Keyword

BaseExtractor

Bases: TransformComponent

Metadata extractor.

Parameters:

Name	Type	Description	Default
is_text_node_only	bool		True
show_progress	bool	Whether to show progress.	True
metadata_mode	MetadataMode	Metadata mode to use when reading nodes.	<MetadataMode.ALL: 'all'>
node_text_template	str	Template to represent how node text is mixed with metadata text.	'[Excerpt from document]\n{metadata_str}\nExcerpt:\n-----\n{content}\n-----\n'
disable_template_rewrite	bool	Disable the node template rewrite.	False
in_place	bool	Whether to process nodes in place.	True
num_workers	int	Number of workers to use for concurrent async processing.	4

Source code in llama_index/core/extractors/interface.py

class BaseExtractor(TransformComponent):
    """Metadata extractor."""

    is_text_node_only: bool = True

    show_progress: bool = Field(default=True, description="Whether to show progress.")

    metadata_mode: MetadataMode = Field(
        default=MetadataMode.ALL, description="Metadata mode to use when reading nodes."
    )

    node_text_template: str = Field(
        default=DEFAULT_NODE_TEXT_TEMPLATE,
        description="Template to represent how node text is mixed with metadata text.",
    )
    disable_template_rewrite: bool = Field(
        default=False, description="Disable the node template rewrite."
    )

    in_place: bool = Field(
        default=True, description="Whether to process nodes in place."
    )

    num_workers: int = Field(
        default=4,
        description="Number of workers to use for concurrent async processing.",
    )

    @classmethod
    def from_dict(cls, data: Dict[str, Any], **kwargs: Any) -> Self:  # type: ignore
        if isinstance(kwargs, dict):
            data.update(kwargs)

        data.pop("class_name", None)

        llm_predictor = data.get("llm_predictor")
        if llm_predictor:
            from llama_index.core.llm_predictor.loading import load_predictor

            llm_predictor = load_predictor(llm_predictor)
            data["llm_predictor"] = llm_predictor

        llm = data.get("llm")
        if llm:
            from llama_index.core.llms.loading import load_llm

            llm = load_llm(llm)
            data["llm"] = llm

        return cls(**data)

    @classmethod
    def class_name(cls) -> str:
        """Get class name."""
        return "MetadataExtractor"

    @abstractmethod
    async def aextract(self, nodes: Sequence[BaseNode]) -> List[Dict]:
        """
        Extracts metadata for a sequence of nodes, returning a list of
        metadata dictionaries corresponding to each node.

        Args:
            nodes (Sequence[Document]): nodes to extract metadata from

        """

    def extract(self, nodes: Sequence[BaseNode]) -> List[Dict]:
        """
        Extracts metadata for a sequence of nodes, returning a list of
        metadata dictionaries corresponding to each node.

        Args:
            nodes (Sequence[Document]): nodes to extract metadata from

        """
        return asyncio_run(self.aextract(nodes))

    async def aprocess_nodes(
        self,
        nodes: Sequence[BaseNode],
        excluded_embed_metadata_keys: Optional[List[str]] = None,
        excluded_llm_metadata_keys: Optional[List[str]] = None,
        **kwargs: Any,
    ) -> List[BaseNode]:
        """
        Post process nodes parsed from documents.

        Allows extractors to be chained.

        Args:
            nodes (List[BaseNode]): nodes to post-process
            excluded_embed_metadata_keys (Optional[List[str]]):
                keys to exclude from embed metadata
            excluded_llm_metadata_keys (Optional[List[str]]):
                keys to exclude from llm metadata

        """
        if self.in_place:
            new_nodes = nodes
        else:
            new_nodes = [deepcopy(node) for node in nodes]

        cur_metadata_list = await self.aextract(new_nodes)
        for idx, node in enumerate(new_nodes):
            node.metadata.update(cur_metadata_list[idx])

        for idx, node in enumerate(new_nodes):
            if excluded_embed_metadata_keys is not None:
                node.excluded_embed_metadata_keys.extend(excluded_embed_metadata_keys)
            if excluded_llm_metadata_keys is not None:
                node.excluded_llm_metadata_keys.extend(excluded_llm_metadata_keys)
            if not self.disable_template_rewrite:
                if isinstance(node, TextNode):
                    cast(TextNode, node).text_template = self.node_text_template

        return new_nodes  # type: ignore

    def process_nodes(
        self,
        nodes: Sequence[BaseNode],
        excluded_embed_metadata_keys: Optional[List[str]] = None,
        excluded_llm_metadata_keys: Optional[List[str]] = None,
        **kwargs: Any,
    ) -> List[BaseNode]:
        return asyncio_run(
            self.aprocess_nodes(
                nodes,
                excluded_embed_metadata_keys=excluded_embed_metadata_keys,
                excluded_llm_metadata_keys=excluded_llm_metadata_keys,
                **kwargs,
            )
        )

    def __call__(self, nodes: Sequence[BaseNode], **kwargs: Any) -> List[BaseNode]:
        """
        Post process nodes parsed from documents.

