Skip to content

Index

Base schema for data structures.

BaseComponent #

Bases: BaseModel

Base component object to capture class names.

Source code in llama-index-core/llama_index/core/schema.py
 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
class BaseComponent(BaseModel):
    """Base component object to capture class names."""

    @classmethod
    def __get_pydantic_json_schema__(
        cls, core_schema: CoreSchema, handler: GetJsonSchemaHandler
    ) -> JsonSchemaValue:
        json_schema = super().__get_pydantic_json_schema__(core_schema, handler)
        if "properties" in json_schema:
            json_schema["properties"]["class_name"] = {
                "title": "Class Name",
                "type": "string",
                "default": cls.class_name(),
            }
        return json_schema

    @classmethod
    def class_name(cls) -> str:
        """
        Get the class name, used as a unique ID in serialization.

        This provides a key that makes serialization robust against actual class
        name changes.
        """
        return "base_component"

    def json(self, **kwargs: Any) -> str:
        return self.to_json(**kwargs)

    @model_serializer(mode="wrap")
    def custom_model_dump(self, handler: Any) -> Dict[str, Any]:
        data = handler(self)
        data["class_name"] = self.class_name()
        return data

    def dict(self, **kwargs: Any) -> Dict[str, Any]:
        return self.model_dump(**kwargs)

    def __getstate__(self) -> Dict[str, Any]:
        state = super().__getstate__()

        # remove attributes that are not pickleable -- kind of dangerous
        keys_to_remove = []
        for key, val in state["__dict__"].items():
            try:
                pickle.dumps(val)
            except Exception:
                keys_to_remove.append(key)

        for key in keys_to_remove:
            logging.warning(f"Removing unpickleable attribute {key}")
            del state["__dict__"][key]

        # remove private attributes if they aren't pickleable -- kind of dangerous
        keys_to_remove = []
        private_attrs = state.get("__pydantic_private__", None)
        if private_attrs:
            for key, val in state["__pydantic_private__"].items():
                try:
                    pickle.dumps(val)
                except Exception:
                    keys_to_remove.append(key)

            for key in keys_to_remove:
                logging.warning(f"Removing unpickleable private attribute {key}")
                del state["__pydantic_private__"][key]

        return state

    def __setstate__(self, state: Dict[str, Any]) -> None:
        # Use the __dict__ and __init__ method to set state
        # so that all variables initialize
        try:
            self.__init__(**state["__dict__"])  # type: ignore
        except Exception:
            # Fall back to the default __setstate__ method
            # This may not work if the class had unpickleable attributes
            super().__setstate__(state)

    def to_dict(self, **kwargs: Any) -> Dict[str, Any]:
        data = self.dict(**kwargs)
        data["class_name"] = self.class_name()
        return data

    def to_json(self, **kwargs: Any) -> str:
        data = self.to_dict(**kwargs)
        return json.dumps(data)

    # TODO: return type here not supported by current mypy version
    @classmethod
    def from_dict(cls, data: Dict[str, Any], **kwargs: Any) -> Self:  # type: ignore
        # In SimpleKVStore we rely on shallow coping. Hence, the data will be modified in the store directly.
        # And it is the same when the user is passing a dictionary to create a component. We can't modify the passed down dictionary.
        data = dict(data)
        if isinstance(kwargs, dict):
            data.update(kwargs)
        data.pop("class_name", None)
        return cls(**data)

    @classmethod
    def from_json(cls, data_str: str, **kwargs: Any) -> Self:  # type: ignore
        data = json.loads(data_str)
        return cls.from_dict(data, **kwargs)

class_name classmethod #

class_name() -> str

Get the class name, used as a unique ID in serialization.

This provides a key that makes serialization robust against actual class name changes.

Source code in llama-index-core/llama_index/core/schema.py
64
65
66
67
68
69
70
71
72
@classmethod
def class_name(cls) -> str:
    """
    Get the class name, used as a unique ID in serialization.

    This provides a key that makes serialization robust against actual class
    name changes.
    """
    return "base_component"

TransformComponent #

Bases: BaseComponent, DispatcherSpanMixin

Base class for transform components.

Source code in llama-index-core/llama_index/core/schema.py
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
class TransformComponent(BaseComponent, DispatcherSpanMixin):
    """Base class for transform components."""

    model_config = ConfigDict(arbitrary_types_allowed=True)

    @abstractmethod
    def __call__(
        self, nodes: Sequence["BaseNode"], **kwargs: Any
    ) -> Sequence["BaseNode"]:
        """Transform nodes."""

    async def acall(
        self, nodes: Sequence["BaseNode"], **kwargs: Any
    ) -> Sequence["BaseNode"]:
        """Async transform nodes."""
        return self.__call__(nodes, **kwargs)

acall async #

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

Async transform nodes.

Source code in llama-index-core/llama_index/core/schema.py
164
165
166
167
168
async def acall(
    self, nodes: Sequence["BaseNode"], **kwargs: Any
) -> Sequence["BaseNode"]:
    """Async transform nodes."""
    return self.__call__(nodes, **kwargs)

NodeRelationship #

Bases: str, Enum

Node relationships used in BaseNode class.

Attributes:

Name Type Description
SOURCE

The node is the source document.

