id stringlengths 14 16 | text stringlengths 4 1.28k | source stringlengths 54 121 |
|---|---|---|
3637c2e10665-3 | "Please install it with `pip install aleph_alpha_client`."
)
values["client"] = Client(token=aleph_alpha_api_key)
return values
[docs] def embed_documents(self, texts: List[str]) -> List[List[float]]:
"""Call out to Aleph Alpha's asymmetric Document endpoint.
Args:
... | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/aleph_alpha.html |
3637c2e10665-4 | for text in texts:
document_params = {
"prompt": Prompt.from_text(text),
"representation": SemanticRepresentation.Document,
"compress_to_size": self.compress_to_size,
"normalize": self.normalize,
"contextual_control_threshold": ... | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/aleph_alpha.html |
3637c2e10665-5 | """
try:
from aleph_alpha_client import (
Prompt,
SemanticEmbeddingRequest,
SemanticRepresentation,
)
except ImportError:
raise ValueError(
"Could not import aleph_alpha_client python package. "
... | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/aleph_alpha.html |
3637c2e10665-6 | )
return symmetric_response.embedding
[docs]class AlephAlphaSymmetricSemanticEmbedding(AlephAlphaAsymmetricSemanticEmbedding):
"""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 SemanticRepresentatio... | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/aleph_alpha.html |
3637c2e10665-7 | )
except ImportError:
raise ValueError(
"Could not import aleph_alpha_client python package. "
"Please install it with `pip install aleph_alpha_client`."
)
query_params = {
"prompt": Prompt.from_text(text),
"representation":... | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/aleph_alpha.html |
3637c2e10665-8 | Args:
texts: The list of texts to embed.
Returns:
List of embeddings, one for each text.
"""
document_embeddings = []
for text in texts:
document_embeddings.append(self._embed(text))
return document_embeddings
[docs] def embed_query(self, te... | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/aleph_alpha.html |
d13c89548180-0 | Source code for langchain.embeddings.modelscope_hub
"""Wrapper around ModelScopeHub embedding models."""
from typing import Any, List
from pydantic import BaseModel, Extra
from langchain.embeddings.base import Embeddings
[docs]class ModelScopeEmbeddings(BaseModel, Embeddings):
"""Wrapper around modelscope_hub embed... | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/modelscope_hub.html |
d13c89548180-1 | """Initialize the modelscope"""
super().__init__(**kwargs)
try:
from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks
self.embed = pipeline(Tasks.sentence_embedding, model=self.model_id)
except ImportError as e:
rais... | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/modelscope_hub.html |
d13c89548180-2 | """
texts = list(map(lambda x: x.replace("\n", " "), texts))
inputs = {"source_sentence": texts}
embeddings = self.embed(input=inputs)["text_embedding"]
return embeddings.tolist()
[docs] def embed_query(self, text: str) -> List[float]:
"""Compute query embeddings using a model... | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/modelscope_hub.html |
5f76bddcf670-0 | Source code for langchain.embeddings.huggingface
"""Wrapper around HuggingFace embedding models."""
from typing import Any, Dict, List, Optional
from pydantic import BaseModel, Extra, Field
from langchain.embeddings.base import Embeddings
DEFAULT_MODEL_NAME = "sentence-transformers/all-mpnet-base-v2"
DEFAULT_INSTRUCT_M... | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/huggingface.html |
5f76bddcf670-1 | model_name = "sentence-transformers/all-mpnet-base-v2"
model_kwargs = {'device': 'cpu'}
encode_kwargs = {'normalize_embeddings': False}
hf = HuggingFaceEmbeddings(
model_name=model_name,
model_kwargs=model_kwargs,
encode_kwargs=encode_k... | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/huggingface.html |
5f76bddcf670-2 | """Key word arguments to pass when calling the `encode` method of the model."""
def __init__(self, **kwargs: Any):
"""Initialize the sentence_transformer."""
super().__init__(**kwargs)
try:
import sentence_transformers
except ImportError as exc:
raise ImportEr... | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/huggingface.html |
5f76bddcf670-3 | Args:
texts: The list of texts to embed.
Returns:
List of embeddings, one for each text.
"""
texts = list(map(lambda x: x.replace("\n", " "), texts))
embeddings = self.client.encode(texts, **self.encode_kwargs)
return embeddings.tolist()
[docs] def embe... | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/huggingface.html |
5f76bddcf670-4 | To use, you should have the ``sentence_transformers``
and ``InstructorEmbedding`` python packages installed.