        Allows extractors to be chained.

        Args:
            nodes (List[BaseNode]): nodes to post-process

        """
        return self.process_nodes(nodes, **kwargs)

    async def acall(self, nodes: Sequence[BaseNode], **kwargs: Any) -> List[BaseNode]:
        """
        Post process nodes parsed from documents.

        Allows extractors to be chained.

        Args:
            nodes (List[BaseNode]): nodes to post-process

        """
        return await self.aprocess_nodes(nodes, **kwargs)

class_name classmethod

class_name() -> str

Get class name.

Source code in llama_index/core/extractors/interface.py

@classmethod
def class_name(cls) -> str:
    """Get class name."""
    return "MetadataExtractor"

aextract abstractmethod async

aextract(nodes: Sequence[BaseNode]) -> List[Dict]

Extracts metadata for a sequence of nodes, returning a list of metadata dictionaries corresponding to each node.

Parameters:

Name	Type	Description	Default
nodes	Sequence[Document]	nodes to extract metadata from	required

Source code in llama_index/core/extractors/interface.py

@abstractmethod
async def aextract(self, nodes: Sequence[BaseNode]) -> List[Dict]:
    """
    Extracts metadata for a sequence of nodes, returning a list of
    metadata dictionaries corresponding to each node.

    Args:
        nodes (Sequence[Document]): nodes to extract metadata from

    """

extract

extract(nodes: Sequence[BaseNode]) -> List[Dict]

Extracts metadata for a sequence of nodes, returning a list of metadata dictionaries corresponding to each node.

Parameters:

Name	Type	Description	Default
nodes	Sequence[Document]	nodes to extract metadata from	required

Source code in llama_index/core/extractors/interface.py

def extract(self, nodes: Sequence[BaseNode]) -> List[Dict]:
    """
    Extracts metadata for a sequence of nodes, returning a list of
    metadata dictionaries corresponding to each node.

    Args:
        nodes (Sequence[Document]): nodes to extract metadata from

    """
    return asyncio_run(self.aextract(nodes))

aprocess_nodes async

aprocess_nodes(nodes: Sequence[BaseNode], excluded_embed_metadata_keys: Optional[List[str]] = None, excluded_llm_metadata_keys: Optional[List[str]] = None, **kwargs: Any) -> List[BaseNode]

Post process nodes parsed from documents.

Allows extractors to be chained.

Parameters:

Name	Type	Description	Default
nodes	List[BaseNode]	nodes to post-process	required
excluded_embed_metadata_keys	Optional[List[str]]	keys to exclude from embed metadata	None
excluded_llm_metadata_keys	Optional[List[str]]	keys to exclude from llm metadata	None

Source code in llama_index/core/extractors/interface.py

async def aprocess_nodes(
    self,
    nodes: Sequence[BaseNode],
    excluded_embed_metadata_keys: Optional[List[str]] = None,
    excluded_llm_metadata_keys: Optional[List[str]] = None,
    **kwargs: Any,
) -> List[BaseNode]:
    """
    Post process nodes parsed from documents.

    Allows extractors to be chained.

    Args:
        nodes (List[BaseNode]): nodes to post-process
        excluded_embed_metadata_keys (Optional[List[str]]):
            keys to exclude from embed metadata
        excluded_llm_metadata_keys (Optional[List[str]]):
            keys to exclude from llm metadata

    """
    if self.in_place:
        new_nodes = nodes
    else:
        new_nodes = [deepcopy(node) for node in nodes]

    cur_metadata_list = await self.aextract(new_nodes)
    for idx, node in enumerate(new_nodes):
        node.metadata.update(cur_metadata_list[idx])

    for idx, node in enumerate(new_nodes):
        if excluded_embed_metadata_keys is not None:
            node.excluded_embed_metadata_keys.extend(excluded_embed_metadata_keys)
        if excluded_llm_metadata_keys is not None:
            node.excluded_llm_metadata_keys.extend(excluded_llm_metadata_keys)
        if not self.disable_template_rewrite:
            if isinstance(node, TextNode):
                cast(TextNode, node).text_template = self.node_text_template

    return new_nodes  # type: ignore

acall async

acall(nodes: Sequence[BaseNode], **kwargs: Any) -> List[BaseNode]

Post process nodes parsed from documents.

Allows extractors to be chained.

