Added python versions for AI connection string pages (7.1, 7.2)

docs/ai-integration/connection-strings/azure-open-ai.mdx

@@ -8,15 +8,20 @@ import LanguageSwitcher from "@site/src/components/LanguageSwitcher";
 import LanguageContent from "@site/src/components/LanguageContent";
 
 import AzureOpenAiCsharp from './content/_azure-open-ai-csharp.mdx';
+import AzureOpenAiPython from './content/_azure-open-ai-python.mdx';
 
-export const supportedLanguages = ["csharp"];
+export const supportedLanguages = ["csharp", "python"];
 
 <LanguageSwitcher supportedLanguages={supportedLanguages} />
 
 <LanguageContent language="csharp">
    <AzureOpenAiCsharp />
 </LanguageContent>
 
+<LanguageContent language="python">
+   <AzureOpenAiPython />
+</LanguageContent>
+
 <!---
 ### Vector Search
 - [Vector search overview](../../ai-integration/vector-search/overview)

docs/ai-integration/connection-strings/content/_azure-open-ai-python.mdx

@@ -0,0 +1,209 @@
+import Admonition from '@theme/Admonition';
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+import CodeBlock from '@theme/CodeBlock';
+
+<Admonition type="note" title="">
+
+* This article explains how to define a connection string to the [Azure OpenAI Service](https://azure.microsoft.com/en-us/products/ai-services/openai-service),  
+  enabling RavenDB to use Azure OpenAI models for [Embeddings generation tasks](../../../ai-integration/generating-embeddings/overview.mdx),
+  [Gen AI tasks](../../../ai-integration/gen-ai-integration/overview.mdx), and [AI agents](../../../ai-integration/ai-agents/overview.mdx).    
+
+* In this article:
+  * [Define the connection string - from the Studio](../../../ai-integration/connection-strings/azure-open-ai.mdx#define-the-connection-string---from-the-studio)
+    * [Configuring a text embedding model](../../../ai-integration/connection-strings/azure-open-ai.mdx#configuring-a-text-embedding-model)
+    * [Configuring a chat model](../../../ai-integration/connection-strings/azure-open-ai.mdx#configuring-a-chat-model)
+  * [Define the connection string - from the Client API](../../../ai-integration/connection-strings/azure-open-ai.mdx#define-the-connection-string---from-the-client-api)
+  * [Syntax](../../../ai-integration/connection-strings/azure-open-ai.mdx#syntax) 
+
+</Admonition>
+
+## Define the connection string - from the Studio
+
+### Configuring a text embedding model
+
+![connection string to azure open ai](../assets/azure-open-ai-1.png)
+
+1. **Name**  
+   Enter a name for this connection string.
+
+2. **Identifier** (optional)  
+   Enter an identifier for this connection string.  
+   Learn more about the identifier in the [connection string identifier](../../../ai-integration/connection-strings/overview.mdx#identifier) section.
+
+3. **Model Type**  
+   Select "Text Embeddings".
+
+4. **Connector**  
+   Select **Azure OpenAI** from the dropdown menu.
+
+5. **API key**  
+   Enter the API key used to authenticate requests to the Azure OpenAI service.
+
+6. **Endpoint**  
+   Enter the base URL of your Azure OpenAI resource.
+
+7. **Model**  
+   Select or enter an Azure OpenAI text embedding model from the dropdown list or enter a new one.
+
+8. **Deployment name**  
+   Specify the unique identifier assigned to your model deployment in your Azure environment.
+
+9. **Dimensions** (optional)  
+   * Specify the number of dimensions for the output embeddings.  
+     Supported only by _text-embedding-3_ and later models.  
+   * If not specified, the model's default dimensionality is used.
+
+10. **Max concurrent query batches**: (optional)  
+    * When making vector search queries, the content of the search terms must also be converted to embeddings to compare them against the stored vectors.  
+      Requests to generate such query embeddings via the AI provider are sent in batches.
+    * This parameter defines the maximum number of these batches that can be processed concurrently.  
+      You can set a default value using the [Ai.Embeddings.MaxConcurrentBatches](../../../server/configuration/ai-integration-configuration.mdx#aiembeddingsmaxconcurrentbatches) configuration key.
+
+11. Click **Test Connection** to confirm the connection string is set up correctly.
+
+12. Click **Save** to store the connection string or **Cancel** to discard changes.
+
+### Configuring a chat model
+
+* When configuring a chat model, the UI displays the same base fields as those used for [text embedding models](../../../ai-integration/connection-strings/azure-open-ai.mdx#configuring-a-text-embedding-model),  
+  including the connection string _Name_, optional _Identifier_, _API Key_, _Endpoint_, _Deployment Name_, and _Model_ name.
+
+* One additional setting is specific to chat models: _Temperature_.
+
+![connection string to azure open ai](../assets/azure-open-ai-2.png)
+
+1. **Model Type**  
+   Select "Chat".
+
+2. **Model**  