PREVIOUS

The node is the previous node in the document.

NEXT

The node is the next node in the document.

PARENT

The node is the parent node in the document.

CHILD

The node is a child node in the document.

Source code in llama-index-core/llama_index/core/schema.py
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
class NodeRelationship(str, Enum):
    """Node relationships used in `BaseNode` class.

    Attributes:
        SOURCE: The node is the source document.
        PREVIOUS: The node is the previous node in the document.
        NEXT: The node is the next node in the document.
        PARENT: The node is the parent node in the document.
        CHILD: The node is a child node in the document.

    """

    SOURCE = auto()
    PREVIOUS = auto()
    NEXT = auto()
    PARENT = auto()
    CHILD = auto()

RelatedNodeInfo #

Bases: BaseComponent

Parameters:

Name Type Description Default
node_id str
required
node_type ObjectType | None
None
metadata Dict[str, Any]
{}
hash str | None
None
Source code in llama-index-core/llama_index/core/schema.py
204
205
206
207
208
209
210
211
212
class RelatedNodeInfo(BaseComponent):
    node_id: str
    node_type: Optional[ObjectType] = None
    metadata: Dict[str, Any] = Field(default_factory=dict)
    hash: Optional[str] = None

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

BaseNode #

Bases: BaseComponent

Base node Object.

Generic abstract interface for retrievable nodes

Parameters:

Name Type Description Default
id_ str

Unique ID of the node.

'bc69a7d2-c633-4122-9b86-fde9d911adac'
embedding List[float] | None

Embedding of the node.

None
metadata Dict[str, Any]

A flat dictionary of metadata fields

{}
excluded_embed_metadata_keys List[str]

Metadata keys that are excluded from text for the embed model.

[]
excluded_llm_metadata_keys List[str]

Metadata keys that are excluded from text for the LLM.

[]
relationships Dict[NodeRelationship, Union[RelatedNodeInfo, List[RelatedNodeInfo]]]

A mapping of relationships to other node information.

{}
Source code in llama-index-core/llama_index/core/schema.py
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
class BaseNode(BaseComponent):
    """Base node Object.

    Generic abstract interface for retrievable nodes

    """

    # hash is computed on local field, during the validation process
    model_config = ConfigDict(populate_by_name=True, validate_assignment=True)

    id_: str = Field(
        default_factory=lambda: str(uuid.uuid4()), description="Unique ID of the node."
    )
    embedding: Optional[List[float]] = Field(
        default=None, description="Embedding of the node."
    )

    """"
    metadata fields
    - injected as part of the text shown to LLMs as context
    - injected as part of the text for generating embeddings
    - used by vector DBs for metadata filtering

    """
    metadata: Dict[str, Any] = Field(
        default_factory=dict,
        description="A flat dictionary of metadata fields",
        alias="extra_info",
    )
    excluded_embed_metadata_keys: List[str] = Field(
        default_factory=list,
        description="Metadata keys that are excluded from text for the embed model.",
    )
    excluded_llm_metadata_keys: List[str] = Field(
        default_factory=list,
        description="Metadata keys that are excluded from text for the LLM.",
    )
    relationships: Dict[NodeRelationship, RelatedNodeType] = Field(
        default_factory=dict,
        description="A mapping of relationships to other node information.",
    )

    @classmethod
    @abstractmethod
    def get_type(cls) -> str:
        """Get Object type."""

    @abstractmethod
    def get_content(self, metadata_mode: MetadataMode = MetadataMode.ALL) -> str:
        """Get object content."""

    @abstractmethod
    def get_metadata_str(self, mode: MetadataMode = MetadataMode.ALL) -> str:
        """Metadata string."""

    @abstractmethod
    def set_content(self, value: Any) -> None:
        """Set the content of the node."""

    @property
    @abstractmethod
    def hash(self) -> str:
        """Get hash of node."""

    @property
    def node_id(self) -> str:
        return self.id_

    @node_id.setter
    def node_id(self, value: str) -> None:
        self.id_ = value

    @property
    def source_node(self) -> Optional[RelatedNodeInfo]:
        """Source object node.

        Extracted from the relationships field.

        """
        if NodeRelationship.SOURCE not in self.relationships:
            return None

        relation = self.relationships[NodeRelationship.SOURCE]
        if isinstance(relation, list):
            raise ValueError("Source object must be a single RelatedNodeInfo object")
        return relation

    @property
    def prev_node(self) -> Optional[RelatedNodeInfo]:
        """Prev node."""
        if NodeRelationship.PREVIOUS not in self.relationships:
            return None

        relation = self.relationships[NodeRelationship.PREVIOUS]
        if not isinstance(relation, RelatedNodeInfo):
            raise ValueError("Previous object must be a single RelatedNodeInfo object")
        return relation

    @property
    def next_node(self) -> Optional[RelatedNodeInfo]:
        """Next node."""
        if NodeRelationship.NEXT not in self.relationships:
            return None

        relation = self.relationships[NodeRelationship.NEXT]
        if not isinstance(relation, RelatedNodeInfo):
            raise ValueError("Next object must be a single RelatedNodeInfo object")
        return relation

