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70abc908f9ed-3 | 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) – | https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.openvino.OpenVINOEmbeddings.html |
70abc908f9ed-4 | 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 | https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.openvino.OpenVINOEmbeddings.html |
7694d707e15d-0 | 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 | https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.dashscope.embed_with_retry.html |
adfb827fd87e-0 | langchain_community.embeddings.premai.PremAIEmbeddings¶
class langchain_community.embeddings.premai.PremAIEmbeddings[source]¶
Bases: BaseModel, Embeddings
Prem’s Embedding APIs
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¶
param max_retries: int = 1¶
Max number of retries for tenacity
param model: str [Required]¶
The Embedding model to choose from
param premai_api_key: Optional[SecretStr] = None¶
Prem AI API Key. Get it here: https://app.premai.io/api_keys/
Constraints
type = string
writeOnly = True
format = password
param project_id: int [Required]¶
The project ID in which the experiments or deployments are carried out.
You can find all your projects here: https://app.premai.io/projects/
param show_progress_bar: bool = False¶
Whether to show a tqdm progress bar. Must have tqdm installed.
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 | https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.premai.PremAIEmbeddings.html |
adfb827fd87e-1 | 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]) – | https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.premai.PremAIEmbeddings.html |
adfb827fd87e-2 | 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 search docs.
Parameters
texts (List[str]) –
Return type
List[List[float]]
embed_query(text: str) → List[float][source]¶
Embed query 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) –
exclude_defaults (bool) –
exclude_none (bool) –
encoder (Optional[Callable[[Any], Any]]) – | https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.premai.PremAIEmbeddings.html |
adfb827fd87e-3 | 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) –
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
classmethod update_forward_refs(**localns: Any) → None¶
Try to update ForwardRefs on fields based on this Model, globalns and localns.
Parameters | https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.premai.PremAIEmbeddings.html |
adfb827fd87e-4 | Parameters
localns (Any) –
Return type
None
classmethod validate(value: Any) → Model¶
Parameters
value (Any) –
Return type
Model
Examples using PremAIEmbeddings¶
PremAI | https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.premai.PremAIEmbeddings.html |
9c92538f2776-0 | langchain_together.embeddings.TogetherEmbeddings¶
class langchain_together.embeddings.TogetherEmbeddings[source]¶
Bases: BaseModel, Embeddings
TogetherEmbeddings embedding model.
To use, set the environment variable TOGETHER_API_KEY with your API key or
pass it as a named parameter to the constructor.
Example
from langchain_together import TogetherEmbeddings
model = TogetherEmbeddings()
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 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.
param max_retries: int = 2¶
Maximum number of retries to make when generating. | https://api.python.langchain.com/en/latest/embeddings/langchain_together.embeddings.TogetherEmbeddings.html |
9c92538f2776-1 | Maximum number of retries to make when generating.
param model: str = 'togethercomputer/m2-bert-80M-8k-retrieval'¶
Embeddings model name to use.
Instead, use ‘togethercomputer/m2-bert-80M-8k-retrieval’ 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 Together 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 together_api_base: str = 'https://api.together.ai/v1/' (alias 'base_url')¶
Endpoint URL to use.
param together_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.
Returns
Embedding for the text.
Return type
List[float] | https://api.python.langchain.com/en/latest/embeddings/langchain_together.embeddings.TogetherEmbeddings.html |
9c92538f2776-2 | Returns
Embedding 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 | https://api.python.langchain.com/en/latest/embeddings/langchain_together.embeddings.TogetherEmbeddings.html |
9c92538f2776-3 | 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 document texts using passage 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]¶
Embed query text using query model.
Parameters
text (str) – The text to embed.
Returns
Embedding for the text.
Return type
List[float]
classmethod from_orm(obj: Any) → Model¶
Parameters
obj (Any) –
Return type
Model | https://api.python.langchain.com/en/latest/embeddings/langchain_together.embeddings.TogetherEmbeddings.html |
9c92538f2776-4 | 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) –
allow_pickle (bool) –
Return type
Model
classmethod parse_obj(obj: Any) → Model¶
Parameters
obj (Any) –
Return type
Model | https://api.python.langchain.com/en/latest/embeddings/langchain_together.embeddings.TogetherEmbeddings.html |
9c92538f2776-5 | 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 TogetherEmbeddings¶
TogetherEmbeddings | https://api.python.langchain.com/en/latest/embeddings/langchain_together.embeddings.TogetherEmbeddings.html |
aeafa8f296c5-0 | langchain_mistralai.embeddings.MistralAIEmbeddings¶
class langchain_mistralai.embeddings.MistralAIEmbeddings[source]¶
Bases: BaseModel, Embeddings
MistralAI embedding models.
To use, set the environment variable MISTRAL_API_KEY is set with your API key or
pass it as a named parameter to the constructor.
Example
from langchain_mistralai import MistralAIEmbeddings
mistral = MistralAIEmbeddings(
model="mistral-embed",
api_key="my-api-key"
)
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 endpoint: str = 'https://api.mistral.ai/v1/'¶
param max_concurrent_requests: int = 64¶
param max_retries: int = 5¶
param mistral_api_key: Optional[SecretStr] = None (alias 'api_key')¶
Constraints
type = string
writeOnly = True
format = password
param model: str = 'mistral-embed'¶
param timeout: int = 120¶
param tokenizer: Tokenizer = None¶
async aembed_documents(texts: List[str]) → List[List[float]][source]¶
Embed a list of document texts.
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]¶
Embed a single query text.
Parameters
text (str) – The text to embed.
Returns
Embedding for the text.
Return type
List[float] | https://api.python.langchain.com/en/latest/embeddings/langchain_mistralai.embeddings.MistralAIEmbeddings.html |
aeafa8f296c5-1 | Returns
Embedding 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 | https://api.python.langchain.com/en/latest/embeddings/langchain_mistralai.embeddings.MistralAIEmbeddings.html |
aeafa8f296c5-2 | 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 document texts.