Parameters:

Name	Type	Description	Default
nodes	List[BaseNode]	nodes to post-process	required

Source code in llama_index/core/extractors/interface.py

async def acall(self, nodes: Sequence[BaseNode], **kwargs: Any) -> List[BaseNode]:
    """
    Post process nodes parsed from documents.

    Allows extractors to be chained.

    Args:
        nodes (List[BaseNode]): nodes to post-process

    """
    return await self.aprocess_nodes(nodes, **kwargs)

KeywordExtractor

Bases: BaseExtractor

Keyword extractor. Node-level extractor. Extracts excerpt_keywords metadata field.

Parameters:

Name	Type	Description	Default
llm	Optional[LLM]	LLM	required
keywords	int	number of keywords to extract	5
prompt_template	str	template for keyword extraction	'{context_str}. Give {keywords} unique keywords for this document. Format as comma separated. Keywords: '

Source code in llama_index/core/extractors/metadata_extractors.py

class KeywordExtractor(BaseExtractor):
    """
    Keyword extractor. Node-level extractor. Extracts
    `excerpt_keywords` metadata field.

    Args:
        llm (Optional[LLM]): LLM
        keywords (int): number of keywords to extract
        prompt_template (str): template for keyword extraction

    """

    llm: SerializeAsAny[LLM] = Field(description="The LLM to use for generation.")
    keywords: int = Field(
        default=5, description="The number of keywords to extract.", gt=0
    )

    prompt_template: str = Field(
        default=DEFAULT_KEYWORD_EXTRACT_TEMPLATE,
        description="Prompt template to use when generating keywords.",
    )

    def __init__(
        self,
        llm: Optional[LLM] = None,
        # TODO: llm_predictor arg is deprecated
        llm_predictor: Optional[LLM] = None,
        keywords: int = 5,
        prompt_template: str = DEFAULT_KEYWORD_EXTRACT_TEMPLATE,
        num_workers: int = DEFAULT_NUM_WORKERS,
        **kwargs: Any,
    ) -> None:
        """Init params."""
        if keywords < 1:
            raise ValueError("num_keywords must be >= 1")

        super().__init__(
            llm=llm or llm_predictor or Settings.llm,
            keywords=keywords,
            prompt_template=prompt_template,
            num_workers=num_workers,
            **kwargs,
        )

    @classmethod
    def class_name(cls) -> str:
        return "KeywordExtractor"

    async def _aextract_keywords_from_node(self, node: BaseNode) -> Dict[str, str]:
        """Extract keywords from a node and return it's metadata dict."""
        if self.is_text_node_only and not isinstance(node, TextNode):
            return {}

        context_str = node.get_content(metadata_mode=self.metadata_mode)
        keywords = await self.llm.apredict(
            PromptTemplate(template=self.prompt_template),
            keywords=self.keywords,
            context_str=context_str,
        )

        return {"excerpt_keywords": keywords.strip()}

    async def aextract(self, nodes: Sequence[BaseNode]) -> List[Dict]:
        keyword_jobs = []
        for node in nodes:
            keyword_jobs.append(self._aextract_keywords_from_node(node))

        metadata_list: List[Dict] = await run_jobs(
            keyword_jobs, show_progress=self.show_progress, workers=self.num_workers
        )

        return metadata_list

PydanticProgramExtractor

Bases: BaseExtractor, Generic[Model]

Pydantic program extractor.

Uses an LLM to extract out a Pydantic object. Return attributes of that object in a dictionary.

Parameters:

Name	Type	Description	Default
program	BasePydanticProgram[TypeVar]	Pydantic program to extract.	required
input_key	str	Key to use as input to the program (the program template string must expose this key).	'input'
extract_template_str	str	Template to use for extraction.	'Here is the content of the section:\n----------------\n{context_str}\n----------------\nGiven the contextual information, extract out a {class_name} object.'

Source code in llama_index/core/extractors/metadata_extractors.py

class PydanticProgramExtractor(BaseExtractor, Generic[Model]):
    """
    Pydantic program extractor.

    Uses an LLM to extract out a Pydantic object. Return attributes of that object
    in a dictionary.

