id stringlengths 14 16 | text stringlengths 13 2.7k | source stringlengths 57 178 |
|---|---|---|
0f45dd3b4937-0 | Source code for langchain.embeddings.localai
from __future__ import annotations
import logging
import warnings
from typing import (
Any,
Callable,
Dict,
List,
Literal,
Optional,
Sequence,
Set,
Tuple,
Union,
)
from tenacity import (
AsyncRetrying,
before_sleep_log,
ret... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/localai.html |
0f45dd3b4937-1 | 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,
stop=stop_after_attempt(embeddings.max_retries),
wait=wait_expone... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/localai.html |
0f45dd3b4937-2 | retry_decorator = _create_retry_decorator(embeddings)
@retry_decorator
def _embed_with_retry(**kwargs: Any) -> Any:
response = embeddings.client.create(**kwargs)
return _check_response(response)
return _embed_with_retry(**kwargs)
[docs]async def async_embed_with_retry(embeddings: LocalAIEmbe... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/localai.html |
0f45dd3b4937-3 | openai_api_base: Optional[str] = None
# to support explicit proxy for LocalAI
openai_proxy: Optional[str] = None
embedding_ctx_length: int = 8191
"""The maximum number of tokens to embed at once."""
openai_api_key: Optional[str] = None
openai_organization: Optional[str] = None
allowed_specia... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/localai.html |
0f45dd3b4937-4 | if field_name not in all_required_field_names:
warnings.warn(
f"""WARNING! {field_name} is not default parameter.
{field_name} was transferred to model_kwargs.
Please confirm that {field_name} is what you intended."""
)
... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/localai.html |
0f45dd3b4937-5 | "OPENAI_ORGANIZATION",
default="",
)
try:
import openai
values["client"] = openai.Embedding
except ImportError:
raise ImportError(
"Could not import openai python package. "
"Please install it with `pip install opena... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/localai.html |
0f45dd3b4937-6 | **self._invocation_params,
)["data"][0]["embedding"]
async def _aembedding_func(self, text: str, *, engine: str) -> List[float]:
"""Call out to LocalAI's embedding endpoint."""
# handle large input text
if self.model.endswith("001"):
# See: https://github.com/openai/opena... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/localai.html |
0f45dd3b4937-7 | specified by the class.
Returns:
List of embeddings, one for each text.
"""
embeddings = []
for text in texts:
response = await self._aembedding_func(text, engine=self.deployment)
embeddings.append(response)
return embeddings
[docs] def embe... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/localai.html |
25a7ac7afa19-0 | Source code for langchain.embeddings.embaas
from typing import Any, Dict, List, Mapping, Optional
import requests
from typing_extensions import NotRequired, TypedDict
from langchain.pydantic_v1 import BaseModel, Extra, root_validator
from langchain.schema.embeddings import Embeddings
from langchain.utils import get_fro... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/embaas.html |
25a7ac7afa19-1 | 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_environment(cls, values: Dict) -> Dict:
"""Validate ... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/embaas.html |
25a7ac7afa19-2 | return embeddings
def _generate_embeddings(self, texts: List[str]) -> List[List[float]]:
"""Generate embeddings using the Embaas API."""
payload = self._generate_payload(texts)
try:
return self._handle_request(payload)
except requests.exceptions.RequestException as e:
... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/embaas.html |
0145e31d213d-0 | Source code for langchain.embeddings.cache
"""Module contains code for a cache backed embedder.
The cache backed embedder is a wrapper around an embedder that caches
embeddings in a key-value store. The cache is used to avoid recomputing
embeddings for the same text.
The text is hashed and the hash is used as the key i... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/cache.html |
0145e31d213d-1 | The interface allows works with any store that implements
the abstract store interface accepting keys of type str and values of list of
floats.
If need be, the interface can be extended to accept other implementations
of the value serializer and deserializer, as well as the key encoder.
Examples:
... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/cache.html |
0145e31d213d-2 | to embed the documents and stores the results in the cache.
Args:
texts: A list of texts to embed.
Returns:
A list of embeddings for the given texts.
"""
vectors: List[Union[List[float], None]] = self.document_embedding_store.mget(
texts
)
... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/cache.html |
0145e31d213d-3 | def from_bytes_store(
cls,
underlying_embeddings: Embeddings,
document_embedding_cache: BaseStore[str, bytes],
*,
namespace: str = "",
) -> CacheBackedEmbeddings:
"""On-ramp that adds the necessary serialization and encoding to the store.