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 single query text.
Parameters
text (str) – The text to embed.
Returns
Embedding for the text.
Return type
List[float]
classmethod from_orm(obj: Any) → Model¶
Parameters
obj (Any) –
Return type
Model | https://api.python.langchain.com/en/latest/embeddings/langchain_mistralai.embeddings.MistralAIEmbeddings.html |
aeafa8f296c5-3 | 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) –
allow_pickle (bool) –
Return type
Model
classmethod parse_obj(obj: Any) → Model¶
Parameters
obj (Any) –
Return type
Model | https://api.python.langchain.com/en/latest/embeddings/langchain_mistralai.embeddings.MistralAIEmbeddings.html |
aeafa8f296c5-4 | 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 MistralAIEmbeddings¶
MistralAI | https://api.python.langchain.com/en/latest/embeddings/langchain_mistralai.embeddings.MistralAIEmbeddings.html |
c707828fd04e-0 | langchain_community.embeddings.nemo.NeMoEmbeddings¶
class langchain_community.embeddings.nemo.NeMoEmbeddings[source]¶
Bases: BaseModel, Embeddings
[Deprecated] NeMo embedding models.
Notes
Deprecated since version 0.0.37: Directly instantiating a NeMoEmbeddings from langchain-community is deprecated. Please use langchain-nvidia-ai-endpoints NVIDIAEmbeddings interface.
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_endpoint_url: str = 'http://localhost:8088/v1/embeddings'¶
param batch_size: int = 16¶
param model: str = 'NV-Embed-QA-003'¶
async aembed_documents(texts: List[str]) → List[List[float]][source]¶
Call out to NeMo’s embedding endpoint async for embedding 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]¶
Call out to NeMo’s embedding endpoint async for embedding query text.
Parameters
text (str) – The text to embed.
Returns
Embedding 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 | https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.nemo.NeMoEmbeddings.html |
c707828fd04e-1 | 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) – | https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.nemo.NeMoEmbeddings.html |
c707828fd04e-2 | exclude_unset (bool) –
exclude_defaults (bool) –
exclude_none (bool) –
Return type
DictStrAny
embed_documents(documents: List[str]) → List[List[float]][source]¶
Embed a list of document texts.
Parameters
texts – The list of texts to embed.
documents (List[str]) –
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) –
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) – | https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.nemo.NeMoEmbeddings.html |
c707828fd04e-3 | 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) –
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
classmethod update_forward_refs(**localns: Any) → None¶ | https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.nemo.NeMoEmbeddings.html |
c707828fd04e-4 | 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 NeMoEmbeddings¶
NVIDIA NeMo embeddings | https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.nemo.NeMoEmbeddings.html |
694bd1ebee00-0 | langchain_community.embeddings.ipex_llm.IpexLLMBgeEmbeddings¶
class langchain_community.embeddings.ipex_llm.IpexLLMBgeEmbeddings[source]¶
Bases: BaseModel, Embeddings
Wrapper around the BGE embedding model
with IPEX-LLM optimizations on Intel CPUs and GPUs.
To use, you should have the ipex-llm
and sentence_transformers package installed. Refer to
here
for installation on Intel CPU.
Example on Intel CPU:from langchain_community.embeddings import IpexLLMBgeEmbeddings
embedding_model = IpexLLMBgeEmbeddings(
model_name="BAAI/bge-large-en-v1.5",
model_kwargs={},
encode_kwargs={"normalize_embeddings": True},
)
Refer to
here
for installation on Intel GPU.
Example on Intel GPU:from langchain_community.embeddings import IpexLLMBgeEmbeddings
embedding_model = IpexLLMBgeEmbeddings(
model_name="BAAI/bge-large-en-v1.5",
model_kwargs={"device": "xpu"},
encode_kwargs={"normalize_embeddings": True},
)
Initialize the sentence_transformer.
param cache_folder: Optional[str] = None¶
Path to store models.
Can be also set by SENTENCE_TRANSFORMERS_HOME environment variable.
param embed_instruction: str = ''¶
Instruction to use for embedding document.
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: str = 'BAAI/bge-small-en-v1.5'¶
Model name to use. | https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.ipex_llm.IpexLLMBgeEmbeddings.html |
694bd1ebee00-1 | Model name to use.
param query_instruction: str = 'Represent this question for searching relevant passages: '¶
Instruction to use for embedding query.
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 | https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.ipex_llm.IpexLLMBgeEmbeddings.html |
694bd1ebee00-2 | 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]¶
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]
classmethod from_orm(obj: Any) → Model¶
Parameters
obj (Any) –
Return type
Model | https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.ipex_llm.IpexLLMBgeEmbeddings.html |
694bd1ebee00-3 | 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) –
allow_pickle (bool) –
Return type
Model
classmethod parse_obj(obj: Any) → Model¶
Parameters
obj (Any) –
Return type
Model | https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.ipex_llm.IpexLLMBgeEmbeddings.html |
694bd1ebee00-4 | 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 | https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.ipex_llm.IpexLLMBgeEmbeddings.html |
324b1e412daf-0 | langchain_community.embeddings.sagemaker_endpoint.SagemakerEndpointEmbeddings¶
class langchain_community.embeddings.sagemaker_endpoint.SagemakerEndpointEmbeddings[source]¶
Bases: BaseModel, Embeddings
Custom Sagemaker Inference Endpoints.
To use, you must supply the endpoint name from your deployed
Sagemaker model & the region where it is deployed.
To authenticate, the AWS client uses the following methods to
automatically load credentials:
https://boto3.amazonaws.com/v1/documentation/api/latest/guide/credentials.html
If a specific credential profile should be used, you must pass
the name of the profile from the ~/.aws/credentials file that is to be used.
Make sure the credentials / roles used have the required policies to
access the Sagemaker endpoint.
