Bump unstable version and create unstable docs

VERSION.txt

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-2.29.0-rc0
+2.30.0-rc0

docs-website/docusaurus.config.js

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           beforeDefaultRemarkPlugins: [require('./src/remark/versionedReferenceLinks')],
           versions: {
             current: {
-              label: '2.29-unstable',
+              label: '2.30-unstable',
               path: 'next',
               banner: 'unreleased',
             },
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         exclude: ['**/_templates/**'],
         versions: {
           current: {
-            label: '2.29-unstable',
+            label: '2.30-unstable',
             path: 'next',
             banner: 'unreleased',
           },

docs-website/reference_versioned_docs/version-2.29-unstable/experiments-api/experimental_chatmessage_store_api.md

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+---
+title: "ChatMessage Store"
+id: experimental-chatmessage-store-api
+description: "Storage for the chat messages."
+slug: "/experimental-chatmessage-store-api"
+---
+
+<a id="haystack_experimental.chat_message_stores.in_memory"></a>
+
+## Module haystack\_experimental.chat\_message\_stores.in\_memory
+
+<a id="haystack_experimental.chat_message_stores.in_memory.InMemoryChatMessageStore"></a>
+
+### InMemoryChatMessageStore
+
+Stores chat messages in-memory.
+
+The `chat_history_id` parameter is used as a unique identifier for each conversation or chat session.
+It acts as a namespace that isolates messages from different sessions. Each `chat_history_id` value corresponds to a
+separate list of `ChatMessage` objects stored in memory.
+
+Typical usage involves providing a unique `chat_history_id` (for example, a session ID or conversation ID)
+whenever you write, read, or delete messages. This ensures that chat messages from different
+conversations do not overlap.
+
+Usage example:
+```python
+from haystack.dataclasses import ChatMessage
+from haystack_experimental.chat_message_stores.in_memory import InMemoryChatMessageStore
+
+message_store = InMemoryChatMessageStore()
+
+messages = [
+    ChatMessage.from_assistant("Hello, how can I help you?"),
+    ChatMessage.from_user("Hi, I have a question about Python. What is a Protocol?"),
+]
+message_store.write_messages(chat_history_id="user_456_session_123", messages=messages)
+retrieved_messages = message_store.retrieve_messages(chat_history_id="user_456_session_123")
+
+print(retrieved_messages)
+```
+
+<a id="haystack_experimental.chat_message_stores.in_memory.InMemoryChatMessageStore.__init__"></a>
+
+#### InMemoryChatMessageStore.\_\_init\_\_
+
+```python
+def __init__(skip_system_messages: bool = True,
+             last_k: int | None = 10) -> None
+```
+
+Create an InMemoryChatMessageStore.
+
+**Arguments**:
+
+- `skip_system_messages`: Whether to skip storing system messages. Defaults to True.
+- `last_k`: The number of last messages to retrieve. Defaults to 10 messages if not specified.
+
+<a id="haystack_experimental.chat_message_stores.in_memory.InMemoryChatMessageStore.to_dict"></a>
+
+#### InMemoryChatMessageStore.to\_dict
+
+```python
+def to_dict() -> dict[str, Any]
+```
+
+Serializes the component to a dictionary.
+
+**Returns**:
+
+Dictionary with serialized data.
+
+<a id="haystack_experimental.chat_message_stores.in_memory.InMemoryChatMessageStore.from_dict"></a>
+
+#### InMemoryChatMessageStore.from\_dict
+
+```python
+@classmethod
+def from_dict(cls, data: dict[str, Any]) -> "InMemoryChatMessageStore"
+```
+
+Deserializes the component from a dictionary.
+
+**Arguments**:
+
+- `data`: The dictionary to deserialize from.
+
+**Returns**:
+
+The deserialized component.
+
+<a id="haystack_experimental.chat_message_stores.in_memory.InMemoryChatMessageStore.count_messages"></a>
+
+#### InMemoryChatMessageStore.count\_messages
+
+```python
+def count_messages(chat_history_id: str) -> int
+```
+
+Returns the number of chat messages stored in this store.
+
+**Arguments**:
+
+- `chat_history_id`: The chat history id for which to count messages.
+
+**Returns**:
+
+The number of messages.
+
+<a id="haystack_experimental.chat_message_stores.in_memory.InMemoryChatMessageStore.write_messages"></a>
+
+#### InMemoryChatMessageStore.write\_messages
+
+```python
+def write_messages(chat_history_id: str, messages: list[ChatMessage]) -> int
+```
+
+Writes chat messages to the ChatMessageStore.
+
+**Arguments**:
+
+- `chat_history_id`: The chat history id under which to store the messages.
+- `messages`: A list of ChatMessages to write.
+
+**Raises**:
+
+- `ValueError`: If messages is not a list of ChatMessages.
+
+**Returns**:
+
+The number of messages written.
+
+<a id="haystack_experimental.chat_message_stores.in_memory.InMemoryChatMessageStore.retrieve_messages"></a>
+
+#### InMemoryChatMessageStore.retrieve\_messages
+
+```python
+def retrieve_messages(chat_history_id: str,
+                      last_k: int | None = None) -> list[ChatMessage]
+```
+
+Retrieves all stored chat messages.
+
+**Arguments**:
+
+- `chat_history_id`: The chat history id from which to retrieve messages.
+- `last_k`: The number of last messages to retrieve. If unspecified, the last_k parameter passed
+to the constructor will be used.
+
+**Raises**:
+
+- `ValueError`: If last_k is not None and is less than 0.
+
+**Returns**:
+
+A list of chat messages.
+
+<a id="haystack_experimental.chat_message_stores.in_memory.InMemoryChatMessageStore.delete_messages"></a>
+
+#### InMemoryChatMessageStore.delete\_messages
+
+```python
+def delete_messages(chat_history_id: str) -> None
+```
+
+Deletes all stored chat messages.
+
+**Arguments**:
+
+- `chat_history_id`: The chat history id from which to delete messages.
+
+<a id="haystack_experimental.chat_message_stores.in_memory.InMemoryChatMessageStore.delete_all_messages"></a>
+
+#### InMemoryChatMessageStore.delete\_all\_messages
+
+```python
+def delete_all_messages() -> None
+```
+
+Deletes all stored chat messages from all chat history ids.
+