Args:
und... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/cache.html |
e413f5101d1f-0 | Source code for langchain.embeddings.tensorflow_hub
from typing import Any, List
from langchain.pydantic_v1 import BaseModel, Extra
from langchain.schema.embeddings import Embeddings
DEFAULT_MODEL_URL = "https://tfhub.dev/google/universal-sentence-encoder-multilingual/3"
[docs]class TensorflowHubEmbeddings(BaseModel, E... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/tensorflow_hub.html |
e413f5101d1f-1 | """Compute doc embeddings using a TensorflowHub embedding model.
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.embed(texts).numpy()
... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/tensorflow_hub.html |
d1618f411224-0 | Source code for langchain.embeddings.fake
import hashlib
from typing import List
import numpy as np
from langchain.pydantic_v1 import BaseModel
from langchain.schema.embeddings import Embeddings
[docs]class FakeEmbeddings(Embeddings, BaseModel):
"""Fake embedding model."""
size: int
"""The size of the embed... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/fake.html |
8486bf4035d9-0 | Source code for langchain.embeddings.clarifai
import logging
from typing import Any, Dict, List, Optional
from langchain.pydantic_v1 import BaseModel, Extra, root_validator
from langchain.schema.embeddings import Embeddings
from langchain.utils import get_from_dict_or_env
logger = logging.getLogger(__name__)
[docs]clas... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/clarifai.html |
8486bf4035d9-1 | extra = Extra.forbid
@root_validator()
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that api key and python package exists in environment."""
values["pat"] = get_from_dict_or_env(values, "pat", "CLARIFAI_PAT")
user_id = values.get("user_id")
app_id = values.ge... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/clarifai.html |
8486bf4035d9-2 | List of embeddings, one for each text.
"""
try:
from clarifai_grpc.grpc.api import (
resources_pb2,
service_pb2,
)
from clarifai_grpc.grpc.api.status import status_code_pb2
except ImportError:
raise ImportError(
... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/clarifai.html |
8486bf4035d9-3 | for o in post_model_outputs_response.outputs
]
)
return embeddings
[docs] def embed_query(self, text: str) -> List[float]:
"""Call out to Clarifai's embedding models.
Args:
text: The text to embed.
Returns:
Embeddings for the text.
... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/clarifai.html |
8486bf4035d9-4 | for o in post_model_outputs_response.outputs
]
return embeddings[0] | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/clarifai.html |
e33a6c51373b-0 | Source code for langchain.embeddings.mosaicml
from typing import Any, Dict, List, Mapping, Optional, Tuple
import requests
from langchain.pydantic_v1 import BaseModel, Extra, root_validator
from langchain.schema.embeddings import Embeddings
from langchain.utils import get_from_dict_or_env
[docs]class MosaicMLInstructor... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/mosaicml.html |
e33a6c51373b-1 | 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... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/mosaicml.html |
e33a6c51373b-2 | # to be robust to multiple response formats.
if isinstance(parsed_response, dict):
output_keys = ["data", "output", "outputs"]
for key in output_keys:
if key in parsed_response:
output_item = parsed_response[key]
... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/mosaicml.html |
b9d18d92cb29-0 | Source code for langchain.embeddings.gradient_ai
import asyncio
import logging
import os
from concurrent.futures import ThreadPoolExecutor
from typing import Any, Callable, Dict, List, Optional, Tuple
import aiohttp
import numpy as np
import requests
from langchain.pydantic_v1 import BaseModel, Extra, root_validator
fr... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/gradient_ai.html |
b9d18d92cb29-1 | """Gradient client."""
# LLM call kwargs
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
@root_validator(allow_reuse=True)
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that api key and python package exists in environment."""
... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/gradient_ai.html |
b9d18d92cb29-2 | model=self.model,
texts=texts,
)
return embeddings
[docs] async def aembed_documents(self, texts: List[str]) -> List[List[float]]:
"""Async call out to Gradient's embedding endpoint.
Args:
texts: The list of texts to embed.