See: https://docs.aws.amazon.com/IAM/latest/UserGuide/access_policies.html
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¶
param content_handler: EmbeddingsContentHandler [Required]¶
The content handler class that provides an input and
output transform functions to handle formats between LLM
and the endpoint.
param credentials_profile_name: Optional[str] = None¶
The name of the profile in the ~/.aws/credentials or ~/.aws/config files, which
has either access keys or role information specified.
If not specified, the default credential profile or, if on an EC2 instance,
credentials from IMDS will be used.
See: https://boto3.amazonaws.com/v1/documentation/api/latest/guide/credentials.html
param endpoint_kwargs: Optional[Dict] = None¶
Optional attributes passed to the invoke_endpoint
function. See `boto3`_. docs for more info. | https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.sagemaker_endpoint.SagemakerEndpointEmbeddings.html |
324b1e412daf-1 | function. See `boto3`_. docs for more info.
.. _boto3: <https://boto3.amazonaws.com/v1/documentation/api/latest/index.html>
param endpoint_name: str = ''¶
The name of the endpoint from the deployed Sagemaker model.
Must be unique within an AWS Region.
param model_kwargs: Optional[Dict] = None¶
Keyword arguments to pass to the model.
param region_name: str = ''¶
The aws region where the Sagemaker model is deployed, eg. us-west-2.
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 | https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.sagemaker_endpoint.SagemakerEndpointEmbeddings.html |
324b1e412daf-2 | 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], chunk_size: int = 64) → List[List[float]][source]¶
Compute doc embeddings using a SageMaker Inference Endpoint.
Parameters
texts (List[str]) – The list of texts to embed. | https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.sagemaker_endpoint.SagemakerEndpointEmbeddings.html |
324b1e412daf-3 | Parameters
texts (List[str]) – The list of texts to embed.
chunk_size (int) – The chunk size defines how many input texts will
be grouped together as request. If None, will use the
chunk size specified by the class.
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 SageMaker inference 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
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]]) – | https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.sagemaker_endpoint.SagemakerEndpointEmbeddings.html |
324b1e412daf-4 | 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) –
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
classmethod update_forward_refs(**localns: Any) → None¶
Try to update ForwardRefs on fields based on this Model, globalns and localns.
Parameters | https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.sagemaker_endpoint.SagemakerEndpointEmbeddings.html |
324b1e412daf-5 | Parameters
localns (Any) –
Return type
None
classmethod validate(value: Any) → Model¶
Parameters
value (Any) –
Return type
Model
Examples using SagemakerEndpointEmbeddings¶
AWS
SageMaker | https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.sagemaker_endpoint.SagemakerEndpointEmbeddings.html |
9aacf9eeaf3f-0 | langchain_community.embeddings.javelin_ai_gateway.JavelinAIGatewayEmbeddings¶
class langchain_community.embeddings.javelin_ai_gateway.JavelinAIGatewayEmbeddings[source]¶
Bases: Embeddings, BaseModel
Javelin AI Gateway embeddings.
To use, you should have the javelin_sdk python package installed.
For more information, see https://docs.getjavelin.io
Example
from langchain_community.embeddings import JavelinAIGatewayEmbeddings
embeddings = JavelinAIGatewayEmbeddings(
gateway_uri="<javelin-ai-gateway-uri>",
route="<your-javelin-gateway-embeddings-route>"
)
param client: Any = None¶
javelin client.
param gateway_uri: Optional[str] = None¶
The URI for the Javelin AI Gateway API.
param javelin_api_key: Optional[str] = None¶
The API key for the Javelin AI Gateway API.
param route: str [Required]¶
The route to use for the Javelin AI Gateway API.
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]
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 | https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.javelin_ai_gateway.JavelinAIGatewayEmbeddings.html |
9aacf9eeaf3f-1 | 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]) – | https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.javelin_ai_gateway.JavelinAIGatewayEmbeddings.html |
9aacf9eeaf3f-2 | 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 search docs.
Parameters
texts (List[str]) –
Return type
List[List[float]]
embed_query(text: str) → List[float][source]¶
Embed query 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) –
exclude_defaults (bool) –
exclude_none (bool) –
encoder (Optional[Callable[[Any], Any]]) – | https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.javelin_ai_gateway.JavelinAIGatewayEmbeddings.html |
9aacf9eeaf3f-3 | 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) –
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
classmethod update_forward_refs(**localns: Any) → None¶
Try to update ForwardRefs on fields based on this Model, globalns and localns.
Parameters | https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.javelin_ai_gateway.JavelinAIGatewayEmbeddings.html |
9aacf9eeaf3f-4 | Parameters
localns (Any) –
Return type
None
classmethod validate(value: Any) → Model¶
Parameters
value (Any) –
Return type
Model
Examples using JavelinAIGatewayEmbeddings¶
Javelin AI Gateway
Javelin AI Gateway Tutorial | https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.javelin_ai_gateway.JavelinAIGatewayEmbeddings.html |
fe8d8ae83caa-0 | langchain_community.embeddings.llm_rails.LLMRailsEmbeddings¶
class langchain_community.embeddings.llm_rails.LLMRailsEmbeddings[source]¶
Bases: BaseModel, Embeddings
LLMRails embedding models.
To use, you should have the environment
variable LLM_RAILS_API_KEY set with your API key or pass it
as a named parameter to the constructor.
Model can be one of [“embedding-english-v1”,”embedding-multi-v1”]
Example
from langchain_community.embeddings import LLMRailsEmbeddings
cohere = LLMRailsEmbeddings(
model="embedding-english-v1", api_key="my-api-key"
)
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: Optional[SecretStr] = None¶
LLMRails API key.
Constraints
type = string
writeOnly = True
format = password
param model: str = 'embedding-english-v1'¶
Model name to use.
