Tony20100/langchain_full huggingface dataset:
Call out to OCIGenAI’s embedding endpoint.
Parameters
text (str) – The text to embed.
Returns
Embeddings for the text.
Return type
List[float]
classmethod from_orm(obj: Any) → Model¶
Parameters
obj (Any) – 
Return type
Model
json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, **dumps_kwargs: Any) → unicode¶
Generate a JSON representation of the model, include and exclude arguments as per dict().
encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().
Parameters
include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – 
exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – 
by_alias (bool) – 
skip_defaults (Optional[bool]) – 
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) –
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
value (Any) – 
Return type
Model
Examples using OCIGenAIEmbeddings¶
# Oracle Cloud Infrastructure Generative AI
Oracle Cloud Infrastructure (OCI)
Oracle Cloud Infrastructure Generative AI
langchain_elasticsearch.embeddings.EmbeddingServiceAdapter¶
class langchain_elasticsearch.embeddings.EmbeddingServiceAdapter(langchain_embeddings: Embeddings)[source]¶
Adapter for LangChain Embeddings to support the EmbeddingService interface from
elasticsearch.helpers.vectorstore.
Methods
__init__(langchain_embeddings)
embed_documents(texts)
Generate embeddings for a list of documents.
embed_query(text)
Generate an embedding for a single query text.
Parameters
langchain_embeddings (Embeddings) – 
__init__(langchain_embeddings: Embeddings)[source]¶
Parameters
langchain_embeddings (Embeddings) – 
embed_documents(texts: List[str]) → List[List[float]][source]¶
Generate embeddings for a list of documents.
Parameters
texts (List[str]) – A list of document text strings to generate embeddings
for.
Returns
A list of embeddings, one for each document in the inputlist.
Return type
List[List[float]]
embed_query(text: str) → List[float][source]¶
Generate an embedding for a single query text.
Parameters
text (str) – The query text to generate an embedding for.
Returns
The embedding for the input query text.
Return type
List[float]
langchain_community.embeddings.bookend.BookendEmbeddings¶
class langchain_community.embeddings.bookend.BookendEmbeddings[source]¶
Bases: BaseModel, Embeddings
Bookend AI sentence_transformers embedding models.
Example
from langchain_community.embeddings import BookendEmbeddings
bookend = BookendEmbeddings(
    domain={domain}
    api_token={api_token}
    model_id={model_id}
)
bookend.embed_documents([
    "Please put on these earmuffs because I can't you hear.",
    "Baby wipes are made of chocolate stardust.",
])
bookend.embed_query(
    "She only paints with bold colors; she does not like pastels."
)
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_token: str [Required]¶
Request for an API token at https://bookend.ai/ to use this embeddings module.
param auth_header: dict [Optional]¶
param domain: str [Required]¶
Request for a domain at https://bookend.ai/ to use this embeddings module.
param model_id: str [Required]¶
Embeddings model ID 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.
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 Bookend deployed embeddings 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 a query using a Bookend deployed embeddings 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
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 BookendEmbeddings¶
Bookend AI
langchain_community.embeddings.voyageai.VoyageEmbeddings¶
class langchain_community.embeddings.voyageai.VoyageEmbeddings[source]¶
Bases: BaseModel, Embeddings
[Deprecated] Voyage embedding models.
To use, you should have the environment variable VOYAGE_API_KEY set with
your API key or pass it as a named parameter to the constructor.
Example
from langchain_community.embeddings import VoyageEmbeddings
voyage = VoyageEmbeddings(voyage_api_key="your-api-key", model="voyage-2")
text = "This is a test query."
query_result = voyage.embed_query(text)
Notes
Deprecated since version 0.0.29.
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 batch_size: int [Required]¶
Maximum number of texts to embed in each API request.
param max_retries: int = 6¶
Maximum number of retries to make when generating.
param model: str [Required]¶
param request_timeout: Optional[Union[float, Tuple[float, float]]] = None¶
Timeout in seconds for the API request.
param show_progress_bar: bool = False¶
Whether to show a progress bar when embedding. Must have tqdm installed if set
to True.
param truncation: bool = True¶
Whether to truncate the input texts to fit within the context length.
If True, over-length input texts will be truncated to fit within the context
length, before vectorized by the embedding model. If False, an error will be
raised if any given text exceeds the context length.
param voyage_api_base: str = 'https://api.voyageai.com/v1/embeddings'¶
param voyage_api_key: Optional[SecretStr] = None¶
Constraints
param voyage_api_key: Optional[SecretStr] = None¶
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¶
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
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 Voyage Embedding endpoint for embedding search docs.
Parameters
texts (List[str]) – The list of texts to embed.
Returns
List of embeddings, one for each text.
