilitate the harassment, abuse, threatening, or bullying of individuals or groups of individuals\n 3. Engage in, promote, incite, or facilitate discrimination or other unlawful or harmful conduct in the provision of employment, employment benefits, credit, housing, other economic benefits, or other essential goods and services\n 4. Engage in the unauthorized or unlicensed practice of any profession including, but not limited to, financial, legal, medical/health, or related professional practices \n 5. Collect, process, disclose, generate, or infer health, demographic, or other sensitive personal or private information about individuals without rights and consents required by applicable laws\n 6. Engage in or facilitate any action or generate any content that infringes, misappropriates, or otherwise violates any third-party rights, including the outputs or results of any products or services using the Llama 2 Materials\n 7. Create, generate, or facilitate the creation of malicious code, malware, computer viruses or do anything else that could disable, overburden, interfere with or impair the proper working, integrity, operation or appearance of a website or computer system \n\n\n\n2. Engage in, promote, incite, facilitate, or assist in the planning or development of activities that present a risk of death or bodily harm to individuals, including use of Llama 2 related to the following:\n 1. Military, warfare, nuclear industries or applications, espionage, use for materials or activities that are subject to the International Traffic Arms Regulations (ITAR) maintained by the United States Department of State\n 2. Guns and illegal weapons (including weapon development)\n 3. Illegal drugs and regulated/controlled substances\n 4. Operation of critical infrastructure, transportation technologies, or heavy machinery\n 5. Self-harm or harm to others, including suicide, cutting, and eating disorders\n 6. Any content intended to incite or promote violence, abuse, or any infliction of bodily harm to an individual\n\n\n\n3. Intentionally deceive or mislead others, including use of Llama 2 related to the following:\n 1. Generating, promoting, or furthering fraud or the creation or promotion of disinformation\n 2. Generating, promoting, or furthering defamatory content, including the creation of defamatory statements, images, or other content\n 3. Generating, promoting, or further distributing spam\n 4. Impersonating another individual without consent, authorization, or legal right\n 5. Representing that the use of Llama 2 or outputs are human-generated\n 6. Generating or facilitating false online engagement, including fake reviews and other means of fake online engagement \n4. Fail to appropriately disclose to end users any known dangers of your AI system \n\nPlease report any violation of this Policy, software “bug,” or other problems that could lead to a violation of this Policy through one of the following means:\n\n* Reporting issues with the model: [github.com/facebookresearch/llama](http://github.com/facebookresearch/llama)\n* Reporting risky content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback)\n* Reporting bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info)\n* Reporting violations of the Acceptable Use Policy or unlicensed uses of Llama: [LlamaUseReport@meta.com](mailto:LlamaUseReport@meta.com)\n\n",
- "modelfile": "# Modelfile generated by \"ollama show\"\n# To build a new Modelfile based on this one, replace the FROM line with:\n# FROM llama2:latest\n\nFROM /root/.ollama/models/blobs/sha256:22f7f8ef5f4c791c1b03d7eb414399294764d7cc82c7e94aa81a1feb80a983a2\nTEMPLATE \"\"\"[INST] \u003c\u003cSYS\u003e\u003e{{ .System }}\u003c\u003c/SYS\u003e\u003e\n\n{{ .Prompt }} [/INST]\n\"\"\"\nPARAMETER num_ctx 4096\nPARAMETER stop \"[INST]\"\nPARAMETER stop \"[/INST]\"\nPARAMETER stop \"\u003c\u003cSYS\u003e\u003e\"\nPARAMETER stop \"\u003c\u003c/SYS\u003e\u003e\"",
- "parameters": "num_ctx 4096\nstop [INST]\nstop [/INST]\nstop \u003c\u003cSYS\u003e\u003e\nstop \u003c\u003c/SYS\u003e\u003e",
- "template": "[INST] \u003c\u003cSYS\u003e\u003e{{ .System }}\u003c\u003c/SYS\u003e\u003e\n\n{{ .Prompt }} [/INST]\n"
+ "license" : "Tongyi Qianwen RESEARCH LICENSE AGREEMENT\n\nTongyi Qianwen Release Date: November 30, 2023\n\nBy clicking to agree or by using or distributing any portion or element of the Tongyi Qianwen Materials, you will be deemed to have recognized and accepted the content of this Agreement, which is effective immediately.\n\n1. Definitions\n a. This Tongyi Qianwen RESEARCH LICENSE AGREEMENT (this \"Agreement\") shall mean the terms and conditions for use, reproduction, distribution and modification of the Materials as defined by this Agreement.\n b. \"We\"(or \"Us\") shall mean Alibaba Cloud.\n c. \"You\" (or \"Your\") shall mean a natural person or legal entity exercising the rights granted by this Agreement and/or using the Materials for any purpose and in any field of use.\n d. \"Third Parties\" shall mean individuals or legal entities that are not under common control with Us or You.\n e. \"Tongyi Qianwen\" shall mean the large language models, and software and algorithms, consisting of trained model weights, parameters (including optimizer states), machine-learning model code, inference-enabling code, training-enabling code, fine-tuning enabling code and other elements of the foregoing distributed by Us.