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101afca65472-0 | langchain_community.embeddings.sparkllm.Url¶
class langchain_community.embeddings.sparkllm.Url(host: str, path: str, schema: str)[source]¶
Methods
__init__(host, path, schema)
Parameters
host (str) –
path (str) –
schema (str) –
__init__(host: str, path: str, schema: str) → None[source]¶
Parameters
host (str) –
path (str) –
schema (str) –
Return type
None | https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.sparkllm.Url.html |
fb9a8eed5ed2-0 | langchain_community.embeddings.vertexai.VertexAIEmbeddings¶
class langchain_community.embeddings.vertexai.VertexAIEmbeddings[source]¶
Bases: _VertexAICommon, Embeddings
[Deprecated] Google Cloud VertexAI embedding models.
Notes
Deprecated since version 0.0.12.
Initialize the sentence_transformer.
param credentials: Any = None¶
The default custom credentials (google.auth.credentials.Credentials) to use
param location: str = 'us-central1'¶
The default location to use when making API calls.
param max_output_tokens: int = 128¶
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 [Required]¶
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 show_progress_bar: bool = False¶
Whether to show a tqdm progress bar. Must have tqdm installed.
param stop: Optional[List[str]] = None¶
Optional list of stop words to use when generating.
param streaming: bool = False¶
Whether to stream the results or not.
param temperature: float = 0.0¶
Sampling temperature, it controls the degree of randomness in token selection.
param top_k: int = 40¶
How the model selects tokens for output, the next token is selected from
param top_p: float = 0.95¶
Tokens are selected from most probable to least until the sum of their | https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.vertexai.VertexAIEmbeddings.html |
fb9a8eed5ed2-1 | Tokens are selected from most probable to least until the sum of their
async aembed_documents(texts: List[str]) → List[List[float]]¶
Asynchronous Embed search docs.
Parameters
texts (List[str]) –
Return type
List[List[float]]
async aembed_query(text: str) → List[float]¶
Asynchronous Embed query text.
Parameters
text (str) –
Return type
List[float]
classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model¶
Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
Behaves as if Config.extra = ‘allow’ was set since it adds all passed values
Parameters
_fields_set (Optional[SetStr]) –
values (Any) –
Return type
Model
copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model¶
Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – fields to include in new model
exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – fields to exclude from new model, as with values this takes precedence over include
update (Optional[DictStrAny]) – values to change/add in the new model. Note: the data is not validated before creating
the new model: you should trust this data
deep (bool) – set to True to make a deep copy of the model
self (Model) –
Returns
new model instance | https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.vertexai.VertexAIEmbeddings.html |
fb9a8eed5ed2-2 | 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(texts: List[str], batch_size: int = 0, embeddings_task_type: Optional[Literal['RETRIEVAL_QUERY', 'RETRIEVAL_DOCUMENT', 'SEMANTIC_SIMILARITY', 'CLASSIFICATION', 'CLUSTERING']] = 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_community.embeddings.vertexai.VertexAIEmbeddings.html |
fb9a8eed5ed2-3 | at the first request, starting from 250, down to 5.
embeddings_task_type (Optional[Literal['RETRIEVAL_QUERY', 'RETRIEVAL_DOCUMENT', 'SEMANTIC_SIMILARITY', 'CLASSIFICATION', 'CLUSTERING']]) – [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.
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_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 | https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.vertexai.VertexAIEmbeddings.html |
fb9a8eed5ed2-4 | 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_community.embeddings.vertexai.VertexAIEmbeddings.html |
fb9a8eed5ed2-5 | 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
property is_codey_model: bool¶
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_community.embeddings.vertexai.VertexAIEmbeddings.html |
ca320176a2fb-0 | langchain_community.embeddings.huggingface.HuggingFaceBgeEmbeddings¶
class langchain_community.embeddings.huggingface.HuggingFaceBgeEmbeddings[source]¶
Bases: BaseModel, Embeddings
HuggingFace sentence_transformers embedding models.
To use, you should have the sentence_transformers python package installed.
To use Nomic, make sure the version of sentence_transformers >= 2.3.0.
Bge Example:
from langchain_community.embeddings import HuggingFaceBgeEmbeddings
model_name = "BAAI/bge-large-en"
model_kwargs = {'device': 'cpu'}
encode_kwargs = {'normalize_embeddings': True}
hf = HuggingFaceBgeEmbeddings(
model_name=model_name,
model_kwargs=model_kwargs,
encode_kwargs=encode_kwargs
)
Nomic Example:from langchain_community.embeddings import HuggingFaceBgeEmbeddings
model_name = "nomic-ai/nomic-embed-text-v1"
model_kwargs = {
'device': 'cpu',
'trust_remote_code':True
}
encode_kwargs = {'normalize_embeddings': True}
hf = HuggingFaceBgeEmbeddings(
model_name=model_name,
model_kwargs=model_kwargs,
encode_kwargs=encode_kwargs,
query_instruction = "search_query:",
embed_instruction = "search_document:"
)
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]¶ | https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.huggingface.HuggingFaceBgeEmbeddings.html |
ca320176a2fb-1 | param model_kwargs: Dict[str, Any] [Optional]¶
Keyword arguments to pass to the model.
param model_name: str = 'BAAI/bge-large-en'¶
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 | https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.huggingface.HuggingFaceBgeEmbeddings.html |
ca320176a2fb-2 | 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 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 | https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.huggingface.HuggingFaceBgeEmbeddings.html |
ca320176a2fb-3 | 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
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.huggingface.HuggingFaceBgeEmbeddings.html |
ca320176a2fb-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 HuggingFaceBgeEmbeddings¶
BGE on Hugging Face
Hugging Face | https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.huggingface.HuggingFaceBgeEmbeddings.html |
b2b62eabf320-0 | langchain_community.embeddings.aleph_alpha.AlephAlphaAsymmetricSemanticEmbedding¶
class langchain_community.embeddings.aleph_alpha.AlephAlphaAsymmetricSemanticEmbedding[source]¶
Bases: BaseModel, Embeddings
Aleph Alpha’s asymmetric semantic embedding.
AA provides you with an endpoint to embed a document and a query.
The models were optimized to make the embeddings of documents and
the query for a document as similar as possible.
