.rst
.pdf
Embeddings
Embeddings#
Wrappers around embedding modules.
pydantic model langchain.embeddings.AlephAlphaAsymmetricSemanticEmbedding[source]#
Wrapper for Aleph Alpha’s Asymmetric Embeddings
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
from aleph_alpha import AlephAlphaAsymmetricSemanticEmbedding
embeddings = AlephAlphaSymmetricSemanticEmbedding()
document = "This is a content of the document"
query = "What is the content of the document?"
doc_result = embeddings.embed_documents([document])
query_result = embeddings.embed_query(query)
field aleph_alpha_api_key: Optional[str] = None#
API key for Aleph Alpha API.
field compress_to_size: Optional[int] = 128#
Should the returned embeddings come back as an original 5120-dim vector,
or should it be compressed to 128-dim.
field contextual_control_threshold: Optional[int] = None#
Attention control parameters only apply to those tokens that have
explicitly been set in the request.
field control_log_additive: Optional[bool] = True#
Apply controls on prompt items by adding the log(control_factor)
to attention scores.
field hosting: Optional[str] = 'https://api.aleph-alpha.com'#
Optional parameter that specifies which datacenters may process the request.
field model: Optional[str] = 'luminous-base'#
Model name to use.
field normalize: Optional[bool] = True#
Should returned embeddings be normalized
embed_documents(texts: List[str]) → List[List[float]][source]#
embed_documents(texts: List[str]) → List[List[float]][source]#
Call out to Aleph Alpha’s asymmetric Document endpoint.
Parameters
texts – The list of texts to embed.
Returns
List of embeddings, one for each text.
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.
pydantic model langchain.embeddings.AlephAlphaSymmetricSemanticEmbedding[source]#
The symmetric version of the Aleph Alpha’s semantic embeddings.
The main difference is that here, both the documents and
queries are embedded with a SemanticRepresentation.Symmetric
.. rubric:: Example
embed_documents(texts: List[str]) → List[List[float]][source]#
Call out to Aleph Alpha’s Document endpoint.
Parameters
texts – The list of texts to embed.
Returns
List of embeddings, one for each text.
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.
pydantic model langchain.embeddings.CohereEmbeddings[source]#
Wrapper around Cohere embedding models.
To use, you should have the cohere python package installed, and the
environment variable COHERE_API_KEY set with your API key or pass it
as a named parameter to the constructor.
Example
from langchain.embeddings import CohereEmbeddings
cohere = CohereEmbeddings(
    model="embed-english-light-v2.0", cohere_api_key="my-api-key"
)
field model: str = 'embed-english-v2.0'#
Model name to use.
field truncate: Optional[str] = None#
Model name to use.
field truncate: Optional[str] = None#
Truncate embeddings that are too long from start or end (“NONE”|”START”|”END”)
embed_documents(texts: List[str]) → List[List[float]][source]#
Call out to Cohere’s embedding endpoint.
Parameters
texts – The list of texts to embed.
Returns
List of embeddings, one for each text.
embed_query(text: str) → List[float][source]#
Call out to Cohere’s embedding endpoint.
Parameters
text – The text to embed.
Returns
Embeddings for the text.
class langchain.embeddings.ElasticsearchEmbeddings(client: MlClient, model_id: str, *, input_field: str = 'text_field')[source]#
Wrapper around Elasticsearch embedding models.
This class provides an interface to generate embeddings using a model deployed
in an Elasticsearch cluster. It requires an Elasticsearch connection object
and the model_id of the model deployed in the cluster.
In Elasticsearch you need to have an embedding model loaded and deployed.
- https://www.elastic.co/guide/en/elasticsearch/reference/current/infer-trained-model.html
- https://www.elastic.co/guide/en/machine-learning/current/ml-nlp-deploy-models.html
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]
Returns
The embedding for the input query text.
Return type
List[float]
classmethod from_credentials(model_id: str, *, es_cloud_id: Optional[str] = None, es_user: Optional[str] = None, es_password: Optional[str] = None, input_field: str = 'text_field') → langchain.embeddings.elasticsearch.ElasticsearchEmbeddings[source]#
Instantiate embeddings from Elasticsearch credentials.
