Tony20100/langchain_full huggingface dataset:
Parameters
obj (Any) – 
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¶
Parameters
b (Union[str, bytes]) – 
content_type (unicode) – 
encoding (unicode) – 
proto (Protocol) – 
allow_pickle (bool) – 
Return type
Model
classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') → DictStrAny¶
Parameters
by_alias (bool) – 
ref_template (unicode) – 
Return type
DictStrAny
classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) → unicode¶
Parameters
by_alias (bool) – 
ref_template (unicode) – 
dumps_kwargs (Any) – 
Return type
unicode
classmethod update_forward_refs(**localns: Any) → None¶
Try to update ForwardRefs on fields based on this Model, globalns and localns.
Parameters
localns (Any) – 
Return type
None
classmethod validate(value: Any) → Model¶
Parameters
value (Any) – 
Return type
Model
Examples using GigaChatEmbeddings¶
GigaChat
Salute Devices
langchain_community.embeddings.jina.JinaEmbeddings¶
class langchain_community.embeddings.jina.JinaEmbeddings[source]¶
Bases: BaseModel, Embeddings
Jina embedding models.
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 jina_api_key: Optional[SecretStr] = None¶
Constraints
type = string
writeOnly = True
format = password
param model_name: str = 'jina-embeddings-v2-base-en'¶
async aembed_documents(texts: List[str]) → List[List[float]]¶
Asynchronous Embed search docs.
Parameters
texts (List[str]) – 
Return type
List[List[float]]
async aembed_query(text: str) → List[float]¶
Asynchronous Embed query text.
Parameters
text (str) – 
Return type
List[float]
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
Parameters
_fields_set (Optional[SetStr]) – 
values (Any) – 
Return type
Model
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.
Parameters
Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – fields to include in new model
exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – fields to exclude from new model, as with values this takes precedence over include
update (Optional[DictStrAny]) – 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) – set to True to make a deep copy of the model
self (Model) – 
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¶
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
Parameters
include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – 
exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – 
by_alias (bool) – 
skip_defaults (Optional[bool]) – 
exclude_unset (bool) – 
exclude_defaults (bool) – 
exclude_none (bool) – 
Return type
DictStrAny
embed_documents(texts: List[str]) → List[List[float]][source]¶
Call out to Jina’s embedding endpoint.
:param texts: The list of texts to embed.
Returns
List of embeddings, one for each text.
Parameters
texts (List[str]) –
List of embeddings, one for each text.
Parameters
texts (List[str]) – 
Return type
List[List[float]]
embed_images(uris: List[str]) → List[List[float]][source]¶
Call out to Jina’s image embedding endpoint.
:param uris: The list of uris to embed.
Returns
List of embeddings, one for each text.
Parameters
uris (List[str]) – 
Return type
List[List[float]]
embed_query(text: str) → List[float][source]¶
Call out to Jina’s embedding endpoint.
:param text: The text to embed.
Returns
Embeddings for the text.
Parameters
text (str) – 
Return type
List[float]
classmethod from_orm(obj: Any) → Model¶
Parameters
obj (Any) – 
Return type
Model
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().
Parameters
include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – 
exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – 
by_alias (bool) – 
skip_defaults (Optional[bool]) – 
exclude_unset (bool) –
dumps_kwargs (Any) – 
Return type
unicode
classmethod update_forward_refs(**localns: Any) → None¶
Try to update ForwardRefs on fields based on this Model, globalns and localns.
Parameters
localns (Any) – 
Return type
None
classmethod validate(value: Any) → Model¶
Parameters
value (Any) – 
Return type
Model
Examples using JinaEmbeddings¶
Jina
Jina Reranker
langchain_community.embeddings.optimum_intel.QuantizedBiEncoderEmbeddings¶
class langchain_community.embeddings.optimum_intel.QuantizedBiEncoderEmbeddings[source]¶
Bases: BaseModel, Embeddings
Quantized bi-encoders embedding models.
Please ensure that you have installed optimum-intel and ipex.
Input:model_name: str = Model name.
max_seq_len: int = The maximum sequence length for tokenization. (default 512)
pooling_strategy: str =
“mean” or “cls”, pooling strategy for the final layer. (default “mean”)
query_instruction: Optional[str] =An instruction to add to the query before embedding. (default None)
document_instruction: Optional[str] =An instruction to add to each document before embedding. (default None)
padding: Optional[bool] =Whether to add padding during tokenization or not. (default True)
model_kwargs: Optional[Dict] =Parameters to add to the model during initialization. (default {})
encode_kwargs: Optional[Dict] =Parameters to add during the embedding forward pass. (default {})
Example:
from langchain_community.embeddings import QuantizedBiEncoderEmbeddings
model_name = “Intel/bge-small-en-v1.5-rag-int8-static”
encode_kwargs = {‘normalize_embeddings’: True}
hf = QuantizedBiEncoderEmbeddings(
model_name,
encode_kwargs=encode_kwargs,
query_instruction=”Represent this sentence for searching relevant passages: “
)
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.
async aembed_documents(texts: List[str]) → List[List[float]]¶
Asynchronous Embed search docs.