Returns:
List of ... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/gradient_ai.html |
b9d18d92cb29-3 | workspace_id="12345614fc0_workspace",
access_token="gradientai-access_token",
)
embeds = mini_client.embed(
model="bge-large",
text=["doc1", "doc2"]
)
# or
embeds = await mini_client.aembed(
model... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/gradient_ai.html |
b9d18d92cb29-4 | self._batch_size = 128
@staticmethod
def _permute(
texts: List[str], sorter: Callable = len
) -> Tuple[List[str], Callable]:
"""Sort texts in ascending order, and
delivers a lambda expr, which can sort a same length list
https://github.com/UKPLab/sentence-transformers/blob/
... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/gradient_ai.html |
b9d18d92cb29-5 | Returns:
List[List[str]]: Batches of List of sentences
"""
if len(texts) == 1:
# special case query
return [texts]
batches = []
for start_index in range(0, len(texts), self._batch_size):
batches.append(texts[start_index : start_index + self... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/gradient_ai.html |
b9d18d92cb29-6 | ) -> List[List[float]]:
response = requests.post(
**self._kwargs_post_request(model=model, texts=batch_texts)
)
if response.status_code != 200:
raise Exception(
f"Gradient returned an unexpected response with status "
f"{response.status_cod... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/gradient_ai.html |
b9d18d92cb29-7 | raise Exception(
f"Gradient returned an unexpected response with status "
f"{response.status}: {response.text}"
)
embedding = (await response.json())["embeddings"]
return [e["embedding"] for e in embedding]
[docs] async def aembed(self, ... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/gradient_ai.html |
00ab8dda6dd1-0 | Source code for langchain.embeddings.spacy_embeddings
import importlib.util
from typing import Any, Dict, List
from langchain.pydantic_v1 import BaseModel, Extra, root_validator
from langchain.schema.embeddings import Embeddings
[docs]class SpacyEmbeddings(BaseModel, Embeddings):
"""Embeddings by SpaCy models.
... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/spacy_embeddings.html |
00ab8dda6dd1-1 | import spacy
values["nlp"] = spacy.load("en_core_web_sm")
except OSError:
# If the model is not found, raise a ValueError
raise ValueError(
"Spacy model 'en_core_web_sm' not found. "
"Please install it with"
" `python -m spacy d... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/spacy_embeddings.html |
00ab8dda6dd1-2 | """
Asynchronously generates an embedding for a single piece of text.
This method is not implemented and raises a NotImplementedError.
Args:
text (str): The text to generate an embedding for.
Raises:
NotImplementedError: This method is not implemented.
"""... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/spacy_embeddings.html |
326b3068e98e-0 | Source code for langchain.embeddings.bedrock
import asyncio
import json
import os
from functools import partial
from typing import Any, Dict, List, Optional
from langchain.pydantic_v1 import BaseModel, Extra, root_validator
from langchain.schema.embeddings import Embeddings
[docs]class BedrockEmbeddings(BaseModel, Embe... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/bedrock.html |
326b3068e98e-1 | 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
"""
model_id: str = "amazon.titan-embed-text-v1"
... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/bedrock.html |
326b3068e98e-2 | raise ModuleNotFoundError(
"Could not import boto3 python package. "
"Please install it with `pip install boto3`."
)
except Exception as e:
raise ValueError(
"Could not load credentials to authenticate with AWS client. "
"Pl... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/bedrock.html |
326b3068e98e-3 | return response_body.get("embedding")
except Exception as e:
raise ValueError(f"Error raised by inference endpoint: {e}")
[docs] def embed_documents(self, texts: List[str]) -> List[List[float]]:
"""Compute doc embeddings using a Bedrock model.
Args:
texts: The list of ... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/bedrock.html |
47325573394b-0 | Source code for langchain.embeddings.llamacpp
from typing import Any, Dict, List, Optional
from langchain.pydantic_v1 import BaseModel, Extra, Field, root_validator
from langchain.schema.embeddings import Embeddings
[docs]class LlamaCppEmbeddings(BaseModel, Embeddings):
"""llama.cpp embedding models.
To use, yo... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/llamacpp.html |
47325573394b-1 | """Force system to keep model in RAM."""
n_threads: Optional[int] = Field(None, alias="n_threads")
"""Number of threads to use. If None, the number
of threads is automatically determined."""
n_batch: Optional[int] = Field(8, alias="n_batch")
"""Number of tokens to process in parallel.
