docs: Update README with new model profile properties and examples for LLM configuration

README.md

@@ -59,6 +59,7 @@ These profiles must be mapped to fixed categories with the following application
     profile.model.s=
     profile.model.m=
     profile.model.l=
+    profile.model.xl=
     profile.model.hot=
     profile.model.cold=
 
@@ -70,6 +71,33 @@ Profiles must be located in the classpath resources location (e.g. src/main/reso
 
 Examples can be found in test resources.
 
+### Model Profile Properties
+
+Each model profile is a `.properties` file that configures how the library interacts with a specific LLM. All properties are loaded from the classpath resources.
+
+| Property | Type | Required | Description | Example Values |
+|----------|------|----------|-------------|----------------|
+| `prompts` | String | Yes | Prompt template strategy name for text completions. Use empty string for chat completions or if no special formatting is needed. | `llama4`, `gpt-oss`, `no`, `` (empty) |
+| `tokenEncoding` | String | Yes | Token encoding scheme used by the model for counting tokens. | `cl100k_base`, `none` |
+| `maxTokens` | Integer | Yes | Maximum number of tokens to generate in the model response. | `1500`, `2500` |
+| `maxContextLength` | Integer | Yes | Maximum total context length (prompt + response) supported by the model. | `16000`, `32767`, `128000` |
+| `temperature` | Double | Yes | Controls randomness in responses. Lower values (0.0-0.5) are more deterministic, higher values (0.5-1.0+) more creative. | `0.2`, `0.4`, `0.7` |
+| `topP` | Double | Yes | Nucleus sampling parameter. Controls diversity via cumulative probability. Typically kept at 1.0. | `1.0`, `0.9` |
+| `modelProvider` | Enum | Yes | The LLM provider/client type to use. | `OAI_CHAT_COMPLETION`, `OAI_TEXT_COMPLETION`, `AZURE_AI` |
+| `modelName` | String | Yes | The specific model identifier or deployment name. Can be empty for local models where it's auto-detected. | `Llama-3.3-70B-Instruct`, `gpt-4`, `` (empty for auto-detect) |
+| `url` | String | Yes | Base URL endpoint for the model API. | `https://api.openai.com/v1`, `http://localhost:5001` |
+| `apiKey` | String | Yes | Authentication key for the API. Can use Spring property placeholders like `${apikey}`. Can be empty for local models. | `sk-...`, `${apikey}`, `` (empty) |
+| `cost1MInTokens` | Double | Yes | Cost per 1 million input tokens in your currency. Use `0` for free/local models. | `0.71`, `0`, `5.00` |
+| `cost1MOutTokens` | Double | Yes | Cost per 1 million output tokens in your currency. Use `0` for free/local models. | `0.71`, `0`, `15.00` |
+| `reasoningEffort` | String | No | For reasoning models (e.g., OpenAI o1): controls depth of reasoning process. | `low`, `medium`, `high` |
+| `reasoningSummaryDetails` | String | No | For reasoning models: controls how reasoning content is summarized in the response. | `auto`, `concise`, `detailed` |
+
+**Notes:**
+- Properties marked "Yes" in the Required column must be present in every profile; missing required properties will cause runtime errors.
+- The `prompts` property determines the prompt formatting strategy. For text completions, specify a strategy name (e.g., `llama3`, `gpt-oss`) or `no` for no formatting. For chat completions, use an empty string.
+- Cost properties are used for statistics tracking and have no impact on API calls. They help you monitor expenses.
+- Reasoning properties are only applicable to models that support separate reasoning output (like OpenAI's o1 series).
+
 ### Reasoning Models
 
 For models that support reasoning (e.g., OpenAI's o1 series), you can configure reasoning parameters in the model profile:
@@ -79,6 +107,58 @@ For models that support reasoning (e.g., OpenAI's o1 series), you can configure
 
 These settings control how the model generates and presents its reasoning process. The reasoning content is captured separately from the main response and can be accessed via the `reasoning` field in `Completion` objects or the `<key>_reasoning` context key in agents.
 
+### Example Profile Files
+
+**Azure AI Chat Completion (llama3.3-70b-azure.properties):**
+```properties
+prompts=
+tokenEncoding=cl100k_base
+maxTokens=1500
+maxContextLength=32767
+temperature=0.2
+topP=1.0
+modelProvider=AZURE_AI
+modelName=Llama-3.3-70B-Instruct
+url=https://your-endpoint.inference.ai.azure.com
+apiKey=${apikey}
+cost1MInTokens=0.71
+cost1MOutTokens=0.71
+```
+
+**Local Chat Completion (local-chatcompletion.properties):**
+```properties
+prompts=
+tokenEncoding=cl100k_base
+maxTokens=2500
+maxContextLength=16000
+temperature=0.4
+topP=1.0
+modelProvider=OAI_CHAT_COMPLETION
+modelName=
+url=http://localhost:5001
+apiKey=
+cost1MInTokens=0
+cost1MOutTokens=0
+reasoningEffort=medium
+reasoningSummaryDetails=auto
+```
+
+**Local Text Completion (local-completion.properties):**
+```properties
+prompts=gpt-oss
+tokenEncoding=cl100k_base
+maxTokens=2500
+maxContextLength=16000
+temperature=0.4
+topP=1.0
+modelProvider=OAI_TEXT_COMPLETION
+modelName=
+url=http://localhost:5001
+apiKey=
+cost1MInTokens=0
+cost1MOutTokens=0
+```
+
 ## OpenAi compatible client
 
 Use tokenizer strategy jtokkit or llamaServer (if the model is hosted on a llama.cpp server)