add logging and model parameter tutorials to docu

docs/docs/tutorials/image-models.md

@@ -4,6 +4,8 @@ sidebar_position: 21
 
 # 12. Image Models
 
-Creating and interpreting
+Creating images: example in https://github.com/langchain4j/langchain4j-examples/blob/main/tutorials/src/main/java/_02_OpenAiImageModelExamples.java
 
-Coming soon
+Interpreting images: coming very soon
+
+More elaborated content coming soon - or help us by adding it <3

docs/docs/tutorials/logging.md

@@ -0,0 +1,44 @@
+---
+sidebar_position: 30
+---
+
+# 15. Logging
+
+### Model requests and responses
+Console output can be switched on and off by setting `.logRequests()` and `.logResponses()` on the model
+
+```
+        ChatLanguageModel model = OpenAiChatModel.builder()
+                .apiKey(ApiKeys.OPENAI_API_KEY)
+                .logRequests(true)
+                .logResponses(true)
+                .build();
+```
+
+### Default logging: Tinylog
+By default, we offer the Tinylog framework. An example can be found in langchain4j-examples/tutorials.
+Logging properties are set in `tinylog.properties`, as follows
+```
+writer.level = info
+```
+Typical log level settings are `error`, `warn`, `info` and `debug`. 
+
+An overview of all the options:
+- `off`: No log messages will be written. This effectively disables logging.
+- `trace`: All log messages, including trace, debug, info, warn, and error, will be written to the log output.
+- `debug`: Log messages of debug, info, warn, and error levels will be written to the log output. Trace messages will be ignored.
+- `info`: Log messages of info, warn, and error levels will be written to the log output. Debug and trace messages will be ignored.
+- `warn`: Log messages of warn and error levels will be written to the log output. Info, debug, and trace messages will be ignored.
+- `error`: Only log messages of error level will be written to the log output. Warn, info, debug, and trace messages will be ignored.
+- `fatal`: This level is not part of the standard log levels in Tinylog. You can use it to specify a custom level for log messages. By default, it behaves the same as the `error` level.
+
+Or you can choose to add you own logger.
+
+## Spring Boot
+In Spring Boot examples, logging properties are set in the `application.properties` file
+```
+logging.level.dev.langchain4j=INFO
+logging.level.dev.ai4j.openai4j=INFO
+```
+
+_This documentation page is a stub - help us make it better_

docs/docs/tutorials/response-streaming.md

@@ -4,6 +4,6 @@ sidebar_position: 9
 
 # 4. Response Streaming
 
-[Streaming of LLM responses](https://github.com/langchain4j/langchain4j-examples/blob/main/other-examples/src/main/java/StreamingExamples.java)
+[Streaming of LLM responses](https://github.com/langchain4j/langchain4j-examples/blob/main/tutorials/src/main/java/_04_Streaming.java)
 
 Tutorial coming soon

docs/docs/tutorials/set-model-parameters.md

@@ -3,5 +3,82 @@ sidebar_position: 4
 ---
 
 # 2. Set Model Parameters
+An example of specifying model parameters can be found [here](https://github.com/langchain4j/langchain4j-examples/blob/main/tutorials/src/main/java/_01_ModelParameters.java
+).
 
-Coming soon
+## What are Parameters in LLMs
+Depending on which model and which model provider you use, you can set a lot of parameters that will influence the model's output, speed, logging, etc.
+Typically, you will find all the parameters and their meaning on the provider's website.
+
+
+For example, OpenAI API's parameters can be found at https://platform.openai.com/docs/api-reference/chat
+and include options like
+
+| Parameter          | Description                                                                                                                                                                                                                                                                                              | Type      |
+|--------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-----------|
+| `modelName`        | The name of the model to use (gtp-3.5-turbo, gpt-4-1106-preview, ...)                                                                                                                                                                                                                                    | `String`  |
+| `temperature`      | The sampling temperature, between 0 and 1. Higher values like 0.8 will make the output more random, while lower values like 0.2 will make it more focused and deterministic. If set to 0, the model will use log probability to automatically increase the temperature until certain thresholds are hit. | `Double`  |
+| `max_tokens`       | The maximum number of tokens that can be generated in the chat completion.                                                                                                                                                                                                                               | `Integer` |
+| `frequencyPenalty` | Number between -2.0 and 2.0. Positive values penalize new tokens based on their existing frequency in the text so far, decreasing the model's likelihood to repeat the same line verbatim.                                                                                                               | `Double`  |
+| `n`                | How many chat completion choices to generate for each input message. Note that you will be charged based on the number of generated tokens across all of the choices. Keep n as 1 to minimize costs.                                                                                                     | `Integer` |
+| `...`              | ...                                                                                                                                                                                                                                                                                                      | `...`     |
+
+For the full list of parameters in OpenAI LLMs, see the [OpenAI Language Model page](/docs/integrations/language-models/openai).
+Full lists of parameters and default values per model can be found under the separate model pages (under Integration, Language Model and Image Model).
+
+## Default Parameter Settings
+The LangChain4j framework for very easy model constructors with a lot of parameters set under the hood to sane defaults. The minimal way to construct a model object is
+```
+ChatLanguageModel model = OpenAiChatModel.withApiKey("demo");
+```
+In this case of an OpenAI Chat Model for example, some of the defaults are
+
+| Parameter      | Default Value | 
+|----------------|---------------|
+| `timeout`      | 60s           |
+| `modelName`    | gpt-3.5-turbo |
+| `temperature`  | 0.7           |
+| `logRequests`  | false         |
+| `logResponses` | false         |
+| `...`          | ...           |
+
+Defaults for all language and image models can be found on the pages of the respective providers under [Integrations](/docs/integrations).
+
+## How to Set Parameter Values
+When we use the builder pattern, we will be able to set all the available parameters of the model as follows:
+```
+ChatLanguageModel model = OpenAiChatModel.builder()
+        .apiKey(ApiKeys.OPENAI_API_KEY)
+        .modelName(GPT_3_5_TURBO)
+        .temperature(0.3)
+        .timeout(ofSeconds(60))
+        .logRequests(true)
+        .logResponses(true)
+        .build();
+```
+
+## Parameter Settings in Quarkus
+LangChain4j parameters in Quarkus applications can be set in the `application.properties` file as follows:
+```
+quarkus.langchain4j.openai.chat-model.temperature=0.5
+quarkus.langchain4j.openai.timeout=60s
+quarkus.langchain4j.openai.api-key=${OPENAI_API_KEY}
+```
+
+Interestingly, for debugging, tweaking or even just knowing all the available parameters, one can have a look in the quarkus DEV UI.
+In this dashboard, you can make changes that will be immediately reflected in your running instance, and your changes are automatically ported to the code.
+The DEV UI can be accessed by running your Quarkus application with the command `quarkus dev`, then you can find it on localhost:8080/q/dev-ui (or wherever you deploy your application).
+
+
+[![](/img/quarkus-dev-ui-parameters.png)](/docs/tutorials/set-model-parameters)
+
+## Parameter Settings in Spring Boot
+LangChain4j parameters in Spring Boot applications can be set in the `application.properties` file as follows:
+```
+langchain4j.open-ai.chat-model.api-key=${OPENAI_API_KEY}
+langchain4j.open-ai.chat-model.model-name=gpt-4-1106-preview
+langchain4j.open-ai.chat-model.temperature=0.0
+langchain4j.open-ai.chat-model.timeout=PT60S
+langchain4j.open-ai.chat-model.log-requests=false
+langchain4j.open-ai.chat-model.log-responses=false
+```