    """

    program: SerializeAsAny[BasePydanticProgram[Model]] = Field(
        ..., description="Pydantic program to extract."
    )
    input_key: str = Field(
        default="input",
        description=(
            "Key to use as input to the program (the program "
            "template string must expose this key)."
        ),
    )
    extract_template_str: str = Field(
        default=DEFAULT_EXTRACT_TEMPLATE_STR,
        description="Template to use for extraction.",
    )

    @classmethod
    def class_name(cls) -> str:
        return "PydanticModelExtractor"

    async def _acall_program(self, node: BaseNode) -> Dict[str, Any]:
        """Call the program on a node."""
        if self.is_text_node_only and not isinstance(node, TextNode):
            return {}

        extract_str = self.extract_template_str.format(
            context_str=node.get_content(metadata_mode=self.metadata_mode),
            class_name=self.program.output_cls.__name__,
        )

        ret_object = await self.program.acall(**{self.input_key: extract_str})
        assert not isinstance(ret_object, list)

        return ret_object.model_dump()

    async def aextract(self, nodes: Sequence[BaseNode]) -> List[Dict]:
        """Extract pydantic program."""
        program_jobs = []
        for node in nodes:
            program_jobs.append(self._acall_program(node))

        metadata_list: List[Dict] = await run_jobs(
            program_jobs, show_progress=self.show_progress, workers=self.num_workers
        )

        return metadata_list

aextract async

aextract(nodes: Sequence[BaseNode]) -> List[Dict]

Extract pydantic program.

Source code in llama_index/core/extractors/metadata_extractors.py

async def aextract(self, nodes: Sequence[BaseNode]) -> List[Dict]:
    """Extract pydantic program."""
    program_jobs = []
    for node in nodes:
        program_jobs.append(self._acall_program(node))

    metadata_list: List[Dict] = await run_jobs(
        program_jobs, show_progress=self.show_progress, workers=self.num_workers
    )

    return metadata_list

QuestionsAnsweredExtractor

Bases: BaseExtractor

Questions answered extractor. Node-level extractor. Extracts questions_this_excerpt_can_answer metadata field.

Parameters:

Name	Type	Description	Default
llm	Optional[LLM]	LLM	required
questions	int	number of questions to extract	5
prompt_template	str	template for question extraction,	'Here is the context:\n{context_str}\n\nGiven the contextual information, generate {num_questions} questions this context can provide specific answers to which are unlikely to be found elsewhere.\n\nHigher-level summaries of surrounding context may be provided as well. Try using these summaries to generate better questions that this context can answer.\n\n'
embedding_only	bool	whether to use embedding only	True

Source code in llama_index/core/extractors/metadata_extractors.py

class QuestionsAnsweredExtractor(BaseExtractor):
    """
    Questions answered extractor. Node-level extractor.
    Extracts `questions_this_excerpt_can_answer` metadata field.

    Args:
        llm (Optional[LLM]): LLM
        questions (int): number of questions to extract
        prompt_template (str): template for question extraction,
        embedding_only (bool): whether to use embedding only

    """

    llm: SerializeAsAny[LLM] = Field(description="The LLM to use for generation.")
    questions: int = Field(
        default=5,
        description="The number of questions to generate.",
        gt=0,
    )
    prompt_template: str = Field(
        default=DEFAULT_QUESTION_GEN_TMPL,
        description="Prompt template to use when generating questions.",
    )
    embedding_only: bool = Field(
        default=True, description="Whether to use metadata for emebddings only."
    )

    def __init__(
        self,
        llm: Optional[LLM] = None,
        # TODO: llm_predictor arg is deprecated
        llm_predictor: Optional[LLM] = None,
        questions: int = 5,
        prompt_template: str = DEFAULT_QUESTION_GEN_TMPL,
        embedding_only: bool = True,
        num_workers: int = DEFAULT_NUM_WORKERS,
        **kwargs: Any,
    ) -> None:
        """Init params."""
        if questions < 1:
            raise ValueError("questions must be >= 1")

        super().__init__(
            llm=llm or llm_predictor or Settings.llm,
            questions=questions,
            prompt_template=prompt_template,
            embedding_only=embedding_only,
            num_workers=num_workers,
            **kwargs,
        )

    @classmethod
    def class_name(cls) -> str:
        return "QuestionsAnsweredExtractor"

    async def _aextract_questions_from_node(self, node: BaseNode) -> Dict[str, str]:
        """Extract questions from a node and return it's metadata dict."""
        if self.is_text_node_only and not isinstance(node, TextNode):
            return {}

        context_str = node.get_content(metadata_mode=self.metadata_mode)
        prompt = PromptTemplate(template=self.prompt_template)
        questions = await self.llm.apredict(
            prompt, num_questions=self.questions, context_str=context_str
        )

        return {"questions_this_excerpt_can_answer": questions.strip()}

    async def aextract(self, nodes: Sequence[BaseNode]) -> List[Dict]:
        questions_jobs = []
        for node in nodes:
            questions_jobs.append(self._aextract_questions_from_node(node))

        metadata_list: List[Dict] = await run_jobs(
            questions_jobs, show_progress=self.show_progress, workers=self.num_workers
        )

        return metadata_list

SummaryExtractor

Bases: BaseExtractor

Summary extractor. Node-level extractor with adjacent sharing. Extracts section_summary, prev_section_summary, next_section_summary metadata fields.