docs-website/reference_versioned_docs/version-2.29-unstable/experiments-api/experimental_generators_api.md

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+---
+title: "Generators"
+id: experimental-generators-api
+description: "Enables text generation using LLMs."
+slug: "/experimental-generators-api"
+---
+
+<a id="haystack_experimental.components.generators.chat.openai"></a>
+
+## Module haystack\_experimental.components.generators.chat.openai
+
+<a id="haystack_experimental.components.generators.chat.openai.OpenAIChatGenerator"></a>
+
+### OpenAIChatGenerator
+
+An OpenAI chat-based text generator component that supports hallucination risk scoring.
+
+This is based on the paper
+[LLMs are Bayesian, in Expectation, not in Realization](https://arxiv.org/abs/2507.11768).
+
+## Usage Example:
+
+    ```python
+    from haystack.dataclasses import ChatMessage
+
+    from haystack_experimental.utils.hallucination_risk_calculator.dataclasses import HallucinationScoreConfig
+    from haystack_experimental.components.generators.chat.openai import OpenAIChatGenerator
+
+    # Evidence-based Example
+    llm = OpenAIChatGenerator(model="gpt-4o")
+    rag_result = llm.run(
+        messages=[
+            ChatMessage.from_user(
+                text="Task: Answer strictly based on the evidence provided below.
+"
+                "Question: Who won the Nobel Prize in Physics in 2019?
+"
+                "Evidence:
+"
+                "- Nobel Prize press release (2019): James Peebles (1/2); Michel Mayor & Didier Queloz (1/2).
+"
+                "Constraints: If evidence is insufficient or conflicting, refuse."
+            )
+        ],
+        hallucination_score_config=HallucinationScoreConfig(skeleton_policy="evidence_erase"),
+    )
+    print(f"Decision: {rag_result['replies'][0].meta['hallucination_decision']}")
+    print(f"Risk bound: {rag_result['replies'][0].meta['hallucination_risk']:.3f}")
+    print(f"Rationale: {rag_result['replies'][0].meta['hallucination_rationale']}")
+    print(f"Answer:
+{rag_result['replies'][0].text}")
+    print("---")
+    ```
+
+<a id="haystack_experimental.components.generators.chat.openai.OpenAIChatGenerator.run"></a>
+
+#### OpenAIChatGenerator.run
+
+```python
+@component.output_types(replies=list[ChatMessage])
+def run(
+    messages: list[ChatMessage],
+    streaming_callback: StreamingCallbackT | None = None,
+    generation_kwargs: dict[str, Any] | None = None,
+    *,
+    tools: ToolsType | None = None,
+    tools_strict: bool | None = None,
+    hallucination_score_config: HallucinationScoreConfig | None = None
+) -> dict[str, list[ChatMessage]]
+```
+
+Invokes chat completion based on the provided messages and generation parameters.
+
+**Arguments**:
+
+- `messages`: A list of ChatMessage instances representing the input messages.
+- `streaming_callback`: A callback function that is called when a new token is received from the stream.
+- `generation_kwargs`: Additional keyword arguments for text generation. These parameters will
+override the parameters passed during component initialization.
+For details on OpenAI API parameters, see [OpenAI documentation](https://platform.openai.com/docs/api-reference/chat/create).
+- `tools`: A list of Tool and/or Toolset objects, or a single Toolset for which the model can prepare calls.
+If set, it will override the `tools` parameter provided during initialization.
+- `tools_strict`: Whether to enable strict schema adherence for tool calls. If set to `True`, the model will follow exactly
+the schema provided in the `parameters` field of the tool definition, but this may increase latency.
+If set, it will override the `tools_strict` parameter set during component initialization.
