Tony20100/langchain_full huggingface dataset:
Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – fields to include in new model
exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – fields to exclude from new model, as with values this takes precedence over include
update (Optional[DictStrAny]) – values to change/add in the new model. Note: the data is not validated before creating
the new model: you should trust this data
deep (bool) – set to True to make a deep copy of the model
self (Model) – 
Returns
new model instance
Return type
Model
dict(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False) → DictStrAny¶
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
Parameters
include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – 
exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – 
by_alias (bool) – 
skip_defaults (Optional[bool]) – 
exclude_unset (bool) – 
exclude_defaults (bool) – 
exclude_none (bool) – 
Return type
DictStrAny
embed_documents(texts: List[str]) → List[List[float]][source]¶
Embed a list of documents using NLP Cloud.
Parameters
texts (List[str]) – The list of texts to embed.
Returns
List of embeddings, one for each text.
Return type
Returns
List of embeddings, one for each text.
Return type
List[List[float]]
embed_query(text: str) → List[float][source]¶
Embed a query using NLP Cloud.
Parameters
text (str) – The text to embed.
Returns
Embeddings for the text.
Return type
List[float]
classmethod from_orm(obj: Any) → Model¶
Parameters
obj (Any) – 
Return type
Model
json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, **dumps_kwargs: Any) → unicode¶
Generate a JSON representation of the model, include and exclude arguments as per dict().
encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().
Parameters
include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – 
exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – 
by_alias (bool) – 
skip_defaults (Optional[bool]) – 
exclude_unset (bool) – 
exclude_defaults (bool) – 
exclude_none (bool) – 
encoder (Optional[Callable[[Any], Any]]) – 
models_as_dict (bool) – 
dumps_kwargs (Any) – 
Return type
unicode
langchain_cohere.embeddings.CohereEmbeddings¶
class langchain_cohere.embeddings.CohereEmbeddings[source]¶
Bases: BaseModel, Embeddings
Implements the Embeddings interface with Cohere’s text representation language
models.
Find out more about us at https://cohere.com and https://huggingface.co/CohereForAI
This implementation uses the Embed API - see https://docs.cohere.com/reference/embed
To use this you’ll need to a Cohere API key - either pass it to cohere_api_key
parameter or set the COHERE_API_KEY environment variable.
API keys are available on https://cohere.com - it’s free to sign up and trial API
keys work with this implementation.
Basic Example:cohere_embeddings = CohereEmbeddings(model="embed-english-light-v3.0")
text = "This is a test document."
query_result = cohere_embeddings.embed_query(text)
print(query_result)
doc_result = cohere_embeddings.embed_documents([text])
print(doc_result)
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param async_client: Any = None¶
Cohere async client.
param base_url: Optional[str] = None¶
Override the default Cohere API URL.
param client: Any = None¶
Cohere client.
param cohere_api_key: Optional[str] = None¶
param max_retries: int = 3¶
Maximum number of retries to make when generating.
param model: str = 'embed-english-v2.0'¶
Model name to use.
param request_timeout: Optional[float] = None¶
Timeout in seconds for the Cohere API request.
param truncate: Optional[str] = None¶
param truncate: Optional[str] = None¶
Truncate embeddings that are too long from start or end (“NONE”|”START”|”END”)
param user_agent: str = 'langchain:partner'¶
Identifier for the application making the request.
async aembed(texts: List[str], *, input_type: Optional[Union[Literal['search_document', 'search_query', 'classification', 'clustering'], Any]] = None) → List[List[float]][source]¶
Parameters
texts (List[str]) – 
input_type (Optional[Union[Literal['search_document', 'search_query', 'classification', 'clustering'], ~typing.Any]]) – 
Return type
List[List[float]]
async aembed_documents(texts: List[str]) → List[List[float]][source]¶
Async call out to Cohere’s embedding endpoint.
Parameters
texts (List[str]) – The list of texts to embed.
Returns
List of embeddings, one for each text.
Return type
List[List[float]]
async aembed_query(text: str) → List[float][source]¶
Async call out to Cohere’s embedding endpoint.