Example:
.. code-block:: python
from langchain.embeddings import HuggingFaceInstructEmbeddings
model_name = "hkunlp/instructor-large"
model_kwargs = {'device':... | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/huggingface.html |
5f76bddcf670-5 | model_kwargs: Dict[str, Any] = Field(default_factory=dict)
"""Key word arguments to pass to the model."""
encode_kwargs: Dict[str, Any] = Field(default_factory=dict)
"""Key word arguments to pass when calling the `encode` method of the model."""
embed_instruction: str = DEFAULT_EMBED_INSTRUCTION
"""... | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/huggingface.html |
5f76bddcf670-6 | class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
[docs] def embed_documents(self, texts: List[str]) -> List[List[float]]:
"""Compute doc embeddings using a HuggingFace instruct model.
Args:
texts: The list of texts to embed.
Returns:... | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/huggingface.html |
5f76bddcf670-7 | """
instruction_pair = [self.query_instruction, text]
embedding = self.client.encode([instruction_pair], **self.encode_kwargs)[0]
return embedding.tolist() | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/huggingface.html |
17b7a0fac055-0 | Source code for langchain.embeddings.tensorflow_hub
"""Wrapper around TensorflowHub embedding models."""
from typing import Any, List
from pydantic import BaseModel, Extra
from langchain.embeddings.base import Embeddings
DEFAULT_MODEL_URL = "https://tfhub.dev/google/universal-sentence-encoder-multilingual/3"
[docs]clas... | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/tensorflow_hub.html |
17b7a0fac055-1 | model_url: str = DEFAULT_MODEL_URL
"""Model name to use."""
def __init__(self, **kwargs: Any):
"""Initialize the tensorflow_hub and tensorflow_text."""
super().__init__(**kwargs)
try:
import tensorflow_hub
except ImportError:
raise ImportError(
... | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/tensorflow_hub.html |
17b7a0fac055-2 | extra = Extra.forbid
[docs] def embed_documents(self, texts: List[str]) -> List[List[float]]:
"""Compute doc embeddings using a TensorflowHub embedding model.
Args:
texts: The list of texts to embed.
Returns:
List of embeddings, one for each text.
"""
t... | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/tensorflow_hub.html |
fd79a69d24d3-0 | Source code for langchain.embeddings.fake
from typing import List
import numpy as np
from pydantic import BaseModel
from langchain.embeddings.base import Embeddings
[docs]class FakeEmbeddings(Embeddings, BaseModel):
size: int
def _get_embedding(self) -> List[float]:
return list(np.random.normal(size=sel... | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/fake.html |
03853e9368cd-0 | Source code for langchain.embeddings.openai
"""Wrapper around OpenAI embedding models."""
from __future__ import annotations
import logging
from typing import (
Any,
Callable,
Dict,
List,
Literal,
Optional,
Sequence,
Set,
Tuple,
Union,
)
import numpy as np
from pydantic import Ba... | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/openai.html |
03853e9368cd-1 | min_seconds = 4
max_seconds = 10
# Wait 2^x * 1 second between each retry starting with
# 4 seconds, then up to 10 seconds, then 10 seconds afterwards
return retry(
reraise=True,
stop=stop_after_attempt(embeddings.max_retries),
wait=wait_exponential(multiplier=1, min=min_seconds,... | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/openai.html |
03853e9368cd-2 | )
def _async_retry_decorator(embeddings: OpenAIEmbeddings) -> Any:
import openai
min_seconds = 4
max_seconds = 10
# Wait 2^x * 1 second between each retry starting with
# 4 seconds, then up to 10 seconds, then 10 seconds afterwards
async_retrying = AsyncRetrying(
reraise=True,
st... | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/openai.html |
03853e9368cd-3 | ),
before_sleep=before_sleep_log(logger, logging.WARNING),
)
def wrap(func: Callable) -> Callable:
async def wrapped_f(*args: Any, **kwargs: Any) -> Callable:
async for _ in async_retrying:
return await func(*args, **kwargs)
raise AssertionError("this is u... | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/openai.html |
03853e9368cd-4 | """Use tenacity to retry the embedding call."""