Return type
List[List[float]]
embed_general_texts(texts: List[str], *, input_type: Optional[str] = None) → List[List[float]][source]¶
Call out to Voyage Embedding endpoint for embedding general text.
Parameters
texts (List[str]) – The list of texts to embed.
input_type (Optional[str]) – Type of the input text. Default to None, meaning the type is
unspecified. Other options: query, document.
Returns
unspecified. Other options: query, document.
Returns
Embedding for the text.
Return type
List[List[float]]
embed_query(text: str) → List[float][source]¶
Call out to Voyage 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¶
Generate a JSON representation of the model, include and exclude arguments as per dict().
encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().
Parameters
include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – 
exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – 
by_alias (bool) – 
skip_defaults (Optional[bool]) – 
exclude_unset (bool) – 
exclude_defaults (bool) – 
exclude_none (bool) – 
encoder (Optional[Callable[[Any], Any]]) – 
models_as_dict (bool) – 
dumps_kwargs (Any) – 
Return type
unicode
langchain_community.embeddings.sagemaker_endpoint.EmbeddingsContentHandler¶
class langchain_community.embeddings.sagemaker_endpoint.EmbeddingsContentHandler[source]¶
Content handler for LLM class.
Attributes
accepts
The MIME type of the response data returned from endpoint
content_type
The MIME type of the input data passed to endpoint
Methods
__init__()
transform_input(prompt, model_kwargs)
Transforms the input to a format that model can accept as the request Body.
transform_output(output)
Transforms the output from the model to string that the LLM class expects.
__init__()¶
abstract transform_input(prompt: INPUT_TYPE, model_kwargs: Dict) → bytes¶
Transforms the input to a format that model can accept
as the request Body. Should return bytes or seekable file
like object in the format specified in the content_type
request header.
Parameters
prompt (INPUT_TYPE) – 
model_kwargs (Dict) – 
Return type
bytes
abstract transform_output(output: bytes) → OUTPUT_TYPE¶
Transforms the output from the model to string that
the LLM class expects.
Parameters
output (bytes) – 
Return type
OUTPUT_TYPE
Examples using EmbeddingsContentHandler¶
SageMaker
langchain_openai.embeddings.azure.AzureOpenAIEmbeddings¶
class langchain_openai.embeddings.azure.AzureOpenAIEmbeddings[source]¶
Bases: OpenAIEmbeddings
Azure OpenAI Embeddings API.
To use, you should have the
environment variable AZURE_OPENAI_API_KEY set with your API key or pass it
as a named parameter to the constructor.
Example
from langchain_openai import AzureOpenAIEmbeddings
openai = AzureOpenAIEmbeddings(model="text-embedding-3-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], None] = None¶
param azure_ad_token: Optional[SecretStr] = None¶
Your Azure Active Directory token.
Automatically inferred from env var AZURE_OPENAI_AD_TOKEN if not provided.
For more:
https://www.microsoft.com/en-us/security/business/identity-access/microsoft-entra-id.
Constraints
type = string
writeOnly = True
format = password
param azure_ad_token_provider: Union[Callable[[], str], None] = None¶
A function that returns an Azure Active Directory token.
Will be invoked on every request.
param azure_endpoint: Union[str, None] = None¶
Your Azure endpoint, including the resource.
Automatically inferred from env var AZURE_OPENAI_ENDPOINT if not provided.
Example: https://example-resource.azure.openai.com/
param check_embedding_ctx_length: bool = True¶
Whether to check the token length of inputs and automatically split inputs
longer than embedding_ctx_length.
param chunk_size: int = 2048¶
Maximum number of texts to embed in each batch
param default_headers: Union[Mapping[str, str], None] = None¶
param default_headers: Union[Mapping[str, str], None] = None¶
param default_query: Union[Mapping[str, object], None] = None¶
param deployment: Optional[str] = None (alias 'azure_deployment')¶
A model deployment.
If given sets the base client URL to include /deployments/{azure_deployment}.
Note: this means you won’t be able to use non-deployment endpoints.
param dimensions: Optional[int] = None¶
The number of dimensions the resulting output embeddings should have.
Only supported in text-embedding-3 and later models.
param disallowed_special: Union[Literal['all'], Set[str], Sequence[str], None] = None¶
param embedding_ctx_length: int = 8191¶
The maximum number of tokens to embed at once.
param headers: Any = None¶
param http_async_client: Union[Any, None] = 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: Union[Any, None] = None¶
Optional httpx.Client. Only used for sync invocations. Must specify
http_async_client as well if you’d like a custom client for async invocations.
param max_retries: int = 2¶
Maximum number of retries to make when generating.
param model: str = 'text-embedding-ada-002'¶
param model_kwargs: Dict[str, Any] [Optional]¶
Holds any model parameters valid for create call not explicitly specified.
param openai_api_base: Optional[str] = None (alias 'base_url')¶
Base URL path for API requests, leave blank if not using a proxy or service
emulator.
param openai_api_key: Optional[SecretStr] = None (alias 'api_key')¶
Automatically inferred from env var AZURE_OPENAI_API_KEY if not provided.
