Module haystack_experimental.components.retrievers.auto_merging_retriever

AutoMergingRetriever

A retriever which returns parent documents of the matched leaf nodes documents, based on a threshold setting.

The AutoMergingRetriever assumes you have a hierarchical tree structure of documents, where the leaf nodes are indexed in a document store. See the HierarchicalDocumentSplitter for more information on how to create such a structure. During retrieval, if the number of matched leaf documents below the same parent is higher than a defined threshold, the retriever will return the parent document instead of the individual leaf documents.

The rational is, given that a paragraph is split into multiple chunks represented as leaf documents, and if for a given query, multiple chunks are matched, the whole paragraph might be more informative than the individual chunks alone.

Currently the AutoMergingRetriever can only be used by the following DocumentStores:

• AstraDB
• ElasticSearch
• OpenSearch
• PGVector
• Qdrant

from haystack import Document
from haystack_experimental.components.splitters import HierarchicalDocumentSplitter
from haystack_experimental.components.retrievers.auto_merging_retriever import AutoMergingRetriever
from haystack.document_stores.in_memory import InMemoryDocumentStore

# create a hierarchical document structure with 2 levels, where the parent document has 3 children
text = "The sun rose early in the morning. It cast a warm glow over the trees. Birds began to sing."
original_document = Document(content=text)
builder = HierarchicalDocumentSplitter(block_sizes=[10, 3], split_overlap=0, split_by="word")
docs = builder.run([original_document])["documents"]

# store level-1 parent documents and initialize the retriever
doc_store_parents = InMemoryDocumentStore()
for doc in docs["documents"]:
    if doc.meta["children_ids"] and doc.meta["level"] == 1:
        doc_store_parents.write_documents([doc])
retriever = AutoMergingRetriever(doc_store_parents, threshold=0.5)

# assume we retrieved 2 leaf docs from the same parent, the parent document should be returned,
# since it has 3 children and the threshold=0.5, and we retrieved 2 children (2/3 > 0.66(6))
leaf_docs = [doc for doc in docs["documents"] if not doc.meta["children_ids"]]
docs = retriever.run(leaf_docs[4:6])
>> {'documents': [Document(id=538..),
>> content: 'warm glow over the trees. Birds began to sing.',
>> meta: {'block_size': 10, 'parent_id': '835..', 'children_ids': ['c17...', '3ff...', '352...'], 'level': 1, 'source_id': '835...',
>> 'page_number': 1, 'split_id': 1, 'split_idx_start': 45})]}

AutoMergingRetriever.__init__

def __init__(document_store: DocumentStore, threshold: float = 0.5)

Initialize the AutoMergingRetriever.

Arguments:

• document_store: DocumentStore from which to retrieve the parent documents
• threshold: Threshold to decide whether the parent instead of the individual documents is returned

AutoMergingRetriever.to_dict

def to_dict() -> Dict[str, Any]

Serializes the component to a dictionary.

Returns:

Dictionary with serialized data.

AutoMergingRetriever.from_dict

@classmethod
def from_dict(cls, data: Dict[str, Any]) -> "AutoMergingRetriever"

Deserializes the component from a dictionary.

Arguments:

• data: Dictionary with serialized data.

Returns:

An instance of the component.

AutoMergingRetriever.run

@component.output_types(documents=List[Document])
def run(matched_leaf_documents: List[Document])

Run the AutoMergingRetriever.

Groups the matched leaf documents by their parent documents and returns the parent documents if the number of matched leaf documents below the same parent is higher than the defined threshold. Otherwise, returns the matched leaf documents.

Arguments:

• matched_leaf_documents: List of leaf documents that were matched by a retriever

Returns:

List of parent documents or matched leaf documents based on the threshold value

Module haystack_experimental.components.retrievers.chat_message_retriever

ChatMessageRetriever

Retrieves chat messages from the underlying ChatMessageStore.

Usage example:

from haystack.dataclasses import ChatMessage
from haystack_experimental.components.retrievers import ChatMessageRetriever
from haystack_experimental.chat_message_stores.in_memory import 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 = InMemoryChatMessageStore()
message_store.write_messages(messages)
retriever = ChatMessageRetriever(message_store)

result = retriever.run()

print(result["messages"])

ChatMessageRetriever.__init__

def __init__(message_store: ChatMessageStore, last_k: int = 10)

Create the ChatMessageRetriever component.

Arguments:

• message_store: An instance of a ChatMessageStore.
• last_k: The number of last messages to retrieve. Defaults to 10 messages if not specified.

ChatMessageRetriever.to_dict

def to_dict() -> Dict[str, Any]

Serializes the component to a dictionary.

Returns:

Dictionary with serialized data.

ChatMessageRetriever.from_dict

@classmethod
def from_dict(cls, data: Dict[str, Any]) -> "ChatMessageRetriever"

Deserializes the component from a dictionary.

Arguments:

• data: The dictionary to deserialize from.

Returns:

The deserialized component.

ChatMessageRetriever.run

@component.output_types(messages=List[ChatMessage])
def run(last_k: Optional[int] = None)

Run the ChatMessageRetriever

Arguments:

• last_k: The number of last messages to retrieve. This parameter takes precedence over the last_k parameter passed to the ChatMessageRetriever constructor. 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 1

Returns:

• messages - The retrieved chat messages.