To learn more, check out: https://docs.aleph-alpha.com/docs/tasks/semantic_embed/
Example
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 aleph_alpha_api_key: Optional[str] = None¶
API key for Aleph Alpha API.
param compress_to_size: Optional[int] = None¶
Should the returned embeddings come back as an original 5120-dim vector,
or should it be compressed to 128-dim.
param contextual_control_threshold: Optional[int] = None¶
Attention control parameters only apply to those tokens that have
explicitly been set in the request.
param control_log_additive: bool = True¶
Apply controls on prompt items by adding the log(control_factor)
to attention scores.
param host: str = 'https://api.aleph-alpha.com'¶
The hostname of the API host.
The default one is “https://api.aleph-alpha.com”)
param hosting: Optional[str] = None¶
Determines in which datacenters the request may be processed.
You can either set the parameter to “aleph-alpha” or omit it (defaulting to None).
Not setting this value, or setting it to None, gives us maximal flexibility
in processing your request in our
own datacenters and on servers hosted with other providers. | https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.aleph_alpha.AlephAlphaAsymmetricSemanticEmbedding.html |
b2b62eabf320-1 | in processing your request in our
own datacenters and on servers hosted with other providers.
Choose this option for maximal availability.
Setting it to “aleph-alpha” allows us to only process the request
in our own datacenters.
Choose this option for maximal data privacy.
param model: str = 'luminous-base'¶
Model name to use.
param nice: bool = False¶
Setting this to True, will signal to the API that you intend to be
nice to other users
by de-prioritizing your request below concurrent ones.
param normalize: bool = False¶
Should returned embeddings be normalized
param request_timeout_seconds: int = 305¶
Client timeout that will be set for HTTP requests in the
requests library’s API calls.
Server will close all requests after 300 seconds with an internal server error.
param total_retries: int = 8¶
The number of retries made in case requests fail with certain retryable
status codes. If the last
retry fails a corresponding exception is raised. Note, that between retries
an exponential backoff
is applied, starting with 0.5 s after the first retry and doubling for each
retry made. So with the
default setting of 8 retries a total wait time of 63.5 s is added between
the retries.
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¶ | https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.aleph_alpha.AlephAlphaAsymmetricSemanticEmbedding.html |
b2b62eabf320-2 | Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
Behaves as if Config.extra = ‘allow’ was set since it adds all passed values
Parameters
_fields_set (Optional[SetStr]) –
values (Any) –
Return type
Model
copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model¶
Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – fields to include in new model
exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – fields to exclude from new model, as with values this takes precedence over include
update (Optional[DictStrAny]) – values to change/add in the new model. Note: the data is not validated before creating
the new model: you should trust this data
deep (bool) – set to True to make a deep copy of the model
self (Model) –
Returns
new model instance
Return type
Model
dict(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False) → DictStrAny¶
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
Parameters | https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.aleph_alpha.AlephAlphaAsymmetricSemanticEmbedding.html |
b2b62eabf320-3 | 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 Aleph Alpha’s asymmetric Document 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 Aleph Alpha’s asymmetric, query embedding endpoint
:param text: The text to embed.
Returns
Embeddings for the text.
Parameters
text (str) –
Return type
List[float]
classmethod from_orm(obj: Any) → Model¶
Parameters
obj (Any) –
Return type
Model
json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, **dumps_kwargs: Any) → unicode¶
Generate a JSON representation of the model, include and exclude arguments as per dict(). | https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.aleph_alpha.AlephAlphaAsymmetricSemanticEmbedding.html |
b2b62eabf320-4 | 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
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) – | https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.aleph_alpha.AlephAlphaAsymmetricSemanticEmbedding.html |
b2b62eabf320-5 | 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 AlephAlphaAsymmetricSemanticEmbedding¶
Aleph Alpha | https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.aleph_alpha.AlephAlphaAsymmetricSemanticEmbedding.html |
71c476e19fb6-0 | langchain_community.embeddings.dashscope.DashScopeEmbeddings¶
class langchain_community.embeddings.dashscope.DashScopeEmbeddings[source]¶
Bases: BaseModel, Embeddings
DashScope embedding models.
To use, you should have the dashscope python package installed, and the
environment variable DASHSCOPE_API_KEY set with your API key or pass it
as a named parameter to the constructor.
Example
from langchain_community.embeddings import DashScopeEmbeddings
embeddings = DashScopeEmbeddings(dashscope_api_key="my-api-key")
Example
import os
os.environ["DASHSCOPE_API_KEY"] = "your DashScope API KEY"
from langchain_community.embeddings.dashscope import DashScopeEmbeddings
embeddings = DashScopeEmbeddings(
model="text-embedding-v1",
)
text = "This is a test query."
query_result = embeddings.embed_query(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 client: Any = None¶
The DashScope client.
param dashscope_api_key: Optional[str] = None¶
param max_retries: int = 5¶
Maximum number of retries to make when generating.
param model: str = 'text-embedding-v1'¶
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¶ | https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.dashscope.DashScopeEmbeddings.html |
71c476e19fb6-1 | Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
Behaves as if Config.extra = ‘allow’ was set since it adds all passed values
Parameters
_fields_set (Optional[SetStr]) –
values (Any) –
Return type
Model
copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model¶
Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – fields to include in new model
exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – fields to exclude from new model, as with values this takes precedence over include
update (Optional[DictStrAny]) – values to change/add in the new model. Note: the data is not validated before creating
the new model: you should trust this data
deep (bool) – set to True to make a deep copy of the model
self (Model) –
Returns
new model instance
Return type
Model
dict(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False) → DictStrAny¶
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
Parameters | https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.dashscope.DashScopeEmbeddings.html |
71c476e19fb6-2 | 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 DashScope’s embedding endpoint for embedding search docs.
Parameters
texts (List[str]) – The list of texts to embed.
chunk_size – The chunk size of embeddings. 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]¶
Call out to DashScope’s embedding endpoint for embedding 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
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¶ | https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.dashscope.DashScopeEmbeddings.html |
71c476e19fb6-3 | 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
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) – | https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.dashscope.DashScopeEmbeddings.html |
71c476e19fb6-4 | 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 DashScopeEmbeddings¶
DashScope
DashVector | https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.dashscope.DashScopeEmbeddings.html |
44d85d6bc3e4-0 | langchain_community.embeddings.solar.SolarEmbeddings¶
class langchain_community.embeddings.solar.SolarEmbeddings[source]¶
Bases: BaseModel, Embeddings
[Deprecated] Solar’s embedding service.
To use, you should have the environment variable``SOLAR_API_KEY`` set
with your API token, or pass it as a named parameter to the constructor.
Example
from langchain_community.embeddings import SolarEmbeddings
embeddings = SolarEmbeddings()
query_text = "This is a test query."
query_result = embeddings.embed_query(query_text)
document_text = "This is a test document."
document_result = embeddings.embed_documents([document_text])
Notes
Deprecated since version 0.0.34.