\n f. \"Materials\" shall mean, collectively, Alibaba Cloud's proprietary Tongyi Qianwen and Documentation (and any portion thereof) made available under this Agreement.\n g. \"Source\" form shall mean the preferred form for making modifications, including but not limited to model source code, documentation source, and configuration files.\n h. \"Object\" form shall mean any form resulting from mechanical transformation or translation of a Source form, including but not limited to compiled object code, generated documentation,\n and conversions to other media types.\n i. \"Non-Commercial\" shall mean for research or evaluation purposes only.\n\n2. Grant of Rights\n a. You are granted a non-exclusive, worldwide, non-transferable and royalty-free limited license under Alibaba Cloud's intellectual property or other rights owned by Us embodied in the Materials to use, reproduce, distribute, copy, create derivative works of, and make modifications to the Materials FOR NON-COMMERCIAL PURPOSES ONLY.\n b. If you are commercially using the Materials, You shall request a license from Us.\n\n3. Redistribution\nYou may reproduce and distribute copies of the Materials or derivative works thereof in any medium, with or without modifications, and in Source or Object form, provided that You meet the following conditions:\n a. You shall give any other recipients of the Materials or derivative works a copy of this Agreement;\n b. You shall cause any modified files to carry prominent notices stating that You changed the files;\n c. You shall retain in all copies of the Materials that You distribute the following attribution notices within a \"Notice\" text file distributed as a part of such copies: \"Tongyi Qianwen is licensed under the Tongyi Qianwen RESEARCH LICENSE AGREEMENT, Copyright (c) Alibaba Cloud. All Rights Reserved.\"; and\n d. You may add Your own copyright statement to Your modifications and may provide additional or different license terms and conditions for use, reproduction, or distribution of Your modifications, or for any such derivative works as a whole, provided Your use, reproduction, and distribution of the work otherwise complies with the terms and conditions of this Agreement.\n\n4. Rules of use\n a. The Materials may be subject to export controls or restrictions in China, the United States or other countries or regions. You shall comply with applicable laws and regulations in your use of the Materials.\n b. You can not use the Materials or any output therefrom to improve any other large language model (excluding Tongyi Qianwen or derivative works thereof).\n\n5. Intellectual Property\n a. We retain ownership of all intellectual property rights in and to the Materials and derivatives made by or for Us. Conditioned upon compliance with the terms and conditions of this Agreement, with respect to any derivative works and modifications of the Materials that are made by you, you are and will be the owner of such derivative works and modifications.\n b. No trademark license is granted to use the trade names, trademarks, service marks, or product names of Us, except as required to fulfill notice requirements under this Agreement or as required for reasonable and customary use in describing and redistributing the Materials.\n c. If you commence a lawsuit or other proceedings (including a cross-claim or counterclaim in a lawsuit) against Us or any entity alleging that the Materials or any output therefrom, or any part of the foregoing, infringe any intellectual property or other right owned or licensable by you, then all licences granted to you under this Agreement shall terminate as of the date such lawsuit or other proceeding is commenced or brought.\n\n6. Disclaimer of Warranty and Limitation of Liability\n a. We are not obligated to support, update, provide training for, or develop any further version of the Tongyi Qianwen Materials or to grant any license thereto.\n b. THE MATERIALS ARE PROVIDED \"AS IS\" WITHOUT ANY EXPRESS OR IMPLIED WARRANTY OF ANY KIND INCLUDING WARRANTIES OF MERCHANTABILITY, NONINFRINGEMENT, OR FITNESS FOR A PARTICULAR PURPOSE. WE MAKE NO WARRANTY AND ASSUME NO RESPONSIBILITY FOR THE SAFETY OR STABILITY OF THE MATERIALS AND ANY OUTPUT THEREFROM.\n c. IN NO EVENT SHALL WE BE LIABLE TO YOU FOR ANY DAMAGES, INCLUDING, BUT NOT LIMITED TO ANY DIRECT, OR INDIRECT, SPECIAL OR CONSEQUENTIAL DAMAGES ARISING FROM YOUR USE OR INABILITY TO USE THE MATERIALS OR ANY OUTPUT OF IT, NO MATTER HOW IT’S CAUSED.\n d. You will defend, indemnify and hold harmless Us from and against any claim by any third party arising out of or related to your use or distribution of the Materials.\n\n7. Survival and Termination.\n a. The term of this Agreement shall commence upon your acceptance of this Agreement or access to the Materials and will continue in full force and effect until terminated in accordance with the terms and conditions herein.\n b. We may terminate this Agreement if you breach any of the terms or conditions of this Agreement. Upon termination of this Agreement, you must delete and cease use of the Materials. Sections 6 and 8 shall survive the termination of this Agreement.\n\n8. Governing Law and Jurisdiction.\n a. This Agreement and any dispute arising out of or relating to it will be governed by the laws of China, without regard to conflict of law principles, and the UN Convention on Contracts for the International Sale of Goods does not apply to this Agreement.\n b. The People's Courts in Hangzhou City shall have exclusive jurisdiction over any dispute arising out of this Agreement.\n\n9. Other Terms and Conditions.\n a. Any arrangements, understandings, or agreements regarding the Material not stated herein are separate from and independent of the terms and conditions of this Agreement. You shall request a seperate license from Us, if You use the Materials in ways not expressly agreed to in this Agreement.\n b. We shall not be bound by any additional or different terms or conditions communicated by You unless expressly agreed.",