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 | https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.llm_rails.LLMRailsEmbeddings.html |
fe8d8ae83caa-1 | 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]) – | https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.llm_rails.LLMRailsEmbeddings.html |
fe8d8ae83caa-2 | 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 Cohere’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 Cohere’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
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]) – | https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.llm_rails.LLMRailsEmbeddings.html |
fe8d8ae83caa-3 | 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) –
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 | https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.llm_rails.LLMRailsEmbeddings.html |
fe8d8ae83caa-4 | 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 LLMRailsEmbeddings¶
LLMRails | https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.llm_rails.LLMRailsEmbeddings.html |
be9b4a7d0714-0 | langchain_community.embeddings.itrex.QuantizedBgeEmbeddings¶
class langchain_community.embeddings.itrex.QuantizedBgeEmbeddings[source]¶
Bases: BaseModel, Embeddings
Leverage Itrex runtime to unlock the performance of compressed NLP models.
Please ensure that you have installed intel-extension-for-transformers.
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 {})
onnx_file_name: Optional[str] =File name of onnx optimized model which is exported by itrex.
(default “int8-model.onnx”)
Example
from langchain_community.embeddings import QuantizedBgeEmbeddings
model_name = "Intel/bge-small-en-v1.5-sts-int8-static-inc"
encode_kwargs = {'normalize_embeddings': True}
hf = QuantizedBgeEmbeddings(
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. | https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.itrex.QuantizedBgeEmbeddings.html |
be9b4a7d0714-1 | 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]]
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 | https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.itrex.QuantizedBgeEmbeddings.html |
be9b4a7d0714-2 | 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 text documents using the Optimized Embedder model.
Input:texts: List[str] = List of text documents to embed.
Output:List[List[float]] = The embeddings of each text document.
Parameters
texts (List[str]) –
Return type
List[List[float]]
embed_query(text: str) → List[float][source]¶
Embed query text.
Parameters
text (str) –
Return type
List[float]
classmethod from_orm(obj: Any) → Model¶
Parameters
obj (Any) –
Return type
Model | https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.itrex.QuantizedBgeEmbeddings.html |
be9b4a7d0714-3 | 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
load_model() → None[source]¶
Return type
None
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 | https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.itrex.QuantizedBgeEmbeddings.html |
be9b4a7d0714-4 | 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 QuantizedBgeEmbeddings¶
Intel
Intel® Extension for Transformers Quantized Text Embeddings | https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.itrex.QuantizedBgeEmbeddings.html |
2aa430c2200f-0 | langchain_community.embeddings.infinity_local.InfinityEmbeddingsLocal¶
class langchain_community.embeddings.infinity_local.InfinityEmbeddingsLocal[source]¶
Bases: BaseModel, Embeddings
Optimized Infinity embedding models.
https://github.com/michaelfeil/infinity
This class deploys a local Infinity instance to embed text.
The class requires async usage.
Infinity is a class to interact with Embedding Models on https://github.com/michaelfeil/infinity
Example
from langchain_community.embeddings import InfinityEmbeddingsLocal
async with InfinityEmbeddingsLocal(
model="BAAI/bge-small-en-v1.5",
revision=None,
device="cpu",
) as embedder:
embeddings = await engine.aembed_documents(["text1", "text2"])
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 backend: str = 'torch'¶
Backend for inference, e.g. ‘torch’ (recommended for ROCm/Nvidia)
param batch_size: int = 32¶
Internal batch size for inference, e.g. 32
param device: str = 'auto'¶
Device to use for inference, e.g. ‘cpu’ or ‘cuda’, or ‘mps’
param engine: Any = None¶
Infinity’s AsyncEmbeddingEngine.
param model: str [Required]¶
Underlying model id from huggingface, e.g. BAAI/bge-small-en-v1.5
param model_warmup: bool = True¶
Warmup the model with the max batch size.
param revision: Optional[str] = None¶
Model version, the commit hash from huggingface
async aembed_documents(texts: List[str]) → List[List[float]][source]¶ | https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.infinity_local.InfinityEmbeddingsLocal.html |
2aa430c2200f-1 | async aembed_documents(texts: List[str]) → List[List[float]][source]¶
Async call out to Infinity’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]]
async aembed_query(text: str) → List[float][source]¶
Async call out to Infinity’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 | https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.infinity_local.InfinityEmbeddingsLocal.html |
2aa430c2200f-2 | 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]¶
This method is async only.
Parameters
texts (List[str]) –
Return type
List[List[float]]
embed_query(text: str) → List[float][source]¶
Parameters
text (str) –
Return type
List[float]
classmethod from_orm(obj: Any) → Model¶
Parameters
obj (Any) –
Return type
Model | https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.infinity_local.InfinityEmbeddingsLocal.html |
2aa430c2200f-3 | 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) –
allow_pickle (bool) –
Return type
Model
classmethod parse_obj(obj: Any) → Model¶
Parameters
obj (Any) –
Return type
Model | https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.infinity_local.InfinityEmbeddingsLocal.html |
2aa430c2200f-4 | 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 InfinityEmbeddingsLocal¶
Infinity | https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.infinity_local.InfinityEmbeddingsLocal.html |
66a814d880c2-0 | langchain_community.embeddings.baichuan.BaichuanTextEmbeddings¶
class langchain_community.embeddings.baichuan.BaichuanTextEmbeddings[source]¶
Bases: BaseModel, Embeddings
Baichuan Text Embedding models.
To use, you should set the environment variable BAICHUAN_API_KEY to
your API key or pass it as a named parameter to the constructor.
Example
from langchain_community.embeddings import BaichuanTextEmbeddings
baichuan = BaichuanTextEmbeddings(baichuan_api_key="my-api-key")
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 baichuan_api_key: Optional[SecretStr] = None¶
Automatically inferred from env var BAICHUAN_API_KEY if not provided.
Constraints
type = string
writeOnly = True
format = password
param model_name: str = 'Baichuan-Text-Embedding'¶
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 | https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.baichuan.BaichuanTextEmbeddings.html |
66a814d880c2-1 | 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) – | https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.baichuan.BaichuanTextEmbeddings.html |
66a814d880c2-2 | exclude_unset (bool) –
exclude_defaults (bool) –
exclude_none (bool) –
Return type
DictStrAny
embed_documents(texts: List[str]) → Optional[List[List[float]]][source]¶
Public method to get embeddings for a list of documents.
