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313 | class DocumentSummaryIndex(BaseIndex[IndexDocumentSummary]):
"""
Document Summary Index.
Args:
response_synthesizer (BaseSynthesizer): A response synthesizer for generating
summaries.
summary_query (str): The query to use to generate the summary for each document.
show_progress (bool): Whether to show tqdm progress bars.
Defaults to False.
embed_summaries (bool): Whether to embed the summaries.
This is required for running the default embedding-based retriever.
Defaults to True.
"""
index_struct_cls = IndexDocumentSummary
def __init__(
self,
nodes: Optional[Sequence[BaseNode]] = None,
objects: Optional[Sequence[IndexNode]] = None,
index_struct: Optional[IndexDocumentSummary] = None,
llm: Optional[LLM] = None,
embed_model: Optional[BaseEmbedding] = None,
storage_context: Optional[StorageContext] = None,
response_synthesizer: Optional[BaseSynthesizer] = None,
summary_query: str = DEFAULT_SUMMARY_QUERY,
show_progress: bool = False,
embed_summaries: bool = True,
**kwargs: Any,
) -> None:
"""Initialize params."""
self._llm = llm or Settings.llm
self._embed_model = embed_model or Settings.embed_model
self._response_synthesizer = response_synthesizer or get_response_synthesizer(
llm=self._llm, response_mode=ResponseMode.TREE_SUMMARIZE
)
self._summary_query = summary_query
self._embed_summaries = embed_summaries
super().__init__(
nodes=nodes,
index_struct=index_struct,
storage_context=storage_context,
show_progress=show_progress,
objects=objects,
**kwargs,
)
@property
def vector_store(self) -> BasePydanticVectorStore:
return self._vector_store
def as_retriever(
self,
retriever_mode: Union[str, _RetrieverMode] = _RetrieverMode.EMBEDDING,
**kwargs: Any,
) -> BaseRetriever:
"""
Get retriever.
Args:
retriever_mode (Union[str, DocumentSummaryRetrieverMode]): A retriever mode.
Defaults to DocumentSummaryRetrieverMode.EMBEDDING.
"""
from llama_index.core.indices.document_summary.retrievers import (
DocumentSummaryIndexEmbeddingRetriever,
DocumentSummaryIndexLLMRetriever,
)
LLMRetriever = DocumentSummaryIndexLLMRetriever
EmbeddingRetriever = DocumentSummaryIndexEmbeddingRetriever
if retriever_mode == _RetrieverMode.EMBEDDING:
if not self._embed_summaries:
raise ValueError(
"Cannot use embedding retriever if embed_summaries is False"
)
return EmbeddingRetriever(
self,
object_map=self._object_map,
embed_model=self._embed_model,
**kwargs,
)
if retriever_mode == _RetrieverMode.LLM:
return LLMRetriever(
self, object_map=self._object_map, llm=self._llm, **kwargs
)
else:
raise ValueError(f"Unknown retriever mode: {retriever_mode}")
def get_document_summary(self, doc_id: str) -> str:
"""
Get document summary by doc id.
Args:
doc_id (str): A document id.
"""
if doc_id not in self._index_struct.doc_id_to_summary_id:
raise ValueError(f"doc_id {doc_id} not in index")
summary_id = self._index_struct.doc_id_to_summary_id[doc_id]
return self.docstore.get_node(summary_id).get_content()
def _add_nodes_to_index(
self,
index_struct: IndexDocumentSummary,
nodes: Sequence[BaseNode],
show_progress: bool = False,
) -> None:
"""Add nodes to index."""
