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. + + + +::::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: + + + +The annotated method can then be used as a tool in the chat session: + + + +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 d5aabe9..d5a147c 100644 --- a/docs/docs/apis-generate/chat.md +++ b/docs/docs/apis-generate/chat.md @@ -9,262 +9,93 @@ import CodeEmbed from '@site/src/components/CodeEmbed'; 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()); - } -} - -``` + 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(""); - - OllamaChatRequest requestModel = builder.withMessage(OllamaChatMessageRole.USER, "").build(); - OllamaChatResult initialChatResult = ollamaAPI.chat(requestModel); - System.out.println(initialChatResult.getResponse()); - - List history = initialChatResult.getChatHistory(); - - while (true) { - OllamaChatResult chatResult = ollamaAPI.chat(builder.withMessages(history).withMessage(OllamaChatMessageRole.USER, ") - OllamaStreamHandler streamHandler = (s) -> { - System.out.println(s); - }; - - OllamaChatResult chatResult = ollamaAPI.chat(requestModel, streamHandler); - } -} -``` - -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. +> +:::: -## 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; + -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); - } -} -``` + -## 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; + + +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()); - } -} -``` + 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 @@ -274,5 +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. - - \ 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; + -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 + -```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, 0.001123205, 0.060663134, -0.06755974, -0.008144066, -0.012715194, 0.031090235, -0.06392361, -0.07458864, 0.11904344, 0.012541205, 0.06530589, 0.014893975, 0.051452246, -0.0850448, 0.010324387, -0.007704823, -0.035547107, -0.115298286, -0.030618181, -0.08327795, 0.013764867, 0.05644683, -0.040965002, 0.042660262, 0.022258658, 0.046835635, -0.051371332, 0.030119192, 0.007202741, -0.004240163, -0.031205537, 0.077798784, 0.034248676, 0.06132544, 0.0074892035, -0.036300045, -0.08461072, 0.021714637, -0.019322984, -0.0398159, 0.054820538, -0.033751138, 0.018145457, -0.105456986, -0.050408557, -0.011556143, 0.037754424, 0.022083722, 0.08057535, 0.007969883, -0.016802859, -0.059379302, -7.2237E-33, 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-0.08174755, 0.026874617, -0.029649356, -0.004314064, 0.012965783, -0.03528545, -0.019647561, 0.055427335, -0.06122222, -0.054911185, 0.012418541, -0.019181116, -0.012523167, -0.015836857, -0.06933424, -0.044998724, -0.049169958, 0.048181616, -0.10435304, -0.1079065, 3.5844724E-33, -5.2857434E-4, -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, 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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); - } -} -``` + 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 -} -```` +[[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, 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-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 embeddings = ollamaAPI.generateEmbeddings(OllamaModelType.LLAMA2, - "Here is an article about llamas..."); - - embeddings.forEach(System.out::println); - } -} -``` + 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 ![Img](https://t3.ftcdn.net/jpg/02/96/63/80/360_F_296638053_0gUVA4WVBKceGsIr7LNqRWSnkusi07dq.jpg) -```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()); - } -} -``` + 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: ![Img](https://t3.ftcdn.net/jpg/02/96/63/80/360_F_296638053_0gUVA4WVBKceGsIr7LNqRWSnkusi07dq.jpg) -```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()); - } -} -``` + 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 arguments) { - String location = arguments.get("location").toString(); - String fuelType = arguments.get("fuelType").toString(); - return "Current price of " + fuelType + " in " + location + " is Rs.103/L"; -} -``` + 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 arguments) { - String location = arguments.get("city").toString(); - return "Currently " + location + "'s weather is nice."; -} -``` + 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 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()); - } -} -``` + ### 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(); -``` + 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(); -``` + 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(); -``` + -### 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 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 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 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()); - } -} -``` + 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 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 0fbed37..f8f438b 100644 --- a/docs/docs/apis-generate/generate.md +++ b/docs/docs/apis-generate/generate.md @@ -4,7 +4,7 @@ sidebar_position: 1 import CodeEmbed from '@site/src/components/CodeEmbed'; -# Generate - Sync +# Generate (Sync) This API lets you ask questions to the LLMs in a synchronous way. This API corresponds to @@ -15,21 +15,24 @@ 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 You will get a response similar to: +::::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 You will get a response similar to: +::::tip[LLM Response] > The > > The capital @@ -43,210 +46,29 @@ You will get a response similar to: > The capital of France is Paris > > 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; + -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 format = new HashMap<>(); - format.put("type", "object"); - format.put("properties", new HashMap() { - { - put("age", new HashMap() { - { - put("type", "integer"); - } - }); - put("available", new HashMap() { - { - 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; + -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 format = new HashMap<>(); - format.put("type", "object"); - format.put("properties", new HashMap() { - { - put("age", new HashMap() { - { - put("type", "integer"); - } - }); - put("available", new HashMap() { - { - 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