    @property
    def parent_node(self) -> Optional[RelatedNodeInfo]:
        """Parent node."""
        if NodeRelationship.PARENT not in self.relationships:
            return None

        relation = self.relationships[NodeRelationship.PARENT]
        if not isinstance(relation, RelatedNodeInfo):
            raise ValueError("Parent object must be a single RelatedNodeInfo object")
        return relation

    @property
    def child_nodes(self) -> Optional[List[RelatedNodeInfo]]:
        """Child nodes."""
        if NodeRelationship.CHILD not in self.relationships:
            return None

        relation = self.relationships[NodeRelationship.CHILD]
        if not isinstance(relation, list):
            raise ValueError("Child objects must be a list of RelatedNodeInfo objects.")
        return relation

    @property
    def ref_doc_id(self) -> Optional[str]:
        """Deprecated: Get ref doc id."""
        source_node = self.source_node
        if source_node is None:
            return None
        return source_node.node_id

    @property
    def extra_info(self) -> Dict[str, Any]:
        """TODO: DEPRECATED: Extra info."""
        return self.metadata

    def __str__(self) -> str:
        source_text_truncated = truncate_text(
            self.get_content().strip(), TRUNCATE_LENGTH
        )
        source_text_wrapped = textwrap.fill(
            f"Text: {source_text_truncated}\n", width=WRAP_WIDTH
        )
        return f"Node ID: {self.node_id}\n{source_text_wrapped}"

    def get_embedding(self) -> List[float]:
        """Get embedding.

        Errors if embedding is None.

        """
        if self.embedding is None:
            raise ValueError("embedding not set.")
        return self.embedding

    def as_related_node_info(self) -> RelatedNodeInfo:
        """Get node as RelatedNodeInfo."""
        return RelatedNodeInfo(
            node_id=self.node_id,
            node_type=self.get_type(),
            metadata=self.metadata,
            hash=self.hash,
        )

embedding class-attribute instance-attribute #

embedding: Optional[List[float]] = Field(default=None, description='Embedding of the node.')

" metadata fields - injected as part of the text shown to LLMs as context - injected as part of the text for generating embeddings - used by vector DBs for metadata filtering

hash abstractmethod property #

hash: str

Get hash of node.

source_node property #

source_node: Optional[RelatedNodeInfo]

Source object node.

Extracted from the relationships field.

prev_node property #

prev_node: Optional[RelatedNodeInfo]

Prev node.

next_node property #

next_node: Optional[RelatedNodeInfo]

Next node.

parent_node property #

parent_node: Optional[RelatedNodeInfo]

Parent node.

child_nodes property #

child_nodes: Optional[List[RelatedNodeInfo]]

Child nodes.

ref_doc_id property #

ref_doc_id: Optional[str]

Deprecated: Get ref doc id.

extra_info property #

extra_info: Dict[str, Any]

TODO: DEPRECATED: Extra info.

get_type abstractmethod classmethod #

get_type() -> str

Get Object type.

Source code in llama-index-core/llama_index/core/schema.py
261
262
263
264
@classmethod
@abstractmethod
def get_type(cls) -> str:
    """Get Object type."""

get_content abstractmethod #

get_content(metadata_mode: MetadataMode = ALL) -> str

Get object content.

Source code in llama-index-core/llama_index/core/schema.py
266
267
268
@abstractmethod
def get_content(self, metadata_mode: MetadataMode = MetadataMode.ALL) -> str:
    """Get object content."""

get_metadata_str abstractmethod #

get_metadata_str(mode: MetadataMode = ALL) -> str

Metadata string.

Source code in llama-index-core/llama_index/core/schema.py
270
271
272
@abstractmethod
def get_metadata_str(self, mode: MetadataMode = MetadataMode.ALL) -> str:
    """Metadata string."""

set_content abstractmethod #

set_content(value: Any) -> None

Set the content of the node.

Source code in llama-index-core/llama_index/core/schema.py
274
275
276
@abstractmethod
def set_content(self, value: Any) -> None:
    """Set the content of the node."""

get_embedding #

get_embedding() -> List[float]

Get embedding.

Errors if embedding is None.

Source code in llama-index-core/llama_index/core/schema.py
372
373
374
375
376
377
378
379
380
def get_embedding(self) -> List[float]:
    """Get embedding.

    Errors if embedding is None.

    """
    if self.embedding is None:
        raise ValueError("embedding not set.")
    return self.embedding
as_related_node_info() -> RelatedNodeInfo

Get node as RelatedNodeInfo.

Source code in llama-index-core/llama_index/core/schema.py
382
383
384
385
386
387
388
389
def as_related_node_info(self) -> RelatedNodeInfo:
    """Get node as RelatedNodeInfo."""
    return RelatedNodeInfo(
        node_id=self.node_id,
        node_type=self.get_type(),
        metadata=self.metadata,
        hash=self.hash,
    )

TextNode #

Bases: BaseNode

Parameters:

Name Type Description Default
text str

Text content of the node.

''
mimetype str

MIME type of the node content.

'text/plain'
start_char_idx int | None

Start char index of the node.

None
end_char_idx int | None

End char index of the node.

None
text_template str

Template for how text is formatted, with {content} and {metadata_str} placeholders.

'{metadata_str}\n\n{content}'
metadata_template str

Template for how metadata is formatted, with {key} and {value} placeholders.