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_url: str = 'https://api.upstage.ai/v1/solar/embeddings'¶
Endpoint URL to use.
param model: str = 'solar-1-mini-embedding-query'¶
Embeddings model name to use.
param solar_api_key: Optional[SecretStr] = None¶
API Key for Solar API.
Constraints
type = string
writeOnly = True
format = password
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¶ | https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.solar.SolarEmbeddings.html |
44d85d6bc3e4-1 | Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
Behaves as if Config.extra = ‘allow’ was set since it adds all passed values
Parameters
_fields_set (Optional[SetStr]) –
values (Any) –
Return type
Model
copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model¶
Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – fields to include in new model
exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – fields to exclude from new model, as with values this takes precedence over include
update (Optional[DictStrAny]) – values to change/add in the new model. Note: the data is not validated before creating
the new model: you should trust this data
deep (bool) – set to True to make a deep copy of the model
self (Model) –
Returns
new model instance
Return type
Model
dict(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False) → DictStrAny¶
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
Parameters | https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.solar.SolarEmbeddings.html |
44d85d6bc3e4-2 | 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(text: str) → List[List[float]][source]¶
Parameters
text (str) –
Return type
List[List[float]]
embed_documents(texts: List[str]) → List[List[float]][source]¶
Embed documents using a Solar 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]¶
Embed a query using a Solar 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¶ | https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.solar.SolarEmbeddings.html |
44d85d6bc3e4-3 | 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
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) – | https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.solar.SolarEmbeddings.html |
44d85d6bc3e4-4 | 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 SolarEmbeddings¶
Solar | https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.solar.SolarEmbeddings.html |
a6ea7172062a-0 | langchain_community.embeddings.fake.FakeEmbeddings¶
class langchain_community.embeddings.fake.FakeEmbeddings[source]¶
Bases: Embeddings, BaseModel
Fake embedding model.
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.FakeEmbeddings.html |
a6ea7172062a-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.FakeEmbeddings.html |
a6ea7172062a-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.FakeEmbeddings.html |
a6ea7172062a-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 FakeEmbeddings¶
Baidu VectorDB
DocArray
Fake Embeddings
Google Memorystore for Redis
PGVecto.rs
Relyt
Tair
Tencent Cloud VectorDB
Vectara
Vectara | https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.fake.FakeEmbeddings.html |
c778090d3066-0 | langchain_community.embeddings.gigachat.GigaChatEmbeddings¶
class langchain_community.embeddings.gigachat.GigaChatEmbeddings[source]¶
Bases: BaseModel, Embeddings
GigaChat Embeddings models.
Example
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param access_token: Optional[str] = None¶
Access token for GigaChat
param auth_url: Optional[str] = None¶
Auth URL
param base_url: Optional[str] = None¶
Base API URL
param ca_bundle_file: Optional[str] = None¶
param cert_file: Optional[str] = None¶
param credentials: Optional[str] = None¶
Auth Token
param key_file: Optional[str] = None¶
param key_file_password: Optional[str] = None¶
param model: Optional[str] = None¶
Model name to use.
param password: Optional[str] = None¶
Password for authenticate
param scope: Optional[str] = None¶
Permission scope for access token
param timeout: Optional[float] = 600¶
Timeout for request. By default it works for long requests.
param user: Optional[str] = None¶
Username for authenticate
param verify_ssl_certs: Optional[bool] = None¶
Check certificates for all requests
async aembed_documents(texts: List[str]) → List[List[float]][source]¶
Embed documents using a GigaChat embeddings models.
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 query using a GigaChat embeddings models.
Parameters
text (str) – The text to embed.
Returns | https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.gigachat.GigaChatEmbeddings.html |
c778090d3066-1 | 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_community.embeddings.gigachat.GigaChatEmbeddings.html |
c778090d3066-2 | 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 documents using a GigaChat embeddings models.
Parameters
texts (List[str]) – The list of texts to embed.
Returns
List of embeddings, one for each text.
Return type
List[List[float]]
embed_query(text: str) → List[float][source]¶
Embed a query using a GigaChat embeddings models.
Parameters
text (str) – The text to embed.
Returns
Embeddings for the text.
Return type
List[float]
classmethod from_orm(obj: Any) → Model¶
Parameters
obj (Any) –
Return type
Model | https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.gigachat.GigaChatEmbeddings.html |
c778090d3066-3 | 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.gigachat.GigaChatEmbeddings.html |
c778090d3066-4 | 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 GigaChatEmbeddings¶
GigaChat
Salute Devices | https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.gigachat.GigaChatEmbeddings.html |
123337754ad5-0 | langchain_community.embeddings.jina.JinaEmbeddings¶
class langchain_community.embeddings.jina.JinaEmbeddings[source]¶
Bases: BaseModel, Embeddings
Jina embedding models.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param jina_api_key: Optional[SecretStr] = None¶
Constraints
type = string
writeOnly = True
format = password
param model_name: str = 'jina-embeddings-v2-base-en'¶
async aembed_documents(texts: List[str]) → List[List[float]]¶
Asynchronous Embed search docs.
Parameters
texts (List[str]) –
Return type
List[List[float]]
async aembed_query(text: str) → List[float]¶
Asynchronous Embed query text.
Parameters
text (str) –
Return type
List[float]
classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model¶
Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
Behaves as if Config.extra = ‘allow’ was set since it adds all passed values
Parameters
_fields_set (Optional[SetStr]) –
values (Any) –
Return type
Model
copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model¶
Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters | https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.jina.JinaEmbeddings.html |
123337754ad5-1 | 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]¶
Call out to Jina’s embedding endpoint.
:param texts: The list of texts to embed.
Returns
List of embeddings, one for each text.
Parameters
texts (List[str]) – | https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.jina.JinaEmbeddings.html |
123337754ad5-2 | List of embeddings, one for each text.
Parameters
texts (List[str]) –
Return type
List[List[float]]
embed_images(uris: List[str]) → List[List[float]][source]¶
Call out to Jina’s image embedding endpoint.
:param uris: The list of uris to embed.
Returns
List of embeddings, one for each text.
Parameters
uris (List[str]) –
Return type
List[List[float]]
embed_query(text: str) → List[float][source]¶
Call out to Jina’s embedding endpoint.
:param text: The text to embed.
Returns
Embeddings for the text.
Parameters
text (str) –
Return type
List[float]
classmethod from_orm(obj: Any) → Model¶
Parameters
obj (Any) –
Return type
Model
json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, **dumps_kwargs: Any) → unicode¶
Generate a JSON representation of the model, include and exclude arguments as per dict().
encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().