Parameters
texts (List[str]) – The list of texts to embed.
Returns
A list of embeddings, one for each text, or None if an error occurs.
Return type
Optional[List[List[float]]]
embed_query(text: str) → Optional[List[float]][source]¶
Public method to get embedding for a single query text.
Parameters
text (str) – The text to embed.
Returns
Embeddings for the text, or None if an error occurs.
Return type
Optional[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) – | https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.baichuan.BaichuanTextEmbeddings.html |
66a814d880c2-3 | 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) –
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 | https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.baichuan.BaichuanTextEmbeddings.html |
66a814d880c2-4 | 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 BaichuanTextEmbeddings¶
Baichuan
Baichuan Text Embeddings | https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.baichuan.BaichuanTextEmbeddings.html |
c0966aba8057-0 | langchain_community.embeddings.openai.embed_with_retry¶
langchain_community.embeddings.openai.embed_with_retry(embeddings: OpenAIEmbeddings, **kwargs: Any) → Any[source]¶
Use tenacity to retry the embedding call.
Parameters
embeddings (OpenAIEmbeddings) –
kwargs (Any) –
Return type
Any | https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.openai.embed_with_retry.html |
ac2f153e50f3-0 | langchain_community.embeddings.fake.DeterministicFakeEmbedding¶
class langchain_community.embeddings.fake.DeterministicFakeEmbedding[source]¶
Bases: Embeddings, BaseModel
Fake embedding model that always returns
the same embedding vector for the same text.
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 size: int [Required]¶
The size of the embedding vector.
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 | https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.fake.DeterministicFakeEmbedding.html |
ac2f153e50f3-1 | 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 search docs.
Parameters
texts (List[str]) –
Return type
List[List[float]]
embed_query(text: str) → List[float][source]¶
Embed query text.
Parameters
text (str) –
Return type
List[float]
classmethod from_orm(obj: Any) → Model¶
Parameters
obj (Any) –
Return type
Model | https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.fake.DeterministicFakeEmbedding.html |
ac2f153e50f3-2 | 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) –
allow_pickle (bool) –
Return type
Model
classmethod parse_obj(obj: Any) → Model¶
Parameters
obj (Any) –
Return type
Model | https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.fake.DeterministicFakeEmbedding.html |
ac2f153e50f3-3 | 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 DeterministicFakeEmbedding¶
Elasticsearch | https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.fake.DeterministicFakeEmbedding.html |
031b3890fa18-0 | langchain_community.embeddings.johnsnowlabs.JohnSnowLabsEmbeddings¶
class langchain_community.embeddings.johnsnowlabs.JohnSnowLabsEmbeddings[source]¶
Bases: BaseModel, Embeddings
JohnSnowLabs embedding models
To use, you should have the johnsnowlabs python package installed.
.. rubric:: Example
from langchain_community.embeddings.johnsnowlabs import JohnSnowLabsEmbeddings
embedding = JohnSnowLabsEmbeddings(model='embed_sentence.bert')
output = embedding.embed_query("foo bar")
Initialize the johnsnowlabs model.
param model: Any = 'embed_sentence.bert'¶
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 | https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.johnsnowlabs.JohnSnowLabsEmbeddings.html |
031b3890fa18-1 | 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]¶
Compute doc embeddings using a JohnSnowLabs transformer model.
Parameters
texts (List[str]) – The list of texts to embed.
Returns
List of embeddings, one for each text. | https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.johnsnowlabs.JohnSnowLabsEmbeddings.html |
031b3890fa18-2 | 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 JohnSnowLabs transformer model.
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 | https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.johnsnowlabs.JohnSnowLabsEmbeddings.html |
031b3890fa18-3 | 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
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 | https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.johnsnowlabs.JohnSnowLabsEmbeddings.html |
031b3890fa18-4 | Return type
None
classmethod validate(value: Any) → Model¶
Parameters
value (Any) –
Return type
Model
Examples using JohnSnowLabsEmbeddings¶
John Snow Labs | https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.johnsnowlabs.JohnSnowLabsEmbeddings.html |
b5e37e4dfaa1-0 | langchain_community.embeddings.databricks.DatabricksEmbeddings¶
class langchain_community.embeddings.databricks.DatabricksEmbeddings[source]¶
Bases: MlflowEmbeddings
Databricks embeddings.
To use, you should have the mlflow python package installed.
For more information, see https://mlflow.org/docs/latest/llms/deployments.
Example
from langchain_community.embeddings import DatabricksEmbeddings
embeddings = DatabricksEmbeddings(
target_uri="databricks",
endpoint="embeddings",
)
param documents_params: Dict[str, str] = {}¶
param endpoint: str [Required]¶
The endpoint to use.
param query_params: Dict[str, str] = {}¶
The parameters to use for documents.
param target_uri: str = 'databricks'¶
The target URI to use. Defaults to databricks.
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 | https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.databricks.DatabricksEmbeddings.html |
b5e37e4dfaa1-1 | 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) – | https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.databricks.DatabricksEmbeddings.html |
b5e37e4dfaa1-2 | exclude_unset (bool) –
exclude_defaults (bool) –
exclude_none (bool) –
Return type
DictStrAny
embed(texts: List[str], params: Dict[str, str]) → List[List[float]]¶
Parameters
texts (List[str]) –
params (Dict[str, str]) –
Return type
List[List[float]]
embed_documents(texts: List[str]) → List[List[float]]¶
Embed search docs.
Parameters
texts (List[str]) –
Return type
List[List[float]]
embed_query(text: str) → List[float]¶
Embed query 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) –
exclude_defaults (bool) – | https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.databricks.DatabricksEmbeddings.html |
b5e37e4dfaa1-3 | 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) –
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
classmethod update_forward_refs(**localns: Any) → None¶ | https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.databricks.DatabricksEmbeddings.html |
b5e37e4dfaa1-4 | 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 DatabricksEmbeddings¶
-> content=’Hello! How can I assist you today?’