+   Enter the name of the Azure OpenAI model to use for chat completions.
+
+3. **Temperature** (optional)  
+   The temperature setting controls the randomness and creativity of the model’s output.  
+   Valid values typically range from `0.0` to `2.0`:  
+     * Higher values (e.g., `1.0` or above) produce more diverse and creative responses.
+     * Lower values (e.g., `0.2`) result in more focused, consistent, and deterministic output.
+     * If not explicitly set, Azure OpenAI uses a default temperature of `1.0`.  
+       See [Azure OpenAI chat completions parameters](https://learn.microsoft.com/en-us/azure/ai-foundry/openai/reference#request-body-2).
+
+---
+
+## Define the connection string - from the Client API
+
+<Tabs groupId='languageSyntax'>
+<TabItem value="Connection_string_for_text_embedding_model" label="Connection_string_for_text_embedding_model">
+```python
+store = DocumentStore([ravendb_url], database_name)
+store.initialize()
+
+# Define the connection string to Azure OpenAI
+connection_string = AiConnectionString(
+    # Connection string name & identifier
+    name="ConnectionStringToAzureOpenAI",
+    identifier="identifier-to-the-connection-string",
+    # Model type
+    model_type=AiModelType.TEXT_EMBEDDINGS,
+    # Azure OpenAI settings
+    azure_openai_settings=AzureOpenAiSettings(
+        api_key="your-api-key",
+        endpoint="https://your-resource-name.openai.azure.com",
+        # Name of text embedding model to use
+        model="text-embedding-3-small",
+        deployment_name="your-deployment-name",
+        # Optionally, override the default maximum number of query embedding batches
+        # that can be processed concurrently.
+        embeddings_max_concurrent_batches=10,
+    ),
+)
+
+# Deploy the connection string to the server
+put_connection_string_op = PutConnectionStringOperation(connection_string)
+put_connection_string_result = store.maintenance.send(put_connection_string_op)
+```
+</TabItem>
+<TabItem value="Connection_string_for_chat_model" label="Connection_string_for_chat_model">
+```python
+store = DocumentStore([ravendb_url], database_name)
+store.initialize()
+
+# Define the connection string to Azure OpenAI
+connection_string = AiConnectionString(
+    # Connection string name & identifier
+    name="ConnectionStringToAzureOpenAI",
+    identifier="identifier-to-the-connection-string",
+    # Model type
+    model_type=AiModelType.CHAT,
+    # Azure OpenAI settings
+    azure_openai_settings=AzureOpenAiSettings(
+        api_key="your-api-key",
+        endpoint="https://your-resource-name.openai.azure.com",
+        # Name of chat model to use
+        model="gpt-4o-mini",
+        deployment_name="your-deployment-name",
+        # Optionally, set the model's temperature
+        temperature=0.4,
+    ),
+)
+
+# Deploy the connection string to the server
+put_connection_string_op = PutConnectionStringOperation(connection_string)
+put_connection_string_result = store.maintenance.send(put_connection_string_op)
+```
+</TabItem>
+</Tabs>
+
+## Syntax
+
+<TabItem value="azure_open_ai_settings" label="azure_open_ai_settings">
+```python
+class AiConnectionString(ConnectionString):
+    def __init__(
+        self,
+        name: str,
+        identifier: str,
+        # ...
+        model_type: AiModelType = None,
+        azure_openai_settings: Optional[AzureOpenAiSettings] = None,
+    ):
+        ...
+
+class AzureOpenAiSettings(OpenAiBaseSettings):
+    def __init__(
+        self,
+        api_key: str = None,
+        endpoint: str = None,
+        model: str = None,
+        deployment_name: str = None,
+        # Relevant only for text embedding models:
+        # Specifies the number of dimensions in the generated embedding vectors.
+        dimensions: int = None,
+        # Relevant only for chat models:
+        # Controls the randomness and creativity of the model's output.
+        # Higher values (e.g., 1.0 or above) produce more diverse and creative responses.
+        # Lower values (e.g., 0.2) result in more focused and deterministic output.
+        # If set to 'null', the temperature is not sent and the model's default will be used.
+        temperature: float = None,
+        embeddings_max_concurrent_batches: int = None,
+    ):
+        super().__init__(api_key, endpoint, model, dimensions, temperature, embeddings_max_concurrent_batches)
+        ...
+
+class OpenAiBaseSettings(AbstractAiSettings, ABC):
+    def __init__(
+        self,
+        api_key: str = None,
+        endpoint: str = None,
+        model: str = None,
+        dimensions: int = None,
+        temperature: float = None,
+        embeddings_max_concurrent_batches: int = None,
+    ):
+        super().__init__(embeddings_max_concurrent_batches)
+        ...
+
+class AbstractAiSettings:
+    def __init__(self, embeddings_max_concurrent_batches: int = None):
+        ...
+```
+</TabItem>

