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192 | class AutoMergingRetriever(BaseRetriever):
"""This retriever will try to merge context into parent context.
The retriever first retrieves chunks from a vector store.
Then, it will try to merge the chunks into a single context.
"""
def __init__(
self,
vector_retriever: VectorIndexRetriever,
storage_context: StorageContext,
simple_ratio_thresh: float = 0.5,
verbose: bool = False,
callback_manager: Optional[CallbackManager] = None,
object_map: Optional[dict] = None,
objects: Optional[List[IndexNode]] = None,
) -> None:
"""Init params."""
self._vector_retriever = vector_retriever
self._storage_context = storage_context
self._simple_ratio_thresh = simple_ratio_thresh
super().__init__(
callback_manager=callback_manager,
object_map=object_map,
objects=objects,
verbose=verbose,
)
def _get_parents_and_merge(
self, nodes: List[NodeWithScore]
) -> Tuple[List[NodeWithScore], bool]:
"""Get parents and merge nodes."""
# retrieve all parent nodes
parent_nodes: Dict[str, BaseNode] = {}
parent_cur_children_dict: Dict[str, List[NodeWithScore]] = defaultdict(list)
for node in nodes:
if node.node.parent_node is None:
continue
parent_node_info = node.node.parent_node
# Fetch actual parent node if doesn't exist in `parent_nodes` cache yet
parent_node_id = parent_node_info.node_id
if parent_node_id not in parent_nodes:
parent_node = self._storage_context.docstore.get_document(
parent_node_id
)
parent_nodes[parent_node_id] = cast(BaseNode, parent_node)
# add reference to child from parent
parent_cur_children_dict[parent_node_id].append(node)
# compute ratios and "merge" nodes
# merging: delete some children nodes, add some parent nodes
node_ids_to_delete = set()
nodes_to_add: Dict[str, NodeWithScore] = {}
for parent_node_id, parent_node in parent_nodes.items():
parent_child_nodes = parent_node.child_nodes
parent_num_children = len(parent_child_nodes) if parent_child_nodes else 1
parent_cur_children = parent_cur_children_dict[parent_node_id]
ratio = len(parent_cur_children) / parent_num_children
# if ratio is high enough, merge
if ratio > self._simple_ratio_thresh:
node_ids_to_delete.update(
set({n.node.node_id for n in parent_cur_children})
)
parent_node_text = truncate_text(
parent_node.get_content(metadata_mode=MetadataMode.NONE), 100
)
info_str = (
f"> Merging {len(parent_cur_children)} nodes into parent node.\n"
f"> Parent node id: {parent_node_id}.\n"
f"> Parent node text: {parent_node_text}\n"
)
logger.info(info_str)
if self._verbose:
print(info_str)
# add parent node
# can try averaging score across embeddings for now
avg_score = sum(
[n.get_score() or 0.0 for n in parent_cur_children]
) / len(parent_cur_children)
parent_node_with_score = NodeWithScore(
node=parent_node, score=avg_score
)
nodes_to_add[parent_node_id] = parent_node_with_score
# delete old child nodes, add new parent nodes
new_nodes = [n for n in nodes if n.node.node_id not in node_ids_to_delete]
# add parent nodes
new_nodes.extend(list(nodes_to_add.values()))
is_changed = len(node_ids_to_delete) > 0
return new_nodes, is_changed
def _fill_in_nodes(
self, nodes: List[NodeWithScore]
) -> Tuple[List[NodeWithScore], bool]:
"""Fill in nodes."""
new_nodes = []
is_changed = False
for idx, node in enumerate(nodes):
new_nodes.append(node)
if idx >= len(nodes) - 1:
continue
cur_node = cast(BaseNode, node.node)
# if there's a node in the middle, add that to the queue
if (
cur_node.next_node is not None
and cur_node.next_node == nodes[idx + 1].node.prev_node
):
is_changed = True
next_node = self._storage_context.docstore.get_document(
cur_node.next_node.node_id
)
next_node = cast(BaseNode, next_node)
next_node_text = truncate_text(
next_node.get_content(metadata_mode=MetadataMode.NONE), 100
)
info_str = (
f"> Filling in node. Node id: {cur_node.next_node.node_id}"
f"> Node text: {next_node_text}\n"
)
logger.info(info_str)
if self._verbose:
print(info_str)
# set score to be average of current node and next node
avg_score = (node.get_score() + nodes[idx + 1].get_score()) / 2
new_nodes.append(NodeWithScore(node=next_node, score=avg_score))
return new_nodes, is_changed
def _try_merging(
self, nodes: List[NodeWithScore]
) -> Tuple[List[NodeWithScore], bool]:
"""Try different ways to merge nodes."""
# first try filling in nodes
nodes, is_changed_0 = self._fill_in_nodes(nodes)
# then try merging nodes
nodes, is_changed_1 = self._get_parents_and_merge(nodes)
return nodes, is_changed_0 or is_changed_1
def _retrieve(self, query_bundle: QueryBundle) -> List[NodeWithScore]:
"""Retrieve nodes given query.
Implemented by the user.
"""
initial_nodes = self._vector_retriever.retrieve(query_bundle)
cur_nodes, is_changed = self._try_merging(initial_nodes)
# cur_nodes, is_changed = self._get_parents_and_merge(initial_nodes)
while is_changed:
cur_nodes, is_changed = self._try_merging(cur_nodes)
# cur_nodes, is_changed = self._get_parents_and_merge(cur_nodes)
# sort by similarity
cur_nodes.sort(key=lambda x: x.get_score(), reverse=True)
return cur_nodes
|