Parameters
text (str) – The text to embed.
Returns
Embeddings for the text.
Return type
List[float]
aembed_with_retry(**kwargs: Any) → Any[source]¶
Use tenacity to retry the embed call.
Parameters
kwargs (Any) – 
Return type
Any
classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model¶
Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
Behaves as if Config.extra = ‘allow’ was set since it adds all passed values
Parameters
by_alias (bool) – 
skip_defaults (Optional[bool]) – 
exclude_unset (bool) – 
exclude_defaults (bool) – 
exclude_none (bool) – 
Return type
DictStrAny
embed(texts: List[str], *, input_type: Optional[Union[Literal['search_document', 'search_query', 'classification', 'clustering'], Any]] = None) → List[List[float]][source]¶
Parameters
texts (List[str]) – 
input_type (Optional[Union[Literal['search_document', 'search_query', 'classification', 'clustering'], ~typing.Any]]) – 
Return type
List[List[float]]
embed_documents(texts: List[str]) → List[List[float]][source]¶
Embed a list of document texts.
Parameters
texts (List[str]) – The list of texts to embed.
Returns
List of embeddings, one for each text.
Return type
List[List[float]]
embed_query(text: str) → List[float][source]¶
Call out to Cohere’s embedding endpoint.
Parameters
text (str) – The text to embed.
Returns
Embeddings for the text.
Return type
List[float]
embed_with_retry(**kwargs: Any) → Any[source]¶
Use tenacity to retry the embed call.
Parameters
kwargs (Any) – 
Return type
Any
classmethod from_orm(obj: Any) → Model¶
Parameters
obj (Any) – 
Return type
Model
Parameters
obj (Any) – 
Return type
Model
classmethod parse_raw(b: Union[str, bytes], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶
Parameters
b (Union[str, bytes]) – 
content_type (unicode) – 
encoding (unicode) – 
proto (Protocol) – 
allow_pickle (bool) – 
Return type
Model
classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') → DictStrAny¶
Parameters
by_alias (bool) – 
ref_template (unicode) – 
Return type
DictStrAny
classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) → unicode¶
Parameters
by_alias (bool) – 
ref_template (unicode) – 
dumps_kwargs (Any) – 
Return type
unicode
classmethod update_forward_refs(**localns: Any) → None¶
Try to update ForwardRefs on fields based on this Model, globalns and localns.
Parameters
localns (Any) – 
Return type
None
classmethod validate(value: Any) → Model¶
Parameters
value (Any) – 
Return type
Model
Examples using CohereEmbeddings¶
Cohere reranker
langchain_google_vertexai.embeddings.VertexAIEmbeddings¶
class langchain_google_vertexai.embeddings.VertexAIEmbeddings[source]¶
Bases: _VertexAICommon, Embeddings
Google Cloud VertexAI embedding models.
Initialize the sentence_transformer.
param additional_headers: Optional[Dict[str, str]] = None¶
A key-value dictionary representing additional headers for the model call
param api_endpoint: Optional[str] = None¶
Desired API endpoint, e.g., us-central1-aiplatform.googleapis.com
param api_transport: Optional[str] = None¶
The desired API transport method, can be either ‘grpc’ or ‘rest’
param client_cert_source: Optional[Callable[[], Tuple[bytes, bytes]]] = None¶
A callback which returns client certificate bytes and private key bytes both
param credentials: Any = None¶
The default custom credentials (google.auth.credentials.Credentials) to use
param full_model_name: Optional[str] = None¶
The full name of the model’s endpoint.
param location: str = 'us-central1'¶
The default location to use when making API calls.
param max_output_tokens: Optional[int] = None (alias 'max_tokens')¶
Token limit determines the maximum amount of text output from one prompt.
param max_retries: int = 6¶
The maximum number of retries to make when generating.
param model_name: str = None (alias 'model')¶
Underlying model name.
param n: int = 1¶
How many completions to generate for each prompt.
param project: Optional[str] = None¶
The default GCP project to use when making Vertex API calls.
param request_parallelism: int = 5¶
The amount of parallelism allowed for requests issued to VertexAI models.
param safety_settings: Optional['SafetySettingsType'] = None¶
param safety_settings: Optional['SafetySettingsType'] = None¶
The default safety settings to use for all generations.