docs/ai-integration/connection-strings/content/_embedded-python.mdx

@@ -0,0 +1,96 @@
+import Admonition from '@theme/Admonition';
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+import CodeBlock from '@theme/CodeBlock';
+
+<Admonition type="note" title="">
+
+* This article explains how to define a connection string to the [bge-micro-v2](https://huggingface.co/TaylorAI/bge-micro-v2) model.  
+  This model, designed exclusively for embeddings generation, is embedded within RavenDB, enabling RavenDB to seamlessly handle its
+  [Embeddings generation tasks](../../../ai-integration/generating-embeddings/overview.mdx) without requiring an external AI service.  
+
+* Running the model locally consumes processor resources and will impact RavenDB's overall performance,  
+  depending on your workload and usage patterns.
+
+* In this article:
+  * [Define the connection string - from the Studio](../../../ai-integration/connection-strings/embedded.mdx#define-the-connection-string---from-the-studio)
+  * [Define the connection string - from the Client API](../../../ai-integration/connection-strings/embedded.mdx#define-the-connection-string---from-the-client-api)
+  * [Syntax](../../../ai-integration/connection-strings/embedded.mdx#syntax)
+
+</Admonition>
+
+## Define the connection string - from the Studio
+
+![connection string to the embedded model](../assets/embedded.png)
+
+1. **Name**  
+   Enter a name for this connection string.
+
+2. **Identifier** (optional)  
+   Learn more about the identifier in the [connection string identifier](../../../ai-integration/connection-strings/overview.mdx#the-connection-string-identifier) section.
+
+3. **Model Type**  
+   Select "Text Embeddings".
+
+4. **Connector**  
+   Select **Embedded (bge-micro-v2)** from the dropdown menu.
+
+5. **Max concurrent query batches**: (optional)
+   * When making vector search queries, the content of the search terms must also be converted to embeddings to compare them against the stored vectors.  
+     Requests to generate such query embeddings via the AI provider are sent in batches.
+   * This parameter defines the maximum number of these batches that can be processed concurrently.  
+     You can set a default value using the [Ai.Embeddings.MaxConcurrentBatches](../../../server/configuration/ai-integration-configuration.mdx#aiembeddingsmaxconcurrentbatches) configuration key.
+
+6. Click **Save** to store the connection string or **Cancel** to discard changes.
+
+## Define the connection string - from the Client API
+
+<TabItem value="embedded_connection_string_embedding" label="embedded_connection_string_embedding">
+```python
+store = DocumentStore([ravendb_url], database_name)
+store.initialize()
+
+# Define the connection string to the embedded model
+connection_string = AiConnectionString(
+    # Connection string name & identifier
+    name="ConnectionStringToEmbedded",
+    identifier="identifier-to-the-connection-string",
+    # Model type
+    model_type=AiModelType.TEXT_EMBEDDINGS,
+    # Embedded model settings
+    # No user configuration is required for the embedded model 
+    # as it uses predefined values managed internally by RavenDB.
+    embedded_settings=EmbeddedSettings()
+)
+
+# Optionally, override the default maximum number of query embedding batches that can be processed concurrently
+connection_string.embedded_settings.embeddings_max_concurrent_batches = 10
+
+# Deploy the connection string to the server
+put_connection_string_op = PutConnectionStringOperation(connection_string)
+put_connection_string_result = store.maintenance.send(put_connection_string_op)
+```
+</TabItem>
+
+## Syntax
+
+<TabItem value="embedded_settings" label="embedded_settings">
+```python
+class AiConnectionString(ConnectionString):
+    def __init__(
+        self,
+        name: str,
+        identifier: str,
+        # ...
+        model_type: AiModelType = None,
+        embedded_settings: Optional[EmbeddedSettings] = None,
+      ): ...
+
+class EmbeddedSettings(AbstractAiSettings):
+    def __init__(self, embeddings_max_concurrent_batches: int = None):
+        super().__init__(embeddings_max_concurrent_batches)
+
+class AbstractAiSettings:
+    def __init__(self, embeddings_max_concurrent_batches: int = None): ...
+```
+</TabItem>