Parameters
include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) –
exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) –
by_alias (bool) –
skip_defaults (Optional[bool]) –
exclude_unset (bool) – | https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.jina.JinaEmbeddings.html |
123337754ad5-3 | 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.jina.JinaEmbeddings.html |
123337754ad5-4 | 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 JinaEmbeddings¶
Jina
Jina Reranker | https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.jina.JinaEmbeddings.html |
d3ec891da3c3-0 | langchain_community.embeddings.optimum_intel.QuantizedBiEncoderEmbeddings¶
class langchain_community.embeddings.optimum_intel.QuantizedBiEncoderEmbeddings[source]¶
Bases: BaseModel, Embeddings
Quantized bi-encoders embedding models.
Please ensure that you have installed optimum-intel and ipex.
Input:model_name: str = Model name.
max_seq_len: int = The maximum sequence length for tokenization. (default 512)
pooling_strategy: str =
“mean” or “cls”, pooling strategy for the final layer. (default “mean”)
query_instruction: Optional[str] =An instruction to add to the query before embedding. (default None)
document_instruction: Optional[str] =An instruction to add to each document before embedding. (default None)
padding: Optional[bool] =Whether to add padding during tokenization or not. (default True)
model_kwargs: Optional[Dict] =Parameters to add to the model during initialization. (default {})
encode_kwargs: Optional[Dict] =Parameters to add during the embedding forward pass. (default {})
Example:
from langchain_community.embeddings import QuantizedBiEncoderEmbeddings
model_name = “Intel/bge-small-en-v1.5-rag-int8-static”
encode_kwargs = {‘normalize_embeddings’: True}
hf = QuantizedBiEncoderEmbeddings(
model_name,
encode_kwargs=encode_kwargs,
query_instruction=”Represent this sentence for searching relevant passages: “
)
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
async aembed_documents(texts: List[str]) → List[List[float]]¶
Asynchronous Embed search docs.
Parameters
texts (List[str]) –
Return type
List[List[float]] | https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.optimum_intel.QuantizedBiEncoderEmbeddings.html |
d3ec891da3c3-1 | Parameters
texts (List[str]) –
Return type
List[List[float]]
async aembed_query(text: str) → List[float]¶
Asynchronous Embed query text.
Parameters
text (str) –
Return type
List[float]
classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model¶
Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
Behaves as if Config.extra = ‘allow’ was set since it adds all passed values
Parameters
_fields_set (Optional[SetStr]) –
values (Any) –
Return type
Model
copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model¶
Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – fields to include in new model
exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – fields to exclude from new model, as with values this takes precedence over include
update (Optional[DictStrAny]) – values to change/add in the new model. Note: the data is not validated before creating
the new model: you should trust this data
deep (bool) – set to True to make a deep copy of the model
self (Model) –
Returns
new model instance
Return type
Model | https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.optimum_intel.QuantizedBiEncoderEmbeddings.html |
d3ec891da3c3-2 | 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.optimum_intel.QuantizedBiEncoderEmbeddings.html |
d3ec891da3c3-3 | 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.optimum_intel.QuantizedBiEncoderEmbeddings.html |
d3ec891da3c3-4 | 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 QuantizedBiEncoderEmbeddings¶
Embedding Documents using Optimized and Quantized Embedders
Intel | https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.optimum_intel.QuantizedBiEncoderEmbeddings.html |
02d53f91fa28-0 | langchain_upstage.embeddings.UpstageEmbeddings¶
class langchain_upstage.embeddings.UpstageEmbeddings[source]¶
Bases: BaseModel, Embeddings
UpstageEmbeddings embedding model.
To use, set the environment variable UPSTAGE_API_KEY with your API key or
pass it as a named parameter to the constructor.
Example
from langchain_upstage import UpstageEmbeddings
model = UpstageEmbeddings(model='solar-embedding-1-large')
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param allowed_special: Union[Literal['all'], Set[str]] = {}¶
Not yet supported.
param chunk_size: int = 1000¶
Maximum number of texts to embed in each batch.
Not yet supported.
param default_headers: Optional[Mapping[str, str]] = None¶
param default_query: Optional[Mapping[str, object]] = None¶
param dimensions: Optional[int] = None¶
The number of dimensions the resulting output embeddings should have.
Not yet supported.
param disallowed_special: Union[Literal['all'], Set[str], Sequence[str]] = 'all'¶
Not yet supported.
param embed_batch_size: int = 10¶
param embedding_ctx_length: int = 4096¶
The maximum number of tokens to embed at once.
Not yet supported.
param http_async_client: Optional[Any] = None¶
Optional httpx.AsyncClient. Only used for async invocations. Must specify
http_client as well if you’d like a custom client for sync invocations.
param http_client: Optional[Any] = None¶
Optional httpx.Client. Only used for sync invocations. Must specify
http_async_client as well if you’d like a custom client for async invocations. | https://api.python.langchain.com/en/latest/embeddings/langchain_upstage.embeddings.UpstageEmbeddings.html |
02d53f91fa28-1 | http_async_client as well if you’d like a custom client for async invocations.
param max_retries: int = 2¶
Maximum number of retries to make when generating.
param model: str [Required]¶
Embeddings model name to use. Do not add suffixes like -query and -passage.
Instead, use ‘solar-embedding-1-large’ for example.
param model_kwargs: Dict[str, Any] [Optional]¶
Holds any model parameters valid for create call not explicitly specified.
param request_timeout: Optional[Union[float, Tuple[float, float], Any]] = None (alias 'timeout')¶
Timeout for requests to Upstage embedding API. Can be float, httpx.Timeout or
None.
param show_progress_bar: bool = False¶
Whether to show a progress bar when embedding.
Not yet supported.
param skip_empty: bool = False¶
Whether to skip empty strings when embedding or raise an error.
Defaults to not skipping.
Not yet supported.
param upstage_api_base: str = 'https://api.upstage.ai/v1/solar' (alias 'base_url')¶
Endpoint URL to use.
param upstage_api_key: Optional[SecretStr] = None (alias 'api_key')¶
API Key for Solar API.
Constraints
type = string
writeOnly = True
format = password
async aembed_documents(texts: List[str]) → List[List[float]][source]¶
Embed a list of document texts using passage model asynchronously.
Parameters
texts (List[str]) – The list of texts to embed.
Returns
List of embeddings, one for each text.
Return type
List[List[float]]
async aembed_query(text: str) → List[float][source]¶
Asynchronous Embed query text using query model.