Parameters
texts (List[str]) – 
Return type
List[List[float]]
Return type
Model
classmethod parse_obj(obj: Any) → Model¶
Parameters
obj (Any) – 
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¶
Parameters
b (Union[str, bytes]) – 
content_type (unicode) – 
encoding (unicode) – 
proto (Protocol) – 
allow_pickle (bool) – 
Return type
Model
classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') → DictStrAny¶
Parameters
by_alias (bool) – 
ref_template (unicode) – 
Return type
DictStrAny
classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) → unicode¶
Parameters
by_alias (bool) – 
ref_template (unicode) – 
dumps_kwargs (Any) – 
Return type
unicode
classmethod update_forward_refs(**localns: Any) → None¶
Try to update ForwardRefs on fields based on this Model, globalns and localns.
Parameters
localns (Any) – 
Return type
None
classmethod validate(value: Any) → Model¶
Parameters
value (Any) – 
Return type
Model
Examples using QuantizedBiEncoderEmbeddings¶
Embedding Documents using Optimized and Quantized Embedders
Intel
langchain_upstage.embeddings.UpstageEmbeddings¶
class langchain_upstage.embeddings.UpstageEmbeddings[source]¶
Bases: BaseModel, Embeddings
UpstageEmbeddings embedding model.
To use, set the environment variable UPSTAGE_API_KEY with your API key or
pass it as a named parameter to the constructor.
Example
from langchain_upstage import UpstageEmbeddings
model = UpstageEmbeddings(model='solar-embedding-1-large')
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 allowed_special: Union[Literal['all'], Set[str]] = {}¶
Not yet supported.
param chunk_size: int = 1000¶
Maximum number of texts to embed in each batch.
Not yet supported.
param default_headers: Optional[Mapping[str, str]] = None¶
param default_query: Optional[Mapping[str, object]] = None¶
param dimensions: Optional[int] = None¶
The number of dimensions the resulting output embeddings should have.
Not yet supported.
param disallowed_special: Union[Literal['all'], Set[str], Sequence[str]] = 'all'¶
Not yet supported.
param embed_batch_size: int = 10¶
param embedding_ctx_length: int = 4096¶
The maximum number of tokens to embed at once.
Not yet supported.
param http_async_client: Optional[Any] = None¶
Optional httpx.AsyncClient. Only used for async invocations. Must specify
http_client as well if you’d like a custom client for sync invocations.
param http_client: Optional[Any] = None¶
Optional httpx.Client. Only used for sync invocations. Must specify
http_async_client as well if you’d like a custom client for async invocations.
http_async_client as well if you’d like a custom client for async invocations.
param max_retries: int = 2¶
Maximum number of retries to make when generating.
param model: str [Required]¶
Embeddings model name to use. Do not add suffixes like -query and -passage.
Instead, use ‘solar-embedding-1-large’ for example.
param model_kwargs: Dict[str, Any] [Optional]¶
Holds any model parameters valid for create call not explicitly specified.
param request_timeout: Optional[Union[float, Tuple[float, float], Any]] = None (alias 'timeout')¶
Timeout for requests to Upstage embedding API. Can be float, httpx.Timeout or
None.
param show_progress_bar: bool = False¶
Whether to show a progress bar when embedding.
Not yet supported.
param skip_empty: bool = False¶
Whether to skip empty strings when embedding or raise an error.
Defaults to not skipping.
Not yet supported.
param upstage_api_base: str = 'https://api.upstage.ai/v1/solar' (alias 'base_url')¶
Endpoint URL to use.
param upstage_api_key: Optional[SecretStr] = None (alias 'api_key')¶
API Key for Solar API.
Constraints
type = string
writeOnly = True
format = password
async aembed_documents(texts: List[str]) → List[List[float]][source]¶
Embed a list of document texts using passage model asynchronously.
Parameters
texts (List[str]) – The list of texts to embed.
Returns
List of embeddings, one for each text.
Return type
List[List[float]]
async aembed_query(text: str) → List[float][source]¶
Asynchronous Embed query text using query model.
Parameters
text (str) – The text to embed.
langchain_community.embeddings.titan_takeoff.TitanTakeoffEmbed¶
class langchain_community.embeddings.titan_takeoff.TitanTakeoffEmbed(base_url: str = 'http://localhost', port: int = 3000, mgmt_port: int = 3001, models: List[ReaderConfig] = [])[source]¶
Interface with Takeoff Inference API for embedding models.