For example:
from langchain_google_vertexai import HarmBlockThreshold, HarmCategory
safety_settings = {HarmCategory.HARM_CATEGORY_UNSPECIFIED: HarmBlockThreshold.BLOCK_NONE,
HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT: HarmBlockThreshold.BLOCK_MEDIUM_AND_ABOVE,
HarmCategory.HARM_CATEGORY_HATE_SPEECH: HarmBlockThreshold.BLOCK_ONLY_HIGH,
HarmCategory.HARM_CATEGORY_HARASSMENT: HarmBlockThreshold.BLOCK_LOW_AND_ABOVE,
HarmCategory.HARM_CATEGORY_SEXUALLY_EXPLICIT: HarmBlockThreshold.BLOCK_NONE,
}
param stop: Optional[List[str]] = None (alias 'stop_sequences')¶
Optional list of stop words to use when generating.
param streaming: bool = False¶
Whether to stream the results or not.
param temperature: Optional[float] = None¶
Sampling temperature, it controls the degree of randomness in token selection.
param top_k: Optional[int] = None¶
How the model selects tokens for output, the next token is selected from
param top_p: Optional[float] = None¶
Tokens are selected from most probable to least until the sum of their
param tuned_model_name: Optional[str] = None¶
The name of a tuned model. If tuned_model_name is passed
model_name will be used to determine the model family
async aembed_documents(texts: List[str]) → List[List[float]]¶
Asynchronous Embed search docs.
Parameters
texts (List[str]) – 
Return type
List[List[float]]
async aembed_query(text: str) → List[float]¶
Asynchronous Embed query text.
Parameters
text (str) – 
Return type
List[float]
self (Model) – 
Returns
new model instance
Return type
Model
dict(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False) → DictStrAny¶
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
Parameters
include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – 
exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – 
by_alias (bool) – 
skip_defaults (Optional[bool]) – 
exclude_unset (bool) – 
exclude_defaults (bool) – 
exclude_none (bool) – 
Return type
DictStrAny
embed(texts: List[str], batch_size: int = 0, embeddings_task_type: Optional[Literal['RETRIEVAL_QUERY', 'RETRIEVAL_DOCUMENT', 'SEMANTIC_SIMILARITY', 'CLASSIFICATION', 'CLUSTERING', 'QUESTION_ANSWERING', 'FACT_VERIFICATION']] = None, dimensions: Optional[int] = None) → List[List[float]][source]¶
Embed a list of strings.
Parameters
texts (List[str]) – List[str] The list of strings to embed.
batch_size (int) – [int] The batch size of embeddings to send to the model.
If zero, then the largest batch size will be detected dynamically
at the first request, starting from 250, down to 5.
at the first request, starting from 250, down to 5.
embeddings_task_type (Optional[Literal['RETRIEVAL_QUERY', 'RETRIEVAL_DOCUMENT', 'SEMANTIC_SIMILARITY', 'CLASSIFICATION', 'CLUSTERING', 'QUESTION_ANSWERING', 'FACT_VERIFICATION']]) – [str] optional embeddings task type,
one of the following
RETRIEVAL_QUERY     - Text is a queryin a search/retrieval setting.
RETRIEVAL_DOCUMENT - Text is a documentin a search/retrieval setting.
SEMANTIC_SIMILARITY - Embeddings will be usedfor Semantic Textual Similarity (STS).
CLASSIFICATION - Embeddings will be used for classification.
CLUSTERING - Embeddings will be used for clustering.
The following are only supported on preview models:
QUESTION_ANSWERING
FACT_VERIFICATION
dimensions (Optional[int]) – [int] optional. Output embeddings dimensions.
Only supported on preview models.
Returns
List of embeddings, one for each text.
Return type
List[List[float]]
embed_documents(texts: List[str], batch_size: int = 0) → List[List[float]][source]¶
Embed a list of documents.
