chore: import upstream snapshot with attribution
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---
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title: "Retrievers"
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id: experimental-retrievers-api
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description: "Sweep through Document Stores and return a set of candidate documents that are relevant to the query."
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slug: "/experimental-retrievers-api"
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---
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<a id="haystack_experimental.components.retrievers.chat_message_retriever"></a>
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## Module haystack\_experimental.components.retrievers.chat\_message\_retriever
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<a id="haystack_experimental.components.retrievers.chat_message_retriever.ChatMessageRetriever"></a>
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### ChatMessageRetriever
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Retrieves chat messages from the underlying ChatMessageStore.
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Usage example:
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```python
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from haystack.dataclasses import ChatMessage
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from haystack_experimental.components.retrievers import ChatMessageRetriever
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from haystack_experimental.chat_message_stores.in_memory import InMemoryChatMessageStore
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messages = [
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ChatMessage.from_assistant("Hello, how can I help you?"),
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ChatMessage.from_user("Hi, I have a question about Python. What is a Protocol?"),
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]
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message_store = InMemoryChatMessageStore()
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message_store.write_messages(chat_history_id="user_456_session_123", messages=messages)
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retriever = ChatMessageRetriever(message_store)
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result = retriever.run(chat_history_id="user_456_session_123")
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print(result["messages"])
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```
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<a id="haystack_experimental.components.retrievers.chat_message_retriever.ChatMessageRetriever.__init__"></a>
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#### ChatMessageRetriever.\_\_init\_\_
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```python
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def __init__(chat_message_store: ChatMessageStore, last_k: int | None = 10)
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```
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Create the ChatMessageRetriever component.
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**Arguments**:
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- `chat_message_store`: An instance of a ChatMessageStore.
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- `last_k`: The number of last messages to retrieve. Defaults to 10 messages if not specified.
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<a id="haystack_experimental.components.retrievers.chat_message_retriever.ChatMessageRetriever.to_dict"></a>
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#### ChatMessageRetriever.to\_dict
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```python
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def to_dict() -> dict[str, Any]
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```
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Serializes the component to a dictionary.
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**Returns**:
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Dictionary with serialized data.
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<a id="haystack_experimental.components.retrievers.chat_message_retriever.ChatMessageRetriever.from_dict"></a>
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#### ChatMessageRetriever.from\_dict
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```python
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@classmethod
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def from_dict(cls, data: dict[str, Any]) -> "ChatMessageRetriever"
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```
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Deserializes the component from a dictionary.
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**Arguments**:
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- `data`: The dictionary to deserialize from.
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**Returns**:
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The deserialized component.
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<a id="haystack_experimental.components.retrievers.chat_message_retriever.ChatMessageRetriever.run"></a>
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#### ChatMessageRetriever.run
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```python
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@component.output_types(messages=list[ChatMessage])
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def run(
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chat_history_id: str,
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*,
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last_k: int | None = None,
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current_messages: list[ChatMessage] | None = None
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) -> dict[str, list[ChatMessage]]
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```
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Run the ChatMessageRetriever
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**Arguments**:
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- `chat_history_id`: A unique identifier for the chat session or conversation whose messages should be retrieved.
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Each `chat_history_id` corresponds to a distinct chat history stored in the underlying ChatMessageStore.
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For example, use a session ID or conversation ID to isolate messages from different chat sessions.
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- `last_k`: The number of last messages to retrieve. This parameter takes precedence over the last_k
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parameter passed to the ChatMessageRetriever constructor. If unspecified, the last_k parameter passed
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to the constructor will be used.
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- `current_messages`: A list of incoming chat messages to combine with the retrieved messages. System messages from this list
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are prepended before the retrieved history, while all other messages (e.g., user messages) are appended
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after. This is useful for including new conversational context alongside stored history so the output
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can be directly used as input to a ChatGenerator or an Agent. If not provided, only the stored messages
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will be returned.
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**Raises**:
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- `ValueError`: If last_k is not None and is less than 0.
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**Returns**:
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A dictionary with the following key:
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- `messages` - The retrieved chat messages combined with any provided current messages.
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