Parameters
text (str) – The text to embed. | https://api.python.langchain.com/en/latest/embeddings/langchain_upstage.embeddings.UpstageEmbeddings.html |
02d53f91fa28-2 | 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
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_upstage.embeddings.UpstageEmbeddings.html |
02d53f91fa28-3 | 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_upstage.embeddings.UpstageEmbeddings.html |
02d53f91fa28-4 | 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_upstage.embeddings.UpstageEmbeddings.html |
02d53f91fa28-5 | 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 | https://api.python.langchain.com/en/latest/embeddings/langchain_upstage.embeddings.UpstageEmbeddings.html |
722e4139607c-0 | langchain_community.embeddings.titan_takeoff.TitanTakeoffEmbed¶
class langchain_community.embeddings.titan_takeoff.TitanTakeoffEmbed(base_url: str = 'http://localhost', port: int = 3000, mgmt_port: int = 3001, models: List[ReaderConfig] = [])[source]¶
Interface with Takeoff Inference API for embedding models.
Use it to send embedding requests and to deploy embedding
readers with Takeoff.
Examples
This is an example how to deploy an embedding model and send requests.
Initialize the Titan Takeoff embedding wrapper.
Parameters
base_url (str, optional) – The base url where Takeoff Inference Server is
"http (listening. Defaults to) – //localhost”.
port (int, optional) – What port is Takeoff Inference API listening on.
3000. (Defaults to) –
mgmt_port (int, optional) – What port is Takeoff Management API listening on.
3001. (Defaults to) –
models (List[ReaderConfig], optional) – Any readers you’d like to spin up on.
[]. (Defaults to) –
Raises
ImportError – If you haven’t installed takeoff-client, you will get an
ImportError. To remedy run pip install 'takeoff-client==0.4.0' –
Attributes
base_url
//localhost".
client
Takeoff Client Python SDK used to interact with Takeoff API
embed_consumer_groups
The consumer groups in Takeoff which contain embedding models
mgmt_port
The management port of the Titan Takeoff (Pro) server.
port
The port of the Titan Takeoff (Pro) server.
Methods
__init__([base_url, port, mgmt_port, models])
Initialize the Titan Takeoff embedding wrapper.
aembed_documents(texts) | https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.titan_takeoff.TitanTakeoffEmbed.html |
722e4139607c-1 | Initialize the Titan Takeoff embedding wrapper.
aembed_documents(texts)
Asynchronous Embed search docs.
aembed_query(text)
Asynchronous Embed query text.
embed_documents(texts[, consumer_group])
Embed documents.
embed_query(text[, consumer_group])
Embed query.
__init__(base_url: str = 'http://localhost', port: int = 3000, mgmt_port: int = 3001, models: List[ReaderConfig] = [])[source]¶
Initialize the Titan Takeoff embedding wrapper.
Parameters
base_url (str, optional) – The base url where Takeoff Inference Server is
"http (listening. Defaults to) – //localhost”.
port (int, optional) – What port is Takeoff Inference API listening on.
3000. (Defaults to) –
mgmt_port (int, optional) – What port is Takeoff Management API listening on.
3001. (Defaults to) –
models (List[ReaderConfig], optional) – Any readers you’d like to spin up on.
[]. (Defaults to) –
Raises
ImportError – If you haven’t installed takeoff-client, you will get an
ImportError. To remedy run pip install 'takeoff-client==0.4.0' –
async aembed_documents(texts: List[str]) → List[List[float]]¶
Asynchronous Embed search docs.
Parameters
texts (List[str]) –
Return type
List[List[float]]
async aembed_query(text: str) → List[float]¶
Asynchronous Embed query text.
Parameters
text (str) –
Return type
List[float]
embed_documents(texts: List[str], consumer_group: Optional[str] = None) → List[List[float]][source]¶
Embed documents.
Parameters | https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.titan_takeoff.TitanTakeoffEmbed.html |
722e4139607c-2 | Embed documents.
Parameters
texts (List[str]) – List of prompts/documents to embed
consumer_group (Optional[str], optional) – Consumer group to send request
None. (to containing embedding model. Defaults to) –
Returns
List of embeddings
Return type
List[List[float]]
embed_query(text: str, consumer_group: Optional[str] = None) → List[float][source]¶
Embed query.
Parameters
text (str) – Prompt/document to embed
consumer_group (Optional[str], optional) – Consumer group to send request
None. (to containing embedding model. Defaults to) –
Returns
Embedding
Return type
List[float]
Examples using TitanTakeoffEmbed¶
Titan Takeoff | https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.titan_takeoff.TitanTakeoffEmbed.html |
4d674ab74177-0 | langchain_community.embeddings.google_palm.embed_with_retry¶
langchain_community.embeddings.google_palm.embed_with_retry(embeddings: GooglePalmEmbeddings, *args: Any, **kwargs: Any) → Any[source]¶
Use tenacity to retry the completion call.
Parameters
embeddings (GooglePalmEmbeddings) –
args (Any) –
kwargs (Any) –
Return type
Any | https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.google_palm.embed_with_retry.html |
aa21f048efe7-0 | langchain_community.embeddings.gradient_ai.GradientEmbeddings¶
class langchain_community.embeddings.gradient_ai.GradientEmbeddings[source]¶
Bases: BaseModel, Embeddings
Gradient.ai Embedding models.
GradientLLM is a class to interact with Embedding Models on gradient.ai
To use, set the environment variable GRADIENT_ACCESS_TOKEN with your
API token and GRADIENT_WORKSPACE_ID for your gradient workspace,
or alternatively provide them as keywords to the constructor of this class.
Example
from langchain_community.embeddings import GradientEmbeddings
GradientEmbeddings(
model="bge-large",
gradient_workspace_id="12345614fc0_workspace",
gradient_access_token="gradientai-access_token",
)
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param client: Any = None¶
Gradient client.
param gradient_access_token: Optional[str] = None¶
gradient.ai API Token, which can be generated by going to
https://auth.gradient.ai/select-workspace
and selecting “Access tokens” under the profile drop-down.
param gradient_api_url: str = 'https://api.gradient.ai/api'¶
Endpoint URL to use.
param gradient_workspace_id: Optional[str] = None¶
Underlying gradient.ai workspace_id.
param model: str [Required]¶
Underlying gradient.ai model id.
param query_prompt_for_retrieval: Optional[str] = None¶
Query pre-prompt
async aembed_documents(texts: List[str]) → List[List[float]][source]¶
Async call out to Gradient’s embedding endpoint.