Constraints
type = string
writeOnly = True
format = password
param openai_api_type: Optional[str] = None¶
param openai_api_version: Optional[str] = None (alias 'api_version')¶
Automatically inferred from env var OPENAI_API_VERSION if not provided.
param openai_organization: Optional[str] = None (alias 'organization')¶
Automatically inferred from env var OPENAI_ORG_ID if not provided.
param openai_proxy: Optional[str] = None¶
param request_timeout: Optional[Union[float, Tuple[float, float], Any]] = None (alias 'timeout')¶
Timeout for requests to OpenAI completion API. Can be float, httpx.Timeout or
None.
param retry_max_seconds: int = 20¶
Max number of seconds to wait between retries
param retry_min_seconds: int = 4¶
Min number of seconds to wait between retries
param show_progress_bar: bool = False¶
Whether to show a progress bar when embedding.
param skip_empty: bool = False¶
Whether to skip empty strings when embedding or raise an error.
Defaults to not skipping.
param tiktoken_enabled: bool = True¶
Set this to False for non-OpenAI implementations of the embeddings API, e.g.
the –extensions openai extension for text-generation-webui
param tiktoken_model_name: Optional[str] = None¶
The model name to pass to tiktoken when using this class.
Tiktoken is used to count the number of tokens in documents to constrain
them to be under a certain limit. By default, when set to None, this will
be the same as the embedding model name. However, there are some cases
where you may want to use this Embedding class with a model name not
where you may want to use this Embedding class with a model name not
supported by tiktoken. This can include when using Azure embeddings or
when using one of the many model providers that expose an OpenAI-like
API but with different models. In those cases, in order to avoid erroring
when tiktoken is called, you can specify a model name to use here.
param validate_base_url: bool = True¶
async aembed_documents(texts: List[str], chunk_size: Optional[int] = 0) → List[List[float]]¶
Call out to OpenAI’s embedding endpoint async for embedding search docs.
Parameters
texts (List[str]) – The list of texts to embed.
chunk_size (Optional[int]) – 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]]
async aembed_query(text: str) → List[float]¶
Call out to OpenAI’s embedding endpoint async for embedding query text.
Parameters
text (str) – The text to embed.
Returns
Embedding for the text.
Return type
List[float]
classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model¶
Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
Behaves as if Config.extra = ‘allow’ was set since it adds all passed values
Parameters
_fields_set (Optional[SetStr]) – 
values (Any) – 
Return type
Model
exclude_unset (bool) – 
exclude_defaults (bool) – 
exclude_none (bool) – 
Return type
DictStrAny
embed_documents(texts: List[str], chunk_size: Optional[int] = 0) → List[List[float]]¶
Call out to OpenAI’s embedding endpoint for embedding search docs.
Parameters
texts (List[str]) – The list of texts to embed.
chunk_size (Optional[int]) – 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]¶
Call out to OpenAI’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¶
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 AzureOpenAIEmbeddings¶
Azure AI Search
Azure OpenAI
Microsoft
langchain_community.embeddings.premai.create_prem_retry_decorator¶
langchain_community.embeddings.premai.create_prem_retry_decorator(embedder: PremAIEmbeddings, *, max_retries: int = 1) → Callable[[Any], Any][source]¶
Create a retry decorator for PremAIEmbeddings.
Parameters
embedder (PremAIEmbeddings) – The PremAIEmbeddings instance
max_retries (int) – The maximum number of retries
Returns
The retry decorator
Return type
Callable[[Any], Any]
langchain_community.embeddings.premai.embed_with_retry¶
langchain_community.embeddings.premai.embed_with_retry(embedder: PremAIEmbeddings, model: str, project_id: int, input: Union[str, List[str]]) → Any[source]¶
Using tenacity for retry in embedding calls
Parameters
embedder (PremAIEmbeddings) – 
model (str) – 
project_id (int) – 
input (Union[str, List[str]]) – 
Return type
Any
langchain_community.embeddings.minimax.embed_with_retry¶
langchain_community.embeddings.minimax.embed_with_retry(embeddings: MiniMaxEmbeddings, *args: Any, **kwargs: Any) → Any[source]¶
Use tenacity to retry the completion call.
Parameters
embeddings (MiniMaxEmbeddings) – 
args (Any) – 
kwargs (Any) – 
Return type
Any