Tony20100/langchain_full huggingface dataset:
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 modelscope embedding 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 modelscope embedding model.
Parameters
text (str) – The text to embed.
Returns
Embeddings for the text.
Return type
List[float]
classmethod from_orm(obj: Any) → Model¶
Parameters
obj (Any) – 
Return type
Model
json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, **dumps_kwargs: Any) → unicode¶
Generate a JSON representation of the model, include and exclude arguments as per dict().
encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().
Parameters
include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – 
exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – 
by_alias (bool) – 
skip_defaults (Optional[bool]) – 
exclude_unset (bool) – 
exclude_defaults (bool) –
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 ModelScopeEmbeddings¶
ModelScope
langchain_ai21.embeddings.AI21Embeddings¶
class langchain_ai21.embeddings.AI21Embeddings[source]¶
Bases: Embeddings, AI21Base
AI21 Embeddings embedding model.
To use, you should have the ‘AI21_API_KEY’ environment variable set
or pass as a named parameter to the constructor.
Example
from langchain_ai21 import AI21Embeddings
embeddings = AI21Embeddings()
query_result = embeddings.embed_query("Hello embeddings world!")
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param api_host: Optional[str] = None¶
param api_key: Optional[SecretStr] = None¶
Constraints
type = string
writeOnly = True
format = password
param batch_size: int = 128¶
Maximum number of texts to embed in each batch
param num_retries: Optional[int] = None¶
param timeout_sec: Optional[float] = None¶
async aembed_documents(texts: List[str]) → List[List[float]]¶
Asynchronous Embed search docs.
Parameters
texts (List[str]) – 
Return type
List[List[float]]
async aembed_query(text: str) → List[float]¶
Asynchronous Embed query text.
Parameters
text (str) – 
Return type
List[float]
classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model¶
Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
Behaves as if Config.extra = ‘allow’ was set since it adds all passed values
Parameters
_fields_set (Optional[SetStr]) – 
values (Any) – 
Return type
Model
exclude_unset (bool) – 
exclude_defaults (bool) – 
exclude_none (bool) – 
Return type
DictStrAny
embed_documents(texts: List[str], *, batch_size: Optional[int] = None, **kwargs: Any) → List[List[float]][source]¶
Embed search docs.
Parameters
texts (List[str]) – 
batch_size (Optional[int]) – 
kwargs (Any) – 
Return type
List[List[float]]
embed_query(text: str, *, batch_size: Optional[int] = None, **kwargs: Any) → List[float][source]¶
Embed query text.
Parameters
text (str) – 
batch_size (Optional[int]) – 
kwargs (Any) – 
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) –
langchain_community.embeddings.clova.ClovaEmbeddings¶
class langchain_community.embeddings.clova.ClovaEmbeddings[source]¶
Bases: BaseModel, Embeddings
Clova’s embedding service.
To use this service,
you should have the following environment variables
set with your API tokens and application ID,
or pass them as named parameters to the constructor:
CLOVA_EMB_API_KEY: API key for accessing Clova’s embedding service.
CLOVA_EMB_APIGW_API_KEY: API gateway key for enhanced security.
CLOVA_EMB_APP_ID: Application ID for identifying your application.
Example
from langchain_community.embeddings import ClovaEmbeddings
embeddings = ClovaEmbeddings(
    clova_emb_api_key='your_clova_emb_api_key',
    clova_emb_apigw_api_key='your_clova_emb_apigw_api_key',
    app_id='your_app_id'
)
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 app_id: Optional[SecretStr] = None¶
Application ID for identifying your application.
Constraints
type = string
writeOnly = True
format = password
param clova_emb_api_key: Optional[SecretStr] = None¶
API key for accessing Clova’s embedding service.
Constraints
type = string
writeOnly = True
format = password
param clova_emb_apigw_api_key: Optional[SecretStr] = None¶
API gateway key for enhanced security.
Constraints
type = string
writeOnly = True
API gateway key for enhanced security.
Constraints
type = string
writeOnly = True
format = password
param endpoint_url: str = 'https://clovastudio.apigw.ntruss.com/testapp/v1/api-tools/embedding'¶
Endpoint URL to use.
param model: str = 'clir-emb-dolphin'¶
Embedding model name to use.
async aembed_documents(texts: List[str]) → List[List[float]]¶
Asynchronous Embed search docs.
Parameters
texts (List[str]) – 
Return type
List[List[float]]
async aembed_query(text: str) → List[float]¶
Asynchronous Embed query text.