Parameters
texts (List[str]) – The list of texts to embed.
Returns
List of embeddings, one for each text.
Return type
List[List[float]] | https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.gradient_ai.GradientEmbeddings.html |
aa21f048efe7-1 | Returns
List of embeddings, one for each text.
Return type
List[List[float]]
async aembed_query(text: str) → List[float][source]¶
Async call out to Gradient’s embedding endpoint.
Parameters
text (str) – The text to embed.
Returns
Embeddings for the text.
Return type
List[float]
classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model¶
Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
Behaves as if Config.extra = ‘allow’ was set since it adds all passed values
Parameters
_fields_set (Optional[SetStr]) –
values (Any) –
Return type
Model
copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model¶
Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – fields to include in new model
exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – fields to exclude from new model, as with values this takes precedence over include
update (Optional[DictStrAny]) – values to change/add in the new model. Note: the data is not validated before creating
the new model: you should trust this data
deep (bool) – set to True to make a deep copy of the model
self (Model) –
Returns
new model instance
Return type
Model | https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.gradient_ai.GradientEmbeddings.html |
aa21f048efe7-2 | 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 Gradient’s embedding endpoint.
Parameters
texts (List[str]) – The list of texts to embed.
Returns
List of embeddings, one for each text.
Return type
List[List[float]]
embed_query(text: str) → List[float][source]¶
Call out to Gradient’s embedding endpoint.
Parameters
text (str) – The text to embed.
Returns
Embeddings for the text.
Return type
List[float]
classmethod from_orm(obj: Any) → Model¶
Parameters
obj (Any) –
Return type
Model | https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.gradient_ai.GradientEmbeddings.html |
aa21f048efe7-3 | 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.gradient_ai.GradientEmbeddings.html |
aa21f048efe7-4 | 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 GradientEmbeddings¶
Gradient | https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.gradient_ai.GradientEmbeddings.html |
a523ed8f43fb-0 | langchain_community.embeddings.edenai.EdenAiEmbeddings¶
class langchain_community.embeddings.edenai.EdenAiEmbeddings[source]¶
Bases: BaseModel, Embeddings
EdenAI embedding.
environment variable EDENAI_API_KEY set with your API key, or pass
it as a named parameter.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param edenai_api_key: Optional[SecretStr] = None¶
EdenAI API Token
Constraints
type = string
writeOnly = True
format = password
param model: Optional[str] = None¶
model name for above provider (eg: ‘gpt-3.5-turbo-instruct’ for openai)
available models are shown on https://docs.edenai.co/ under ‘available providers’
param provider: str = 'openai'¶
embedding provider to use (eg: openai,google etc.)
async aembed_documents(texts: List[str]) → List[List[float]]¶
Asynchronous Embed search docs.
Parameters
texts (List[str]) –
Return type
List[List[float]]
async aembed_query(text: str) → List[float]¶
Asynchronous Embed query text.
Parameters
text (str) –
Return type
List[float]
classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model¶
Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
Behaves as if Config.extra = ‘allow’ was set since it adds all passed values
Parameters
_fields_set (Optional[SetStr]) –
values (Any) –
Return type
Model | https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.edenai.EdenAiEmbeddings.html |
a523ed8f43fb-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.edenai.EdenAiEmbeddings.html |
a523ed8f43fb-2 | exclude_unset (bool) –
exclude_defaults (bool) –
exclude_none (bool) –
Return type
DictStrAny
embed_documents(texts: List[str]) → List[List[float]][source]¶
Embed a list of documents using EdenAI.
Parameters
texts (List[str]) – The list of texts to embed.
Returns
List of embeddings, one for each text.
Return type
List[List[float]]
embed_query(text: str) → List[float][source]¶
Embed a query using EdenAI.
Parameters
text (str) – The text to embed.
Returns
Embeddings for the text.
Return type
List[float]
classmethod from_orm(obj: Any) → Model¶
Parameters
obj (Any) –
Return type
Model
static get_user_agent() → str[source]¶
Return type
str
json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, **dumps_kwargs: Any) → unicode¶
Generate a JSON representation of the model, include and exclude arguments as per dict().
encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().
Parameters
include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) –
exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) –
by_alias (bool) –
skip_defaults (Optional[bool]) – | https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.edenai.EdenAiEmbeddings.html |
a523ed8f43fb-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.edenai.EdenAiEmbeddings.html |
a523ed8f43fb-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 EdenAiEmbeddings¶
EDEN AI
Eden AI | https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.edenai.EdenAiEmbeddings.html |
6f8bbbc702b3-0 | langchain_aws.embeddings.bedrock.BedrockEmbeddings¶
class langchain_aws.embeddings.bedrock.BedrockEmbeddings[source]¶
Bases: BaseModel, Embeddings
Bedrock embedding models.
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 Bedrock service.
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¶
Bedrock client.
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_url: Optional[str] = None¶
Needed if you don’t want to default to us-east-1 endpoint
param model_id: str = 'amazon.titan-embed-text-v1'¶
Id of the model to call, e.g., amazon.titan-embed-text-v1, this is
equivalent to the modelId property in the list-foundation-models api
param model_kwargs: Optional[Dict] = None¶
Keyword arguments to pass to the model.
param normalize: bool = False¶
Whether the embeddings should be normalized to unit vectors
param region_name: Optional[str] = None¶ | https://api.python.langchain.com/en/latest/embeddings/langchain_aws.embeddings.bedrock.BedrockEmbeddings.html |
6f8bbbc702b3-1 | param region_name: Optional[str] = None¶
The aws region e.g., us-west-2. Fallsback to AWS_DEFAULT_REGION env variable
or region specified in ~/.aws/config in case it is not provided here.
async aembed_documents(texts: List[str]) → List[List[float]][source]¶
Asynchronous compute doc embeddings using a Bedrock model.
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 compute query embeddings using a Bedrock model.
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 | https://api.python.langchain.com/en/latest/embeddings/langchain_aws.embeddings.bedrock.BedrockEmbeddings.html |
6f8bbbc702b3-2 | 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], dim: int = 1024, norm: bool = True) → List[List[float]][source]¶
Compute doc embeddings using a Bedrock model.
Parameters
texts (List[str]) – The list of texts to embed
dim (int) –
norm (bool) –
Returns
List of embeddings, one for each text.
Return type
List[List[float]] | https://api.python.langchain.com/en/latest/embeddings/langchain_aws.embeddings.bedrock.BedrockEmbeddings.html |
6f8bbbc702b3-3 | Returns
List of embeddings, one for each text.
