Skip to content

Index

Node parser interface.

BaseExtractor #

Bases: TransformComponent

Metadata extractor.

Source code in llama-index-core/llama_index/core/extractors/interface.py 
 21
 22
 23
 24
 25
 26
 27
 28
 29
 30
 31
 32
 33
 34
 35
 36
 37
 38
 39
 40
 41
 42
 43
 44
 45
 46
 47
 48
 49
 50
 51
 52
 53
 54
 55
 56
 57
 58
 59
 60
 61
 62
 63
 64
 65
 66
 67
 68
 69
 70
 71
 72
 73
 74
 75
 76
 77
 78
 79
 80
 81
 82
 83
 84
 85
 86
 87
 88
 89
 90
 91
 92
 93
 94
 95
 96
 97
 98
 99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
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", None)
        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", None)
        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: List[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

    def process_nodes(
        self,
        nodes: List[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: List[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: List[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/llama_index/core/extractors/interface.py 
72
73
74
75
@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/llama_index/core/extractors/interface.py 
77
78
79
80
81
82
83
84
85
@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/llama_index/core/extractors/interface.py 
87
88
89
90
91
92
93
94
95
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: List[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/llama_index/core/extractors/interface.py 
 97
 98
 99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
async def aprocess_nodes(
    self,
    nodes: List[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

acall async #

acall(nodes: List[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/llama_index/core/extractors/interface.py 
161
162
163
164
165
166
167
168
169
async def acall(self, nodes: List[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)