'{key}: {value}'
metadata_seperator str

Separator between metadata fields when converting to string.

'\n'
Source code in llama-index-core/llama_index/core/schema.py
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
class TextNode(BaseNode):
    text: str = Field(default="", description="Text content of the node.")
    mimetype: str = Field(
        default="text/plain", description="MIME type of the node content."
    )
    start_char_idx: Optional[int] = Field(
        default=None, description="Start char index of the node."
    )
    end_char_idx: Optional[int] = Field(
        default=None, description="End char index of the node."
    )
    text_template: str = Field(
        default=DEFAULT_TEXT_NODE_TMPL,
        description=(
            "Template for how text is formatted, with {content} and "
            "{metadata_str} placeholders."
        ),
    )
    metadata_template: str = Field(
        default=DEFAULT_METADATA_TMPL,
        description=(
            "Template for how metadata is formatted, with {key} and "
            "{value} placeholders."
        ),
    )
    metadata_seperator: str = Field(
        default="\n",
        description="Separator between metadata fields when converting to string.",
    )

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

    @property
    def hash(self) -> str:
        doc_identity = str(self.text) + str(self.metadata)
        return str(sha256(doc_identity.encode("utf-8", "surrogatepass")).hexdigest())

    @classmethod
    def get_type(cls) -> str:
        """Get Object type."""
        return ObjectType.TEXT

    def get_content(self, metadata_mode: MetadataMode = MetadataMode.NONE) -> str:
        """Get object content."""
        metadata_str = self.get_metadata_str(mode=metadata_mode).strip()
        if not metadata_str:
            return self.text

        return self.text_template.format(
            content=self.text, metadata_str=metadata_str
        ).strip()

    def get_metadata_str(self, mode: MetadataMode = MetadataMode.ALL) -> str:
        """Metadata info string."""
        if mode == MetadataMode.NONE:
            return ""

        usable_metadata_keys = set(self.metadata.keys())
        if mode == MetadataMode.LLM:
            for key in self.excluded_llm_metadata_keys:
                if key in usable_metadata_keys:
                    usable_metadata_keys.remove(key)
        elif mode == MetadataMode.EMBED:
            for key in self.excluded_embed_metadata_keys:
                if key in usable_metadata_keys:
                    usable_metadata_keys.remove(key)

        return self.metadata_seperator.join(
            [
                self.metadata_template.format(key=key, value=str(value))
                for key, value in self.metadata.items()
                if key in usable_metadata_keys
            ]
        )

    def set_content(self, value: str) -> None:
        """Set the content of the node."""
        self.text = value

    def get_node_info(self) -> Dict[str, Any]:
        """Get node info."""
        return {"start": self.start_char_idx, "end": self.end_char_idx}

    def get_text(self) -> str:
        return self.get_content(metadata_mode=MetadataMode.NONE)

    @property
    def node_info(self) -> Dict[str, Any]:
        """Deprecated: Get node info."""
        return self.get_node_info()

node_info property #

node_info: Dict[str, Any]

Deprecated: Get node info.

get_type classmethod #

get_type() -> str

Get Object type.

Source code in llama-index-core/llama_index/core/schema.py
431
432
433
434
@classmethod
def get_type(cls) -> str:
    """Get Object type."""
    return ObjectType.TEXT

get_content #

get_content(metadata_mode: MetadataMode = NONE) -> str

Get object content.

Source code in llama-index-core/llama_index/core/schema.py
436
437
438
439
440
441
442
443
444
def get_content(self, metadata_mode: MetadataMode = MetadataMode.NONE) -> str:
    """Get object content."""
    metadata_str = self.get_metadata_str(mode=metadata_mode).strip()
    if not metadata_str:
        return self.text

    return self.text_template.format(
        content=self.text, metadata_str=metadata_str
    ).strip()

get_metadata_str #

get_metadata_str(mode: MetadataMode = ALL) -> str

Metadata info string.

Source code in llama-index-core/llama_index/core/schema.py
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
def get_metadata_str(self, mode: MetadataMode = MetadataMode.ALL) -> str:
    """Metadata info string."""
    if mode == MetadataMode.NONE:
        return ""

    usable_metadata_keys = set(self.metadata.keys())
    if mode == MetadataMode.LLM:
        for key in self.excluded_llm_metadata_keys:
            if key in usable_metadata_keys:
                usable_metadata_keys.remove(key)
    elif mode == MetadataMode.EMBED:
        for key in self.excluded_embed_metadata_keys:
            if key in usable_metadata_keys:
                usable_metadata_keys.remove(key)

    return self.metadata_seperator.join(
        [
            self.metadata_template.format(key=key, value=str(value))
            for key, value in self.metadata.items()
            if key in usable_metadata_keys
        ]
    )

set_content #

set_content(value: str) -> None

Set the content of the node.

Source code in llama-index-core/llama_index/core/schema.py
469
470
471
def set_content(self, value: str) -> None:
    """Set the content of the node."""
    self.text = value

get_node_info #

get_node_info() -> Dict[str, Any]

Get node info.

Source code in llama-index-core/llama_index/core/schema.py
473
474
475
def get_node_info(self) -> Dict[str, Any]:
    """Get node info."""
    return {"start": self.start_char_idx, "end": self.end_char_idx}

ImageNode #

Bases: TextNode

Node with image.