Parameters:

Name	Type	Description	Default
llm	Optional[LLM]	LLM	required
summaries	List[str]	list of summaries to extract: 'self', 'prev', 'next'	required
prompt_template	str	template for summary extraction	'Here is the content of the section:\n{context_str}\n\nSummarize the key topics and entities of the section. \nSummary: '

Source code in llama_index/core/extractors/metadata_extractors.py

class SummaryExtractor(BaseExtractor):
    """
    Summary extractor. Node-level extractor with adjacent sharing.
    Extracts `section_summary`, `prev_section_summary`, `next_section_summary`
    metadata fields.

    Args:
        llm (Optional[LLM]): LLM
        summaries (List[str]): list of summaries to extract: 'self', 'prev', 'next'
        prompt_template (str): template for summary extraction

    """

    llm: SerializeAsAny[LLM] = Field(description="The LLM to use for generation.")
    summaries: List[str] = Field(
        description="List of summaries to extract: 'self', 'prev', 'next'"
    )
    prompt_template: str = Field(
        default=DEFAULT_SUMMARY_EXTRACT_TEMPLATE,
        description="Template to use when generating summaries.",
    )

    _self_summary: bool = PrivateAttr()
    _prev_summary: bool = PrivateAttr()
    _next_summary: bool = PrivateAttr()

    def __init__(
        self,
        llm: Optional[LLM] = None,
        # TODO: llm_predictor arg is deprecated
        llm_predictor: Optional[LLM] = None,
        summaries: List[str] = ["self"],
        prompt_template: str = DEFAULT_SUMMARY_EXTRACT_TEMPLATE,
        num_workers: int = DEFAULT_NUM_WORKERS,
        **kwargs: Any,
    ):
        # validation
        if not all(s in ["self", "prev", "next"] for s in summaries):
            raise ValueError("summaries must be one of ['self', 'prev', 'next']")

        super().__init__(
            llm=llm or llm_predictor or Settings.llm,
            summaries=summaries,
            prompt_template=prompt_template,
            num_workers=num_workers,
            **kwargs,
        )

        self._self_summary = "self" in summaries
        self._prev_summary = "prev" in summaries
        self._next_summary = "next" in summaries

    @classmethod
    def class_name(cls) -> str:
        return "SummaryExtractor"

    async def _agenerate_node_summary(self, node: BaseNode) -> str:
        """Generate a summary for a node."""
        if self.is_text_node_only and not isinstance(node, TextNode):
            return ""

        context_str = node.get_content(metadata_mode=self.metadata_mode)
        summary = await self.llm.apredict(
            PromptTemplate(template=self.prompt_template), context_str=context_str
        )

        return summary.strip()

    async def aextract(self, nodes: Sequence[BaseNode]) -> List[Dict]:
        if not all(isinstance(node, TextNode) for node in nodes):
            raise ValueError("Only `TextNode` is allowed for `Summary` extractor")

        node_summaries_jobs = []
        for node in nodes:
            node_summaries_jobs.append(self._agenerate_node_summary(node))

        node_summaries = await run_jobs(
            node_summaries_jobs,
            show_progress=self.show_progress,
            workers=self.num_workers,
        )

        # Extract node-level summary metadata
        metadata_list: List[Dict] = [{} for _ in nodes]
        for i, metadata in enumerate(metadata_list):
            if i > 0 and self._prev_summary and node_summaries[i - 1]:
                metadata["prev_section_summary"] = node_summaries[i - 1]
            if i < len(nodes) - 1 and self._next_summary and node_summaries[i + 1]:
                metadata["next_section_summary"] = node_summaries[i + 1]
            if self._self_summary and node_summaries[i]:
                metadata["section_summary"] = node_summaries[i]

        return metadata_list

TitleExtractor

Bases: BaseExtractor

Title extractor. Useful for long documents. Extracts document_title metadata field.

Parameters:

Name	Type	Description	Default
llm	Optional[LLM]	LLM	required
nodes	int	number of nodes from front to use for title extraction	5
node_template	str	template for node-level title clues extraction	'Context: {context_str}. Give a title that summarizes all of the unique entities, titles or themes found in the context. Title: '
combine_template	str	template for combining node-level clues into a document-level title	'{context_str}. Based on the above candidate titles and content, what is the comprehensive title for this document? Title: '
is_text_node_only	bool		False