Databricks | https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.databricks.DatabricksEmbeddings.html |
7c80899514e9-0 | langchain_community.embeddings.voyageai.embed_with_retry¶
langchain_community.embeddings.voyageai.embed_with_retry(embeddings: VoyageEmbeddings, **kwargs: Any) → Any[source]¶
Use tenacity to retry the embedding call.
Parameters
embeddings (VoyageEmbeddings) –
kwargs (Any) –
Return type
Any | https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.voyageai.embed_with_retry.html |
2e8786358772-0 | langchain_huggingface.embeddings.huggingface_endpoint.HuggingFaceEndpointEmbeddings¶
class langchain_huggingface.embeddings.huggingface_endpoint.HuggingFaceEndpointEmbeddings[source]¶
Bases: BaseModel, Embeddings
HuggingFaceHub embedding models.
To use, you should have the huggingface_hub python package installed, and the
environment variable HUGGINGFACEHUB_API_TOKEN set with your API token, or pass
it as a named parameter to the constructor.
Example
from langchain_huggingface import HuggingFaceEndpointEmbeddings
model = "sentence-transformers/all-mpnet-base-v2"
hf = HuggingFaceEndpointEmbeddings(
model=model,
task="feature-extraction",
huggingfacehub_api_token="my-api-key",
)
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 huggingfacehub_api_token: Optional[str] = None¶
param model: Optional[str] = None¶
Model name to use.
param model_kwargs: Optional[dict] = None¶
Keyword arguments to pass to the model.
param repo_id: Optional[str] = None¶
Huggingfacehub repository id, for backward compatibility.
param task: Optional[str] = 'feature-extraction'¶
Task to call the model with.
async aembed_documents(texts: List[str]) → List[List[float]][source]¶
Async Call to HuggingFaceHub’s embedding endpoint for embedding 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]¶ | https://api.python.langchain.com/en/latest/embeddings/langchain_huggingface.embeddings.huggingface_endpoint.HuggingFaceEndpointEmbeddings.html |
2e8786358772-1 | async aembed_query(text: str) → List[float][source]¶
Async Call to HuggingFaceHub’s embedding endpoint for embedding 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
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 | https://api.python.langchain.com/en/latest/embeddings/langchain_huggingface.embeddings.huggingface_endpoint.HuggingFaceEndpointEmbeddings.html |
2e8786358772-2 | 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 HuggingFaceHub’s embedding endpoint for embedding 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]¶
Call out to HuggingFaceHub’s embedding endpoint for embedding 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 | https://api.python.langchain.com/en/latest/embeddings/langchain_huggingface.embeddings.huggingface_endpoint.HuggingFaceEndpointEmbeddings.html |
2e8786358772-3 | 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) –
allow_pickle (bool) –
Return type
Model
classmethod parse_obj(obj: Any) → Model¶
Parameters
obj (Any) –
Return type
Model | https://api.python.langchain.com/en/latest/embeddings/langchain_huggingface.embeddings.huggingface_endpoint.HuggingFaceEndpointEmbeddings.html |
2e8786358772-4 | 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 HuggingFaceEndpointEmbeddings¶
Hugging Face
Text Embeddings Inference | https://api.python.langchain.com/en/latest/embeddings/langchain_huggingface.embeddings.huggingface_endpoint.HuggingFaceEndpointEmbeddings.html |
be1f5e2af03a-0 | langchain_community.embeddings.nlpcloud.NLPCloudEmbeddings¶
class langchain_community.embeddings.nlpcloud.NLPCloudEmbeddings[source]¶
Bases: BaseModel, Embeddings
NLP Cloud embedding models.
To use, you should have the nlpcloud python package installed
Example
from langchain_community.embeddings import NLPCloudEmbeddings
embeddings = NLPCloudEmbeddings()
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 gpu: bool [Required]¶
param model_name: str [Required]¶
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 | https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.nlpcloud.NLPCloudEmbeddings.html |
be1f5e2af03a-1 | 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 NLP Cloud.
Parameters
texts (List[str]) – The list of texts to embed.
Returns
List of embeddings, one for each text.
Return type | https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.nlpcloud.NLPCloudEmbeddings.html |
be1f5e2af03a-2 | Returns
List of embeddings, one for each text.
Return type
List[List[float]]
embed_query(text: str) → List[float][source]¶
Embed a query using NLP Cloud.
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 | https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.nlpcloud.NLPCloudEmbeddings.html |
be1f5e2af03a-3 | 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
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 | https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.nlpcloud.NLPCloudEmbeddings.html |
be1f5e2af03a-4 | Return type
None
classmethod validate(value: Any) → Model¶
Parameters
value (Any) –
Return type
Model
Examples using NLPCloudEmbeddings¶
NLP Cloud
NLPCloud | https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.nlpcloud.NLPCloudEmbeddings.html |
db1e63e512f6-0 | langchain_cohere.embeddings.CohereEmbeddings¶
class langchain_cohere.embeddings.CohereEmbeddings[source]¶
Bases: BaseModel, Embeddings
Implements the Embeddings interface with Cohere’s text representation language
models.
Find out more about us at https://cohere.com and https://huggingface.co/CohereForAI
This implementation uses the Embed API - see https://docs.cohere.com/reference/embed
To use this you’ll need to a Cohere API key - either pass it to cohere_api_key
parameter or set the COHERE_API_KEY environment variable.
API keys are available on https://cohere.com - it’s free to sign up and trial API
keys work with this implementation.