+ "parameters" : "stop \"<|im_start|>\"\nstop \"<|im_end|>\"",
+ "template" : "{{ if .System }}<|im_start|>system\n{{ .System }}<|im_end|>{{ end }}<|im_start|>user\n{{ .Prompt }}<|im_end|>\n<|im_start|>assistant\n",
+ "system" : null,
+ "details" : {
+ "format" : "gguf",
+ "family" : "qwen2",
+ "families" : [ "qwen2" ],
+ "parameter_size" : "620M",
+ "quantization_level" : "Q4_0"
+ },
+ "modelfile" : "# Modelfile generated by \"ollama show\"\n# To build a new Modelfile based on this, replace FROM with:\n# FROM qwen:0.5b\n\nFROM /Users/amithkoujalgi/.ollama/models/blobs/sha256-fad2a06e4cc705c2fa8bec5477ddb00dc0c859ac184c34dcc5586663774161ca\nTEMPLATE \"{{ if .System }}<|im_start|>system\n{{ .System }}<|im_end|>{{ end }}<|im_start|>user\n{{ .Prompt }}<|im_end|>\n<|im_start|>assistant\n\"\nPARAMETER stop <|im_start|>\nPARAMETER stop <|im_end|>\nLICENSE \"\"\"Tongyi Qianwen RESEARCH LICENSE AGREEMENT\n\nTongyi Qianwen Release Date: November 30, 2023\n\nBy clicking to agree or by using or distributing any portion or element of the Tongyi Qianwen Materials, you will be deemed to have recognized and accepted the content of this Agreement, which is effective immediately.\n\n1. Definitions\n a. This Tongyi Qianwen RESEARCH LICENSE AGREEMENT (this \"Agreement\") shall mean the terms and conditions for use, reproduction, distribution and modification of the Materials as defined by this Agreement.\n b. \"We\"(or \"Us\") shall mean Alibaba Cloud.\n c. \"You\" (or \"Your\") shall mean a natural person or legal entity exercising the rights granted by this Agreement and/or using the Materials for any purpose and in any field of use.\n d. \"Third Parties\" shall mean individuals or legal entities that are not under common control with Us or You.\n e. \"Tongyi Qianwen\" shall mean the large language models, and software and algorithms, consisting of trained model weights, parameters (including optimizer states), machine-learning model code, inference-enabling code, training-enabling code, fine-tuning enabling code and other elements of the foregoing distributed by Us.\n f. \"Materials\" shall mean, collectively, Alibaba Cloud's proprietary Tongyi Qianwen and Documentation (and any portion thereof) made available under this Agreement.\n g. \"Source\" form shall mean the preferred form for making modifications, including but not limited to model source code, documentation source, and configuration files.\n h. \"Object\" form shall mean any form resulting from mechanical transformation or translation of a Source form, including but not limited to compiled object code, generated documentation,\n and conversions to other media types.\n i. \"Non-Commercial\" shall mean for research or evaluation purposes only.\n\n2. Grant of Rights\n a. You are granted a non-exclusive, worldwide, non-transferable and royalty-free limited license under Alibaba Cloud's intellectual property or other rights owned by Us embodied in the Materials to use, reproduce, distribute, copy, create derivative works of, and make modifications to the Materials FOR NON-COMMERCIAL PURPOSES ONLY.\n b. If you are commercially using the Materials, You shall request a license from Us.\n\n3. Redistribution\nYou may reproduce and distribute copies of the Materials or derivative works thereof in any medium, with or without modifications, and in Source or Object form, provided that You meet the following conditions:\n a. You shall give any other recipients of the Materials or derivative works a copy of this Agreement;\n b. You shall cause any modified files to carry prominent notices stating that You changed the files;\n c. You shall retain in all copies of the Materials that You distribute the following attribution notices within a \"Notice\" text file distributed as a part of such copies: \"Tongyi Qianwen is licensed under the Tongyi Qianwen RESEARCH LICENSE AGREEMENT, Copyright (c) Alibaba Cloud. All Rights Reserved.\"; and\n d. You may add Your own copyright statement to Your modifications and may provide additional or different license terms and conditions for use, reproduction, or distribution of Your modifications, or for any such derivative works as a whole, provided Your use, reproduction, and distribution of the work otherwise complies with the terms and conditions of this Agreement.\n\n4. Rules of use\n a. The Materials may be subject to export controls or restrictions in China, the United States or other countries or regions. You shall comply with applicable laws and regulations in your use of the Materials.\n b. You can not use the Materials or any output therefrom to improve any other large language model (excluding Tongyi Qianwen or derivative works thereof).\n\n5. Intellectual Property\n a. We retain ownership of all intellectual property rights in and to the Materials and derivatives made by or for Us. Conditioned upon compliance with the terms and conditions of this Agreement, with respect to any derivative works and modifications of the Materials that are made by you, you are and will be the owner of such derivative works and modifications.