Source code in llama_index/core/extractors/metadata_extractors.py

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:
        jobs = []
        final_dict = {}

        async def get_titles_by_doc(nodes: List[BaseNode], key: str) -> Dict:
            titles_by_doc_id = {}
            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

        for key, nodes in nodes_by_doc_id.items():
            jobs.append(get_titles_by_doc(nodes, key))
        list_dict_titles: List[Dict] = await run_jobs(
            jobs=jobs,
            show_progress=self.show_progress,
        )
        for d in list_dict_titles:
            for key, value in d.items():
                final_dict.update({key: value})
        return final_dict

    async def get_title_candidates(self, nodes: List[BaseNode]) -> List[str]:
        return [
            await self.llm.apredict(
                PromptTemplate(template=self.node_template),
                context_str=cast(TextNode, node).text,
            )
            for node in nodes
        ]

DocumentContextExtractor

Bases: BaseExtractor

An LLM-based context extractor for enhancing RAG accuracy through document analysis.

! Nodes that already have the 'key' in node.metadata will NOT be processed - will be skipped !

This extractor processes documents and their nodes to generate contextual metadata, implementing the approach described in the Anthropic "Contextual Retrieval" blog post. It handles rate limits, document size constraints, and parallel processing of nodes.

Parameters:

Name	Type	Description	Default
num_workers	int		4
llm	LLM		required
docstore	BaseDocumentStore		required
key	str		required
prompt	str		required
doc_ids	Set[str]		required
max_context_length	int		required
max_output_tokens	int		required
oversized_document_strategy	Literal['warn', 'error', 'ignore']		required
DEFAULT_KEY	str		'context'

Attributes:

Name	Type	Description
llm	LLM	Language model instance for generating context
docstore	BaseDocumentStore	Storage for parent documents
key	str	Metadata key for storing extracted context
prompt	str	Prompt template for context generation
doc_ids	Set[str]	Set of processed document IDs
max_context_length	int	Maximum allowed document context length
max_output_tokens	int	Maximum tokens in generated context
oversized_document_strategy	OversizeStrategy	Strategy for handling large documents

Example

extractor = DocumentContextExtractor(
    docstore=my_docstore,
    llm=my_llm,
    max_context_length=64000,
    max_output_tokens=256
)
metadata_list = await extractor.aextract(nodes)

Source code in llama_index/core/extractors/document_context.py

class DocumentContextExtractor(BaseExtractor):
    """
    An LLM-based context extractor for enhancing RAG accuracy through document analysis.

    ! Nodes that already have the 'key' in node.metadata will NOT be processed - will be skipped !

    This extractor processes documents and their nodes to generate contextual metadata,
    implementing the approach described in the Anthropic "Contextual Retrieval" blog post.
    It handles rate limits, document size constraints, and parallel processing of nodes.

    Attributes:
        llm (LLM): Language model instance for generating context
        docstore (BaseDocumentStore): Storage for parent documents
        key (str): Metadata key for storing extracted context
        prompt (str): Prompt template for context generation
        doc_ids (Set[str]): Set of processed document IDs
        max_context_length (int): Maximum allowed document context length
        max_output_tokens (int): Maximum tokens in generated context
        oversized_document_strategy (OversizeStrategy): Strategy for handling large documents

    Example:
        ```python
        extractor = DocumentContextExtractor(
            docstore=my_docstore,
            llm=my_llm,
            max_context_length=64000,
            max_output_tokens=256
        )
        metadata_list = await extractor.aextract(nodes)
        ```

    """

    # Pydantic fields
    llm: LLM
    docstore: BaseDocumentStore
    key: str
    prompt: str
    doc_ids: Set[str]
    max_context_length: int
    max_output_tokens: int
    oversized_document_strategy: OversizeStrategy
    num_workers: int = DEFAULT_NUM_WORKERS

    ORIGINAL_CONTEXT_PROMPT: ClassVar[str] = ORIGINAL_CONTEXT_PROMPT
    SUCCINCT_CONTEXT_PROMPT: ClassVar[str] = SUCCINCT_CONTEXT_PROMPT

    DEFAULT_KEY: str = "context"

    def __init__(
        self,
        docstore: BaseDocumentStore,
        llm: Optional[LLM] = None,
        max_context_length: int = 1000,
        key: str = DEFAULT_KEY,
        prompt: str = ORIGINAL_CONTEXT_PROMPT,
        num_workers: int = DEFAULT_NUM_WORKERS,
        max_output_tokens: int = 512,
        oversized_document_strategy: OversizeStrategy = "warn",
        **kwargs: Any,
    ) -> None:
        """Init params."""
        assert hasattr(
            llm, "achat"
        )  # not all LLMs have this, particularly the huggingfaceapi ones.

        super().__init__(
            llm=llm or Settings.llm,
            docstore=docstore,
            key=key,
            prompt=prompt,
            doc_ids=set(),
            max_context_length=max_context_length,
            max_output_tokens=max_output_tokens,
            oversized_document_strategy=oversized_document_strategy,
            num_workers=num_workers,
            **kwargs,
        )