Should be... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/llamacpp.html |
47325573394b-2 | except ImportError:
raise ModuleNotFoundError(
"Could not import llama-cpp-python library. "
"Please install the llama-cpp-python library to "
"use this embedding model: pip install llama-cpp-python"
)
except Exception as e:
rai... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/llamacpp.html |
0a2b118f60fc-0 | Source code for langchain.embeddings.sagemaker_endpoint
from typing import Any, Dict, List, Optional
from langchain.llms.sagemaker_endpoint import ContentHandlerBase
from langchain.pydantic_v1 import BaseModel, Extra, root_validator
from langchain.schema.embeddings import Embeddings
[docs]class EmbeddingsContentHandler... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/sagemaker_endpoint.html |
0a2b118f60fc-1 | )
#Use with boto3 client
client = boto3.client(
"sagemaker-runtime",
region_name=region_name
)
se = SagemakerEndpointEmbeddings(
endpoint_name=endpoint_name,
client=client
... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/sagemaker_endpoint.html |
0a2b118f60fc-2 | def transform_output(self, output: bytes) -> List[List[float]]:
response_json = json.loads(output.read().decode("utf-8"))
return response_json["vectors"]
""" # noqa: E501
model_kwargs: Optional[Dict] = None
"""Keyword arguments to pass to the model."""
endpoint_k... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/sagemaker_endpoint.html |
0a2b118f60fc-3 | raise ImportError(
"Could not import boto3 python package. "
"Please install it with `pip install boto3`."
)
return values
def _embedding_func(self, texts: List[str]) -> List[List[float]]:
"""Call out to SageMaker Inference embedding endpoint."""
#... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/sagemaker_endpoint.html |
0a2b118f60fc-4 | for i in range(0, len(texts), _chunk_size):
response = self._embedding_func(texts[i : i + _chunk_size])
results.extend(response)
return results
[docs] def embed_query(self, text: str) -> List[float]:
"""Compute query embeddings using a SageMaker inference endpoint.
Arg... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/sagemaker_endpoint.html |
df7ccf0cb694-0 | Source code for langchain.embeddings.ernie
import asyncio
import logging
import threading
from functools import partial
from typing import Dict, List, Optional
import requests
from langchain.pydantic_v1 import BaseModel, root_validator
from langchain.schema.embeddings import Embeddings
from langchain.utils import get_f... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/ernie.html |
df7ccf0cb694-1 | )
resp = requests.post(
f"{base_url}/embedding-v1",
headers={
"Content-Type": "application/json",
},
params={"access_token": self.access_token},
json=json,
)
return resp.json()
def _refresh_access_token_with_lock(sel... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/ernie.html |
df7ccf0cb694-2 | self._refresh_access_token_with_lock()
resp = self._embedding({"input": [text for text in chunk]})
else:
raise ValueError(f"Error from Ernie: {resp}")
lst.extend([i["embedding"] for i in resp["data"]])
return lst
[docs] def embed_query(self,... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/ernie.html |
df7ccf0cb694-3 | List[List[float]]: List of embeddings, one for each text.
"""
result = await asyncio.gather(*[self.aembed_query(text) for text in texts])
return list(result) | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/ernie.html |
451253574251-0 | Source code for langchain.embeddings.aleph_alpha
from typing import Any, Dict, List, Optional
from langchain.pydantic_v1 import BaseModel, root_validator
from langchain.schema.embeddings import Embeddings
from langchain.utils import get_from_dict_or_env
[docs]class AlephAlphaAsymmetricSemanticEmbedding(BaseModel, Embed... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/aleph_alpha.html |
451253574251-1 | explicitly been set in the request."""
control_log_additive: bool = True
"""Apply controls on prompt items by adding the log(control_factor)
to attention scores."""
# Client params
aleph_alpha_api_key: Optional[str] = None
"""API key for Aleph Alpha API."""
host: str = "https://api.aleph-al... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/aleph_alpha.html |
451253574251-2 | retry made. So with the
default setting of 8 retries a total wait time of 63.5 s is added between
the retries."""
nice: bool = False
"""Setting this to True, will signal to the API that you intend to be
nice to other users
by de-prioritizing your request below concurrent ones."""
@root_val... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/aleph_alpha.html |
451253574251-3 | SemanticRepresentation,
)
except ImportError:
raise ValueError(
"Could not import aleph_alpha_client python package. "
"Please install it with `pip install aleph_alpha_client`."
