dosubot[bot]
commented
4 months ago The discrepancy between nodes and all_nodes usage in the VectorStoreIndex of the DenseXRetrievalPack class could be due to the filtering and querying logic implemented in the monkey_query function. Specifically, the function filters nodes based on metadata and node ID restrictions before performing the query. If all_nodes is not being considered in this filtering process, it could lead to a discrepancy.
To configure VectorStoreIndex to successfully query using embeddings from all_nodes, ensure that the filtering logic in the monkey_query function includes all relevant nodes. Here is the relevant part of the code that handles the filtering and querying:
def monkey_query(
self,
query: VectorStoreQuery,
**kwargs: Any,
):
"""Get nodes for response."""
# Prevent metadata filtering on stores that were persisted without metadata.
if (
query.filters is not None
and self._data.embedding_dict
and not self._data.metadata_dict
):
raise ValueError(
"Cannot filter stores that were persisted without metadata. "
"Please rebuild the store with metadata to enable filtering."
)
# Prefilter nodes based on the query filter and node ID restrictions.
query_filter_fn = _build_metadata_filter_fn(
lambda node_id: self._data.metadata_dict[node_id], query.filters
)
if query.node_ids is not None:
available_ids = set(query.node_ids)
def node_filter_fn(node_id: str) -> bool:
return node_id in available_ids
else:
def node_filter_fn(node_id: str) -> bool:
return True
node_ids = []
embeddings = []
# TODO: consolidate with get_query_text_embedding_similarities
for node_id, embedding in self._data.embedding_dict.items():
if node_filter_fn(node_id) and query_filter_fn(node_id):
node_ids.append(node_id)
embeddings.append(embedding)
query_embedding = cast(List[float], query.query_embedding)
if query.mode in LEARNER_MODES:
top_similarities, top_ids = get_top_k_embeddings_learner(
query_embedding,
embeddings,
similarity_top_k=query.similarity_top_k,
embedding_ids=node_ids,
)
elif query.mode == MMR_MODE:
mmr_threshold = kwargs.get("mmr_threshold", None)
top_similarities, top_ids = get_top_k_mmr_embeddings(
query_embedding,
embeddings,
similarity_top_k=query.similarity_top_k,
embedding_ids=node_ids,
mmr_threshold=mmr_threshold,
)
elif query.mode == VectorStoreQueryMode.DEFAULT:
top_similarities, top_ids = get_top_k_embeddings(
query_embedding,
embeddings,
similarity_top_k=query.similarity_top_k,
embedding_ids=node_ids,
)
elif query.mode == VectorStoreQueryMode.HYBRID: # where I made my changes
top_similarities, top_ids = get_top_k_embeddings(
query_embedding,
embeddings,
similarity_top_k=query.similarity_top_k,
embedding_ids=node_ids,
)
else:
raise ValueError(f"Invalid query mode: {query.mode}")
return VectorStoreQueryResult(similarities=top_similarities, ids=top_ids)Ensure that the node_filter_fn and query_filter_fn functions are correctly configured to include all relevant nodes from all_nodes. If all_nodes is a broader set than the current filtering allows, you may need to adjust the filtering logic to ensure it includes all necessary nodes.
