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 LaserEmbeddings¶
Facebook - Meta
LASER Language-Agnostic SEntence Representations Embeddings by Meta AI
langchain_community.embeddings.yandex.YandexGPTEmbeddings¶
class langchain_community.embeddings.yandex.YandexGPTEmbeddings[source]¶
Bases: BaseModel, Embeddings
YandexGPT Embeddings models.
To use, you should have the yandexcloud python package installed.
There are two authentication options for the service account
with the ai.languageModels.user role:
You can specify the token in a constructor parameter iam_token
or in an environment variable YC_IAM_TOKEN.
- You can specify the key in a constructor parameter api_key
or in an environment variable YC_API_KEY.
To use the default model specify the folder ID in a parameter folder_id
or in an environment variable YC_FOLDER_ID.
Example
from langchain_community.embeddings.yandex import YandexGPTEmbeddings
embeddings = YandexGPTEmbeddings(iam_token="t1.9eu...", folder_id=<folder-id>)
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 api_key: SecretStr = ''¶
Yandex Cloud Api Key for service account
with the ai.languageModels.user role
Constraints
type = string
writeOnly = True
format = password
param disable_request_logging: bool = False¶
YandexGPT API logs all request data by default.
If you provide personal data, confidential information, disable logging.
param doc_model_name: str = 'text-search-doc'¶
Doc model name to use.
param doc_model_uri: str = ''¶
Doc model uri to use.
param folder_id: str = ''¶
Yandex Cloud folder ID
param iam_token: SecretStr = ''¶
Yandex Cloud IAM token for service account
with the ai.languageModels.user role
Constraints
type = string
with the ai.languageModels.user role
Constraints
type = string
writeOnly = True
format = password
param max_retries: int = 6¶
Maximum number of retries to make when generating.
param model_name: str = 'text-search-query' (alias 'query_model_name')¶
Query model name to use.
param model_uri: str = '' (alias 'query_model_uri')¶
Query model uri to use.
param model_version: str = 'latest'¶
Model version to use.
param sleep_interval: float = 0.0¶
Delay between API requests
param url: str = 'llm.api.cloud.yandex.net:443'¶
The url of the API.
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
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]¶
Embed documents using a YandexGPT embeddings models.
Parameters
texts (List[str]) – The list of texts to embed.
Returns
List of embeddings, one for each text.
Return type
Returns
List of embeddings, one for each text.
Return type
List[List[float]]
embed_query(text: str) → List[float][source]¶
Embed a query using a YandexGPT embeddings models.
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
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) – 
exclude_defaults (bool) – 
exclude_none (bool) – 
encoder (Optional[Callable[[Any], Any]]) – 
models_as_dict (bool) – 
dumps_kwargs (Any) – 
Return type
unicode
langchain_community.embeddings.ernie.ErnieEmbeddings¶
class langchain_community.embeddings.ernie.ErnieEmbeddings[source]¶
Bases: BaseModel, Embeddings
[Deprecated] Ernie Embeddings V1 embedding models.
Notes
Deprecated since version 0.0.13: Use langchain_community.embeddings.QianfanEmbeddingsEndpoint instead.
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 access_token: Optional[str] = None¶
param chunk_size: int = 16¶
param ernie_api_base: Optional[str] = None¶
param ernie_client_id: Optional[str] = None¶
param ernie_client_secret: Optional[str] = None¶
async aembed_documents(texts: List[str]) → List[List[float]][source]¶
Asynchronous Embed search docs.
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.
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
exclude_unset (bool) – 
exclude_defaults (bool) – 
exclude_none (bool) – 
Return type
DictStrAny
embed_documents(texts: List[str]) → List[List[float]][source]¶
Embed search docs.
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 query text.
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
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) – 
exclude_defaults (bool) – 
exclude_none (bool) –
langchain_community.embeddings.awa.AwaEmbeddings¶
class langchain_community.embeddings.awa.AwaEmbeddings[source]¶
Bases: BaseModel, Embeddings
Embedding documents and queries with Awa DB.
client¶
The AwaEmbedding client.
model¶
The name of the model used for embedding.
Default is “all-mpnet-base-v2”.
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 model: str = 'all-mpnet-base-v2'¶
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]¶
Embed a list of documents using AwaEmbedding.
Parameters
texts (List[str]) – The list of texts need to be embedded
Returns
List of embeddings, one for each text.
Returns
List of embeddings, one for each text.
Return type
List[List[float]]
embed_query(text: str) → List[float][source]¶
Compute query embeddings using AwaEmbedding.