\n b. No trademark license is granted to use the trade names, trademarks, service marks, or product names of Us, except as required to fulfill notice requirements under this Agreement or as required for reasonable and customary use in describing and redistributing the Materials.\n c. If you commence a lawsuit or other proceedings (including a cross-claim or counterclaim in a lawsuit) against Us or any entity alleging that the Materials or any output therefrom, or any part of the foregoing, infringe any intellectual property or other right owned or licensable by you, then all licences granted to you under this Agreement shall terminate as of the date such lawsuit or other proceeding is commenced or brought.\n\n6. Disclaimer of Warranty and Limitation of Liability\n a. We are not obligated to support, update, provide training for, or develop any further version of the Tongyi Qianwen Materials or to grant any license thereto.\n b. THE MATERIALS ARE PROVIDED \"AS IS\" WITHOUT ANY EXPRESS OR IMPLIED WARRANTY OF ANY KIND INCLUDING WARRANTIES OF MERCHANTABILITY, NONINFRINGEMENT, OR FITNESS FOR A PARTICULAR PURPOSE. WE MAKE NO WARRANTY AND ASSUME NO RESPONSIBILITY FOR THE SAFETY OR STABILITY OF THE MATERIALS AND ANY OUTPUT THEREFROM.\n c. IN NO EVENT SHALL WE BE LIABLE TO YOU FOR ANY DAMAGES, INCLUDING, BUT NOT LIMITED TO ANY DIRECT, OR INDIRECT, SPECIAL OR CONSEQUENTIAL DAMAGES ARISING FROM YOUR USE OR INABILITY TO USE THE MATERIALS OR ANY OUTPUT OF IT, NO MATTER HOW IT’S CAUSED.\n d. You will defend, indemnify and hold harmless Us from and against any claim by any third party arising out of or related to your use or distribution of the Materials.\n\n7. Survival and Termination.\n a. The term of this Agreement shall commence upon your acceptance of this Agreement or access to the Materials and will continue in full force and effect until terminated in accordance with the terms and conditions herein.\n b. We may terminate this Agreement if you breach any of the terms or conditions of this Agreement. Upon termination of this Agreement, you must delete and cease use of the Materials. Sections 6 and 8 shall survive the termination of this Agreement.\n\n8. Governing Law and Jurisdiction.\n a. This Agreement and any dispute arising out of or relating to it will be governed by the laws of China, without regard to conflict of law principles, and the UN Convention on Contracts for the International Sale of Goods does not apply to this Agreement.\n b. The People's Courts in Hangzhou City shall have exclusive jurisdiction over any dispute arising out of this Agreement.\n\n9. Other Terms and Conditions.\n a. Any arrangements, understandings, or agreements regarding the Material not stated herein are separate from and independent of the terms and conditions of this Agreement. You shall request a seperate license from Us, if You use the Materials in ways not expressly agreed to in this Agreement.\n b. We shall not be bound by any additional or different terms or conditions communicated by You unless expressly agreed.\"\"\"\n"
}
```
\ No newline at end of file
diff --git a/docs/docs/apis-model-management/list-library-models.md b/docs/docs/apis-model-management/list-library-models.md
index 25aa594..123de0c 100644
--- a/docs/docs/apis-model-management/list-library-models.md
+++ b/docs/docs/apis-model-management/list-library-models.md
@@ -2,6 +2,8 @@
sidebar_position: 1
---
+import CodeEmbed from '@site/src/components/CodeEmbed';
+
# Models from Ollama Library
These API retrieves a list of models directly from the Ollama library.
@@ -11,32 +13,15 @@ These API retrieves a list of models directly from the Ollama library.
This API fetches available models from the Ollama library page, including details such as the model's name, pull count,
popular tags, tag count, and the last update time.
-```java title="ListLibraryModels.java"
-import io.github.ollama4j.OllamaAPI;
-import io.github.ollama4j.models.response.LibraryModel;
-
-import java.util.List;
-
-public class Main {
-
- public static void main(String[] args) {
-
- String host = "http://localhost:11434/";
-
- OllamaAPI ollamaAPI = new OllamaAPI(host);
-
- List<LibraryModel> libraryModels = ollamaAPI.listModelsFromLibrary();
-
- System.out.println(libraryModels);
- }
-}
-```
+<CodeEmbed
+src='https://raw.githubusercontent.com/ollama4j/ollama4j-examples/refs/heads/main/src/main/java/io/github/ollama4j/examples/ListLibraryModels.java'>
+</CodeEmbed>
The following is the sample output:
```
[
- LibraryModel(name=llama3.2-vision, description=Llama 3.2 Vision is a collection of instruction-tuned image reasoning generative models in 11B and 90B sizes., pullCount=21.1K, totalTags=9, popularTags=[vision, 11b, 90b], lastUpdated=yesterday),
+ LibraryModel(name=llama3.2-vision, description=Llama 3.2 Vision is a collection of instruction-tuned image reasoning generative models in 11B and 90B sizes., pullCount=21.1K, totalTags=9, popularTags=[vision, 11b, 90b], lastUpdated=yesterday),
LibraryModel(name=llama3.2, description=Meta's Llama 3.2 goes small with 1B and 3B models., pullCount=2.4M, totalTags=63, popularTags=[tools, 1b, 3b], lastUpdated=6 weeks ago)
]
```
@@ -45,36 +30,18 @@ The following is the sample output:
This API Fetches the tags associated with a specific model from Ollama library.