Parameters
model_id (str) – The model_id of the model deployed in the Elasticsearch
cluster.
input_field (str) – The name of the key for the input text field in the
document. Defaults to ‘text_field’.
es_cloud_id – (str, optional): The Elasticsearch cloud ID to connect to.
es_user – (str, optional): Elasticsearch username.
es_password – (str, optional): Elasticsearch password.
Example Usage:from langchain.embeddings import ElasticsearchEmbeddings
# Define the model ID and input field name (if different from default)
model_id = “your_model_id”
# Optional, only if different from ‘text_field’
input_field = “your_input_field”
# Credentials can be passed in two ways. Either set the env vars
# ES_CLOUD_ID, ES_USER, ES_PASSWORD and they will be automatically pulled
# in, or pass them in directly as kwargs.
embeddings = ElasticsearchEmbeddings.from_credentials(
model_id,
input_field=input_field,
# es_cloud_id=”foo”,
# es_user=”bar”,
# es_password=”baz”,
)
documents = [“This is an example document.”,
“Another example document to generate embeddings for.”,
]
embeddings_generator.embed_documents(documents)
pydantic model langchain.embeddings.FakeEmbeddings[source]#
embed_documents(texts: List[str]) → List[List[float]][source]#
Embed search docs.
Embed search docs.
embed_query(text: str) → List[float][source]#
Embed query text.
pydantic model langchain.embeddings.HuggingFaceEmbeddings[source]#
Wrapper around sentence_transformers embedding models.
To use, you should have the sentence_transformers python package installed.
Example
from langchain.embeddings import HuggingFaceEmbeddings
model_name = "sentence-transformers/all-mpnet-base-v2"
model_kwargs = {'device': 'cpu'}
hf = HuggingFaceEmbeddings(model_name=model_name, model_kwargs=model_kwargs)
field cache_folder: Optional[str] = None#
Path to store models.
Can be also set by SENTENCE_TRANSFORMERS_HOME environment variable.
field encode_kwargs: Dict[str, Any] [Optional]#
Key word arguments to pass when calling the encode method of the model.
field model_kwargs: Dict[str, Any] [Optional]#
Key word arguments to pass to the model.
field model_name: str = 'sentence-transformers/all-mpnet-base-v2'#
Model name to use.
embed_documents(texts: List[str]) → List[List[float]][source]#
Compute doc embeddings using a HuggingFace transformer model.
Parameters
texts – The list of texts to embed.
Returns
List of embeddings, one for each text.
embed_query(text: str) → List[float][source]#
Compute query embeddings using a HuggingFace transformer model.
Parameters
text – The text to embed.
Returns
Embeddings for the text.
pydantic model langchain.embeddings.HuggingFaceHubEmbeddings[source]#
Wrapper around HuggingFaceHub embedding models.
To use, you should have the huggingface_hub python package installed, and the
environment variable HUGGINGFACEHUB_API_TOKEN set with your API token, or pass
environment variable HUGGINGFACEHUB_API_TOKEN set with your API token, or pass
it as a named parameter to the constructor.
Example
from langchain.embeddings import HuggingFaceHubEmbeddings
repo_id = "sentence-transformers/all-mpnet-base-v2"
hf = HuggingFaceHubEmbeddings(
    repo_id=repo_id,
    task="feature-extraction",
    huggingfacehub_api_token="my-api-key",
)
field model_kwargs: Optional[dict] = None#
Key word arguments to pass to the model.
field repo_id: str = 'sentence-transformers/all-mpnet-base-v2'#
Model name to use.
field task: Optional[str] = 'feature-extraction'#
Task to call the model with.
embed_documents(texts: List[str]) → List[List[float]][source]#
Call out to HuggingFaceHub’s embedding endpoint for embedding search docs.
Parameters
texts – The list of texts to embed.
Returns
List of embeddings, one for each text.
embed_query(text: str) → List[float][source]#
Call out to HuggingFaceHub’s embedding endpoint for embedding query text.