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
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) – 
exclude_defaults (bool) – 
exclude_none (bool) – 
encoder (Optional[Callable[[Any], Any]]) – 
models_as_dict (bool) – 
dumps_kwargs (Any) – 
Return type
unicode
dumps_kwargs (Any) – 
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¶
Parameters
path (Union[str, Path]) – 
content_type (unicode) – 
encoding (unicode) – 
proto (Protocol) – 
allow_pickle (bool) – 
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
set_model(model_name: str) → None[source]¶
Set the model used for embedding.
The default model used is all-mpnet-base-v2
Parameters
model_name (str) – A string which represents the name of model.
Return type
None
langchain_core.embeddings.embeddings.Embeddings¶
class langchain_core.embeddings.embeddings.Embeddings[source]¶
Interface for embedding models.
Methods
__init__()
aembed_documents(texts)
Asynchronous Embed search docs.
aembed_query(text)
Asynchronous Embed query text.
embed_documents(texts)
Embed search docs.
embed_query(text)
Embed query text.
__init__()¶
async aembed_documents(texts: List[str]) → List[List[float]][source]¶
Asynchronous Embed search docs.
Parameters
texts (List[str]) – 
Return type
List[List[float]]
async aembed_query(text: str) → List[float][source]¶
Asynchronous Embed query text.
Parameters
text (str) – 
Return type
List[float]
abstract embed_documents(texts: List[str]) → List[List[float]][source]¶
Embed search docs.
Parameters
texts (List[str]) – 
Return type
List[List[float]]
abstract embed_query(text: str) → List[float][source]¶
Embed query text.
Parameters
text (str) – 
Return type
List[float]
Examples using Embeddings¶
Elasticsearch
Infinispan
langchain_community.embeddings.openvino.OpenVINOEmbeddings¶
class langchain_community.embeddings.openvino.OpenVINOEmbeddings[source]¶
Bases: BaseModel, Embeddings
OpenVINO embedding models.
Example
from langchain_community.embeddings import OpenVINOEmbeddings
model_name = "sentence-transformers/all-mpnet-base-v2"
model_kwargs = {'device': 'CPU'}
encode_kwargs = {'normalize_embeddings': True}
ov = OpenVINOEmbeddings(
    model_name_or_path=model_name,
    model_kwargs=model_kwargs,
    encode_kwargs=encode_kwargs
)
Initialize the sentence_transformer.
param encode_kwargs: Dict[str, Any] [Optional]¶
Keyword arguments to pass when calling the encode method of the model.
param model_kwargs: Dict[str, Any] [Optional]¶
Keyword arguments to pass to the model.
param model_name_or_path: str [Required]¶
HuggingFace model id.
param ov_model: Any = None¶
OpenVINO model object.
param show_progress: bool = False¶
Whether to show a progress bar.
param tokenizer: Any = None¶
Tokenizer for embedding model.
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.
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]¶
Compute doc embeddings using a HuggingFace transformer model.
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]¶
Compute query embeddings using a HuggingFace transformer model.
Parameters
text (str) – The text to embed.
Returns
Embeddings for the text.
Return type
List[float]
encode(sentences: Any, batch_size: int = 4, show_progress_bar: bool = False, convert_to_numpy: bool = True, convert_to_tensor: bool = False, mean_pooling: bool = False, normalize_embeddings: bool = True) → Any[source]¶
Computes sentence embeddings.
Parameters
sentences (Any) – the sentences to embed.
batch_size (int) – the batch size used for the computation.
show_progress_bar (bool) – Whether to output a progress bar.
convert_to_numpy (bool) – Whether the output should be a list of numpy vectors.
convert_to_tensor (bool) – Whether the output should be one large tensor.
mean_pooling (bool) – Whether to pool returned vectors.
normalize_embeddings (bool) – Whether to normalize returned vectors.
Returns
By default, a 2d numpy array with shape [num_inputs, output_dimension].
By default, a 2d numpy array with shape [num_inputs, output_dimension].
Return type
Any
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) – 
exclude_defaults (bool) – 
exclude_none (bool) – 
encoder (Optional[Callable[[Any], Any]]) – 
models_as_dict (bool) – 
dumps_kwargs (Any) – 
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¶
Parameters
path (Union[str, Path]) – 
content_type (unicode) – 
encoding (unicode) – 
proto (Protocol) –
encoding (unicode) – 
proto (Protocol) – 
allow_pickle (bool) – 
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
save_model(model_path: str) → bool[source]¶
Parameters
model_path (str) – 
Return type
bool
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 OpenVINOEmbeddings¶
OpenVINO
OpenVINO Reranker
langchain_community.embeddings.dashscope.embed_with_retry¶
langchain_community.embeddings.dashscope.embed_with_retry(embeddings: DashScopeEmbeddings, **kwargs: Any) → Any[source]¶
Use tenacity to retry the embedding call.
Parameters
embeddings (DashScopeEmbeddings) – 
kwargs (Any) – 
Return type
Any