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Adds documentation for annotation based Tool registration
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@ -464,21 +464,155 @@ A typical final response of the above could be:
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This tool calling can also be done using the streaming API.
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### Potential Improvements
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### Using Annotation based Tool Registration
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Instead of explicitly registering `ollamaAPI.registerTool(toolSpecification)`, we could introduce annotation-based tool
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registration. For example:
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Instead of explicitly registering each tool, ollama4j supports declarative tool specification and registration via java
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Annotations and reflection calling.
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To declare a method to be used as a tool for a chat call, the following steps have to be considered:
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* Annotate a method and its Parameters to be used as a tool
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* Annotate a method with the `ToolSpec` annotation
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* Annotate the methods parameters with the `ToolProperty` annotation. Only the following datatypes are supported for now:
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* `java.lang.String`
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* `java.lang.Integer`
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* `java.lang.Boolean`
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* `java.math.BigDecimal`
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* Annotate the class that calls the `OllamaAPI` client with the `OllamaToolService` annotation, referencing the desired provider-classes that contain `ToolSpec` methods.
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* Before calling the `OllamaAPI` chat request, call the method `OllamaAPI.registerAnnotatedTools()` method to add tools to the chat.
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#### Example
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Let's say, we have an ollama4j service class that should ask a llm a specific tool based question.
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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:
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```java
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public class BackendService{
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public BackendService(){}
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@ToolSpec(name = "current-fuel-price", desc = "Get current fuel price")
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public String getCurrentFuelPrice(Map<String, Object> arguments) {
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String location = arguments.get("location").toString();
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String fuelType = arguments.get("fuelType").toString();
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return "Current price of " + fuelType + " in " + location + " is Rs.103/L";
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@ToolSpec(desc = "Computes the most important constant all around the globe!")
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public String computeMkeConstant(@ToolProperty(name = "noOfDigits",desc = "Number of digits that shall be returned") Integer noOfDigits ){
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return BigDecimal.valueOf((long)(Math.random()*1000000L),noOfDigits).toString();
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}
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}
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```
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The caller API can then be written as:
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```java
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import io.github.ollama4j.tools.annotations.OllamaToolService;
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@OllamaToolService(providers = BackendService.class)
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public class MyOllamaService{
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public void chatWithAnnotatedTool(){
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// inject the annotated method to the ollama toolsregistry
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ollamaAPI.registerAnnotatedTools();
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OllamaChatRequest requestModel = builder
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.withMessage(OllamaChatMessageRole.USER,
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"Compute the most important constant in the world using 5 digits")
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.build();
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OllamaChatResult chatResult = ollamaAPI.chat(requestModel);
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}
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}
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```
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The request should be the following:
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```json
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{
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"model" : "llama3.2:1b",
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"stream" : false,
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"messages" : [ {
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"role" : "user",
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"content" : "Compute the most important constant in the world using 5 digits",
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"images" : null,
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"tool_calls" : [ ]
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} ],
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"tools" : [ {
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"type" : "function",
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"function" : {
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"name" : "computeImportantConstant",
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"description" : "Computes the most important constant all around the globe!",
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"parameters" : {
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"type" : "object",
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"properties" : {
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"noOfDigits" : {
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"type" : "java.lang.Integer",
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"description" : "Number of digits that shall be returned"
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}
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},
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"required" : [ "noOfDigits" ]
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}
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}
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} ]
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}
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```
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The result could be something like the following:
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```json
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{
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"chatHistory" : [ {
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"role" : "user",
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"content" : "Compute the most important constant in the world using 5 digits",
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"images" : null,
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"tool_calls" : [ ]
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}, {
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"role" : "assistant",
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"content" : "",
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"images" : null,
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"tool_calls" : [ {
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"function" : {
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"name" : "computeImportantConstant",
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"arguments" : {
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"noOfDigits" : "5"
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}
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}
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} ]
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}, {
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"role" : "tool",
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"content" : "[TOOL_RESULTS]computeImportantConstant([noOfDigits]) : 1.51019[/TOOL_RESULTS]",
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"images" : null,
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"tool_calls" : null
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}, {
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"role" : "assistant",
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"content" : "The most important constant in the world with 5 digits is: **1.51019**",
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"images" : null,
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"tool_calls" : null
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} ],
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"responseModel" : {
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"model" : "llama3.2:1b",
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"message" : {
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"role" : "assistant",
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"content" : "The most important constant in the world with 5 digits is: **1.51019**",
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"images" : null,
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"tool_calls" : null
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},
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"done" : true,
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"error" : null,
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"context" : null,
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"created_at" : "2024-12-27T21:55:39.3232495Z",
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"done_reason" : "stop",
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"total_duration" : 1075444300,
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"load_duration" : 13558600,
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"prompt_eval_duration" : 509000000,
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"eval_duration" : 550000000,
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"prompt_eval_count" : 124,
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"eval_count" : 20
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},
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"response" : "The most important constant in the world with 5 digits is: **1.51019**",
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"responseTime" : 1075444300,
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"httpStatusCode" : 200
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}
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```
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### Potential Improvements
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Instead of passing a map of args `Map<String, Object> arguments` to the tool functions, we could support passing
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specific args separately with their data types. For example:
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@ -317,7 +317,7 @@ class TestRealAPIs {
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List<OllamaChatToolCalls> toolCalls = chatResult.getChatHistory().get(1).getToolCalls();
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assertEquals(1, toolCalls.size());
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OllamaToolCallsFunction function = toolCalls.get(0).getFunction();
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assertEquals("computeMkeConstant", function.getName());
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assertEquals("computeImportantConstant", function.getName());
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assertEquals(1, function.getArguments().size());
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Object noOfDigits = function.getArguments().get("noOfDigits");
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assertNotNull(noOfDigits);
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@ -8,7 +8,7 @@ import java.math.BigDecimal;
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public class AnnotatedTool {
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@ToolSpec(desc = "Computes the most important constant all around the globe!")
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public String computeMkeConstant(@ToolProperty(name = "noOfDigits",desc = "Number of digits that shall be returned") Integer noOfDigits ){
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public String computeImportantConstant(@ToolProperty(name = "noOfDigits",desc = "Number of digits that shall be returned") Integer noOfDigits ){
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return BigDecimal.valueOf((long)(Math.random()*1000000L),noOfDigits).toString();
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}
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