Use it to send embedding requests and to deploy embedding
readers with Takeoff.
Examples
This is an example how to deploy an embedding model and send requests.
Initialize the Titan Takeoff embedding wrapper.
Parameters
base_url (str, optional) – The base url where Takeoff Inference Server is
"http (listening. Defaults to) – //localhost”.
port (int, optional) – What port is Takeoff Inference API listening on.
3000. (Defaults to) – 
mgmt_port (int, optional) – What port is Takeoff Management API listening on.
3001. (Defaults to) – 
models (List[ReaderConfig], optional) – Any readers you’d like to spin up on.
[]. (Defaults to) – 
Raises
ImportError – If you haven’t installed takeoff-client, you will get an
ImportError. To remedy run pip install 'takeoff-client==0.4.0' – 
Attributes
base_url
//localhost".
client
Takeoff Client Python SDK used to interact with Takeoff API
embed_consumer_groups
The consumer groups in Takeoff which contain embedding models
mgmt_port
The management port of the Titan Takeoff (Pro) server.
port
The port of the Titan Takeoff (Pro) server.
Methods
__init__([base_url, port, mgmt_port, models])
Initialize the Titan Takeoff embedding wrapper.
aembed_documents(texts)
Initialize the Titan Takeoff embedding wrapper.
aembed_documents(texts)
Asynchronous Embed search docs.
aembed_query(text)
Asynchronous Embed query text.
embed_documents(texts[, consumer_group])
Embed documents.
embed_query(text[, consumer_group])
Embed query.
__init__(base_url: str = 'http://localhost', port: int = 3000, mgmt_port: int = 3001, models: List[ReaderConfig] = [])[source]¶
Initialize the Titan Takeoff embedding wrapper.
Parameters
base_url (str, optional) – The base url where Takeoff Inference Server is
"http (listening. Defaults to) – //localhost”.
port (int, optional) – What port is Takeoff Inference API listening on.
3000. (Defaults to) – 
mgmt_port (int, optional) – What port is Takeoff Management API listening on.
3001. (Defaults to) – 
models (List[ReaderConfig], optional) – Any readers you’d like to spin up on.
[]. (Defaults to) – 
Raises
ImportError – If you haven’t installed takeoff-client, you will get an
ImportError. To remedy run pip install 'takeoff-client==0.4.0' – 
async aembed_documents(texts: List[str]) → List[List[float]]¶
Asynchronous Embed search docs.
Parameters
texts (List[str]) – 
Return type
List[List[float]]
async aembed_query(text: str) → List[float]¶
Asynchronous Embed query text.
Parameters
text (str) – 
Return type
List[float]
embed_documents(texts: List[str], consumer_group: Optional[str] = None) → List[List[float]][source]¶
Embed documents.
Parameters
Embed documents.
Parameters
texts (List[str]) – List of prompts/documents to embed
consumer_group (Optional[str], optional) – Consumer group to send request
None. (to containing embedding model. Defaults to) – 
Returns
List of embeddings
Return type
List[List[float]]
embed_query(text: str, consumer_group: Optional[str] = None) → List[float][source]¶
Embed query.
Parameters
text (str) – Prompt/document to embed
consumer_group (Optional[str], optional) – Consumer group to send request
None. (to containing embedding model. Defaults to) – 
Returns
Embedding
Return type
List[float]
Examples using TitanTakeoffEmbed¶
Titan Takeoff
langchain_community.embeddings.google_palm.embed_with_retry¶
langchain_community.embeddings.google_palm.embed_with_retry(embeddings: GooglePalmEmbeddings, *args: Any, **kwargs: Any) → Any[source]¶
Use tenacity to retry the completion call.
Parameters
embeddings (GooglePalmEmbeddings) – 
args (Any) – 
kwargs (Any) – 
Return type
Any
langchain_community.embeddings.gradient_ai.GradientEmbeddings¶
class langchain_community.embeddings.gradient_ai.GradientEmbeddings[source]¶
Bases: BaseModel, Embeddings
Gradient.ai Embedding models.
GradientLLM is a class to interact with Embedding Models on gradient.ai
To use, set the environment variable GRADIENT_ACCESS_TOKEN with your
API token and GRADIENT_WORKSPACE_ID for your gradient workspace,
or alternatively provide them as keywords to the constructor of this class.
Example
from langchain_community.embeddings import GradientEmbeddings
GradientEmbeddings(
    model="bge-large",
    gradient_workspace_id="12345614fc0_workspace",
    gradient_access_token="gradientai-access_token",
)
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 client: Any = None¶
Gradient client.
param gradient_access_token: Optional[str] = None¶
gradient.ai API Token, which can be generated by going to
https://auth.gradient.ai/select-workspace
and selecting “Access tokens” under the profile drop-down.
param gradient_api_url: str = 'https://api.gradient.ai/api'¶
Endpoint URL to use.
param gradient_workspace_id: Optional[str] = None¶
Underlying gradient.ai workspace_id.
param model: str [Required]¶
Underlying gradient.ai model id.
param query_prompt_for_retrieval: Optional[str] = None¶
Query pre-prompt
async aembed_documents(texts: List[str]) → List[List[float]][source]¶
Async call out to Gradient’s embedding endpoint.