@_async_retry_decorator(embeddings)
async def _async_embed_with_retry(**kwargs: Any) -> Any:
return await embeddings.client.acreate(**kwargs)
return await _async_embed_with_retry(**kwargs)
[docs]class OpenAIEmbeddings(BaseModel, Embeddings):
"""Wra... | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/openai.html |
03853e9368cd-5 | 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:
.. code-block:: python
... | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/openai.html |
03853e9368cd-6 | embeddings = OpenAIEmbeddings(
deployment="your-embeddings-deployment-name",
model="your-embeddings-model-name",
openai_api_base="https://your-endpoint.openai.azure.com/",
openai_api_type="azure",
)
text = "This is a test query."
... | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/openai.html |
03853e9368cd-7 | # to support explicit proxy for OpenAI
openai_proxy: Optional[str] = None
embedding_ctx_length: int = 8191
openai_api_key: Optional[str] = None
openai_organization: Optional[str] = None
allowed_special: Union[Literal["all"], Set[str]] = set()
disallowed_special: Union[Literal["all"], Set[str], S... | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/openai.html |
03853e9368cd-8 | """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... | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/openai.html |
03853e9368cd-9 | @root_validator()
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that api key and python package exists in environment."""
values["openai_api_key"] = get_from_dict_or_env(
values, "openai_api_key", "OPENAI_API_KEY"
)
values["openai_api_base"] = get_from_... | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/openai.html |
03853e9368cd-10 | default="",
)
if values["openai_api_type"] in ("azure", "azure_ad", "azuread"):
default_api_version = "2022-12-01"
else:
default_api_version = ""
values["openai_api_version"] = get_from_dict_or_env(
values,
"openai_api_version",
... | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/openai.html |
03853e9368cd-11 | )
return values
@property
def _invocation_params(self) -> Dict:
openai_args = {
"engine": self.deployment,
"request_timeout": self.request_timeout,
"headers": self.headers,
"api_key": self.openai_api_key,
"organization": self.openai_org... | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/openai.html |
03853e9368cd-12 | # please refer to
# https://github.com/openai/openai-cookbook/blob/main/examples/Embedding_long_inputs.ipynb
def _get_len_safe_embeddings(
self, texts: List[str], *, engine: str, chunk_size: Optional[int] = None
) -> List[List[float]]:
embeddings: List[List[float]] = [[] for _ in range(len(t... | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/openai.html |
03853e9368cd-13 | except KeyError:
logger.warning("Warning: model not found. Using cl100k_base encoding.")
model = "cl100k_base"
encoding = tiktoken.get_encoding(model)
for i, text in enumerate(texts):
if self.model.endswith("001"):
# See: https://github.com/openai/... | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/openai.html |
03853e9368cd-14 | batched_embeddings = []
_chunk_size = chunk_size or self.chunk_size
for i in range(0, len(tokens), _chunk_size):
response = embed_with_retry(
self,
input=tokens[i : i + _chunk_size],
**self._invocation_params,
)
batched_... | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/openai.html |
03853e9368cd-15 | average = embed_with_retry(
self,
input="",
**self._invocation_params,
)[
"data"
][0]["embedding"]
else:
average = np.average(_result, axis=0, weights=num_tokens_in_batch[i])
... | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/openai.html |
03853e9368cd-16 | except ImportError:
raise ImportError(
"Could not import tiktoken python package. "
"This is needed in order to for OpenAIEmbeddings. "
"Please install it with `pip install tiktoken`."
)
tokens = []
indices = []
model_name =... | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/openai.html |
03853e9368cd-17 | text = text.replace("\n", " ")
token = encoding.encode(
text,
allowed_special=self.allowed_special,
disallowed_special=self.disallowed_special,
)
for j in range(0, len(token), self.embedding_ctx_length):
tokens += [token... | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/openai.html |
03853e9368cd-18 | num_tokens_in_batch: List[List[int]] = [[] for _ in range(len(texts))]
for i in range(len(indices)):
results[indices[i]].append(batched_embeddings[i])
num_tokens_in_batch[indices[i]].append(len(tokens[i]))
for i in range(len(texts)):
_result = results[i]
i... | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/openai.html |
03853e9368cd-19 | """Call out to OpenAI's embedding endpoint."""
# handle large input text
if len(text) > self.embedding_ctx_length:
return self._get_len_safe_embeddings([text], engine=engine)[0]
else:
if self.model.endswith("001"):
# See: https://github.com/openai/openai-p... | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/openai.html |
03853e9368cd-20 | # handle large input text
if len(text) > self.embedding_ctx_length:
return (await self._aget_len_safe_embeddings([text], engine=engine))[0]
else:
if self.model.endswith("001"):
# See: https://github.com/openai/openai-python/issues/418#issuecomment-1525939500
... | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/openai.html |
03853e9368cd-21 | Args:
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.