    # this can take a surprisingly long time on longer docs so we cache it. For oversized docs, we end up counting twice, the 2nd time without the cache.
    # but if you're repeateddly running way oversize docs, the time that takes won't be what matters anyways.
    @staticmethod
    @lru_cache(maxsize=1000)
    def _count_tokens(text: str) -> int:
        """
        This can take a surprisingly long time on longer docs so we cache it, and we need to call it on every doc, regardless of size.
        """
        encoder = Settings.tokenizer
        tokens = encoder(text)
        return len(tokens)

    async def _agenerate_node_context(
        self,
        node: Union[Node, TextNode],
        metadata: Dict,
        document: Union[Node, TextNode],
        prompt: str,
        key: str,
    ) -> Dict:
        """
        Generate context for a node using LLM with retry logic.

        Implements exponential backoff for rate limit handling and uses prompt
        caching when available. The function retries on rate limits.

        Args:
            node: Node to generate context for
            metadata: Metadata dictionary to update
            document: Parent document containing the node
            prompt: Prompt template for context generation
            key: Metadata key for storing generated context

        Returns:
            Updated metadata dictionary with generated context

        Note:
            Uses exponential backoff starting at 60 seconds with up to 5 retries
            for rate limit handling.

        """
        cached_text = f"<document>{document.get_content()}</document>"
        messages = [
            ChatMessage(
                role="user",
                content=[
                    TextBlock(
                        text=cached_text,
                        type="text",
                    )
                ],
                additional_kwargs={"cache_control": {"type": "ephemeral"}},
            ),
            ChatMessage(
                role="user",
                content=[
                    TextBlock(
                        text=f"Here is the chunk we want to situate within the whole document:\n<chunk>{node.get_content()}</chunk>\n{prompt}",
                        type="text",
                    )
                ],
            ),
        ]

        max_retries = 5
        base_delay = 60

        for attempt in range(max_retries):
            try:
                # Extra headers typically dont cause issues
                headers = {"anthropic-beta": "prompt-caching-2024-07-31"}

                response: ChatResponse = await self.llm.achat(
                    messages, max_tokens=self.max_output_tokens, extra_headers=headers
                )

                first_block: Union[TextBlock, ImageBlock, AudioBlock, DocumentBlock] = (
                    cast(
                        Union[TextBlock, ImageBlock, AudioBlock, DocumentBlock],
                        response.message.blocks[0],
                    )
                )
                if isinstance(first_block, TextBlock):
                    metadata[key] = first_block.text
                else:
                    logging.warning(
                        f"Received non-text block type: {type(first_block)}"
                    )
                return metadata

            except Exception as e:
                is_rate_limit = any(
                    message in str(e).lower()
                    for message in ["rate limit", "too many requests", "429"]
                )

                if is_rate_limit and attempt < max_retries - 1:
                    delay = (base_delay * (2**attempt)) + (random.random() * 0.5)
                    logging.warning(
                        f"Rate limit hit, retrying in {delay:.1f} seconds "
                        f"(attempt {attempt + 1}/{max_retries})"
                    )
                    await asyncio.sleep(delay)
                    continue

                if is_rate_limit:
                    logging.error(
                        f"Failed after {max_retries} retries due to rate limiting"
                    )
                else:
                    logging.warning(
                        f"Error generating context for node {node.node_id}: {e}",
                        exc_info=True,
                    )
                return metadata

        return metadata

    async def _get_document(self, doc_id: str) -> Optional[Union[Node, TextNode]]:
        """Counting tokens can be slow, as can awaiting the docstore (potentially), so we keep a small lru_cache."""
        # first we need to get the document
        try:
            doc = await self.docstore.aget_document(doc_id)
        except ValueError as e:
            if "not found" in str(e):
                logging.warning(f"Document {doc_id} not found in docstore")
                return None
        if not doc:
            logging.warning(f"Document {doc_id} not found in docstore")
            return None
        if not is_text_node(doc):
            logging.warning(f"Document {doc_id} is not an instance of (TextNode, Node)")
            return None

        # then truncate if necessary.
        if self.max_context_length is not None:
            strategy = self.oversized_document_strategy
            token_count = self._count_tokens(doc.get_content())
            if token_count > self.max_context_length:
                message = (
                    f"Document {doc.node_id} is too large ({token_count} tokens) "
                    f"to be processed. Doc metadata: {doc.metadata}"
                )

                if strategy == "warn":
                    logging.warning(message)
                elif strategy == "error":
                    raise ValueError(message)
                elif strategy == "ignore":
                    pass
                else:
                    raise ValueError(f"Unknown oversized document strategy: {strategy}")

        return doc

    async def aextract(self, nodes: Sequence[BaseNode]) -> List[Dict]:
        """
        Extract context for multiple nodes asynchronously, optimized for loosely ordered nodes.
        Processes each node independently without guaranteeing sequential document handling.
        Nodes will be *mostly* processed in document-order assuming nodes get passed in document-order.