Parameters
texts (List[str]) – The list of texts to embed.
Returns
List of embeddings, one for each text.
Return type
List[List[float]]
Returns
List of embeddings, one for each text.
Return type
List[List[float]]
async aembed_query(text: str) → List[float][source]¶
Async call out to Gradient’s embedding endpoint.
Parameters
text (str) – The text to embed.
Returns
Embeddings for the text.
Return type
List[float]
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
Parameters
_fields_set (Optional[SetStr]) – 
values (Any) – 
Return type
Model
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.
Parameters
include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – fields to include in new model
exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – fields to exclude from new model, as with values this takes precedence over include
update (Optional[DictStrAny]) – 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) – set to True to make a deep copy of the model
self (Model) – 
Returns
new model instance
Return type
Model
self (Model) – 
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¶
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
Parameters
include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – 
exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – 
by_alias (bool) – 
skip_defaults (Optional[bool]) – 
exclude_unset (bool) – 
exclude_defaults (bool) – 
exclude_none (bool) – 
Return type
DictStrAny
embed_documents(texts: List[str]) → List[List[float]][source]¶
Call out to Gradient’s embedding endpoint.
Parameters
texts (List[str]) – The list of texts to embed.
Returns
List of embeddings, one for each text.
Return type
List[List[float]]
embed_query(text: str) → List[float][source]¶
Call out to Gradient’s embedding endpoint.
Parameters
text (str) – The text to embed.
Returns
Embeddings for the text.
Return type
List[float]
classmethod from_orm(obj: Any) → Model¶
Parameters
obj (Any) – 
Return type
Model
Parameters
obj (Any) – 
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¶
Parameters
b (Union[str, bytes]) – 
content_type (unicode) – 
encoding (unicode) – 
proto (Protocol) – 
allow_pickle (bool) – 
Return type
Model
classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') → DictStrAny¶
Parameters
by_alias (bool) – 
ref_template (unicode) – 
Return type
DictStrAny
classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) → unicode¶
Parameters
by_alias (bool) – 
ref_template (unicode) – 
dumps_kwargs (Any) – 
Return type
unicode
classmethod update_forward_refs(**localns: Any) → None¶
Try to update ForwardRefs on fields based on this Model, globalns and localns.
Parameters
localns (Any) – 
Return type
None
classmethod validate(value: Any) → Model¶
Parameters
value (Any) – 
Return type
Model
Examples using GradientEmbeddings¶
Gradient
langchain_community.embeddings.edenai.EdenAiEmbeddings¶
class langchain_community.embeddings.edenai.EdenAiEmbeddings[source]¶
Bases: BaseModel, Embeddings
EdenAI embedding.
environment variable EDENAI_API_KEY set with your API key, or pass
it as a named parameter.
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 edenai_api_key: Optional[SecretStr] = None¶
EdenAI API Token
Constraints
type = string
writeOnly = True
format = password
param model: Optional[str] = None¶
model name for above provider (eg: ‘gpt-3.5-turbo-instruct’ for openai)
available models are shown on https://docs.edenai.co/ under ‘available providers’
param provider: str = 'openai'¶
embedding provider to use (eg: openai,google etc.)
async aembed_documents(texts: List[str]) → List[List[float]]¶
Asynchronous Embed search docs.
Parameters
texts (List[str]) – 
Return type
List[List[float]]
async aembed_query(text: str) → List[float]¶
Asynchronous Embed query text.
Parameters
text (str) – 
Return type
List[float]
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
Parameters
_fields_set (Optional[SetStr]) – 
values (Any) – 
Return type
Model
exclude_unset (bool) – 
exclude_defaults (bool) – 
exclude_none (bool) – 
Return type
DictStrAny
embed_documents(texts: List[str]) → List[List[float]][source]¶
Embed a list of documents using EdenAI.
Parameters
texts (List[str]) – The list of texts to embed.
Returns
List of embeddings, one for each text.
Return type
List[List[float]]
embed_query(text: str) → List[float][source]¶
Embed a query using EdenAI.
Parameters
text (str) – The text to embed.
Returns
Embeddings for the text.
Return type
List[float]
classmethod from_orm(obj: Any) → Model¶
Parameters
obj (Any) – 
Return type
Model
static get_user_agent() → str[source]¶
Return type
str
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().
Parameters
include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – 
exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – 
by_alias (bool) – 
skip_defaults (Optional[bool]) –