"""
# NOTE: to keep things simple, we assume the list ma... | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/openai.html |
03853e9368cd-22 | specified by the class.
Returns:
List of embeddings, one for each text.
"""
# NOTE: to keep things simple, we assume the list may contain texts longer
# than the maximum context and use length-safe embedding function.
return await self._aget_len_safe_embeddings(... | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/openai.html |
03853e9368cd-23 | Args:
text: The text to embed.
Returns:
Embedding for the text.
"""
embedding = await self._aembedding_func(text, engine=self.deployment)
return embedding | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/openai.html |
3f4fad71d899-0 | Source code for langchain.embeddings.huggingface_hub
"""Wrapper around HuggingFace Hub embedding models."""
from typing import Any, Dict, List, Optional
from pydantic import BaseModel, Extra, root_validator
from langchain.embeddings.base import Embeddings
from langchain.utils import get_from_dict_or_env
DEFAULT_REPO_ID... | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/huggingface_hub.html |
3f4fad71d899-1 | .. code-block:: python
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... | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/huggingface_hub.html |
3f4fad71d899-2 | extra = Extra.forbid
@root_validator()
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that api key and python package exists in environment."""
huggingfacehub_api_token = get_from_dict_or_env(
values, "huggingfacehub_api_token", "HUGGINGFACEHUB_API_TOKEN"
)
... | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/huggingface_hub.html |
3f4fad71d899-3 | )
if client.task not in VALID_TASKS:
raise ValueError(
f"Got invalid task {client.task}, "
f"currently only {VALID_TASKS} are supported"
)
values["client"] = client
except ImportError:
raise ValueError(
... | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/huggingface_hub.html |
3f4fad71d899-4 | texts = [text.replace("\n", " ") for text in texts]
_model_kwargs = self.model_kwargs or {}
responses = self.client(inputs=texts, params=_model_kwargs)
return responses
[docs] def embed_query(self, text: str) -> List[float]:
"""Call out to HuggingFaceHub's embedding endpoint for embed... | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/huggingface_hub.html |
446fd1452acb-0 | Source code for langchain.embeddings.deepinfra
from typing import Any, Dict, List, Mapping, Optional
import requests
from pydantic import BaseModel, Extra, root_validator
from langchain.embeddings.base import Embeddings
from langchain.utils import get_from_dict_or_env
DEFAULT_MODEL_ID = "sentence-transformers/clip-ViT-... | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/deepinfra.html |
446fd1452acb-1 | deepinfra_emb = DeepInfraEmbeddings(
model_id="sentence-transformers/clip-ViT-B-32",
deepinfra_api_token="my-api-key"
)
r1 = deepinfra_emb.embed_documents(
[
"Alpha is the first letter of Greek alphabet",
"Be... | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/deepinfra.html |
446fd1452acb-2 | """Instruction used to embed the query."""
model_kwargs: Optional[dict] = None
"""Other model keyword args"""
deepinfra_api_token: Optional[str] = None
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
@root_validator()
def validate_environment(cls,... | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/deepinfra.html |
446fd1452acb-3 | def _embed(self, input: List[str]) -> List[List[float]]:
_model_kwargs = self.model_kwargs or {}
# HTTP headers for authorization
headers = {
"Authorization": f"bearer {self.deepinfra_api_token}",
"Content-Type": "application/json",
}
# send request
... | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/deepinfra.html |
446fd1452acb-4 | )
try:
t = res.json()
embeddings = t["embeddings"]
except requests.exceptions.JSONDecodeError as e:
raise ValueError(
f"Error raised by inference API: {e}.\nResponse: {res.text}"
)
return embeddings
[docs] def embed_documents(sel... | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/deepinfra.html |
446fd1452acb-5 | """Embed a query using a Deep Infra deployed embedding model.
Args:
text: The text to embed.
Returns:
Embeddings for the text.