+- `hallucination_score_config`: If provided, the generator will evaluate the hallucination risk of its responses using
+the OpenAIPlanner and annotate each response with hallucination metrics.
+This involves generating multiple samples and analyzing their consistency, which may increase
+latency and cost. Use this option when you need to assess the reliability of the generated content
+in scenarios where accuracy is critical.
+For details, see the [research paper](https://arxiv.org/abs/2507.11768)
+
+**Returns**:
+
+A dictionary with the following key:
+- `replies`: A list containing the generated responses as ChatMessage instances. If hallucination
+scoring is enabled, each message will include additional metadata:
+  - `hallucination_decision`: "ANSWER" if the model decided to answer, "REFUSE" if it abstained.
+  - `hallucination_risk`: The EDFL hallucination risk bound.
+  - `hallucination_rationale`: The rationale behind the hallucination decision.
+
+<a id="haystack_experimental.components.generators.chat.openai.OpenAIChatGenerator.run_async"></a>
+
+#### OpenAIChatGenerator.run\_async
+
+```python
+@component.output_types(replies=list[ChatMessage])
+async def run_async(
+    messages: list[ChatMessage],
+    streaming_callback: StreamingCallbackT | None = None,
+    generation_kwargs: dict[str, Any] | None = None,
+    *,
+    tools: ToolsType | None = None,
+    tools_strict: bool | None = None,
+    hallucination_score_config: HallucinationScoreConfig | None = None
+) -> dict[str, list[ChatMessage]]
+```
+
+Asynchronously invokes chat completion based on the provided messages and generation parameters.
+
+This is the asynchronous version of the `run` method. It has the same parameters and return values
+but can be used with `await` in async code.
+
+**Arguments**:
+
+- `messages`: A list of ChatMessage instances representing the input messages.
+- `streaming_callback`: A callback function that is called when a new token is received from the stream.
+Must be a coroutine.
+- `generation_kwargs`: Additional keyword arguments for text generation. These parameters will
+override the parameters passed during component initialization.
+For details on OpenAI API parameters, see [OpenAI documentation](https://platform.openai.com/docs/api-reference/chat/create).
+- `tools`: A list of Tool and/or Toolset objects, or a single Toolset for which the model can prepare calls.
+If set, it will override the `tools` parameter provided during initialization.
+- `tools_strict`: Whether to enable strict schema adherence for tool calls. If set to `True`, the model will follow exactly
+the schema provided in the `parameters` field of the tool definition, but this may increase latency.
+If set, it will override the `tools_strict` parameter set during component initialization.
+- `hallucination_score_config`: If provided, the generator will evaluate the hallucination risk of its responses using
+the OpenAIPlanner and annotate each response with hallucination metrics.
+This involves generating multiple samples and analyzing their consistency, which may increase
+latency and cost. Use this option when you need to assess the reliability of the generated content
+in scenarios where accuracy is critical.
+For details, see the [research paper](https://arxiv.org/abs/2507.11768)
+
+**Returns**:
+
+A dictionary with the following key:
+- `replies`: A list containing the generated responses as ChatMessage instances. If hallucination
+scoring is enabled, each message will include additional metadata:
+  - `hallucination_decision`: "ANSWER" if the model decided to answer, "REFUSE" if it abstained.
+  - `hallucination_risk`: The EDFL hallucination risk bound.
+  - `hallucination_rationale`: The rationale behind the hallucination decision.
+