doc_id_to_nodes = defaultdict(list)
for node in nodes:
if node.ref_doc_id is None:
raise ValueError(
"ref_doc_id of node cannot be None when building a document "
"summary index"
)
doc_id_to_nodes[node.ref_doc_id].append(node)
summary_node_dict = {}
items = doc_id_to_nodes.items()
iterable_with_progress = get_tqdm_iterable(
items, show_progress, "Summarizing documents"
)
for doc_id, nodes in iterable_with_progress:
print(f"current doc id: {doc_id}")
nodes_with_scores = [NodeWithScore(node=n) for n in nodes]
# get the summary for each doc_id
summary_response = self._response_synthesizer.synthesize(
query=self._summary_query,
nodes=nodes_with_scores,
)
summary_response = cast(Response, summary_response)
docid_first_node = doc_id_to_nodes.get(doc_id, [TextNode()])[0]
summary_node_dict[doc_id] = TextNode(
text=summary_response.response,
relationships={
NodeRelationship.SOURCE: RelatedNodeInfo(node_id=doc_id)
},
metadata=docid_first_node.metadata,
excluded_embed_metadata_keys=docid_first_node.excluded_embed_metadata_keys,
excluded_llm_metadata_keys=docid_first_node.excluded_llm_metadata_keys,
)
self.docstore.add_documents([summary_node_dict[doc_id]])
logger.info(
f"> Generated summary for doc {doc_id}: " f"{summary_response.response}"
)
for doc_id, nodes in doc_id_to_nodes.items():
index_struct.add_summary_and_nodes(summary_node_dict[doc_id], nodes)
if self._embed_summaries:
summary_nodes = list(summary_node_dict.values())
id_to_embed_map = embed_nodes(
summary_nodes, self._embed_model, show_progress=show_progress
)
summary_nodes_with_embedding = []
for node in summary_nodes:
node_with_embedding = node.model_copy()
node_with_embedding.embedding = id_to_embed_map[node.node_id]
summary_nodes_with_embedding.append(node_with_embedding)
self._vector_store.add(summary_nodes_with_embedding)
def _build_index_from_nodes(
self,
nodes: Sequence[BaseNode],
**build_kwargs: Any,
) -> IndexDocumentSummary:
"""Build index from nodes."""
# first get doc_id to nodes_dict, generate a summary for each doc_id,
# then build the index struct
index_struct = IndexDocumentSummary()
self._add_nodes_to_index(index_struct, nodes, self._show_progress)
return index_struct
def _insert(self, nodes: Sequence[BaseNode], **insert_kwargs: Any) -> None:
"""Insert a document."""
self._add_nodes_to_index(self._index_struct, nodes)
def _delete_node(self, node_id: str, **delete_kwargs: Any) -> None:
pass
def delete_nodes(
self,
node_ids: List[str],
delete_from_docstore: bool = False,
**delete_kwargs: Any,
) -> None:
"""
Delete a list of nodes from the index.
Args:
node_ids (List[str]): A list of node_ids from the nodes to delete
"""
index_nodes = self._index_struct.node_id_to_summary_id.keys()
for node in node_ids:
if node not in index_nodes:
logger.warning(f"node_id {node} not found, will not be deleted.")
node_ids.remove(node)
self._index_struct.delete_nodes(node_ids)
remove_summary_ids = [
summary_id
for summary_id in self._index_struct.summary_id_to_node_ids
if len(self._index_struct.summary_id_to_node_ids[summary_id]) == 0
]
remove_docs = [
doc_id
for doc_id in self._index_struct.doc_id_to_summary_id
if self._index_struct.doc_id_to_summary_id[doc_id] in remove_summary_ids
]
for doc_id in remove_docs:
self.delete_ref_doc(doc_id)
def delete_ref_doc(
self, ref_doc_id: str, delete_from_docstore: bool = False, **delete_kwargs: Any
) -> None:
"""
Delete a document from the index.
All nodes in the index related to the document will be deleted.
"""
ref_doc_info = self.docstore.get_ref_doc_info(ref_doc_id)
if ref_doc_info is None:
logger.warning(f"ref_doc_id {ref_doc_id} not found, nothing deleted.")
return
self._index_struct.delete(ref_doc_id)
self._vector_store.delete(ref_doc_id)
if delete_from_docstore:
self.docstore.delete_ref_doc(ref_doc_id, raise_error=False)
self._storage_context.index_store.add_index_struct(self._index_struct)
@property
def ref_doc_info(self) -> Dict[str, RefDocInfo]:
"""Retrieve a dict mapping of ingested documents and their nodes+metadata."""
ref_doc_ids = list(self._index_struct.doc_id_to_summary_id.keys())
all_ref_doc_info = {}
for ref_doc_id in ref_doc_ids:
ref_doc_info = self.docstore.get_ref_doc_info(ref_doc_id)
if not ref_doc_info:
continue
all_ref_doc_info[ref_doc_id] = ref_doc_info
return all_ref_doc_info
|