Parameters:

Name Type Description Default
image str | None
None
image_path str | None
None
image_url str | None
None
image_mimetype str | None
None
text_embedding List[float] | None

Text embedding of image node, if text field is filled out

None
Source code in llama-index-core/llama_index/core/schema.py
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
class ImageNode(TextNode):
    """Node with image."""

    # TODO: store reference instead of actual image
    # base64 encoded image str
    image: Optional[str] = None
    image_path: Optional[str] = None
    image_url: Optional[str] = None
    image_mimetype: Optional[str] = None
    text_embedding: Optional[List[float]] = Field(
        default=None,
        description="Text embedding of image node, if text field is filled out",
    )

    @classmethod
    def get_type(cls) -> str:
        return ObjectType.IMAGE

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

    def resolve_image(self) -> ImageType:
        """Resolve an image such that PIL can read it."""
        if self.image is not None:
            import base64

            return BytesIO(base64.b64decode(self.image))
        elif self.image_path is not None:
            return self.image_path
        elif self.image_url is not None:
            # load image from URL
            import requests

            response = requests.get(self.image_url)
            return BytesIO(response.content)
        else:
            raise ValueError("No image found in node.")

    @property
    def hash(self) -> str:
        """Get hash of node."""
        # doc identity depends on if image, image_path, or image_url is set
        image_str = self.image or "None"
        image_path_str = self.image_path or "None"
        image_url_str = self.image_url or "None"
        image_text = self.text or "None"
        doc_identity = f"{image_str}-{image_path_str}-{image_url_str}-{image_text}"
        return str(sha256(doc_identity.encode("utf-8", "surrogatepass")).hexdigest())

hash property #

hash: str

Get hash of node.

resolve_image #

resolve_image() -> ImageType

Resolve an image such that PIL can read it.

Source code in llama-index-core/llama_index/core/schema.py
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
def resolve_image(self) -> ImageType:
    """Resolve an image such that PIL can read it."""
    if self.image is not None:
        import base64

        return BytesIO(base64.b64decode(self.image))
    elif self.image_path is not None:
        return self.image_path
    elif self.image_url is not None:
        # load image from URL
        import requests

        response = requests.get(self.image_url)
        return BytesIO(response.content)
    else:
        raise ValueError("No image found in node.")

IndexNode #

Bases: TextNode

Node with reference to any object.

This can include other indices, query engines, retrievers.

This can also include other nodes (though this is overlapping with relationships on the Node class).

Parameters:

Name Type Description Default
index_id str
required
obj Any
None
Source code in llama-index-core/llama_index/core/schema.py
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
class IndexNode(TextNode):
    """Node with reference to any object.

    This can include other indices, query engines, retrievers.

    This can also include other nodes (though this is overlapping with `relationships`
    on the Node class).

    """

    index_id: str
    obj: Any = None

    def dict(self, **kwargs: Any) -> Dict[str, Any]:
        from llama_index.core.storage.docstore.utils import doc_to_json

        data = super().dict(**kwargs)

        try:
            if self.obj is None:
                data["obj"] = None
            elif isinstance(self.obj, BaseNode):
                data["obj"] = doc_to_json(self.obj)
            elif isinstance(self.obj, BaseModel):
                data["obj"] = self.obj.model_dump()
            else:
                data["obj"] = json.dumps(self.obj)
        except Exception:
            raise ValueError("IndexNode obj is not serializable: " + str(self.obj))

        return data

    @classmethod
    def from_text_node(
        cls,
        node: TextNode,
        index_id: str,
    ) -> "IndexNode":
        """Create index node from text node."""
        # copy all attributes from text node, add index id
        return cls(
            **node.dict(),
            index_id=index_id,
        )

    # TODO: return type here not supported by current mypy version
    @classmethod
    def from_dict(cls, data: Dict[str, Any], **kwargs: Any) -> Self:  # type: ignore
        output = super().from_dict(data, **kwargs)

        obj = data.get("obj", None)
        parsed_obj = None

        if isinstance(obj, str):
            parsed_obj = TextNode(text=obj)
        elif isinstance(obj, dict):
            from llama_index.core.storage.docstore.utils import json_to_doc

            # check if its a node, else assume stringable
            try:
                parsed_obj = json_to_doc(obj)  # type: ignore[assignment]
            except Exception:
                parsed_obj = TextNode(text=str(obj))

        output.obj = parsed_obj

        return output

    @classmethod
    def get_type(cls) -> str:
        return ObjectType.INDEX

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

from_text_node classmethod #

from_text_node(node: TextNode, index_id: str) -> IndexNode

Create index node from text node.