)
document_embeddings = []
for text in texts:
... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/aleph_alpha.html |
451253574251-4 | "control_log_additive": self.control_log_additive,
}
symmetric_request = SemanticEmbeddingRequest(**symmetric_params)
symmetric_response = self.client.semantic_embed(
request=symmetric_request, model=self.model
)
return symmetric_response.embedding
[docs]class AlephAl... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/aleph_alpha.html |
451253574251-5 | query_response = self.client.semantic_embed(
request=query_request, model=self.model
)
return query_response.embedding
[docs] def embed_documents(self, texts: List[str]) -> List[List[float]]:
"""Call out to Aleph Alpha's Document endpoint.
Args:
texts: The list... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/aleph_alpha.html |
29877826ff73-0 | 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.schema.embeddings imp... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/elasticsearch.html |
29877826ff73-1 | 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
... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/elasticsearch.html |
29877826ff73-2 | from elasticsearch.client import MlClient
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")
... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/elasticsearch.html |
29877826ff73-3 | Example:
.. code-block:: python
from elasticsearch import Elasticsearch
from langchain.embeddings import ElasticsearchEmbeddings
# Define the model ID and input field name (if different from default)
model_id = "your_model_id"
#... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/elasticsearch.html |
29877826ff73-4 | list.
"""
response = self.client.infer_trained_model(
model_id=self.model_id, docs=[{self.input_field: text} for text in texts]
)
embeddings = [doc["predicted_value"] for doc in response["inference_results"]]
return embeddings
[docs] def embed_documents(self, texts... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/elasticsearch.html |
cc0e57560807-0 | Source code for langchain.embeddings.voyageai
from __future__ import annotations
import json
import logging
from typing import (
Any,
Callable,
Dict,
List,
Optional,
Tuple,
Union,
cast,
)
import requests
from tenacity import (
before_sleep_log,
retry,
stop_after_attempt,
... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/voyageai.html |
cc0e57560807-1 | @retry_decorator
def _embed_with_retry(**kwargs: Any) -> Any:
response = requests.post(**kwargs)
return _check_response(response.json())
return _embed_with_retry(**kwargs)
[docs]class VoyageEmbeddings(BaseModel, Embeddings):
"""Voyage embedding models.
To use, you should have the environ... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/voyageai.html |
cc0e57560807-2 | values["voyage_api_key"] = convert_to_secret_str(
get_from_dict_or_env(values, "voyage_api_key", "VOYAGE_API_KEY")
)
return values
def _invocation_params(
self, input: List[str], input_type: Optional[str] = None
) -> Dict:
api_key = cast(SecretStr, self.voyage_api_key... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/voyageai.html |
cc0e57560807-3 | self,
**self._invocation_params(
input=texts[i : i + batch_size], input_type=input_type
),
)
embeddings.extend(r["embedding"] for r in response["data"])
return embeddings
[docs] def embed_documents(self, texts: List[str]) -> List[Lis... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/voyageai.html |
493efd198652-0 | Source code for langchain.embeddings.llm_rails
""" This file is for LLMRails Embedding """
import logging
import os
from typing import List, Optional
import requests
from langchain.pydantic_v1 import BaseModel, Extra
from langchain.schema.embeddings import Embeddings
[docs]class LLMRailsEmbeddings(BaseModel, Embeddings... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/llm_rails.html |
493efd198652-1 | response = requests.post(
"https://api.llmrails.com/v1/embeddings",
headers={"X-API-KEY": api_key},
json={"input": texts, "model": self.model},
timeout=60,
)
return [item["embedding"] for item in response.json()["data"]]
[docs] def embed_query(self, tex... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/llm_rails.html |
f55490bf7f65-0 | Source code for langchain.embeddings.self_hosted_hugging_face
import importlib
import logging
from typing import Any, Callable, List, Optional
from langchain.embeddings.self_hosted import SelfHostedEmbeddings
DEFAULT_MODEL_NAME = "sentence-transformers/all-mpnet-base-v2"
DEFAULT_INSTRUCT_MODEL = "hkunlp/instructor-larg... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/self_hosted_hugging_face.html |
f55490bf7f65-1 | 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... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/self_hosted_hugging_face.html |
f55490bf7f65-2 | """Function to load the model remotely on the server."""
load_fn_kwargs: Optional[dict] = None
"""Keyword arguments to pass to the model load function."""
inference_fn: Callable = _embed_documents
"""Inference function to extract the embeddings."""
def __init__(self, **kwargs: Any):
"""Initi... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/self_hosted_hugging_face.html |
f55490bf7f65-3 | """
model_id: str = DEFAULT_INSTRUCT_MODEL
"""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] = ["... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/self_hosted_hugging_face.html |
f55490bf7f65-4 | Returns:
Embeddings for the text.