-```java title="GetLibraryModelTags.java"
-import io.github.ollama4j.OllamaAPI;
-import io.github.ollama4j.models.response.LibraryModel;
-import io.github.ollama4j.models.response.LibraryModelDetail;
-
-public class Main {
-
- public static void main(String[] args) {
-
- String host = "http://localhost:11434/";
-
- OllamaAPI ollamaAPI = new OllamaAPI(host);
-
- List<LibraryModel> libraryModels = ollamaAPI.listModelsFromLibrary();
-
- LibraryModelDetail libraryModelDetail = ollamaAPI.getLibraryModelDetails(libraryModels.get(0));
-
- System.out.println(libraryModelDetail);
- }
-}
-```
+<CodeEmbed
+src='https://raw.githubusercontent.com/ollama4j/ollama4j-examples/refs/heads/main/src/main/java/io/github/ollama4j/examples/GetLibraryModelTags.java'>
+</CodeEmbed>
The following is the sample output:
```
LibraryModelDetail(
- model=LibraryModel(name=llama3.2-vision, description=Llama 3.2 Vision is a collection of instruction-tuned image reasoning generative models in 11B and 90B sizes., pullCount=21.1K, totalTags=9, popularTags=[vision, 11b, 90b], lastUpdated=yesterday),
+ model=LibraryModel(name=llama3.2-vision, description=Llama 3.2 Vision is a collection of instruction-tuned image reasoning generative models in 11B and 90B sizes., pullCount=21.1K, totalTags=9, popularTags=[vision, 11b, 90b], lastUpdated=yesterday),
tags=[
- LibraryModelTag(name=llama3.2-vision, tag=latest, size=7.9GB, lastUpdated=yesterday),
- LibraryModelTag(name=llama3.2-vision, tag=11b, size=7.9GB, lastUpdated=yesterday),
+ LibraryModelTag(name=llama3.2-vision, tag=latest, size=7.9GB, lastUpdated=yesterday),
+ LibraryModelTag(name=llama3.2-vision, tag=11b, size=7.9GB, lastUpdated=yesterday),
LibraryModelTag(name=llama3.2-vision, tag=90b, size=55GB, lastUpdated=yesterday)
]
)
@@ -84,24 +51,9 @@ LibraryModelDetail(
This API finds a specific model using model `name` and `tag` from Ollama library.
-```java title="FindLibraryModel.java"
-import io.github.ollama4j.OllamaAPI;
-import io.github.ollama4j.models.response.LibraryModelTag;
-
-public class Main {
-
- public static void main(String[] args) {
-
- String host = "http://localhost:11434/";
-
- OllamaAPI ollamaAPI = new OllamaAPI(host);
-
- LibraryModelTag libraryModelTag = ollamaAPI.findModelTagFromLibrary("qwen2.5", "7b");
-
- System.out.println(libraryModelTag);
- }
-}
-```
+<CodeEmbed
+src='https://raw.githubusercontent.com/ollama4j/ollama4j-examples/refs/heads/main/src/main/java/io/github/ollama4j/examples/FindLibraryModel.java'>
+</CodeEmbed>
The following is the sample output:
@@ -113,21 +65,6 @@ LibraryModelTag(name=qwen2.5, tag=7b, size=4.7GB, lastUpdated=7 weeks ago)
You can use `LibraryModelTag` to pull models into Ollama server.
-```java title="PullLibraryModelTags.java"
-import io.github.ollama4j.OllamaAPI;
-import io.github.ollama4j.models.response.LibraryModelTag;
-
-public class Main {
-
- public static void main(String[] args) {
-
- String host = "http://localhost:11434/";
-
- OllamaAPI ollamaAPI = new OllamaAPI(host);
-
- LibraryModelTag libraryModelTag = ollamaAPI.findModelTagFromLibrary("qwen2.5", "7b");
-
- ollamaAPI.pullModel(libraryModelTag);
- }
-}
-```
\ No newline at end of file
+<CodeEmbed
+src='https://raw.githubusercontent.com/ollama4j/ollama4j-examples/refs/heads/main/src/main/java/io/github/ollama4j/examples/PullLibraryModelTags.java'>
+</CodeEmbed>
\ No newline at end of file
diff --git a/docs/docs/apis-model-management/list-models.md b/docs/docs/apis-model-management/list-models.md
index 8ed9a24..f1895cc 100644
--- a/docs/docs/apis-model-management/list-models.md
+++ b/docs/docs/apis-model-management/list-models.md
@@ -2,34 +2,23 @@
sidebar_position: 2
---
+import CodeEmbed from '@site/src/components/CodeEmbed';
+
# List Local Models
This API lets you list downloaded/available models on the Ollama server.
-```java title="ListModels.java"
-import io.github.ollama4j.OllamaAPI;
-import io.github.ollama4j.models.response.Model;
+<CodeEmbed
+src='https://raw.githubusercontent.com/ollama4j/ollama4j-examples/refs/heads/main/src/main/java/io/github/ollama4j/examples/ListLocalModels.java'>
+</CodeEmbed>
-import java.util.List;
-
-public class ListModels {
-
- public static void main(String[] args) {
-
- String host = "http://localhost:11434/";
-
- OllamaAPI ollamaAPI = new OllamaAPI(host);
-
- List<Model> models = ollamaAPI.listModels();
-
- models.forEach(model -> System.out.println(model.getName()));
- }
-}
-```
If you have any models already downloaded on Ollama server, you would have them listed as follows:
```bash
llama2:latest
+llama3.2:1b
+qwen2:0.5b
+qwen:0.5b
sqlcoder:latest
```
\ No newline at end of file
diff --git a/docs/docs/apis-model-management/pull-model.md b/docs/docs/apis-model-management/pull-model.md
index 69fd013..28e1484 100644
--- a/docs/docs/apis-model-management/pull-model.md
+++ b/docs/docs/apis-model-management/pull-model.md
@@ -2,26 +2,15 @@
sidebar_position: 3
---
+import CodeEmbed from '@site/src/components/CodeEmbed';
+
# Pull Model
This API lets you pull a model on the Ollama server.
