节点后处理器 - LlamaIndex 🦙 0.6.18

class llama_index.indices.postprocessor.AutoPrevNextNodePostprocessor(*, docstore: BaseDocumentStore, service_context: ServiceContext, num_nodes: int = 1, infer_prev_next_tmpl: str = "The current context information is provided. \nA question is also provided. \nYou are a retrieval agent deciding whether to search the document store for additional prior context or future context. \nGiven the context and question, return PREVIOUS or NEXT or NONE. \nExamples: \n\nContext: Describes the author's experience at Y Combinator.Question: What did the author do after his time at Y Combinator? \nAnswer: NEXT \n\nContext: Describes the author's experience at Y Combinator.Question: What did the author do before his time at Y Combinator? \nAnswer: PREVIOUS \n\nContext: Describe the author's experience at Y Combinator.Question: What did the author do at Y Combinator? \nAnswer: NONE \n\nContext: {context_str}\nQuestion: {query_str}\nAnswer: ", refine_prev_next_tmpl: str = 'The current context information is provided. \nA question is also provided. \nAn existing answer is also provided.\nYou are a retrieval agent deciding whether to search the document store for additional prior context or future context. \nGiven the context, question, and previous answer, return PREVIOUS or NEXT or NONE.\nExamples: \n\nContext: {context_msg}\nQuestion: {query_str}\nExisting Answer: {existing_answer}\nAnswer: ', verbose: bool = False)

Previous/Next Node post-processor.

Allows users to fetch additional nodes from the document store, based on the prev/next relationships of the nodes.

NOTE: difference with PrevNextPostprocessor is that this infers forward/backwards direction.

NOTE: this is a beta feature.

参数

docstore (BaseDocumentStore) -- The document store.
llm_predictor (LLMPredictor) -- The LLM predictor.
num_nodes (int) -- The number of nodes to return (default: 1)
infer_prev_next_tmpl (str) -- The template to use for inference. Required fields are {context_str} and {query_str}.

class Config: Configuration for this pydantic object.

classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model: 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 = 'allow' was set since it adds all passed values

copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model

Duplicate a model, optionally choose which fields to include, exclude and change.

参数

include -- fields to include in new model
exclude -- fields to exclude from new model, as with values this takes precedence over include
update -- 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 -- set to True to make a deep copy of the model

返回

new model instance

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: Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

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

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().

postprocess_nodes(nodes: List[NodeWithScore], query_bundle: Optional[QueryBundle] = None) → List[NodeWithScore]: Postprocess nodes.

classmethod update_forward_refs(**localns: Any) → None: Try to update ForwardRefs on fields based on this Model, globalns and localns.

class llama_index.indices.postprocessor.CohereRerank(top_n: int = 2, model: str = 'rerank-english-v2.0', api_key: Optional[str] = None)

postprocess_nodes(nodes: List[NodeWithScore], query_bundle: Optional[QueryBundle] = None) → List[NodeWithScore]: Postprocess nodes.

class llama_index.indices.postprocessor.EmbeddingRecencyPostprocessor(*, service_context: ServiceContext, date_key: str = 'date', in_extra_info: bool = True, similarity_cutoff: float = 0.7, query_embedding_tmpl: str = 'The current document is provided.\n----------------\n{context_str}\n----------------\nGiven the document, we wish to find documents that contain \nsimilar context. Note that these documents are older than the current document, meaning that certain details may be changed. \nHowever, the high-level context should be similar.\n')

Recency post-processor.

This post-processor does the following steps:

Decides if we need to use the post-processor given the query (is it temporal-related?)
If yes, sorts nodes by date.
For each node, look at subsequent nodes and filter out nodes that have high embedding similarity with the current node. Because this means the subsequent node may have overlapping content with the current node but is also out of date

classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model: 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 = 'allow' was set since it adds all passed values

copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model

Duplicate a model, optionally choose which fields to include, exclude and change.