Parameters
text – The text to embed.
Returns
Embeddings for the text.
pydantic model langchain.embeddings.HuggingFaceInstructEmbeddings[source]#
Wrapper around sentence_transformers embedding models.
To use, you should have the sentence_transformers
and InstructorEmbedding python packages installed.
Example
from langchain.embeddings import HuggingFaceInstructEmbeddings
model_name = "hkunlp/instructor-large"
model_kwargs = {'device': 'cpu'}
hf = HuggingFaceInstructEmbeddings(
    model_name=model_name, model_kwargs=model_kwargs
)
field cache_folder: Optional[str] = None#
)
field cache_folder: Optional[str] = None#
Path to store models.
Can be also set by SENTENCE_TRANSFORMERS_HOME environment variable.
field embed_instruction: str = 'Represent the document for retrieval: '#
Instruction to use for embedding documents.
field model_kwargs: Dict[str, Any] [Optional]#
Key word arguments to pass to the model.
field model_name: str = 'hkunlp/instructor-large'#
Model name to use.
field query_instruction: str = 'Represent the question for retrieving supporting documents: '#
Instruction to use for embedding query.
embed_documents(texts: List[str]) → List[List[float]][source]#
Compute doc embeddings using a HuggingFace instruct model.
Parameters
texts – The list of texts to embed.
Returns
List of embeddings, one for each text.
embed_query(text: str) → List[float][source]#
Compute query embeddings using a HuggingFace instruct model.
Parameters
text – The text to embed.
Returns
Embeddings for the text.
pydantic model langchain.embeddings.LlamaCppEmbeddings[source]#
Wrapper around llama.cpp embedding models.
To use, you should have the llama-cpp-python library installed, and provide the
path to the Llama model as a named parameter to the constructor.
Check out: abetlen/llama-cpp-python
Example
from langchain.embeddings import LlamaCppEmbeddings
llama = LlamaCppEmbeddings(model_path="/path/to/model.bin")
field f16_kv: bool = False#
Use half-precision for key/value cache.
field logits_all: bool = False#
Return logits for all tokens, not just the last token.
field n_batch: Optional[int] = 8#
Number of tokens to process in parallel.
Should be a number between 1 and n_ctx.
Number of tokens to process in parallel.
Should be a number between 1 and n_ctx.
field n_ctx: int = 512#
Token context window.
field n_gpu_layers: Optional[int] = None#
Number of layers to be loaded into gpu memory. Default None.
field n_parts: int = -1#
Number of parts to split the model into.
If -1, the number of parts is automatically determined.
field n_threads: Optional[int] = None#
Number of threads to use. If None, the number
of threads is automatically determined.
field seed: int = -1#
Seed. If -1, a random seed is used.
field use_mlock: bool = False#
Force system to keep model in RAM.
field vocab_only: bool = False#
Only load the vocabulary, no weights.
embed_documents(texts: List[str]) → List[List[float]][source]#
Embed a list of documents using the Llama model.
Parameters
texts – The list of texts to embed.
Returns
List of embeddings, one for each text.
embed_query(text: str) → List[float][source]#
Embed a query using the Llama model.
Parameters
text – The text to embed.
Returns
Embeddings for the text.
pydantic model langchain.embeddings.ModelScopeEmbeddings[source]#
Wrapper around modelscope_hub embedding models.
To use, you should have the modelscope python package installed.
Example
from langchain.embeddings import ModelScopeEmbeddings
model_id = "damo/nlp_corom_sentence-embedding_english-base"
embed = ModelScopeEmbeddings(model_id=model_id)
field model_id: str = 'damo/nlp_corom_sentence-embedding_english-base'#
Model name to use.
Model name to use.
embed_documents(texts: List[str]) → List[List[float]][source]#
Compute doc embeddings using a modelscope embedding model.
Parameters
texts – The list of texts to embed.
Returns
List of embeddings, one for each text.
embed_query(text: str) → List[float][source]#
Compute query embeddings using a modelscope embedding model.
Parameters
text – The text to embed.