-```java title="PullModel.java"
-import io.github.ollama4j.OllamaAPI;
-import io.github.ollama4j.types.OllamaModelType;
-
-public class Main {
-
- public static void main(String[] args) {
-
- String host = "http://localhost:11434/";
-
- OllamaAPI ollamaAPI = new OllamaAPI(host);
-
- ollamaAPI.pullModel(OllamaModelType.LLAMA2);
- }
-}
-```
+<CodeEmbed
+src='https://raw.githubusercontent.com/ollama4j/ollama4j-examples/refs/heads/main/src/main/java/io/github/ollama4j/examples/PullModel.java'>
+</CodeEmbed>
Once downloaded, you can see them when you use [list models](./list-models) API.
diff --git a/docs/docusaurus.config.js b/docs/docusaurus.config.js
index 8f0dae3..8d3eddc 100644
--- a/docs/docusaurus.config.js
+++ b/docs/docusaurus.config.js
@@ -99,20 +99,32 @@ const config = {
style: 'dark',
links: [
{
- title: 'Docs',
+ title: 'Quick Links',
items: [
{
- label: 'Tutorial',
- to: '/intro',
+ label: 'Ollama4j Examples',
+ to: 'https://github.com/ollama4j/ollama4j-examples',
+ },
+ {
+ label: 'Blog',
+ to: '/blog',
+ },
+ {
+ label: 'GitHub',
+ href: 'https://github.com/ollama4j/ollama4j',
},
],
},
{
- title: 'Usage',
+ title: 'Stuff built with Ollama4j',
items: [
{
- label: 'Examples',
- to: 'https://github.com/ollama4j/ollama4j-examples',
+ label: 'Ollama4j Web UI',
+ to: 'https://github.com/ollama4j/ollama4j-web-ui',
+ },
+ {
+ label: 'Ollama4j Desktop UI with Swing',
+ to: 'https://github.com/ollama4j/ollama4j-ui',
},
],
},
@@ -128,20 +140,7 @@ const config = {
href: 'https://twitter.com/ollama4j',
},
],
- },
- {
- title: 'More',
- items: [
- {
- label: 'Blog',
- to: '/blog',
- },
- {
- label: 'GitHub',
- href: 'https://github.com/ollama4j/ollama4j',
- },
- ],
- },
+ }
],
copyright: `Ollama4j Documentation ${new Date().getFullYear()}. Built with Docusaurus.`,
},
diff --git a/docs/package-lock.json b/docs/package-lock.json
index 8b1ee8a..c3425e0 100644
--- a/docs/package-lock.json
+++ b/docs/package-lock.json
@@ -13,11 +13,14 @@
"@docusaurus/plugin-google-gtag": "^3.4.0",
"@docusaurus/preset-classic": "^3.4.0",
"@docusaurus/theme-mermaid": "^3.4.0",
+ "@iconify/react": "^5.2.1",
"@mdx-js/react": "^3.0.0",
"clsx": "^2.0.0",
+ "font-awesome": "^4.7.0",
"prism-react-renderer": "^2.3.0",
"react": "^18.0.0",
- "react-dom": "^18.0.0"
+ "react-dom": "^18.0.0",
+ "react-icons": "^5.5.0"
},
"devDependencies": {
"@docusaurus/module-type-aliases": "^3.4.0",
@@ -3066,6 +3069,27 @@
"@hapi/hoek": "^9.0.0"
}
},
+ "node_modules/@iconify/react": {
+ "version": "5.2.1",
+ "resolved": "https://registry.npmjs.org/@iconify/react/-/react-5.2.1.tgz",
+ "integrity": "sha512-37GDR3fYDZmnmUn9RagyaX+zca24jfVOMY8E1IXTqJuE8pxNtN51KWPQe3VODOWvuUurq7q9uUu3CFrpqj5Iqg==",
+ "license": "MIT",
+ "dependencies": {
+ "@iconify/types": "^2.0.0"
+ },
+ "funding": {
+ "url": "https://github.com/sponsors/cyberalien"
+ },
+ "peerDependencies": {
+ "react": ">=16"
+ }
+ },
+ "node_modules/@iconify/types": {
+ "version": "2.0.0",
+ "resolved": "https://registry.npmjs.org/@iconify/types/-/types-2.0.0.tgz",
+ "integrity": "sha512-+wluvCrRhXrhyOmRDJ3q8mux9JkKy5SJ/v8ol2tu4FVjyYvtEzkc/3pK15ET6RKg4b4w4BmTk1+gsCUhf21Ykg==",
+ "license": "MIT"
+ },
"node_modules/@jest/schemas": {
"version": "29.6.3",
"resolved": "https://registry.npmjs.org/@jest/schemas/-/schemas-29.6.3.tgz",
@@ -4780,9 +4804,9 @@
}
},
"node_modules/caniuse-lite": {
- "version": "1.0.30001641",
- "resolved": "https://registry.npmjs.org/caniuse-lite/-/caniuse-lite-1.0.30001641.tgz",
- "integrity": "sha512-Phv5thgl67bHYo1TtMY/MurjkHhV4EDaCosezRXgZ8jzA/Ub+wjxAvbGvjoFENStinwi5kCyOYV3mi5tOGykwA==",
+ "version": "1.0.30001714",
+ "resolved": "https://registry.npmjs.org/caniuse-lite/-/caniuse-lite-1.0.30001714.tgz",
+ "integrity": "sha512-mtgapdwDLSSBnCI3JokHM7oEQBLxiJKVRtg10AxM1AyeiKcM96f0Mkbqeq+1AbiCtvMcHRulAAEMu693JrSWqg==",
"funding": [
{