参数

include -- fields to include in new model
exclude -- fields to exclude from new model, as with values this takes precedence over include
update -- 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 -- set to True to make a deep copy of the model

返回

new model instance

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: Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

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

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().

postprocess_nodes(nodes: List[NodeWithScore], query_bundle: Optional[QueryBundle] = None) → List[NodeWithScore]: Postprocess nodes.

classmethod update_forward_refs(**localns: Any) → None: Try to update ForwardRefs on fields based on this Model, globalns and localns.

class llama_index.indices.postprocessor.FixedRecencyPostprocessor(*, service_context: ServiceContext, top_k: int = 1, date_key: str = 'date', in_extra_info: bool = True)

Recency post-processor.

This post-processor does the following steps:

Decides if we need to use the post-processor given the query (is it temporal-related?)
If yes, sorts nodes by date.
Take the first k nodes (by default 1), and use that to synthesize an answer.

classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model: 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 = 'allow' was set since it adds all passed values

copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model

Duplicate a model, optionally choose which fields to include, exclude and change.

参数

include -- fields to include in new model
exclude -- fields to exclude from new model, as with values this takes precedence over include
update -- 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 -- set to True to make a deep copy of the model

返回

new model instance

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: Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

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

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().

postprocess_nodes(nodes: List[NodeWithScore], query_bundle: Optional[QueryBundle] = None) → List[NodeWithScore]: Postprocess nodes.

classmethod update_forward_refs(**localns: Any) → None: Try to update ForwardRefs on fields based on this Model, globalns and localns.

class llama_index.indices.postprocessor.KeywordNodePostprocessor(*, required_keywords: List[str] = None, exclude_keywords: List[str] = None)

Keyword-based Node processor.

classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model: 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 = 'allow' was set since it adds all passed values

copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model

Duplicate a model, optionally choose which fields to include, exclude and change.

参数

include -- fields to include in new model
exclude -- fields to exclude from new model, as with values this takes precedence over include
update -- 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 -- set to True to make a deep copy of the model

返回

new model instance

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: Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

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

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().

postprocess_nodes(nodes: List[NodeWithScore], query_bundle: Optional[QueryBundle] = None) → List[NodeWithScore]: Postprocess nodes.

classmethod update_forward_refs(**localns: Any) → None: Try to update ForwardRefs on fields based on this Model, globalns and localns.

class llama_index.indices.postprocessor.LLMRerank(choice_select_prompt: Optional[Prompt] = None, choice_batch_size: int = 10, format_node_batch_fn: Optional[Callable] = None, parse_choice_select_answer_fn: Optional[Callable] = None, service_context: Optional[ServiceContext] = None, top_n: int = 10)

LLM-based reranker.

postprocess_nodes(nodes: List[NodeWithScore], query_bundle: Optional[QueryBundle] = None) → List[NodeWithScore]: Postprocess nodes.

class llama_index.indices.postprocessor.NERPIINodePostprocessor(*, pii_node_info_key: str = '__pii_node_info__')

NER PII Node processor.

Uses a HF transformers model.

classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model: 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 = 'allow' was set since it adds all passed values

copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model

Duplicate a model, optionally choose which fields to include, exclude and change.

参数

include -- fields to include in new model
exclude -- fields to exclude from new model, as with values this takes precedence over include
update -- 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 -- set to True to make a deep copy of the model

返回

new model instance

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: Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

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

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().

mask_pii(ner: Callable, text: str) → Tuple[str, Dict]: Mask PII in text.

postprocess_nodes(nodes: List[NodeWithScore], query_bundle: Optional[QueryBundle] = None) → List[NodeWithScore]: Postprocess nodes.

classmethod update_forward_refs(**localns: Any) → None: Try to update ForwardRefs on fields based on this Model, globalns and localns.

class llama_index.indices.postprocessor.PIINodePostprocessor(*, service_context: ServiceContext, pii_str_tmpl: str = 'The current context information is provided. \nA task is also provided to mask the PII within the context. \nReturn the text, with all PII masked out, and a mapping of the original PII to the masked PII. \nReturn the output of the task in JSON. \nContext:\nHello Zhang Wei, I am John. Your AnyCompany Financial Services, LLC credit card account 1111-0000-1111-0008 has a minimum payment of $24.53 that is due by July 31st. Based on your autopay settings, we will withdraw your payment. Task: Mask out the PII, replace each PII with a tag, and return the text. Return the mapping in JSON. \nOutput: \nHello [NAME1], I am [NAME2]. Your AnyCompany Financial Services, LLC credit card account [CREDIT_CARD_NUMBER] has a minimum payment of $24.53 that is due by [DATE_TIME]. Based on your autopay settings, we will withdraw your payment. Output Mapping:\n{{"NAME1": "Zhang Wei", "NAME2": "John", "CREDIT_CARD_NUMBER": "1111-0000-1111-0008", "DATE_TIME": "July 31st"}}\nContext:\n{context_str}\nTask: {query_str}\nOutput: \n', pii_node_info_key: str = '__pii_node_info__')

PII Node processor.