Basic Example:cohere_embeddings = CohereEmbeddings(model="embed-english-light-v3.0")
text = "This is a test document."
query_result = cohere_embeddings.embed_query(text)
print(query_result)
doc_result = cohere_embeddings.embed_documents([text])
print(doc_result)
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 async_client: Any = None¶
Cohere async client.
param base_url: Optional[str] = None¶
Override the default Cohere API URL.
param client: Any = None¶
Cohere client.
param cohere_api_key: Optional[str] = None¶
param max_retries: int = 3¶
Maximum number of retries to make when generating.
param model: str = 'embed-english-v2.0'¶
Model name to use.
param request_timeout: Optional[float] = None¶
Timeout in seconds for the Cohere API request.
param truncate: Optional[str] = None¶ | https://api.python.langchain.com/en/latest/embeddings/langchain_cohere.embeddings.CohereEmbeddings.html |
db1e63e512f6-1 | param truncate: Optional[str] = None¶
Truncate embeddings that are too long from start or end (“NONE”|”START”|”END”)
param user_agent: str = 'langchain:partner'¶
Identifier for the application making the request.
async aembed(texts: List[str], *, input_type: Optional[Union[Literal['search_document', 'search_query', 'classification', 'clustering'], Any]] = None) → List[List[float]][source]¶
Parameters
texts (List[str]) –
input_type (Optional[Union[Literal['search_document', 'search_query', 'classification', 'clustering'], ~typing.Any]]) –
Return type
List[List[float]]
async aembed_documents(texts: List[str]) → List[List[float]][source]¶
Async call out to Cohere’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]]
async aembed_query(text: str) → List[float][source]¶
Async call out to Cohere’s embedding endpoint.
Parameters
text (str) – The text to embed.
Returns
Embeddings for the text.
Return type
List[float]
aembed_with_retry(**kwargs: Any) → Any[source]¶
Use tenacity to retry the embed call.
Parameters
kwargs (Any) –
Return type
Any
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 | https://api.python.langchain.com/en/latest/embeddings/langchain_cohere.embeddings.CohereEmbeddings.html |
db1e63e512f6-2 | 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]) – | https://api.python.langchain.com/en/latest/embeddings/langchain_cohere.embeddings.CohereEmbeddings.html |
db1e63e512f6-3 | by_alias (bool) –
skip_defaults (Optional[bool]) –
exclude_unset (bool) –
exclude_defaults (bool) –
exclude_none (bool) –
Return type
DictStrAny
embed(texts: List[str], *, input_type: Optional[Union[Literal['search_document', 'search_query', 'classification', 'clustering'], Any]] = None) → List[List[float]][source]¶
Parameters
texts (List[str]) –
input_type (Optional[Union[Literal['search_document', 'search_query', 'classification', 'clustering'], ~typing.Any]]) –
Return type
List[List[float]]
embed_documents(texts: List[str]) → List[List[float]][source]¶
Embed a list of document texts.
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 Cohere’s embedding endpoint.
Parameters
text (str) – The text to embed.
Returns
Embeddings for the text.
Return type
List[float]
embed_with_retry(**kwargs: Any) → Any[source]¶
Use tenacity to retry the embed call.
Parameters
kwargs (Any) –
Return type
Any
classmethod from_orm(obj: Any) → Model¶
Parameters
obj (Any) –
Return type
Model | https://api.python.langchain.com/en/latest/embeddings/langchain_cohere.embeddings.CohereEmbeddings.html |
db1e63e512f6-4 | 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) –
allow_pickle (bool) –
Return type
Model
classmethod parse_obj(obj: Any) → Model¶
Parameters
obj (Any) –
Return type
Model | https://api.python.langchain.com/en/latest/embeddings/langchain_cohere.embeddings.CohereEmbeddings.html |
db1e63e512f6-5 | 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 CohereEmbeddings¶
Cohere reranker | https://api.python.langchain.com/en/latest/embeddings/langchain_cohere.embeddings.CohereEmbeddings.html |
0789344032ce-0 | langchain_google_vertexai.embeddings.VertexAIEmbeddings¶
class langchain_google_vertexai.embeddings.VertexAIEmbeddings[source]¶
Bases: _VertexAICommon, Embeddings
Google Cloud VertexAI embedding models.
Initialize the sentence_transformer.
param additional_headers: Optional[Dict[str, str]] = None¶
A key-value dictionary representing additional headers for the model call
param api_endpoint: Optional[str] = None¶
Desired API endpoint, e.g., us-central1-aiplatform.googleapis.com
param api_transport: Optional[str] = None¶
The desired API transport method, can be either ‘grpc’ or ‘rest’
param client_cert_source: Optional[Callable[[], Tuple[bytes, bytes]]] = None¶
A callback which returns client certificate bytes and private key bytes both
param credentials: Any = None¶
The default custom credentials (google.auth.credentials.Credentials) to use
param full_model_name: Optional[str] = None¶
The full name of the model’s endpoint.
param location: str = 'us-central1'¶
The default location to use when making API calls.
param max_output_tokens: Optional[int] = None (alias 'max_tokens')¶
Token limit determines the maximum amount of text output from one prompt.
param max_retries: int = 6¶
The maximum number of retries to make when generating.
param model_name: str = None (alias 'model')¶
Underlying model name.
param n: int = 1¶
How many completions to generate for each prompt.
param project: Optional[str] = None¶
The default GCP project to use when making Vertex API calls.
param request_parallelism: int = 5¶
The amount of parallelism allowed for requests issued to VertexAI models.
param safety_settings: Optional['SafetySettingsType'] = None¶ | https://api.python.langchain.com/en/latest/embeddings/langchain_google_vertexai.embeddings.VertexAIEmbeddings.html |
0789344032ce-1 | param safety_settings: Optional['SafetySettingsType'] = None¶
The default safety settings to use for all generations.