"type": "opencollective",
@@ -4796,7 +4820,8 @@
"type": "github",
"url": "https://github.com/sponsors/ai"
}
- ]
+ ],
+ "license": "CC-BY-4.0"
},
"node_modules/ccount": {
"version": "2.0.1",
@@ -7266,6 +7291,15 @@
}
}
},
+ "node_modules/font-awesome": {
+ "version": "4.7.0",
+ "resolved": "https://registry.npmjs.org/font-awesome/-/font-awesome-4.7.0.tgz",
+ "integrity": "sha512-U6kGnykA/6bFmg1M/oT9EkFeIYv7JlX3bozwQJWiiLz6L0w3F5vBVPxHlwyX/vtNq1ckcpRKOB9f2Qal/VtFpg==",
+ "license": "(OFL-1.1 AND MIT)",
+ "engines": {
+ "node": ">=0.10.3"
+ }
+ },
"node_modules/fork-ts-checker-webpack-plugin": {
"version": "6.5.3",
"resolved": "https://registry.npmjs.org/fork-ts-checker-webpack-plugin/-/fork-ts-checker-webpack-plugin-6.5.3.tgz",
@@ -13384,6 +13418,15 @@
"react-dom": "^16.6.0 || ^17.0.0 || ^18.0.0"
}
},
+ "node_modules/react-icons": {
+ "version": "5.5.0",
+ "resolved": "https://registry.npmjs.org/react-icons/-/react-icons-5.5.0.tgz",
+ "integrity": "sha512-MEFcXdkP3dLo8uumGI5xN3lDFNsRtrjbOEKDLD7yv76v4wpnEq2Lt2qeHaQOr34I/wPN3s3+N08WkQ+CW37Xiw==",
+ "license": "MIT",
+ "peerDependencies": {
+ "react": "*"
+ }
+ },
"node_modules/react-is": {
"version": "16.13.1",
"resolved": "https://registry.npmjs.org/react-is/-/react-is-16.13.1.tgz",
diff --git a/docs/package.json b/docs/package.json
index 674c828..f486dfd 100644
--- a/docs/package.json
+++ b/docs/package.json
@@ -19,11 +19,14 @@
"@docusaurus/plugin-google-gtag": "^3.4.0",
"@docusaurus/preset-classic": "^3.4.0",
"@docusaurus/theme-mermaid": "^3.4.0",
+ "@iconify/react": "^5.2.1",
"@mdx-js/react": "^3.0.0",
"clsx": "^2.0.0",
+ "font-awesome": "^4.7.0",
"prism-react-renderer": "^2.3.0",
"react": "^18.0.0",
- "react-dom": "^18.0.0"
+ "react-dom": "^18.0.0",
+ "react-icons": "^5.5.0"
},
"devDependencies": {
"@docusaurus/module-type-aliases": "^3.4.0",
diff --git a/docs/src/components/CodeEmbed/index.js b/docs/src/components/CodeEmbed/index.js
new file mode 100644
index 0000000..1a269d1
--- /dev/null
+++ b/docs/src/components/CodeEmbed/index.js
@@ -0,0 +1,170 @@
+// import React, { useState, useEffect } from 'react';
+// import CodeBlock from '@theme/CodeBlock';
+// import Icon from '@site/src/components/Icon';
+
+
+// const CodeEmbed = ({ src }) => {
+// const [code, setCode] = useState('');
+// const [loading, setLoading] = useState(true);
+// const [error, setError] = useState(null);
+
+// useEffect(() => {
+// let isMounted = true;
+
+// const fetchCodeFromUrl = async (url) => {
+// if (!isMounted) return;
+
+// setLoading(true);
+// setError(null);
+
+// try {
+// const response = await fetch(url);
+// if (!response.ok) {
+// throw new Error(`HTTP error! status: ${response.status}`);
+// }
+// const data = await response.text();
+// if (isMounted) {
+// setCode(data);
+// }
+// } catch (err) {
+// console.error('Failed to fetch code:', err);
+// if (isMounted) {
+// setError(err);
+// setCode(`// Failed to load code from ${url}\n// ${err.message}`);
+// }
+// } finally {
+// if (isMounted) {
+// setLoading(false);
+// }
+// }
+// };
+
+// if (src) {
+// fetchCodeFromUrl(src);
+// }
+
+// return () => {
+// isMounted = false;
+// };
+// }, [src]);
+
+// const githubUrl = src ? src.replace('https://raw.githubusercontent.com', 'https://github.com').replace('/refs/heads/', '/blob/') : null;
+// const fileName = src ? src.substring(src.lastIndexOf('/') + 1) : null;
+
+// return (
+// loading ? (
+// <div>Loading code...</div>
+// ) : error ? (
+// <div>Error: {error.message}</div>
+// ) : (
+// <div style={{ backgroundColor: 'transparent', padding: '0px', borderRadius: '5px' }}>
+// <div style={{ textAlign: 'right' }}>
+// {githubUrl && (
+// <a href={githubUrl} target="_blank" rel="noopener noreferrer" style={{ paddingRight: '15px', color: 'gray', fontSize: '0.8em', fontStyle: 'italic', display: 'inline-flex', alignItems: 'center' }}>