Returns
Embeddings for the text.
pydantic model langchain.embeddings.MosaicMLInstructorEmbeddings[source]#
Wrapper around MosaicML’s embedding inference service.
To use, you should have the
environment variable MOSAICML_API_TOKEN set with your API token, or pass
it as a named parameter to the constructor.
Example
from langchain.llms import MosaicMLInstructorEmbeddings
endpoint_url = (
    "https://models.hosted-on.mosaicml.hosting/instructor-large/v1/predict"
)
mosaic_llm = MosaicMLInstructorEmbeddings(
    endpoint_url=endpoint_url,
    mosaicml_api_token="my-api-key"
)
field embed_instruction: str = 'Represent the document for retrieval: '#
Instruction used to embed documents.
field endpoint_url: str = 'https://models.hosted-on.mosaicml.hosting/instructor-large/v1/predict'#
Endpoint URL to use.
field query_instruction: str = 'Represent the question for retrieving supporting documents: '#
Instruction used to embed the query.
field retry_sleep: float = 1.0#
How long to try sleeping for if a rate limit is encountered
embed_documents(texts: List[str]) → List[List[float]][source]#
Embed documents using a MosaicML deployed instructor embedding model.
Parameters
texts – The list of texts to embed.
Returns
Parameters
texts – The list of texts to embed.
Returns
List of embeddings, one for each text.
embed_query(text: str) → List[float][source]#
Embed a query using a MosaicML deployed instructor embedding model.
Parameters
text – The text to embed.
Returns
Embeddings for the text.
pydantic model langchain.embeddings.OpenAIEmbeddings[source]#
Wrapper around OpenAI embedding models.
To use, you should have the openai python package installed, and the
environment variable OPENAI_API_KEY set with your API key or pass it
as a named parameter to the constructor.
Example
from langchain.embeddings import OpenAIEmbeddings
openai = OpenAIEmbeddings(openai_api_key="my-api-key")
In order to use the library with Microsoft Azure endpoints, you need to set
the OPENAI_API_TYPE, OPENAI_API_BASE, OPENAI_API_KEY and OPENAI_API_VERSION.
The OPENAI_API_TYPE must be set to ‘azure’ and the others correspond to
the properties of your endpoint.
In addition, the deployment name must be passed as the model parameter.
Example
import os
os.environ["OPENAI_API_TYPE"] = "azure"
os.environ["OPENAI_API_BASE"] = "https://<your-endpoint.openai.azure.com/"
os.environ["OPENAI_API_KEY"] = "your AzureOpenAI key"
os.environ["OPENAI_API_VERSION"] = "2023-03-15-preview"
from langchain.embeddings.openai import OpenAIEmbeddings
embeddings = OpenAIEmbeddings(
    deployment="your-embeddings-deployment-name",
    model="your-embeddings-model-name",
    api_base="https://your-endpoint.openai.azure.com/",
    api_type="azure",
)
text = "This is a test query."
api_type="azure",
)
text = "This is a test query."
query_result = embeddings.embed_query(text)
field chunk_size: int = 1000#
Maximum number of texts to embed in each batch
field max_retries: int = 6#
Maximum number of retries to make when generating.
field request_timeout: Optional[Union[float, Tuple[float, float]]] = None#
Timeout in seconds for the OpenAPI request.
embed_documents(texts: List[str], chunk_size: Optional[int] = 0) → List[List[float]][source]#
Call out to OpenAI’s embedding endpoint for embedding search docs.
Parameters
texts – 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.
embed_query(text: str) → List[float][source]#
Call out to OpenAI’s embedding endpoint for embedding query text.
Parameters
text – The text to embed.
Returns
Embedding for the text.
pydantic model langchain.embeddings.SagemakerEndpointEmbeddings[source]#
Wrapper around custom Sagemaker Inference Endpoints.
To use, you must supply the endpoint name from your deployed
Sagemaker model & the region where it is deployed.
To authenticate, the AWS client uses the following methods to
automatically load credentials:
https://boto3.amazonaws.com/v1/documentation/api/latest/guide/credentials.html
If a specific credential profile should be used, you must pass
the name of the profile from the ~/.aws/credentials file that is to be used.