NOTE: the ServiceContext should contain a LOCAL model, not an external API.

NOTE: this is a beta feature, the API might change.

参数: service_context (ServiceContext) -- Service context.

classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model: 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 = 'allow' was set since it adds all passed values

copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model

Duplicate a model, optionally choose which fields to include, exclude and change.

参数

include -- fields to include in new model
exclude -- fields to exclude from new model, as with values this takes precedence over include
update -- 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 -- set to True to make a deep copy of the model

返回

new model instance

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: Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

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

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().

mask_pii(text: str) → Tuple[str, Dict]: Mask PII in text.

postprocess_nodes(nodes: List[NodeWithScore], query_bundle: Optional[QueryBundle] = None) → List[NodeWithScore]: Postprocess nodes.

classmethod update_forward_refs(**localns: Any) → None: Try to update ForwardRefs on fields based on this Model, globalns and localns.

class llama_index.indices.postprocessor.PrevNextNodePostprocessor(*, docstore: BaseDocumentStore, num_nodes: int = 1, mode: str = 'next')

Previous/Next Node post-processor.

Allows users to fetch additional nodes from the document store, based on the relationships of the nodes.

NOTE: this is a beta feature.

参数

docstore (BaseDocumentStore) -- The document store.
num_nodes (int) -- The number of nodes to return (default: 1)
mode (str) -- The mode of the post-processor. Can be "previous", "next", or "both.

classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model: 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 = 'allow' was set since it adds all passed values

copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model

Duplicate a model, optionally choose which fields to include, exclude and change.

参数

include -- fields to include in new model
exclude -- fields to exclude from new model, as with values this takes precedence over include
update -- 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 -- set to True to make a deep copy of the model

返回

new model instance

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: Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

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

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().

postprocess_nodes(nodes: List[NodeWithScore], query_bundle: Optional[QueryBundle] = None) → List[NodeWithScore]: Postprocess nodes.

classmethod update_forward_refs(**localns: Any) → None: Try to update ForwardRefs on fields based on this Model, globalns and localns.

class llama_index.indices.postprocessor.SimilarityPostprocessor(*, similarity_cutoff: float = None)

Similarity-based Node processor.

classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model: 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 = 'allow' was set since it adds all passed values

copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model

Duplicate a model, optionally choose which fields to include, exclude and change.

参数

include -- fields to include in new model
exclude -- fields to exclude from new model, as with values this takes precedence over include
update -- 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 -- set to True to make a deep copy of the model

返回

new model instance

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: Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

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

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().

postprocess_nodes(nodes: List[NodeWithScore], query_bundle: Optional[QueryBundle] = None) → List[NodeWithScore]: Postprocess nodes.

classmethod update_forward_refs(**localns: Any) → None: Try to update ForwardRefs on fields based on this Model, globalns and localns.

class llama_index.indices.postprocessor.TimeWeightedPostprocessor(*, time_decay: float = 0.99, last_accessed_key: str = '__last_accessed__', time_access_refresh: bool = True, now: Optional[float] = None, top_k: int = 1)

Time-weighted post-processor.

Reranks a set of nodes based on their recency.

classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model: 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 = 'allow' was set since it adds all passed values

copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model

Duplicate a model, optionally choose which fields to include, exclude and change.

参数

include -- fields to include in new model
exclude -- fields to exclude from new model, as with values this takes precedence over include
update -- 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 -- set to True to make a deep copy of the model

返回

new model instance

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: Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

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

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().

postprocess_nodes(nodes: List[NodeWithScore], query_bundle: Optional[QueryBundle] = None) → List[NodeWithScore]: Postprocess nodes.

classmethod update_forward_refs(**localns: Any) → None: Try to update ForwardRefs on fields based on this Model, globalns and localns.

节点后处理器