Source code in llama-index-core/llama_index/core/schema.py
573
574
575
576
577
578
579
580
581
582
583
584
@classmethod
def from_text_node(
    cls,
    node: TextNode,
    index_id: str,
) -> "IndexNode":
    """Create index node from text node."""
    # copy all attributes from text node, add index id
    return cls(
        **node.dict(),
        index_id=index_id,
    )

NodeWithScore #

Bases: BaseComponent

Parameters:

Name Type Description Default
node BaseNode
required
score float | None
None
Source code in llama-index-core/llama_index/core/schema.py
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
class NodeWithScore(BaseComponent):
    node: SerializeAsAny[BaseNode]
    score: Optional[float] = None

    def __str__(self) -> str:
        score_str = "None" if self.score is None else f"{self.score: 0.3f}"
        return f"{self.node}\nScore: {score_str}\n"

    def get_score(self, raise_error: bool = False) -> float:
        """Get score."""
        if self.score is None:
            if raise_error:
                raise ValueError("Score not set.")
            else:
                return 0.0
        else:
            return self.score

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

    ##### pass through methods to BaseNode #####
    @property
    def node_id(self) -> str:
        return self.node.node_id

    @property
    def id_(self) -> str:
        return self.node.id_

    @property
    def text(self) -> str:
        if isinstance(self.node, TextNode):
            return self.node.text
        else:
            raise ValueError("Node must be a TextNode to get text.")

    @property
    def metadata(self) -> Dict[str, Any]:
        return self.node.metadata

    @property
    def embedding(self) -> Optional[List[float]]:
        return self.node.embedding

    def get_text(self) -> str:
        if isinstance(self.node, TextNode):
            return self.node.get_text()
        else:
            raise ValueError("Node must be a TextNode to get text.")

    def get_content(self, metadata_mode: MetadataMode = MetadataMode.NONE) -> str:
        return self.node.get_content(metadata_mode=metadata_mode)

    def get_embedding(self) -> List[float]:
        return self.node.get_embedding()

get_score #

get_score(raise_error: bool = False) -> float

Get score.

Source code in llama-index-core/llama_index/core/schema.py
626
627
628
629
630
631
632
633
634
def get_score(self, raise_error: bool = False) -> float:
    """Get score."""
    if self.score is None:
        if raise_error:
            raise ValueError("Score not set.")
        else:
            return 0.0
    else:
        return self.score

Document #

Bases: TextNode

Generic interface for a data document.

This document connects to data sources.

Parameters:

Name Type Description Default
id_ str

Unique ID of the node.

'804c3332-bda6-4495-9971-42fb4b9bc8c2'
Source code in llama-index-core/llama_index/core/schema.py
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
class Document(TextNode):
    """Generic interface for a data document.

    This document connects to data sources.

    """

    # TODO: A lot of backwards compatibility logic here, clean up
    id_: str = Field(
        default_factory=lambda: str(uuid.uuid4()),
        description="Unique ID of the node.",
        alias="doc_id",
    )

    _compat_fields = {"doc_id": "id_", "extra_info": "metadata"}

    @classmethod
    def get_type(cls) -> str:
        """Get Document type."""
        return ObjectType.DOCUMENT

    @property
    def doc_id(self) -> str:
        """Get document ID."""
        return self.id_

    def __str__(self) -> str:
        source_text_truncated = truncate_text(
            self.get_content().strip(), TRUNCATE_LENGTH
        )
        source_text_wrapped = textwrap.fill(
            f"Text: {source_text_truncated}\n", width=WRAP_WIDTH
        )
        return f"Doc ID: {self.doc_id}\n{source_text_wrapped}"

    def get_doc_id(self) -> str:
        """TODO: Deprecated: Get document ID."""
        return self.id_

    def __setattr__(self, name: str, value: object) -> None:
        if name in self._compat_fields:
            name = self._compat_fields[name]
        super().__setattr__(name, value)

    def to_langchain_format(self) -> "LCDocument":
        """Convert struct to LangChain document format."""
        from llama_index.core.bridge.langchain import Document as LCDocument

        metadata = self.metadata or {}
        return LCDocument(page_content=self.text, metadata=metadata, id=self.id_)

    @classmethod
    def from_langchain_format(cls, doc: "LCDocument") -> "Document":
        """Convert struct from LangChain document format."""
        if doc.id:
            return cls(text=doc.page_content, metadata=doc.metadata, id_=doc.id)
        return cls(text=doc.page_content, metadata=doc.metadata)

    def to_haystack_format(self) -> "HaystackDocument":
        """Convert struct to Haystack document format."""
        from haystack.schema import Document as HaystackDocument

        return HaystackDocument(
            content=self.text, meta=self.metadata, embedding=self.embedding, id=self.id_
        )

    @classmethod
    def from_haystack_format(cls, doc: "HaystackDocument") -> "Document":
        """Convert struct from Haystack document format."""
        return cls(
            text=doc.content, metadata=doc.meta, embedding=doc.embedding, id_=doc.id
        )

    def to_embedchain_format(self) -> Dict[str, Any]:
        """Convert struct to EmbedChain document format."""
        return {
            "doc_id": self.id_,
            "data": {"content": self.text, "meta_data": self.metadata},
        }

    @classmethod
    def from_embedchain_format(cls, doc: Dict[str, Any]) -> "Document":
        """Convert struct from EmbedChain document format."""
        return cls(
            text=doc["data"]["content"],
            metadata=doc["data"]["meta_data"],
            id_=doc["doc_id"],
        )

    def to_semantic_kernel_format(self) -> "MemoryRecord":
        """Convert struct to Semantic Kernel document format."""
        import numpy as np
        from semantic_kernel.memory.memory_record import MemoryRecord

        return MemoryRecord(
            id=self.id_,
            text=self.text,
            additional_metadata=self.get_metadata_str(),
            embedding=np.array(self.embedding) if self.embedding else None,
        )