Parameters
texts (List[str]) – List[str] The list of texts to embed.
batch_size (int) – [int] The batch size of embeddings to send to the model.
If zero, then the largest batch size will be detected dynamically
at the first request, starting from 250, down to 5.
Returns
List of embeddings, one for each text.
Return type
List[List[float]]
embed_image(image_path: str, contextual_text: Optional[str] = None) → List[float][source]¶
Embed an image.
Parameters
Embed an image.
Parameters
image_path (str) – Path to image (local, Google Cloud Storage or web) to generate
for. (embeddings) – 
contextual_text (Optional[str]) – Text to generate embeddings for.
Returns
Embedding for the image.
Return type
List[float]
embed_query(text: str) → List[float][source]¶
Embed a text.
Parameters
text (str) – The text to embed.
Returns
Embedding for the text.
Return type
List[float]
classmethod from_orm(obj: Any) → Model¶
Parameters
obj (Any) – 
Return type
Model
get_num_tokens(text: str) → int¶
Get the number of tokens present in the text.
Useful for checking if an input will fit in a model’s context window.
Parameters
text (str) – The string input to tokenize.
Returns
The integer number of tokens in the text.
Return type
int
json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, **dumps_kwargs: Any) → unicode¶
Generate a JSON representation of the model, include and exclude arguments as per dict().
encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().
Parameters
include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) –
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
ref_template (unicode) – 
Return type
DictStrAny
classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) → unicode¶
Parameters
by_alias (bool) – 
ref_template (unicode) – 
dumps_kwargs (Any) – 
Return type
unicode
classmethod update_forward_refs(**localns: Any) → None¶
Try to update ForwardRefs on fields based on this Model, globalns and localns.
Parameters
localns (Any) – 
Return type
None
classmethod validate(value: Any) → Model¶
Parameters
value (Any) – 
Return type
Model
property async_prediction_client: PredictionServiceAsyncClient¶
Returns PredictionServiceClient.
property model_type: str¶
property model_version: GoogleEmbeddingModelVersion¶
property prediction_client: PredictionServiceClient¶
Returns PredictionServiceClient.
task_executor: ClassVar[Optional[Executor]] = FieldInfo(exclude=True, extra={})¶
Examples using VertexAIEmbeddings¶
Google
Google AlloyDB for PostgreSQL
Google BigQuery Vector Search
Google Cloud SQL for MySQL
Google Cloud SQL for PostgreSQL
Google Cloud Vertex AI Reranker
Google Firestore (Native Mode)
Google Spanner
Google Vertex AI PaLM 
Google Vertex AI Vector Search
langchain_community.embeddings.infinity.TinyAsyncOpenAIInfinityEmbeddingClient¶
class langchain_community.embeddings.infinity.TinyAsyncOpenAIInfinityEmbeddingClient(host: str = 'http://localhost:7797/v1', aiosession: Optional[ClientSession] = None)[source]¶
Helper tool to embed Infinity.
It is not a part of Langchain’s stable API,
direct use discouraged.
Example
mini_client = TinyAsyncInfinityEmbeddingClient(
)
embeds = mini_client.embed(
    model="BAAI/bge-small",
    text=["doc1", "doc2"]
)
# or
embeds = await mini_client.aembed(
    model="BAAI/bge-small",
    text=["doc1", "doc2"]
)
Methods
__init__([host, aiosession])
aembed(model, texts)
call the embedding of model, async method
embed(model, texts)
call the embedding of model
Parameters
host (str) – 
aiosession (Optional[ClientSession]) – 
__init__(host: str = 'http://localhost:7797/v1', aiosession: Optional[ClientSession] = None) → None[source]¶
Parameters
host (str) – 
aiosession (Optional[ClientSession]) – 
Return type
None
async aembed(model: str, texts: List[str]) → List[List[float]][source]¶
call the embedding of model, async method
Parameters
model (str) – to embedding model
texts (List[str]) – List of sentences to embed.