"""
instruction_pair = f"{self.query_instruction}{text}"
embedding = self._embed([instruction_pair])[0]
return embedding | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/deepinfra.html |
4f6f9f594d0b-0 | Source code for langchain.embeddings.elasticsearch
from __future__ import annotations
from typing import TYPE_CHECKING, List, Optional
from langchain.utils import get_from_env
if TYPE_CHECKING:
from elasticsearch import Elasticsearch
from elasticsearch.client import MlClient
from langchain.embeddings.base impor... | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/elasticsearch.html |
4f6f9f594d0b-1 | """ # noqa: E501
def __init__(
self,
client: MlClient,
model_id: str,
*,
input_field: str = "text_field",
):
"""
Initialize the ElasticsearchEmbeddings instance.
Args:
client (MlClient): An Elasticsearch ML client object.
m... | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/elasticsearch.html |
4f6f9f594d0b-2 | es_user: Optional[str] = None,
es_password: Optional[str] = None,
input_field: str = "text_field",
) -> ElasticsearchEmbeddings:
"""Instantiate embeddings from Elasticsearch credentials.
Args:
model_id (str): The model_id of the model deployed in the Elasticsearch
... | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/elasticsearch.html |
4f6f9f594d0b-3 | 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
... | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/elasticsearch.html |
4f6f9f594d0b-4 | except ImportError:
raise ImportError(
"elasticsearch package not found, please install with 'pip install "
"elasticsearch'"
)
es_cloud_id = es_cloud_id or get_from_env("es_cloud_id", "ES_CLOUD_ID")
es_user = es_user or get_from_env("es_user", "ES_... | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/elasticsearch.html |
4f6f9f594d0b-5 | ) -> ElasticsearchEmbeddings:
"""
Instantiate embeddings from an existing Elasticsearch connection.
This method provides a way to create an instance of the ElasticsearchEmbeddings
class using an existing Elasticsearch connection. The connection object is used
to create an MlClien... | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/elasticsearch.html |
4f6f9f594d0b-6 | # 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"
# Create Elasticsearch connection
es_connection = Elasticsear... | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/elasticsearch.html |
4f6f9f594d0b-7 | from elasticsearch.client import MlClient
# Create an MlClient from the given Elasticsearch connection
client = MlClient(es_connection)
# Return a new instance of the ElasticsearchEmbeddings class with
# the MlClient, model_id, and input_field
return cls(client, model_id, input_f... | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/elasticsearch.html |
4f6f9f594d0b-8 | )
embeddings = [doc["predicted_value"] for doc in response["inference_results"]]
return embeddings
[docs] def embed_documents(self, texts: List[str]) -> List[List[float]]:
"""
Generate embeddings for a list of documents.
Args:
texts (List[str]): A list of document ... | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/elasticsearch.html |
4f6f9f594d0b-9 | """
return self._embedding_func([text])[0] | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/elasticsearch.html |
0ae9510e2d56-0 | Source code for langchain.embeddings.minimax
"""Wrapper around MiniMax APIs."""
from __future__ import annotations
import logging
from typing import Any, Callable, Dict, List, Optional
import requests
from pydantic import BaseModel, Extra, root_validator
from tenacity import (
before_sleep_log,
retry,
stop_... | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/minimax.html |
0ae9510e2d56-1 | wait=wait_exponential(multiplier=multiplier, min=min_seconds, max=max_seconds),
before_sleep=before_sleep_log(logger, logging.WARNING),
)
def embed_with_retry(embeddings: MiniMaxEmbeddings, *args: Any, **kwargs: Any) -> Any:
"""Use tenacity to retry the completion call."""
retry_decorator = _create_... | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/minimax.html |
0ae9510e2d56-2 | ``MINIMAX_API_KEY`` set with your API token, or pass it as a named parameter to
the constructor.
Example:
.. code-block:: python
from langchain.embeddings import MiniMaxEmbeddings
embeddings = MiniMaxEmbeddings()
query_text = "This is a test query."
query_... | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/minimax.html |
0ae9510e2d56-3 | """For embed_query"""
minimax_group_id: Optional[str] = None
"""Group ID for MiniMax API."""
minimax_api_key: Optional[str] = None
"""API Key for MiniMax API."""