    @classmethod
    def from_semantic_kernel_format(cls, doc: "MemoryRecord") -> "Document":
        """Convert struct from Semantic Kernel document format."""
        return cls(
            text=doc._text,
            metadata={"additional_metadata": doc._additional_metadata},
            embedding=doc._embedding.tolist() if doc._embedding is not None else None,
            id_=doc._id,
        )

    def to_vectorflow(self, client: Any) -> None:
        """Send a document to vectorflow, since they don't have a document object."""
        # write document to temp file
        import tempfile

        with tempfile.NamedTemporaryFile() as f:
            f.write(self.text.encode("utf-8"))
            f.flush()
            client.embed(f.name)

    @classmethod
    def example(cls) -> "Document":
        return Document(
            text=SAMPLE_TEXT,
            metadata={"filename": "README.md", "category": "codebase"},
        )

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

    def to_cloud_document(self) -> "CloudDocument":
        """Convert to LlamaCloud document type."""
        from llama_cloud.types.cloud_document import CloudDocument

        return CloudDocument(
            text=self.text,
            metadata=self.metadata,
            excluded_embed_metadata_keys=self.excluded_embed_metadata_keys,
            excluded_llm_metadata_keys=self.excluded_llm_metadata_keys,
            id=self.id_,
        )

    @classmethod
    def from_cloud_document(
        cls,
        doc: "CloudDocument",
    ) -> "Document":
        """Convert from LlamaCloud document type."""
        return Document(
            text=doc.text,
            metadata=doc.metadata,
            excluded_embed_metadata_keys=doc.excluded_embed_metadata_keys,
            excluded_llm_metadata_keys=doc.excluded_llm_metadata_keys,
            id_=doc.id,
        )

doc_id property #

doc_id: str

Get document ID.

get_type classmethod #

get_type() -> str

Get Document type.

Source code in llama-index-core/llama_index/core/schema.py
696
697
698
699
@classmethod
def get_type(cls) -> str:
    """Get Document type."""
    return ObjectType.DOCUMENT

get_doc_id #

get_doc_id() -> str

TODO: Deprecated: Get document ID.

Source code in llama-index-core/llama_index/core/schema.py
715
716
717
def get_doc_id(self) -> str:
    """TODO: Deprecated: Get document ID."""
    return self.id_

to_langchain_format #

to_langchain_format() -> Document

Convert struct to LangChain document format.

Source code in llama-index-core/llama_index/core/schema.py
724
725
726
727
728
729
def to_langchain_format(self) -> "LCDocument":
    """Convert struct to LangChain document format."""
    from llama_index.core.bridge.langchain import Document as LCDocument

    metadata = self.metadata or {}
    return LCDocument(page_content=self.text, metadata=metadata, id=self.id_)

from_langchain_format classmethod #

from_langchain_format(doc: Document) -> Document

Convert struct from LangChain document format.

Source code in llama-index-core/llama_index/core/schema.py
731
732
733
734
735
736
@classmethod
def from_langchain_format(cls, doc: "LCDocument") -> "Document":
    """Convert struct from LangChain document format."""
    if doc.id:
        return cls(text=doc.page_content, metadata=doc.metadata, id_=doc.id)
    return cls(text=doc.page_content, metadata=doc.metadata)

to_haystack_format #

to_haystack_format() -> Document

Convert struct to Haystack document format.

Source code in llama-index-core/llama_index/core/schema.py
738
739
740
741
742
743
744
def to_haystack_format(self) -> "HaystackDocument":
    """Convert struct to Haystack document format."""
    from haystack.schema import Document as HaystackDocument

    return HaystackDocument(
        content=self.text, meta=self.metadata, embedding=self.embedding, id=self.id_
    )

from_haystack_format classmethod #

from_haystack_format(doc: Document) -> Document

Convert struct from Haystack document format.

Source code in llama-index-core/llama_index/core/schema.py
746
747
748
749
750
751
@classmethod
def from_haystack_format(cls, doc: "HaystackDocument") -> "Document":
    """Convert struct from Haystack document format."""
    return cls(
        text=doc.content, metadata=doc.meta, embedding=doc.embedding, id_=doc.id
    )

to_embedchain_format #

to_embedchain_format() -> Dict[str, Any]

Convert struct to EmbedChain document format.

Source code in llama-index-core/llama_index/core/schema.py
753
754
755
756
757
758
def to_embedchain_format(self) -> Dict[str, Any]:
    """Convert struct to EmbedChain document format."""
    return {
        "doc_id": self.id_,
        "data": {"content": self.text, "meta_data": self.metadata},
    }

from_embedchain_format classmethod #

from_embedchain_format(doc: Dict[str, Any]) -> Document

Convert struct from EmbedChain document format.

Source code in llama-index-core/llama_index/core/schema.py
760
761
762
763
764
765
766
767
@classmethod
def from_embedchain_format(cls, doc: Dict[str, Any]) -> "Document":
    """Convert struct from EmbedChain document format."""
    return cls(
        text=doc["data"]["content"],
        metadata=doc["data"]["meta_data"],
        id_=doc["doc_id"],
    )

to_semantic_kernel_format #

to_semantic_kernel_format() -> MemoryRecord

Convert struct to Semantic Kernel document format.