Returns
List of vectors for each sentence
Return type
List[List[float]]
embed(model: str, texts: List[str]) → List[List[float]][source]¶
call the embedding of model
Parameters
langchain_community.embeddings.localai.async_embed_with_retry¶
async langchain_community.embeddings.localai.async_embed_with_retry(embeddings: LocalAIEmbeddings, **kwargs: Any) → Any[source]¶
Use tenacity to retry the embedding call.
Parameters
embeddings (LocalAIEmbeddings) – 
kwargs (Any) – 
Return type
Any
langchain_community.embeddings.ollama.OllamaEmbeddings¶
class langchain_community.embeddings.ollama.OllamaEmbeddings[source]¶
Bases: BaseModel, Embeddings
Ollama locally runs large language models.
To use, follow the instructions at https://ollama.ai/.
Example
from langchain_community.embeddings import OllamaEmbeddings
ollama_emb = OllamaEmbeddings(
    model="llama:7b",
)
r1 = ollama_emb.embed_documents(
    [
        "Alpha is the first letter of Greek alphabet",
        "Beta is the second letter of Greek alphabet",
    ]
)
r2 = ollama_emb.embed_query(
    "What is the second letter of 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:11434'¶
Base url the model is hosted under.
param embed_instruction: str = 'passage: '¶
Instruction used to embed documents.
param headers: Optional[dict] = None¶
Additional headers to pass to endpoint (e.g. Authorization, Referer).
This is useful when Ollama is hosted on cloud services that require
tokens for authentication.
param mirostat: Optional[int] = None¶
Enable Mirostat sampling for controlling perplexity.
(default: 0, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0)
param mirostat_eta: Optional[float] = None¶
Influences how quickly the algorithm responds to feedback
from the generated text. A lower learning rate will result in
slower adjustments, while a higher learning rate will make
slower adjustments, while a higher learning rate will make
the algorithm more responsive. (Default: 0.1)
param mirostat_tau: Optional[float] = None¶
Controls the balance between coherence and diversity
of the output. A lower value will result in more focused and
coherent text. (Default: 5.0)
param model: str = 'llama2'¶
Model name to use.
param model_kwargs: Optional[dict] = None¶
Other model keyword args
param num_ctx: Optional[int] = None¶
Sets the size of the context window used to generate the
next token. (Default: 2048)
param num_gpu: Optional[int] = None¶
The number of GPUs to use. On macOS it defaults to 1 to
enable metal support, 0 to disable.
param num_thread: Optional[int] = None¶
Sets the number of threads to use during computation.
By default, Ollama will detect this for optimal performance.
It is recommended to set this value to the number of physical
CPU cores your system has (as opposed to the logical number of cores).
param query_instruction: str = 'query: '¶
Instruction used to embed the query.
param repeat_last_n: Optional[int] = None¶
Sets how far back for the model to look back to prevent
repetition. (Default: 64, 0 = disabled, -1 = num_ctx)
param repeat_penalty: Optional[float] = None¶
Sets how strongly to penalize repetitions. A higher value (e.g., 1.5)
will penalize repetitions more strongly, while a lower value (e.g., 0.9)
will be more lenient. (Default: 1.1)
param show_progress: bool = False¶
param show_progress: bool = False¶
Whether to show a tqdm progress bar. Must have tqdm installed.
param stop: Optional[List[str]] = None¶
Sets the stop tokens to use.
param temperature: Optional[float] = None¶
The temperature of the model. Increasing the temperature will
make the model answer more creatively. (Default: 0.8)
param tfs_z: Optional[float] = None¶
Tail free sampling is used to reduce the impact of less probable
tokens from the output. A higher value (e.g., 2.0) will reduce the
impact more, while a value of 1.0 disables this setting. (default: 1)
param top_k: Optional[int] = None¶
Reduces the probability of generating nonsense. A higher value (e.g. 100)
will give more diverse answers, while a lower value (e.g. 10)
will be more conservative. (Default: 40)
param top_p: Optional[float] = None¶
Works together with top-k. A higher value (e.g., 0.95) will lead
to more diverse text, while a lower value (e.g., 0.5) will
generate more focused and conservative text. (Default: 0.9)
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.