Additionally, the VectorIndexRetriever class in the llama-index-legacy/llama_index/legacy/indices/vector_store/retrievers/retriever.py module provides a method to build and execute queries against the VectorStoreIndex. Here is an example of how to configure and use this class:
class VectorIndexRetriever(BaseRetriever):
def __init__(
self,
index: VectorStoreIndex,
similarity_top_k: int = DEFAULT_SIMILARITY_TOP_K,
vector_store_query_mode: VectorStoreQueryMode = VectorStoreQueryMode.DEFAULT,
filters: Optional[MetadataFilters] = None,
alpha: Optional[float] = None,
node_ids: Optional[List[str]] = None,
doc_ids: Optional[List[str]] = None,
sparse_top_k: Optional[int] = None,
callback_manager: Optional[CallbackManager] = None,
object_map: Optional[dict] = None,
verbose: bool = False,
**kwargs: Any,
) -> None:
self._index = index
self._vector_store = self._index.vector_store
self._service_context = self._index.service_context
self._docstore = self._index.docstore
self._similarity_top_k = similarity_top_k
self._vector_store_query_mode = VectorStoreQueryMode(vector_store_query_mode)
self._alpha = alpha
self._node_ids = node_ids
self._doc_ids = doc_ids
self._filters = filters
self._sparse_top_k = sparse_top_k
self._kwargs: Dict[str, Any] = kwargs.get("vector_store_kwargs", {})
super().__init__(
callback_manager=callback_manager, object_map=object_map, verbose=verbose
)
def _build_vector_store_query(
self, query_bundle_with_embeddings: QueryBundle
) -> VectorStoreQuery:
return VectorStoreQuery(
query_embedding=query_bundle_with_embeddings.embedding,
similarity_top_k=self._similarity_top_k,
node_ids=self._node_ids,
doc_ids=self._doc_ids,
query_str=query_bundle_with_embeddings.query_str,
mode=self._vector_store_query_mode,
alpha=self._alpha,
filters=self._filters,
sparse_top_k=self._sparse_top_k,
)
def _build_node_list_from_query_result(
self, query_result: VectorStoreQueryResult
) -> List[NodeWithScore]:
if query_result.nodes is None:
if query_result.ids is None:
raise ValueError(
"Vector store query result should return at "
"least one of nodes or ids."
)
assert isinstance(self._index.index_struct, IndexDict)
node_ids = [
self._index.index_struct.nodes_dict[idx] for idx in query_result.ids
]
nodes = self._docstore.get_nodes(node_ids)
query_result.nodes = nodes
else:
for i in range(len(query_result.nodes)):
source_node = query_result.nodes[i].source_node
if (not self._vector_store.stores_text) or (
source_node is not None and source_node.node_type != ObjectType.TEXT
):
node_id = query_result.nodes[i].node_id
if self._docstore.document_exists(node_id):
query_result.nodes[i] = self._docstore.get_node(
node_id
) # type: ignore[index]
log_vector_store_query_result(query_result)
node_with_scores: List[NodeWithScore] = []
for ind, node in enumerate(query_result.nodes):
score: Optional[float] = None
if query_result.similarities is not None:
score = query_result.similarities[ind]
node_with_scores.append(NodeWithScore(node=node, score=score))
return node_with_scores
def _get_nodes_with_embeddings(
self, query_bundle_with_embeddings: QueryBundle
) -> List[NodeWithScore]:
query = self._build_vector_store_query(query_bundle_with_embeddings)
query_result = self._vector_store.query(query, **self._kwargs)
return self._build_node_list_from_query_result(query_result)
async def _aget_nodes_with_embeddings(
self, query_bundle_with_embeddings: QueryBundle
) -> List[NodeWithScore]:
query = self._build_vector_store_query(query_bundle_with_embeddings)
query_result = await self._vector_store.aquery(query, **self._kwargs)
return self._build_node_list_from_query_result(query_result)By using the VectorIndexRetriever class, you can ensure that the query includes all relevant nodes from all_nodes by properly configuring the node_ids and filters parameters. This will help in resolving the discrepancy and successfully querying using embeddings from all_nodes [1][2][3][4].
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Question Validation
Question
In the DenseXRetrievalPack class, I'm encountering an issue with VectorStoreIndex when using different sets of nodes (nodes versus all_nodes). Relevant Code Lines:
Issue: The issue occurs with both ChromaDB and Elasticsearch. When using nodes alone, the embeddings can be reused without any issue. The problem arises specifically when all_nodes is used, which includes both nodes and sub_nodes, result in the error:
ValueError: Query id d28e3de4-1c4f-420a-9184-97bf8556b11b not found in either retriever_dict or query_engine_dict.Expected Behavior: I expect VectorStoreIndex to handle querying using embeddings from all_nodes (nodes+sub_nodes)without errors, similar to when using nodes.
Questions:
Why does the error occur when using all_nodes but not with nodes alone? How can I modify the code to successfully reuse embeddings with all_nodes? Is there a specific way to handle all_nodes with ChromaDB and Elasticsearch to avoid this issue?