Source code in llama-index-core/llama_index/core/schema.py
769
770
771
772
773
774
775
776
777
778
779
def to_semantic_kernel_format(self) -> "MemoryRecord":
    """Convert struct to Semantic Kernel document format."""
    import numpy as np
    from semantic_kernel.memory.memory_record import MemoryRecord

    return MemoryRecord(
        id=self.id_,
        text=self.text,
        additional_metadata=self.get_metadata_str(),
        embedding=np.array(self.embedding) if self.embedding else None,
    )

from_semantic_kernel_format classmethod #

from_semantic_kernel_format(doc: MemoryRecord) -> Document

Convert struct from Semantic Kernel document format.

Source code in llama-index-core/llama_index/core/schema.py
781
782
783
784
785
786
787
788
789
@classmethod
def from_semantic_kernel_format(cls, doc: "MemoryRecord") -> "Document":
    """Convert struct from Semantic Kernel document format."""
    return cls(
        text=doc._text,
        metadata={"additional_metadata": doc._additional_metadata},
        embedding=doc._embedding.tolist() if doc._embedding is not None else None,
        id_=doc._id,
    )

to_vectorflow #

to_vectorflow(client: Any) -> None

Send a document to vectorflow, since they don't have a document object.

Source code in llama-index-core/llama_index/core/schema.py
791
792
793
794
795
796
797
798
799
def to_vectorflow(self, client: Any) -> None:
    """Send a document to vectorflow, since they don't have a document object."""
    # write document to temp file
    import tempfile

    with tempfile.NamedTemporaryFile() as f:
        f.write(self.text.encode("utf-8"))
        f.flush()
        client.embed(f.name)

to_cloud_document #

to_cloud_document() -> CloudDocument

Convert to LlamaCloud document type.

Source code in llama-index-core/llama_index/core/schema.py
812
813
814
815
816
817
818
819
820
821
822
def to_cloud_document(self) -> "CloudDocument":
    """Convert to LlamaCloud document type."""
    from llama_cloud.types.cloud_document import CloudDocument

    return CloudDocument(
        text=self.text,
        metadata=self.metadata,
        excluded_embed_metadata_keys=self.excluded_embed_metadata_keys,
        excluded_llm_metadata_keys=self.excluded_llm_metadata_keys,
        id=self.id_,
    )

from_cloud_document classmethod #

from_cloud_document(doc: CloudDocument) -> Document

Convert from LlamaCloud document type.

Source code in llama-index-core/llama_index/core/schema.py
824
825
826
827
828
829
830
831
832
833
834
835
836
@classmethod
def from_cloud_document(
    cls,
    doc: "CloudDocument",
) -> "Document":
    """Convert from LlamaCloud document type."""
    return Document(
        text=doc.text,
        metadata=doc.metadata,
        excluded_embed_metadata_keys=doc.excluded_embed_metadata_keys,
        excluded_llm_metadata_keys=doc.excluded_llm_metadata_keys,
        id_=doc.id,
    )

ImageDocument #

Bases: Document, ImageNode

Data document containing an image.

Source code in llama-index-core/llama_index/core/schema.py
839
840
841
842
843
844
class ImageDocument(Document, ImageNode):
    """Data document containing an image."""

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

QueryBundle dataclass #

Bases: DataClassJsonMixin

Query bundle.

This dataclass contains the original query string and associated transformations.

Parameters:

Name Type Description Default
query_str str

the original user-specified query string. This is currently used by all non embedding-based queries.

required
custom_embedding_strs list[str]

list of strings used for embedding the query. This is currently used by all embedding-based queries.

required
embedding list[float]

the stored embedding for the query.

required
image_path str | None
None
Source code in llama-index-core/llama_index/core/schema.py
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
@dataclass
class QueryBundle(DataClassJsonMixin):
    """
    Query bundle.

    This dataclass contains the original query string and associated transformations.

    Args:
        query_str (str): the original user-specified query string.
            This is currently used by all non embedding-based queries.
        custom_embedding_strs (list[str]): list of strings used for embedding the query.
            This is currently used by all embedding-based queries.
        embedding (list[float]): the stored embedding for the query.
    """

    query_str: str
    # using single image path as query input
    image_path: Optional[str] = None
    custom_embedding_strs: Optional[List[str]] = None
    embedding: Optional[List[float]] = None

    @property
    def embedding_strs(self) -> List[str]:
        """Use custom embedding strs if specified, otherwise use query str."""
        if self.custom_embedding_strs is None:
            if len(self.query_str) == 0:
                return []
            return [self.query_str]
        else:
            return self.custom_embedding_strs

    @property
    def embedding_image(self) -> List[ImageType]:
        """Use image path for image retrieval."""
        if self.image_path is None:
            return []
        return [self.image_path]

    def __str__(self) -> str:
        """Convert to string representation."""
        return self.query_str

embedding_strs property #

embedding_strs: List[str]

Use custom embedding strs if specified, otherwise use query str.

embedding_image property #

embedding_image: List[ImageType]

Use image path for image retrieval.