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158 | class TitleExtractor(BaseExtractor):
"""Title extractor. Useful for long documents. Extracts `document_title`
metadata field.
Args:
llm (Optional[LLM]): LLM
nodes (int): number of nodes from front to use for title extraction
node_template (str): template for node-level title clues extraction
combine_template (str): template for combining node-level clues into
a document-level title
"""
is_text_node_only: bool = False # can work for mixture of text and non-text nodes
llm: SerializeAsAny[LLM] = Field(description="The LLM to use for generation.")
nodes: int = Field(
default=5,
description="The number of nodes to extract titles from.",
gt=0,
)
node_template: str = Field(
default=DEFAULT_TITLE_NODE_TEMPLATE,
description="The prompt template to extract titles with.",
)
combine_template: str = Field(
default=DEFAULT_TITLE_COMBINE_TEMPLATE,
description="The prompt template to merge titles with.",
)
def __init__(
self,
llm: Optional[LLM] = None,
# TODO: llm_predictor arg is deprecated
llm_predictor: Optional[LLM] = None,
nodes: int = 5,
node_template: str = DEFAULT_TITLE_NODE_TEMPLATE,
combine_template: str = DEFAULT_TITLE_COMBINE_TEMPLATE,
num_workers: int = DEFAULT_NUM_WORKERS,
**kwargs: Any,
) -> None:
"""Init params."""
if nodes < 1:
raise ValueError("num_nodes must be >= 1")
super().__init__(
llm=llm or llm_predictor or Settings.llm,
nodes=nodes,
node_template=node_template,
combine_template=combine_template,
num_workers=num_workers,
**kwargs,
)
@classmethod
def class_name(cls) -> str:
return "TitleExtractor"
async def aextract(self, nodes: Sequence[BaseNode]) -> List[Dict]:
nodes_by_doc_id = self.separate_nodes_by_ref_id(nodes)
titles_by_doc_id = await self.extract_titles(nodes_by_doc_id)
return [{"document_title": titles_by_doc_id[node.ref_doc_id]} for node in nodes]
def filter_nodes(self, nodes: Sequence[BaseNode]) -> List[BaseNode]:
filtered_nodes: List[BaseNode] = []
for node in nodes:
if self.is_text_node_only and not isinstance(node, TextNode):
continue
filtered_nodes.append(node)
return filtered_nodes
def separate_nodes_by_ref_id(self, nodes: Sequence[BaseNode]) -> Dict:
separated_items: Dict[Optional[str], List[BaseNode]] = {}
for node in nodes:
key = node.ref_doc_id
if key not in separated_items:
separated_items[key] = []
if len(separated_items[key]) < self.nodes:
separated_items[key].append(node)
return separated_items
async def extract_titles(self, nodes_by_doc_id: Dict) -> Dict:
titles_by_doc_id = {}
for key, nodes in nodes_by_doc_id.items():
title_candidates = await self.get_title_candidates(nodes)
combined_titles = ", ".join(title_candidates)
titles_by_doc_id[key] = await self.llm.apredict(
PromptTemplate(template=self.combine_template),
context_str=combined_titles,
)
return titles_by_doc_id
async def get_title_candidates(self, nodes: List[BaseNode]) -> List[str]:
title_jobs = [
self.llm.apredict(
PromptTemplate(template=self.node_template),
context_str=cast(TextNode, node).text,
)
for node in nodes
]
return await run_jobs(
title_jobs, show_progress=self.show_progress, workers=self.num_workers
)
|