Parameters
text (str) – 
Return type
List[float]
classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model¶
Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
Behaves as if Config.extra = ‘allow’ was set since it adds all passed values
Parameters
_fields_set (Optional[SetStr]) – 
values (Any) – 
Return type
Model
copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model¶
Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – fields to include in new model
exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – fields to exclude from new model, as with values this takes precedence over include
update (Optional[DictStrAny]) – values to change/add in the new model. Note: the data is not validated before creating
the new model: you should trust this data
deep (bool) – set to True to make a deep copy of the model
self (Model) – 
Returns
new model instance
Return type
Model
dict(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False) → DictStrAny¶
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
Parameters
include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – 
exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – 
by_alias (bool) – 
skip_defaults (Optional[bool]) – 
exclude_unset (bool) – 
exclude_defaults (bool) – 
exclude_none (bool) – 
Return type
DictStrAny
embed_documents(texts: List[str]) → List[List[float]][source]¶
Embed a list of texts and return their embeddings.
Parameters
texts (List[str]) – The list of texts to embed.
Returns
List of embeddings, one for each text.
Return type
List[List[float]]
embed_query(text: str) → List[float][source]¶
Embed a single query text and return its embedding.
Parameters
text (str) – The text to embed.
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
value (Any) – 
Return type
Model
Examples using ClovaEmbeddings¶
Clova Embeddings
langchain_community.embeddings.cloudflare_workersai.CloudflareWorkersAIEmbeddings¶
class langchain_community.embeddings.cloudflare_workersai.CloudflareWorkersAIEmbeddings[source]¶
Bases: BaseModel, Embeddings
Cloudflare Workers AI embedding model.
To use, you need to provide an API token and
account ID to access Cloudflare Workers AI.
Example
from langchain_community.embeddings import CloudflareWorkersAIEmbeddings
account_id = "my_account_id"
api_token = "my_secret_api_token"
model_name =  "@cf/baai/bge-small-en-v1.5"
cf = CloudflareWorkersAIEmbeddings(
    account_id=account_id,
    api_token=api_token,
    model_name=model_name
)
Initialize the Cloudflare Workers AI client.
param account_id: str [Required]¶
param api_base_url: str = 'https://api.cloudflare.com/client/v4/accounts'¶
param api_token: str [Required]¶
param batch_size: int = 50¶
param headers: Dict[str, str] = {'Authorization': 'Bearer '}¶
param model_name: str = '@cf/baai/bge-base-en-v1.5'¶
param strip_new_lines: bool = True¶
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.
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 Cloudflare Workers AI.
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 Cloudflare Workers AI.
Parameters
text (str) – The text to embed.
Returns
Embeddings for the text.
Return type
List[float]
classmethod from_orm(obj: Any) → Model¶
Parameters
obj (Any) – 
Return type
Model
json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, **dumps_kwargs: Any) → unicode¶
Generate a JSON representation of the model, include and exclude arguments as per dict().
encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().
Parameters
include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) –
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 CloudflareWorkersAIEmbeddings¶
Cloudflare
Cloudflare Workers AI
langchain_community.embeddings.mlflow.MlflowEmbeddings¶
class langchain_community.embeddings.mlflow.MlflowEmbeddings[source]¶
Bases: Embeddings, BaseModel
Embedding LLMs in MLflow.
To use, you should have the mlflow[genai] python package installed.
For more information, see https://mlflow.org/docs/latest/llms/deployments.
Example
from langchain_community.embeddings import MlflowEmbeddings
embeddings = MlflowEmbeddings(
    target_uri="http://localhost:5000",
    endpoint="embeddings",
)
param documents_params: Dict[str, str] = {}¶
param endpoint: str [Required]¶
The endpoint to use.
param query_params: Dict[str, str] = {}¶
The parameters to use for documents.
param target_uri: str [Required]¶
The target URI to use.
async aembed_documents(texts: List[str]) → List[List[float]]¶
Asynchronous Embed search docs.
Parameters
texts (List[str]) – 
Return type
List[List[float]]
async aembed_query(text: str) → List[float]¶
Asynchronous Embed query text.
Parameters
text (str) – 
Return type
List[float]
classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model¶
Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
Behaves as if Config.extra = ‘allow’ was set since it adds all passed values
Parameters
_fields_set (Optional[SetStr]) – 
values (Any) – 
Return type
Model
exclude_unset (bool) – 
exclude_defaults (bool) – 
exclude_none (bool) – 
Return type
DictStrAny
embed(texts: List[str], params: Dict[str, str]) → List[List[float]][source]¶
Parameters
texts (List[str]) – 
params (Dict[str, str]) – 
Return type
List[List[float]]
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) –
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 MlflowEmbeddings¶
MLflow Deployments for LLMs
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
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
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
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.
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