langchain.indexes.graph
.GraphIndexCreatorΒΆ
- class langchain.indexes.graph.GraphIndexCreator[source]ΒΆ
Bases:
BaseModel
Functionality to create graph index.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
- param graph_type: Type[NetworkxEntityGraph] = <class 'langchain_community.graphs.networkx_graph.NetworkxEntityGraph'>ΒΆ
- param llm: Optional[BaseLanguageModel] = NoneΒΆ
- async afrom_text(text: str, prompt: BasePromptTemplate = PromptTemplate(input_variables=['text'], template="You are a networked intelligence helping a human track knowledge triples about all relevant people, things, concepts, etc. and integrating them with your knowledge stored within your weights as well as that stored in a knowledge graph. Extract all of the knowledge triples from the text. A knowledge triple is a clause that contains a subject, a predicate, and an object. The subject is the entity being described, the predicate is the property of the subject that is being described, and the object is the value of the property.\n\nEXAMPLE\nIt's a state in the US. It's also the number 1 producer of gold in the US.\n\nOutput: (Nevada, is a, state)<|>(Nevada, is in, US)<|>(Nevada, is the number 1 producer of, gold)\nEND OF EXAMPLE\n\nEXAMPLE\nI'm going to the store.\n\nOutput: NONE\nEND OF EXAMPLE\n\nEXAMPLE\nOh huh. I know Descartes likes to drive antique scooters and play the mandolin.\nOutput: (Descartes, likes to drive, antique scooters)<|>(Descartes, plays, mandolin)\nEND OF EXAMPLE\n\nEXAMPLE\n{text}Output:")) NetworkxEntityGraph [source]ΒΆ
Create graph index from text asynchronously.
- Parameters
text (str) β
prompt (BasePromptTemplate) β
- Return type
- classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) Model ΒΆ
Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed. Behaves as if Config.extra = βallowβ was set since it adds all passed values
- Parameters
_fields_set (Optional[SetStr]) β
values (Any) β
- Return type
Model
- copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) Model ΒΆ
Duplicate a model, optionally choose which fields to include, exclude and change.
- Parameters
include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) β fields to include in new model
exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) β fields to exclude from new model, as with values this takes precedence over include
update (Optional[DictStrAny]) β values to change/add in the new model. Note: the data is not validated before creating the new model: you should trust this data
deep (bool) β set to True to make a deep copy of the model
self (Model) β
- Returns
new model instance
- Return type
Model
- dict(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False) DictStrAny ΒΆ
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters
include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) β
exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) β
by_alias (bool) β
skip_defaults (Optional[bool]) β
exclude_unset (bool) β
exclude_defaults (bool) β
exclude_none (bool) β
- Return type
DictStrAny
- classmethod from_orm(obj: Any) Model ΒΆ
- Parameters
obj (Any) β
- Return type
Model
- from_text(text: str, prompt: BasePromptTemplate = PromptTemplate(input_variables=['text'], template="You are a networked intelligence helping a human track knowledge triples about all relevant people, things, concepts, etc. and integrating them with your knowledge stored within your weights as well as that stored in a knowledge graph. Extract all of the knowledge triples from the text. A knowledge triple is a clause that contains a subject, a predicate, and an object. The subject is the entity being described, the predicate is the property of the subject that is being described, and the object is the value of the property.\n\nEXAMPLE\nIt's a state in the US. It's also the number 1 producer of gold in the US.\n\nOutput: (Nevada, is a, state)<|>(Nevada, is in, US)<|>(Nevada, is the number 1 producer of, gold)\nEND OF EXAMPLE\n\nEXAMPLE\nI'm going to the store.\n\nOutput: NONE\nEND OF EXAMPLE\n\nEXAMPLE\nOh huh. I know Descartes likes to drive antique scooters and play the mandolin.\nOutput: (Descartes, likes to drive, antique scooters)<|>(Descartes, plays, mandolin)\nEND OF EXAMPLE\n\nEXAMPLE\n{text}Output:")) NetworkxEntityGraph [source]ΒΆ
Create graph index from text.
- Parameters
text (str) β
prompt (BasePromptTemplate) β
- Return type
- json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, **dumps_kwargs: Any) unicode ΒΆ
Generate a JSON representation of the model, include and exclude arguments as per dict().
encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().
- Parameters
include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) β
exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) β
by_alias (bool) β
skip_defaults (Optional[bool]) β
exclude_unset (bool) β
exclude_defaults (bool) β
exclude_none (bool) β
encoder (Optional[Callable[[Any], Any]]) β
models_as_dict (bool) β
dumps_kwargs (Any) β
- Return type
unicode
- classmethod parse_file(path: Union[str, Path], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) Model ΒΆ
- Parameters
path (Union[str, Path]) β
content_type (unicode) β
encoding (unicode) β
proto (Protocol) β
allow_pickle (bool) β
- Return type
Model
- classmethod parse_obj(obj: Any) Model ΒΆ
- Parameters
obj (Any) β
- Return type
Model
- classmethod parse_raw(b: Union[str, bytes], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) Model ΒΆ
- Parameters
b (Union[str, bytes]) β
content_type (unicode) β
encoding (unicode) β
proto (Protocol) β
allow_pickle (bool) β
- Return type
Model
- classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') DictStrAny ΒΆ
- Parameters
by_alias (bool) β
ref_template (unicode) β
- Return type
DictStrAny
- classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) unicode ΒΆ
- Parameters
by_alias (bool) β
ref_template (unicode) β
dumps_kwargs (Any) β
- Return type
unicode
- classmethod update_forward_refs(**localns: Any) None ΒΆ
Try to update ForwardRefs on fields based on this Model, globalns and localns.
- Parameters
localns (Any) β
- Return type
None
- classmethod validate(value: Any) Model ΒΆ
- Parameters
value (Any) β
- Return type
Model