"""
instruction_pair = [self.query_instruction, text]
embedding = self.client(self.pipeline_ref, [instruction_pair])[0]
return embedding.tolist() | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/self_hosted_hugging_face.html |
55f1e90718f2-0 | Source code for langchain.embeddings.awa
from typing import Any, Dict, List
from langchain.pydantic_v1 import BaseModel, root_validator
from langchain.schema.embeddings import Embeddings
[docs]class AwaEmbeddings(BaseModel, Embeddings):
"""Embedding documents and queries with Awa DB.
Attributes:
client:... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/awa.html |
55f1e90718f2-1 | Returns:
List of embeddings, one for each text.
"""
return self.client.EmbeddingBatch(texts)
[docs] def embed_query(self, text: str) -> List[float]:
"""Compute query embeddings using AwaEmbedding.
Args:
text: The text to embed.
Returns:
Embe... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/awa.html |
9451bfd14ba2-0 | Source code for langchain.embeddings.baidu_qianfan_endpoint
from __future__ import annotations
import logging
from typing import Any, Dict, List, Optional
from langchain.pydantic_v1 import BaseModel, root_validator
from langchain.schema.embeddings import Embeddings
from langchain.utils import get_from_dict_or_env
logge... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/baidu_qianfan_endpoint.html |
9451bfd14ba2-1 | configuration file are available or not.
init qianfan embedding client with `ak`, `sk`, `model`, `endpoint`
Args:
values: a dictionary containing configuration information, must include the
fields of qianfan_ak and qianfan_sk
Returns:
a dictionary containing c... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/baidu_qianfan_endpoint.html |
9451bfd14ba2-2 | resp = self.embed_documents([text])
return resp[0]
[docs] def embed_documents(self, texts: List[str]) -> List[List[float]]:
"""
Embeds a list of text documents using the AutoVOT algorithm.
Args:
texts (List[str]): A list of text documents to embed.
Returns:
... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/baidu_qianfan_endpoint.html |
d314afe4a826-0 | Source code for langchain.embeddings.javelin_ai_gateway
from __future__ import annotations
from typing import Any, Iterator, List, Optional
from langchain.pydantic_v1 import BaseModel
from langchain.schema.embeddings import Embeddings
def _chunk(texts: List[str], size: int) -> Iterator[List[str]]:
for i in range(0,... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/javelin_ai_gateway.html |
d314afe4a826-1 | raise ImportError(
"Could not import javelin_sdk python package. "
"Please install it with `pip install javelin_sdk`."
)
super().__init__(**kwargs)
if self.gateway_uri:
try:
self.client = JavelinClient(
base_url=... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/javelin_ai_gateway.html |
d314afe4a826-2 | print("Failed to query route: " + str(e))
return embeddings
[docs] def embed_documents(self, texts: List[str]) -> List[List[float]]:
return self._query(texts)
[docs] def embed_query(self, text: str) -> List[float]:
return self._query([text])[0]
[docs] async def aembed_documents(self, te... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/javelin_ai_gateway.html |
4274382abceb-0 | Source code for langchain.embeddings.self_hosted
from typing import Any, Callable, List
from langchain.llms.self_hosted import SelfHostedPipeline
from langchain.pydantic_v1 import Extra
from langchain.schema.embeddings import Embeddings
def _embed_documents(pipeline: Any, *args: Any, **kwargs: Any) -> List[List[float]]... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/self_hosted.html |
4274382abceb-1 | model_load_fn=get_pipeline,
hardware=gpu
model_reqs=["./", "torch", "transformers"],
)
Example passing in a pipeline path:
.. code-block:: python
from langchain.embeddings import SelfHostedHFEmbeddings
import runhouse as rh
from... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/self_hosted.html |
4274382abceb-2 | [docs] def embed_query(self, text: str) -> List[float]:
"""Compute query embeddings using a HuggingFace transformer model.
Args:
text: The text to embed.
Returns:
Embeddings for the text.
"""
text = text.replace("\n", " ")
embeddings = self.clie... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/self_hosted.html |
671b55d78689-0 | Source code for langchain.embeddings.nlpcloud
from typing import Any, Dict, List
from langchain.pydantic_v1 import BaseModel, root_validator
from langchain.schema.embeddings import Embeddings
from langchain.utils import get_from_dict_or_env
[docs]class NLPCloudEmbeddings(BaseModel, Embeddings):
"""NLP Cloud embeddi... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/nlpcloud.html |
671b55d78689-1 | "Please install it with `pip install nlpcloud`."