        Args:
            nodes: List of nodes to process, ideally grouped by source document

        Returns:
            List of metadata dictionaries with generated context

        """
        metadata_list: List[Dict] = []
        for _ in nodes:
            metadata_list.append({})
        metadata_map = {
            node.node_id: metadata_dict
            for metadata_dict, node in zip(metadata_list, nodes)
        }

        # sorting takes a tiny amount of time - 0.4s for 1_000_000 nodes. but 1_000_000 nodes takes potentially hours to process
        # considering sorting CAN save the users hundreds of dollars in API costs, we just sort and leave no option to do otherwise.
        # The math always works out in the user's favor and we can't guarantee things are sorted in the first place.
        sorted_nodes = sorted(
            nodes, key=lambda n: n.source_node.node_id if n.source_node else ""
        )

        # iterate over all the nodes and generate the jobs
        node_tasks: List[Coroutine[Any, Any, Any]] = []
        for node in sorted_nodes:
            if not (node.source_node and is_text_node(node)):
                continue

            # Skip already processed nodes
            if self.key in node.metadata:
                continue

            doc: Optional[Union[Node, TextNode]] = await self._get_document(
                node.source_node.node_id
            )
            if not doc:
                continue

            metadata = metadata_map[node.node_id]
            # this modifies metadata in-place, adding a new key to the dictionary - we needed do anytyhing with the return value
            task = self._agenerate_node_context(
                node, metadata, doc, self.prompt, self.key
            )
            node_tasks.append(task)

        # then run the jobs - this does return the metadata list, but we already have it
        await run_jobs(
            node_tasks,
            show_progress=self.show_progress,
            workers=self.num_workers,
        )

        return metadata_list

aextract async

aextract(nodes: Sequence[BaseNode]) -> List[Dict]

Extract context for multiple nodes asynchronously, optimized for loosely ordered nodes. Processes each node independently without guaranteeing sequential document handling. Nodes will be mostly processed in document-order assuming nodes get passed in document-order.

Parameters:

Name	Type	Description	Default
nodes	Sequence[BaseNode]	List of nodes to process, ideally grouped by source document	required

Returns:

Type	Description
List[Dict]	List of metadata dictionaries with generated context

Source code in llama_index/core/extractors/document_context.py

async def aextract(self, nodes: Sequence[BaseNode]) -> List[Dict]:
    """
    Extract context for multiple nodes asynchronously, optimized for loosely ordered nodes.
    Processes each node independently without guaranteeing sequential document handling.
    Nodes will be *mostly* processed in document-order assuming nodes get passed in document-order.

    Args:
        nodes: List of nodes to process, ideally grouped by source document

    Returns:
        List of metadata dictionaries with generated context

    """
    metadata_list: List[Dict] = []
    for _ in nodes:
        metadata_list.append({})
    metadata_map = {
        node.node_id: metadata_dict
        for metadata_dict, node in zip(metadata_list, nodes)
    }

    # sorting takes a tiny amount of time - 0.4s for 1_000_000 nodes. but 1_000_000 nodes takes potentially hours to process
    # considering sorting CAN save the users hundreds of dollars in API costs, we just sort and leave no option to do otherwise.
    # The math always works out in the user's favor and we can't guarantee things are sorted in the first place.
    sorted_nodes = sorted(
        nodes, key=lambda n: n.source_node.node_id if n.source_node else ""
    )

    # iterate over all the nodes and generate the jobs
    node_tasks: List[Coroutine[Any, Any, Any]] = []
    for node in sorted_nodes:
        if not (node.source_node and is_text_node(node)):
            continue

        # Skip already processed nodes
        if self.key in node.metadata:
            continue

        doc: Optional[Union[Node, TextNode]] = await self._get_document(
            node.source_node.node_id
        )
        if not doc:
            continue

        metadata = metadata_map[node.node_id]
        # this modifies metadata in-place, adding a new key to the dictionary - we needed do anytyhing with the return value
        task = self._agenerate_node_context(
            node, metadata, doc, self.prompt, self.key
        )
        node_tasks.append(task)

    # then run the jobs - this does return the metadata list, but we already have it
    await run_jobs(
        node_tasks,
        show_progress=self.show_progress,
        workers=self.num_workers,
    )

    return metadata_list

options: members: - KeywordExtractor