Make sure the credentials / roles used have the required policies to
access the Sagemaker endpoint.
See: https://docs.aws.amazon.com/IAM/latest/UserGuide/access_policies.html
See: https://docs.aws.amazon.com/IAM/latest/UserGuide/access_policies.html
field content_handler: langchain.embeddings.sagemaker_endpoint.EmbeddingsContentHandler [Required]#
The content handler class that provides an input and
output transform functions to handle formats between LLM
and the endpoint.
field 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
field 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>
field endpoint_name: str = ''#
The name of the endpoint from the deployed Sagemaker model.
Must be unique within an AWS Region.
field model_kwargs: Optional[Dict] = None#
Key word arguments to pass to the model.
field region_name: str = ''#
The aws region where the Sagemaker model is deployed, eg. us-west-2.
embed_documents(texts: List[str], chunk_size: int = 64) → List[List[float]][source]#
Compute doc embeddings using a SageMaker Inference Endpoint.
Parameters
texts – The list of texts to embed.
chunk_size – The chunk size defines how many input texts will
be grouped together as request. If None, will use the
chunk size specified by the class.
Returns
List of embeddings, one for each text.
chunk size specified by the class.
Returns
List of embeddings, one for each text.
embed_query(text: str) → List[float][source]#
Compute query embeddings using a SageMaker inference endpoint.
Parameters
text – The text to embed.
Returns
Embeddings for the text.
pydantic model langchain.embeddings.SelfHostedEmbeddings[source]#
Runs custom embedding models on self-hosted remote hardware.
Supported hardware includes auto-launched instances on AWS, GCP, Azure,
and Lambda, as well as servers specified
by IP address and SSH credentials (such as on-prem, or another
cloud like Paperspace, Coreweave, etc.).
To use, you should have the runhouse python package installed.
Example using a model load function:from langchain.embeddings import SelfHostedEmbeddings
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
import runhouse as rh
gpu = rh.cluster(name="rh-a10x", instance_type="A100:1")
def get_pipeline():
    model_id = "facebook/bart-large"
    tokenizer = AutoTokenizer.from_pretrained(model_id)
    model = AutoModelForCausalLM.from_pretrained(model_id)
    return pipeline("feature-extraction", model=model, tokenizer=tokenizer)
embeddings = SelfHostedEmbeddings(
    model_load_fn=get_pipeline,
    hardware=gpu
    model_reqs=["./", "torch", "transformers"],
)
Example passing in a pipeline path:from langchain.embeddings import SelfHostedHFEmbeddings
import runhouse as rh
from transformers import pipeline
gpu = rh.cluster(name="rh-a10x", instance_type="A100:1")
pipeline = pipeline(model="bert-base-uncased", task="feature-extraction")
rh.blob(pickle.dumps(pipeline),
rh.blob(pickle.dumps(pipeline),
    path="models/pipeline.pkl").save().to(gpu, path="models")
embeddings = SelfHostedHFEmbeddings.from_pipeline(
    pipeline="models/pipeline.pkl",
    hardware=gpu,
    model_reqs=["./", "torch", "transformers"],
)
Validators
raise_deprecation » all fields
set_verbose » verbose
field inference_fn: Callable = <function _embed_documents>#
Inference function to extract the embeddings on the remote hardware.
field inference_kwargs: Any = None#
Any kwargs to pass to the model’s inference function.
embed_documents(texts: List[str]) → List[List[float]][source]#
Compute doc embeddings using a HuggingFace transformer model.
Parameters
texts – The list of texts to embed.s
Returns
List of embeddings, one for each text.
embed_query(text: str) → List[float][source]#
Compute query embeddings using a HuggingFace transformer model.
Parameters
text – The text to embed.
Returns
Embeddings for the text.
pydantic model langchain.embeddings.SelfHostedHuggingFaceEmbeddings[source]#
Runs sentence_transformers embedding models on self-hosted remote hardware.