)
return values
[docs] def embed_documents(self, texts: List[str]) -> List[List[float]]:
"""Embed a list of documents using NLP Cloud.
Args:
texts: The list of texts to embed.
Returns:
List of embeddi... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/nlpcloud.html |
567d392a6389-0 | Source code for langchain.embeddings.cohere
from typing import Any, Dict, List, Optional
from langchain.pydantic_v1 import BaseModel, Extra, root_validator
from langchain.schema.embeddings import Embeddings
from langchain.utils import get_from_dict_or_env
[docs]class CohereEmbeddings(BaseModel, Embeddings):
"""Cohe... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/cohere.html |
567d392a6389-1 | extra = Extra.forbid
@root_validator()
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that api key and python package exists in environment."""
cohere_api_key = get_from_dict_or_env(
values, "cohere_api_key", "COHERE_API_KEY"
)
max_retries = values.g... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/cohere.html |
567d392a6389-2 | """Async call out to Cohere's embedding endpoint.
Args:
texts: The list of texts to embed.
Returns:
List of embeddings, one for each text.
"""
embeddings = await self.async_client.embed(
model=self.model,
texts=texts,
input_type... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/cohere.html |
3f0ad75bcb3a-0 | Source code for langchain.embeddings.mlflow_gateway
from __future__ import annotations
from typing import Any, Iterator, List, Optional
from langchain.pydantic_v1 import BaseModel
from langchain.schema.embeddings import Embeddings
def _chunk(texts: List[str], size: int) -> Iterator[List[str]]:
for i in range(0, len... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/mlflow_gateway.html |
3f0ad75bcb3a-1 | if self.gateway_uri:
mlflow.gateway.set_gateway_uri(self.gateway_uri)
def _query(self, texts: List[str]) -> List[List[float]]:
try:
import mlflow.gateway
except ImportError as e:
raise ImportError(
"Could not import `mlflow.gateway` module. "
... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/mlflow_gateway.html |
0b8b0f0e24a0-0 | Source code for langchain.embeddings.ollama
from typing import Any, Dict, List, Mapping, Optional
import requests
from langchain.pydantic_v1 import BaseModel, Extra
from langchain.schema.embeddings import Embeddings
[docs]class OllamaEmbeddings(BaseModel, Embeddings):
"""Ollama locally runs large language models.
... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/ollama.html |
0b8b0f0e24a0-1 | from the generated text. A lower learning rate will result in
slower adjustments, while a higher learning rate will make
the algorithm more responsive. (Default: 0.1)"""
mirostat_tau: Optional[float] = None
"""Controls the balance between coherence and diversity
of the output. A lower value will res... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/ollama.html |
0b8b0f0e24a0-2 | make the model answer more creatively. (Default: 0.8)"""
stop: Optional[List[str]] = None
"""Sets the stop tokens to use."""
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... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/ollama.html |
0b8b0f0e24a0-3 | "repeat_penalty": self.repeat_penalty,
"temperature": self.temperature,
"stop": self.stop,
"tfs_z": self.tfs_z,
"top_k": self.top_k,
"top_p": self.top_p,
},
}
model_kwargs: Optional[dict] = None
"""Other model ke... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/ollama.html |
0b8b0f0e24a0-4 | except requests.exceptions.JSONDecodeError as e:
raise ValueError(
f"Error raised by inference API: {e}.\nResponse: {res.text}"
)
def _embed(self, input: List[str]) -> List[List[float]]:
embeddings_list: List[List[float]] = []
for prompt in input:
... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/ollama.html |
053d28d89110-0 | Source code for langchain.embeddings.edenai
from typing import Any, Dict, List, Optional
from langchain.pydantic_v1 import BaseModel, Extra, Field, root_validator
from langchain.schema.embeddings import Embeddings
from langchain.utilities.requests import Requests
from langchain.utils import get_from_dict_or_env
[docs]c... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/edenai.html |
053d28d89110-1 | """Compute embeddings using EdenAi api."""
url = "https://api.edenai.run/v2/text/embeddings"
headers = {
"accept": "application/json",
"content-type": "application/json",
"authorization": f"Bearer {self.edenai_api_key}",
"User-Agent": self.get_user_agent()... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/edenai.html |
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