+// View on GitHub
+// <Icon icon="mdi:github" height="48" />
+// </a>
+// )}
+// </div>
+// <CodeBlock title={fileName} className="language-java">{code}</CodeBlock>
+// </div>
+// )
+// );
+// };
+
+// export default CodeEmbed;
+import React, { useState, useEffect } from 'react';
+import CodeBlock from '@theme/CodeBlock';
+import Icon from '@site/src/components/Icon';
+
+
+const CodeEmbed = ({ src }) => {
+ const [code, setCode] = useState('');
+ const [loading, setLoading] = useState(true);
+ const [error, setError] = useState(null);
+
+ useEffect(() => {
+ let isMounted = true;
+
+ const fetchCodeFromUrl = async (url) => {
+ if (!isMounted) return;
+
+ setLoading(true);
+ setError(null);
+
+ try {
+ const response = await fetch(url);
+ if (!response.ok) {
+ throw new Error(`HTTP error! status: ${response.status}`);
+ }
+ const data = await response.text();
+ if (isMounted) {
+ setCode(data);
+ }
+ } catch (err) {
+ console.error('Failed to fetch code:', err);
+ if (isMounted) {
+ setError(err);
+ setCode(`// Failed to load code from ${url}\n// ${err.message}`);
+ }
+ } finally {
+ if (isMounted) {
+ setLoading(false);
+ }
+ }
+ };
+
+ if (src) {
+ fetchCodeFromUrl(src);
+ }
+
+ return () => {
+ isMounted = false;
+ };
+ }, [src]);
+
+ const githubUrl = src ? src.replace('https://raw.githubusercontent.com', 'https://github.com').replace('/refs/heads/', '/blob/') : null;
+ const fileName = src ? src.substring(src.lastIndexOf('/') + 1) : null;
+
+ const title = (
+ <div style={{ display: 'flex', justifyContent: 'space-between', alignItems: 'center' }}>
+ <a
+ href={githubUrl}
+ target="_blank"
+ rel="noopener noreferrer"
+ style={{
+ color: 'gray',
+ textDecoration: 'none',
+ }}
+ onMouseOver={e => {
+ e.target.style.textDecoration = 'underline';
+ }}
+ onMouseOut={e => {
+ e.target.style.textDecoration = 'none';
+ }}
+ >
+ <span>{fileName}</span>
+ </a>
+ {githubUrl && (
+ <a href={githubUrl} target="_blank" rel="noopener noreferrer" style={{ color: 'gray', fontSize: '0.9em', fontStyle: 'italic', display: 'inline-flex', alignItems: 'center' }}>
+ View on GitHub
+ <Icon icon="mdi:github" height="1em" />
+ </a>
+ )}
+ </div>
+ );
+
+ return (
+ loading ? (
+ <div>Loading code...</div>
+ ) : error ? (
+ <div>Error: {error.message}</div>
+ ) : (
+ <div style={{ backgroundColor: 'transparent', padding: '0px', borderRadius: '5px' }}>
+ <CodeBlock title={title} className="language-java">{code}</CodeBlock>
+ </div>
+ )
+ );
+};
+
+export default CodeEmbed;
diff --git a/docs/src/components/Icon/index.js b/docs/src/components/Icon/index.js
new file mode 100644
index 0000000..e2eaa09
--- /dev/null
+++ b/docs/src/components/Icon/index.js
@@ -0,0 +1,9 @@
+// @site/src/components/Icon.js
+import React from 'react';
+import { Icon as IconifyIcon } from '@iconify/react';
+
+const IIcon = ({ icon, color, width = '24', height = '24' }) => (
+ <IconifyIcon icon={icon} color={color} width={width} height={height} />
+);
+
+export default IIcon;
\ No newline at end of file
diff --git a/docs/src/css/custom.css b/docs/src/css/custom.css
index 9da6f53..6a2d6a2 100644
--- a/docs/src/css/custom.css
+++ b/docs/src/css/custom.css
@@ -4,6 +4,8 @@
* work well for content-centric websites.
*/
+ @import 'font-awesome/css/font-awesome.min.css';
+
/* You can override the default Infima variables here. */
:root {
--ifm-color-primary: #2e8555;
diff --git a/docs/src/pages/index.js b/docs/src/pages/index.js
index 8f7a1d6..8d4dc46 100644
--- a/docs/src/pages/index.js
+++ b/docs/src/pages/index.js
@@ -32,7 +32,7 @@ function HomepageHeader() {
export default function Home() {
const {siteConfig} = useDocusaurusContext();
return (<Layout
- title={`Hello from ${siteConfig.title}`}
+ title={`${siteConfig.title}`}
description="Description will go into a meta tag in <head />">
<HomepageHeader/>
<main>