Return type
List[List[float]]
embed_query(text: str, dim: int = 1024, norm: bool = True) → List[float][source]¶
Compute query embeddings using a Bedrock model.
Parameters
text (str) – The text to embed.
dim (int) –
norm (bool) –
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_aws.embeddings.bedrock.BedrockEmbeddings.html |
6f8bbbc702b3-4 | 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_aws.embeddings.bedrock.BedrockEmbeddings.html |
6f8bbbc702b3-5 | Return type
None
classmethod validate(value: Any) → Model¶
Parameters
value (Any) –
Return type
Model
Examples using BedrockEmbeddings¶
AWS
Bedrock | https://api.python.langchain.com/en/latest/embeddings/langchain_aws.embeddings.bedrock.BedrockEmbeddings.html |
38b789a896c9-0 | langchain_fireworks.embeddings.FireworksEmbeddings¶
class langchain_fireworks.embeddings.FireworksEmbeddings[source]¶
Bases: BaseModel, Embeddings
FireworksEmbeddings embedding model.
Example
from langchain_fireworks import FireworksEmbeddings
model = FireworksEmbeddings(
model='nomic-ai/nomic-embed-text-v1.5'
)
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 fireworks_api_key: SecretStr = SecretStr('')¶
Constraints
type = string
writeOnly = True
format = password
param model: str = 'nomic-ai/nomic-embed-text-v1.5'¶
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_fireworks.embeddings.FireworksEmbeddings.html |
38b789a896c9-1 | 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_fireworks.embeddings.FireworksEmbeddings.html |
38b789a896c9-2 | 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]]) –
models_as_dict (bool) –
dumps_kwargs (Any) –
Return type
unicode | https://api.python.langchain.com/en/latest/embeddings/langchain_fireworks.embeddings.FireworksEmbeddings.html |
38b789a896c9-3 | 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_fireworks.embeddings.FireworksEmbeddings.html |
38b789a896c9-4 | Return type
None
classmethod validate(value: Any) → Model¶
Parameters
value (Any) –
Return type
Model
Examples using FireworksEmbeddings¶
FireworksEmbeddings | https://api.python.langchain.com/en/latest/embeddings/langchain_fireworks.embeddings.FireworksEmbeddings.html |
ad45ba992eac-0 | langchain_community.embeddings.titan_takeoff.ReaderConfig¶
class langchain_community.embeddings.titan_takeoff.ReaderConfig[source]¶
Bases: BaseModel
Configuration for the reader to be deployed in Takeoff.
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 consumer_group: str = 'primary'¶
The consumer group to place the reader into
param device: Device = Device.cuda¶
The device to use for inference, cuda or cpu
param model_name: str [Required]¶
The name of the model to use
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 | https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.titan_takeoff.ReaderConfig.html |
ad45ba992eac-1 | 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
classmethod from_orm(obj: Any) → Model¶
Parameters
obj (Any) –
Return type
Model | https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.titan_takeoff.ReaderConfig.html |
ad45ba992eac-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.titan_takeoff.ReaderConfig.html |
ad45ba992eac-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 | https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.titan_takeoff.ReaderConfig.html |
22ecd8cc6724-0 | langchain_community.embeddings.openai.async_embed_with_retry¶
async langchain_community.embeddings.openai.async_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.async_embed_with_retry.html |
2ee4503386a3-0 | langchain_community.embeddings.llamafile.LlamafileEmbeddings¶
class langchain_community.embeddings.llamafile.LlamafileEmbeddings[source]¶
Bases: BaseModel, Embeddings
Llamafile lets you distribute and run large language models with a
single file.
To get started, see: https://github.com/Mozilla-Ocho/llamafile
To use this class, you will need to first:
Download a llamafile.
Make the downloaded file executable: chmod +x path/to/model.llamafile
Start the llamafile in server mode with embeddings enabled:
./path/to/model.llamafile –server –nobrowser –embedding
Example
from langchain_community.embeddings import LlamafileEmbeddings
embedder = LlamafileEmbeddings()
doc_embeddings = embedder.embed_documents(
[
"Alpha is the first letter of the Greek alphabet",
"Beta is the second letter of the Greek alphabet",
]
)
query_embedding = embedder.embed_query(
"What is the second letter of the Greek alphabet"
)
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 base_url: str = 'http://localhost:8080'¶
Base url where the llamafile server is listening.
param request_timeout: Optional[int] = None¶
Timeout for server requests
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_community.embeddings.llamafile.LlamafileEmbeddings.html |
2ee4503386a3-1 | 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_community.embeddings.llamafile.LlamafileEmbeddings.html |
2ee4503386a3-2 | 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 documents using a llamafile server running at self.base_url.
llamafile server should be started in a separate process before invoking
this method.
Parameters
texts (List[str]) – The list of texts to embed.
Returns
List of embeddings, one for each text.
Return type
List[List[float]]
embed_query(text: str) → List[float][source]¶
Embed a query using a llamafile server running at self.base_url.
llamafile server should be started in a separate process before invoking
this method.
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.llamafile.LlamafileEmbeddings.html |
2ee4503386a3-3 | 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.llamafile.LlamafileEmbeddings.html |
2ee4503386a3-4 | 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 LlamafileEmbeddings¶
llamafile | https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.llamafile.LlamafileEmbeddings.html |
77bdaab20d14-0 | langchain_community.embeddings.minimax.MiniMaxEmbeddings¶
class langchain_community.embeddings.minimax.MiniMaxEmbeddings[source]¶
Bases: BaseModel, Embeddings
MiniMax’s embedding service.
To use, you should have the environment variable MINIMAX_GROUP_ID and
MINIMAX_API_KEY set with your API token, or pass it as a named parameter to
the constructor.
Example
from langchain_community.embeddings import MiniMaxEmbeddings
embeddings = MiniMaxEmbeddings()
query_text = "This is a test query."
query_result = embeddings.embed_query(query_text)
document_text = "This is a test document."
document_result = embeddings.embed_documents([document_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 embed_type_db: str = 'db'¶
For embed_documents
param embed_type_query: str = 'query'¶
For embed_query
param endpoint_url: str = 'https://api.minimax.chat/v1/embeddings'¶
Endpoint URL to use.
param minimax_api_key: Optional[SecretStr] = None¶
API Key for MiniMax API.