For example:
from langchain_google_vertexai import HarmBlockThreshold, HarmCategory
safety_settings = {HarmCategory.HARM_CATEGORY_UNSPECIFIED: HarmBlockThreshold.BLOCK_NONE,
HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT: HarmBlockThreshold.BLOCK_MEDIUM_AND_ABOVE,
HarmCategory.HARM_CATEGORY_HATE_SPEECH: HarmBlockThreshold.BLOCK_ONLY_HIGH,
HarmCategory.HARM_CATEGORY_HARASSMENT: HarmBlockThreshold.BLOCK_LOW_AND_ABOVE,
HarmCategory.HARM_CATEGORY_SEXUALLY_EXPLICIT: HarmBlockThreshold.BLOCK_NONE,
}
param stop: Optional[List[str]] = None (alias 'stop_sequences')¶
Optional list of stop words to use when generating.
param streaming: bool = False¶
Whether to stream the results or not.
param temperature: Optional[float] = None¶
Sampling temperature, it controls the degree of randomness in token selection.
param top_k: Optional[int] = None¶
How the model selects tokens for output, the next token is selected from
param top_p: Optional[float] = None¶
Tokens are selected from most probable to least until the sum of their
param tuned_model_name: Optional[str] = None¶
The name of a tuned model. If tuned_model_name is passed
model_name will be used to determine the model family
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] | https://api.python.langchain.com/en/latest/embeddings/langchain_google_vertexai.embeddings.VertexAIEmbeddings.html |
0789344032ce-2 | 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 | https://api.python.langchain.com/en/latest/embeddings/langchain_google_vertexai.embeddings.VertexAIEmbeddings.html |
0789344032ce-3 | 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(texts: List[str], batch_size: int = 0, embeddings_task_type: Optional[Literal['RETRIEVAL_QUERY', 'RETRIEVAL_DOCUMENT', 'SEMANTIC_SIMILARITY', 'CLASSIFICATION', 'CLUSTERING', 'QUESTION_ANSWERING', 'FACT_VERIFICATION']] = None, dimensions: Optional[int] = None) → List[List[float]][source]¶
Embed a list of strings.
Parameters
texts (List[str]) – List[str] The list of strings to embed.
batch_size (int) – [int] The batch size of embeddings to send to the model.
If zero, then the largest batch size will be detected dynamically
at the first request, starting from 250, down to 5. | https://api.python.langchain.com/en/latest/embeddings/langchain_google_vertexai.embeddings.VertexAIEmbeddings.html |
0789344032ce-4 | at the first request, starting from 250, down to 5.
embeddings_task_type (Optional[Literal['RETRIEVAL_QUERY', 'RETRIEVAL_DOCUMENT', 'SEMANTIC_SIMILARITY', 'CLASSIFICATION', 'CLUSTERING', 'QUESTION_ANSWERING', 'FACT_VERIFICATION']]) – [str] optional embeddings task type,
one of the following
RETRIEVAL_QUERY - Text is a queryin a search/retrieval setting.
RETRIEVAL_DOCUMENT - Text is a documentin a search/retrieval setting.
SEMANTIC_SIMILARITY - Embeddings will be usedfor Semantic Textual Similarity (STS).
CLASSIFICATION - Embeddings will be used for classification.
CLUSTERING - Embeddings will be used for clustering.
The following are only supported on preview models:
QUESTION_ANSWERING
FACT_VERIFICATION
dimensions (Optional[int]) – [int] optional. Output embeddings dimensions.
Only supported on preview models.
Returns
List of embeddings, one for each text.
Return type
List[List[float]]
embed_documents(texts: List[str], batch_size: int = 0) → List[List[float]][source]¶
Embed a list of documents.
Parameters
texts (List[str]) – List[str] The list of texts to embed.
batch_size (int) – [int] The batch size of embeddings to send to the model.
If zero, then the largest batch size will be detected dynamically
at the first request, starting from 250, down to 5.
Returns
List of embeddings, one for each text.
Return type
List[List[float]]
embed_image(image_path: str, contextual_text: Optional[str] = None) → List[float][source]¶
Embed an image.
Parameters | https://api.python.langchain.com/en/latest/embeddings/langchain_google_vertexai.embeddings.VertexAIEmbeddings.html |
0789344032ce-5 | Embed an image.
Parameters
image_path (str) – Path to image (local, Google Cloud Storage or web) to generate
for. (embeddings) –
contextual_text (Optional[str]) – Text to generate embeddings for.
Returns
Embedding for the image.
Return type
List[float]
embed_query(text: str) → List[float][source]¶
Embed a text.
Parameters
text (str) – The text to embed.
Returns
Embedding for the text.
Return type
List[float]
classmethod from_orm(obj: Any) → Model¶
Parameters
obj (Any) –
Return type
Model
get_num_tokens(text: str) → int¶
Get the number of tokens present in the text.
Useful for checking if an input will fit in a model’s context window.
Parameters
text (str) – The string input to tokenize.
Returns
The integer number of tokens in the text.
Return type
int
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]]) – | https://api.python.langchain.com/en/latest/embeddings/langchain_google_vertexai.embeddings.VertexAIEmbeddings.html |
0789344032ce-6 | 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) –
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 | https://api.python.langchain.com/en/latest/embeddings/langchain_google_vertexai.embeddings.VertexAIEmbeddings.html |
0789344032ce-7 | 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
property async_prediction_client: PredictionServiceAsyncClient¶
Returns PredictionServiceClient.
property model_type: str¶
property model_version: GoogleEmbeddingModelVersion¶
property prediction_client: PredictionServiceClient¶
Returns PredictionServiceClient.
task_executor: ClassVar[Optional[Executor]] = FieldInfo(exclude=True, extra={})¶
Examples using VertexAIEmbeddings¶
Google
Google AlloyDB for PostgreSQL
Google BigQuery Vector Search
Google Cloud SQL for MySQL
Google Cloud SQL for PostgreSQL
Google Cloud Vertex AI Reranker
Google Firestore (Native Mode)
Google Spanner
Google Vertex AI PaLM
Google Vertex AI Vector Search | https://api.python.langchain.com/en/latest/embeddings/langchain_google_vertexai.embeddings.VertexAIEmbeddings.html |
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