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
@root_validator()
def validate_en... | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/minimax.html |
0ae9510e2d56-4 | values["minimax_api_key"] = minimax_api_key
return values
def embed(
self,
texts: List[str],
embed_type: str,
) -> List[List[float]]:
payload = {
"model": self.model,
"type": embed_type,
"texts": texts,
}
# HTTP headers ... | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/minimax.html |
0ae9510e2d56-5 | raise ValueError(
f"MiniMax API returned an error: {parsed_response['base_resp']}"
)
embeddings = parsed_response["vectors"]
return embeddings
[docs] def embed_documents(self, texts: List[str]) -> List[List[float]]:
"""Embed documents using a MiniMax embedding endp... | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/minimax.html |
0ae9510e2d56-6 | embeddings = embed_with_retry(
self, texts=[text], embed_type=self.embed_type_query
)
return embeddings[0] | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/minimax.html |
19d4c5653c1b-0 | Source code for langchain.embeddings.cohere
"""Wrapper around Cohere embedding models."""
from typing import Any, Dict, List, Optional
from pydantic import BaseModel, Extra, root_validator
from langchain.embeddings.base import Embeddings
from langchain.utils import get_from_dict_or_env
[docs]class CohereEmbeddings(Base... | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/cohere.html |
19d4c5653c1b-1 | )
"""
client: Any #: :meta private:
model: str = "embed-english-v2.0"
"""Model name to use."""
truncate: Optional[str] = None
"""Truncate embeddings that are too long from start or end ("NONE"|"START"|"END")"""
cohere_api_key: Optional[str] = None
class Config:
"""Configuration ... | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/cohere.html |
19d4c5653c1b-2 | except ImportError:
raise ValueError(
"Could not import cohere python package. "
"Please install it with `pip install cohere`."
)
return values
[docs] def embed_documents(self, texts: List[str]) -> List[List[float]]:
"""Call out to Cohere's embe... | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/cohere.html |
19d4c5653c1b-3 | Returns:
Embeddings for the text.
"""
embedding = self.client.embed(
model=self.model, texts=[text], truncate=self.truncate
).embeddings[0]
return list(map(float, embedding)) | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/cohere.html |
902140f2f17c-0 | Source code for langchain.embeddings.mosaicml
"""Wrapper around MosaicML APIs."""
from typing import Any, Dict, List, Mapping, Optional, Tuple
import requests
from pydantic import BaseModel, Extra, root_validator
from langchain.embeddings.base import Embeddings
from langchain.utils import get_from_dict_or_env
[docs]cla... | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/mosaicml.html |
902140f2f17c-1 | )
mosaic_llm = MosaicMLInstructorEmbeddings(
endpoint_url=endpoint_url,
mosaicml_api_token="my-api-key"
)
"""
endpoint_url: str = (
"https://models.hosted-on.mosaicml.hosting/instructor-xl/v1/predict"
)
"""Endpoint URL to use."""
embed_... | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/mosaicml.html |
902140f2f17c-2 | extra = Extra.forbid
@root_validator()
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that api key and python package exists in environment."""
mosaicml_api_token = get_from_dict_or_env(
values, "mosaicml_api_token", "MOSAICML_API_TOKEN"
)
values["mo... | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/mosaicml.html |
902140f2f17c-3 | headers = {
"Authorization": f"{self.mosaicml_api_token}",
"Content-Type": "application/json",
}
# send request
try:
response = requests.post(self.endpoint_url, headers=headers, json=payload)
except requests.exceptions.RequestException as e:
... | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/mosaicml.html |
902140f2f17c-4 | )
# The inference API has changed a couple of times, so we add some handling
# to be robust to multiple response formats.
if isinstance(parsed_response, dict):
if "data" in parsed_response:
output_item = parsed_response["data"]
elif... | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/mosaicml.html |
902140f2f17c-5 | if "output" in first_item:
embeddings = [item["output"] for item in parsed_response]
else:
raise ValueError(
f"No key data or output in response: {parsed_response}"
)
else:
... | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/mosaicml.html |
902140f2f17c-6 | """
instruction_pairs = [(self.embed_instruction, text) for text in texts]
embeddings = self._embed(instruction_pairs)
return embeddings
[docs] def embed_query(self, text: str) -> List[float]:
"""Embed a query using a MosaicML deployed instructor embedding model.