Constraints
type = string
writeOnly = True
format = password
param minimax_group_id: Optional[str] = None¶
Group ID for MiniMax API.
param model: str = 'embo-01'¶
Embeddings 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] | https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.minimax.MiniMaxEmbeddings.html |
77bdaab20d14-1 | 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_community.embeddings.minimax.MiniMaxEmbeddings.html |
77bdaab20d14-2 | 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(texts: List[str], embed_type: str) → List[List[float]][source]¶
Parameters
texts (List[str]) –
embed_type (str) –
Return type
List[List[float]]
embed_documents(texts: List[str]) → List[List[float]][source]¶
Embed documents using a MiniMax 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]¶
Embed a query using a MiniMax 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 | https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.minimax.MiniMaxEmbeddings.html |
77bdaab20d14-3 | 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.minimax.MiniMaxEmbeddings.html |
77bdaab20d14-4 | 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 MiniMaxEmbeddings¶
MiniMax
Minimax | https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.minimax.MiniMaxEmbeddings.html |
2fa34dc1d190-0 | langchain_text_splitters.sentence_transformers.SentenceTransformersTokenTextSplitter¶
class langchain_text_splitters.sentence_transformers.SentenceTransformersTokenTextSplitter(chunk_overlap: int = 50, model_name: str = 'sentence-transformers/all-mpnet-base-v2', tokens_per_chunk: Optional[int] = None, **kwargs: Any)[source]¶
Splitting text to tokens using sentence model tokenizer.
Create a new TextSplitter.
Methods
__init__([chunk_overlap, model_name, ...])
Create a new TextSplitter.
atransform_documents(documents, **kwargs)
Asynchronously transform a list of documents.
count_tokens(*, text)
create_documents(texts[, metadatas])
Create documents from a list of texts.
from_huggingface_tokenizer(tokenizer, **kwargs)
Text splitter that uses HuggingFace tokenizer to count length.
from_tiktoken_encoder([encoding_name, ...])
Text splitter that uses tiktoken encoder to count length.
split_documents(documents)
Split documents.
split_text(text)
Split text into multiple components.
transform_documents(documents, **kwargs)
Transform sequence of documents by splitting them.
Parameters
chunk_overlap (int) –
model_name (str) –
tokens_per_chunk (Optional[int]) –
kwargs (Any) –
__init__(chunk_overlap: int = 50, model_name: str = 'sentence-transformers/all-mpnet-base-v2', tokens_per_chunk: Optional[int] = None, **kwargs: Any) → None[source]¶
Create a new TextSplitter.
Parameters
chunk_overlap (int) –
model_name (str) –
tokens_per_chunk (Optional[int]) –
kwargs (Any) –
Return type
None | https://api.python.langchain.com/en/latest/sentence_transformers/langchain_text_splitters.sentence_transformers.SentenceTransformersTokenTextSplitter.html |
2fa34dc1d190-1 | kwargs (Any) –
Return type
None
async atransform_documents(documents: Sequence[Document], **kwargs: Any) → Sequence[Document]¶
Asynchronously transform a list of documents.
Parameters
documents (Sequence[Document]) – A sequence of Documents to be transformed.
kwargs (Any) –
Returns
A list of transformed Documents.
Return type
Sequence[Document]
count_tokens(*, text: str) → int[source]¶
Parameters
text (str) –
Return type
int
create_documents(texts: List[str], metadatas: Optional[List[dict]] = None) → List[Document]¶
Create documents from a list of texts.
Parameters
texts (List[str]) –
metadatas (Optional[List[dict]]) –
Return type
List[Document]
classmethod from_huggingface_tokenizer(tokenizer: Any, **kwargs: Any) → TextSplitter¶
Text splitter that uses HuggingFace tokenizer to count length.
Parameters
tokenizer (Any) –
kwargs (Any) –
Return type
TextSplitter
classmethod from_tiktoken_encoder(encoding_name: str = 'gpt2', model_name: Optional[str] = None, allowed_special: Union[Literal['all'], AbstractSet[str]] = {}, disallowed_special: Union[Literal['all'], Collection[str]] = 'all', **kwargs: Any) → TS¶
Text splitter that uses tiktoken encoder to count length.
Parameters
encoding_name (str) –
model_name (Optional[str]) –
allowed_special (Union[Literal['all'], ~typing.AbstractSet[str]]) –
disallowed_special (Union[Literal['all'], ~typing.Collection[str]]) –
kwargs (Any) –
Return type
TS
split_documents(documents: Iterable[Document]) → List[Document]¶ | https://api.python.langchain.com/en/latest/sentence_transformers/langchain_text_splitters.sentence_transformers.SentenceTransformersTokenTextSplitter.html |
2fa34dc1d190-2 | TS
split_documents(documents: Iterable[Document]) → List[Document]¶
Split documents.
Parameters
documents (Iterable[Document]) –
Return type
List[Document]
split_text(text: str) → List[str][source]¶
Split text into multiple components.
Parameters
text (str) –
Return type
List[str]
transform_documents(documents: Sequence[Document], **kwargs: Any) → Sequence[Document]¶
Transform sequence of documents by splitting them.
Parameters
documents (Sequence[Document]) –
kwargs (Any) –
Return type
Sequence[Document]
Examples using SentenceTransformersTokenTextSplitter¶
How to split text by tokens | https://api.python.langchain.com/en/latest/sentence_transformers/langchain_text_splitters.sentence_transformers.SentenceTransformersTokenTextSplitter.html |
b84f4c839b44-0 | langchain_community.cross_encoders.sagemaker_endpoint.SagemakerEndpointCrossEncoder¶
class langchain_community.cross_encoders.sagemaker_endpoint.SagemakerEndpointCrossEncoder[source]¶
Bases: BaseModel, BaseCrossEncoder
SageMaker Inference CrossEncoder endpoint.
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 content_handler: CrossEncoderContentHandler = <langchain_community.cross_encoders.sagemaker_endpoint.CrossEncoderContentHandler object>¶
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.
.. _boto3: <https://boto3.amazonaws.com/v1/documentation/api/latest/index.html> | https://api.python.langchain.com/en/latest/cross_encoders/langchain_community.cross_encoders.sagemaker_endpoint.SagemakerEndpointCrossEncoder.html |
b84f4c839b44-1 | 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.
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/cross_encoders/langchain_community.cross_encoders.sagemaker_endpoint.SagemakerEndpointCrossEncoder.html |
b84f4c839b44-2 | 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
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 | https://api.python.langchain.com/en/latest/cross_encoders/langchain_community.cross_encoders.sagemaker_endpoint.SagemakerEndpointCrossEncoder.html |
b84f4c839b44-3 | 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
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/cross_encoders/langchain_community.cross_encoders.sagemaker_endpoint.SagemakerEndpointCrossEncoder.html |
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