Supported hardware includes auto-launched instances on AWS, GCP, Azure,
and Lambda, as well as servers specified
by IP address and SSH credentials (such as on-prem, or another cloud
like Paperspace, Coreweave, etc.).
To use, you should have the runhouse python package installed.
Example
from langchain.embeddings import SelfHostedHuggingFaceEmbeddings
import runhouse as rh
model_name = "sentence-transformers/all-mpnet-base-v2"
gpu = rh.cluster(name="rh-a10x", instance_type="A100:1")
gpu = rh.cluster(name="rh-a10x", instance_type="A100:1")
hf = SelfHostedHuggingFaceEmbeddings(model_name=model_name, hardware=gpu)
Validators
raise_deprecation » all fields
set_verbose » verbose
field hardware: Any = None#
Remote hardware to send the inference function to.
field inference_fn: Callable = <function _embed_documents>#
Inference function to extract the embeddings.
field load_fn_kwargs: Optional[dict] = None#
Key word arguments to pass to the model load function.
field model_id: str = 'sentence-transformers/all-mpnet-base-v2'#
Model name to use.
field model_load_fn: Callable = <function load_embedding_model>#
Function to load the model remotely on the server.
field model_reqs: List[str] = ['./', 'sentence_transformers', 'torch']#
Requirements to install on hardware to inference the model.
pydantic model langchain.embeddings.SelfHostedHuggingFaceInstructEmbeddings[source]#
Runs InstructorEmbedding embedding models on self-hosted remote hardware.
Supported hardware includes auto-launched instances on AWS, GCP, Azure,
and Lambda, as well as servers specified
by IP address and SSH credentials (such as on-prem, or another
cloud like Paperspace, Coreweave, etc.).
To use, you should have the runhouse python package installed.
Example
from langchain.embeddings import SelfHostedHuggingFaceInstructEmbeddings
import runhouse as rh
model_name = "hkunlp/instructor-large"
gpu = rh.cluster(name='rh-a10x', instance_type='A100:1')
hf = SelfHostedHuggingFaceInstructEmbeddings(
    model_name=model_name, hardware=gpu)
Validators
raise_deprecation » all fields
set_verbose » verbose
Validators
raise_deprecation » all fields
set_verbose » verbose
field embed_instruction: str = 'Represent the document for retrieval: '#
Instruction to use for embedding documents.
field model_id: str = 'hkunlp/instructor-large'#
Model name to use.
field model_reqs: List[str] = ['./', 'InstructorEmbedding', 'torch']#
Requirements to install on hardware to inference the model.
field query_instruction: str = 'Represent the question for retrieving supporting documents: '#
Instruction to use for embedding query.
embed_documents(texts: List[str]) → List[List[float]][source]#
Compute doc embeddings using a HuggingFace instruct model.
Parameters
texts – The list of texts to embed.
Returns
List of embeddings, one for each text.
embed_query(text: str) → List[float][source]#
Compute query embeddings using a HuggingFace instruct model.
Parameters
text – The text to embed.
Returns
Embeddings for the text.
langchain.embeddings.SentenceTransformerEmbeddings#
alias of langchain.embeddings.huggingface.HuggingFaceEmbeddings
pydantic model langchain.embeddings.TensorflowHubEmbeddings[source]#
Wrapper around tensorflow_hub embedding models.
To use, you should have the tensorflow_text python package installed.
Example
from langchain.embeddings import TensorflowHubEmbeddings
url = "https://tfhub.dev/google/universal-sentence-encoder-multilingual/3"
tf = TensorflowHubEmbeddings(model_url=url)
field model_url: str = 'https://tfhub.dev/google/universal-sentence-encoder-multilingual/3'#
Model name to use.
embed_documents(texts: List[str]) → List[List[float]][source]#
Compute doc embeddings using a TensorflowHub embedding model.
Parameters
texts – The list of texts to embed.
Returns
Parameters
texts – The list of texts to embed.
Returns
List of embeddings, one for each text.
embed_query(text: str) → List[float][source]#
Compute query embeddings using a TensorflowHub embedding model.
Parameters
text – The text to embed.
Returns
Embeddings for the text.

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By Harrison Chase