Args:
... | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/mosaicml.html |
d6e7ae57a822-0 | Source code for langchain.embeddings.self_hosted_hugging_face
"""Wrapper around HuggingFace embedding models for self-hosted remote hardware."""
import importlib
import logging
from typing import Any, Callable, List, Optional
from langchain.embeddings.self_hosted import SelfHostedEmbeddings
DEFAULT_MODEL_NAME = "senten... | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/self_hosted_hugging_face.html |
d6e7ae57a822-1 | """
return client.encode(*args, **kwargs)
def load_embedding_model(model_id: str, instruct: bool = False, device: int = 0) -> Any:
"""Load the embedding model."""
if not instruct:
import sentence_transformers
client = sentence_transformers.SentenceTransformer(model_id)
else:
from... | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/self_hosted_hugging_face.html |
d6e7ae57a822-2 | logger.warning(
"Device has %d GPUs available. "
"Provide device={deviceId} to `from_model_id` to use available"
"GPUs for execution. deviceId is -1 for CPU and "
"can be a positive integer associated with CUDA device id.",
cuda_device_coun... | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/self_hosted_hugging_face.html |
d6e7ae57a822-3 | Example:
.. code-block:: python
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")
hf = SelfHostedHuggin... | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/self_hosted_hugging_face.html |
d6e7ae57a822-4 | load_fn_kwargs: Optional[dict] = None
"""Key word arguments to pass to the model load function."""
inference_fn: Callable = _embed_documents
"""Inference function to extract the embeddings."""
def __init__(self, **kwargs: Any):
"""Initialize the remote inference function."""
load_fn_kwar... | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/self_hosted_hugging_face.html |
d6e7ae57a822-5 | """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... | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/self_hosted_hugging_face.html |
d6e7ae57a822-6 | """Model name to use."""
embed_instruction: str = DEFAULT_EMBED_INSTRUCTION
"""Instruction to use for embedding documents."""
query_instruction: str = DEFAULT_QUERY_INSTRUCTION
"""Instruction to use for embedding query."""
model_reqs: List[str] = ["./", "InstructorEmbedding", "torch"]
"""Require... | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/self_hosted_hugging_face.html |
d6e7ae57a822-7 | super().__init__(load_fn_kwargs=load_fn_kwargs, **kwargs)
[docs] def embed_documents(self, texts: List[str]) -> List[List[float]]:
"""Compute doc embeddings using a HuggingFace instruct model.
Args:
texts: The list of texts to embed.
Returns:
List of embeddings, one fo... | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/self_hosted_hugging_face.html |
d6e7ae57a822-8 | """
instruction_pair = [self.query_instruction, text]
embedding = self.client(self.pipeline_ref, [instruction_pair])[0]
return embedding.tolist() | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/self_hosted_hugging_face.html |
09186a9d7fbb-0 | Source code for langchain.embeddings.embaas
"""Wrapper around embaas embeddings API."""
from typing import Any, Dict, List, Mapping, Optional
import requests
from pydantic import BaseModel, Extra, root_validator
from typing_extensions import NotRequired, TypedDict
from langchain.embeddings.base import Embeddings
from l... | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/embaas.html |
09186a9d7fbb-1 | To use, you should have the
environment variable ``EMBAAS_API_KEY`` set with your API key, or pass
it as a named parameter to the constructor.
Example:
.. code-block:: python
# Initialise with default model and instruction
from langchain.embeddings import EmbaasEmbeddings
... | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/embaas.html |
09186a9d7fbb-2 | """Instruction used for domain-specific embeddings."""
api_url: str = EMBAAS_API_URL
"""The URL for the embaas embeddings API."""
embaas_api_key: Optional[str] = None
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
@root_validator()
def validate_e... | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/embaas.html |
09186a9d7fbb-3 | """Get the identifying params."""
return {"model": self.model, "instruction": self.instruction}
def _generate_payload(self, texts: List[str]) -> EmbaasEmbeddingsPayload:
"""Generates payload for the API request."""
payload = EmbaasEmbeddingsPayload(texts=texts, model=self.model)
if s... | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/embaas.html |
09186a9d7fbb-4 | response.raise_for_status()
parsed_response = response.json()
embeddings = [item["embedding"] for item in parsed_response["data"]]
return embeddings
def _generate_embeddings(self, texts: List[str]) -> List[List[float]]:
"""Generate embeddings using the Embaas API."""
payload ... | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/embaas.html |
09186a9d7fbb-5 | )
raise
[docs] def embed_documents(self, texts: List[str]) -> List[List[float]]:
"""Get embeddings for a list of texts.
Args:
texts: The list of texts to get embeddings for.
Returns:
List of embeddings, one for each text.
"""
batches = [
... | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/embaas.html |
09186a9d7fbb-6 | Returns:
List of embeddings.
"""
return self.embed_documents([text])[0] | https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/embaas.html |
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