id stringlengths 14 16 | source stringlengths 49 117 | text stringlengths 16 2.73k |
|---|---|---|
f93c35a5f1e3-0 | https://python.langchain.com/en/latest/_modules/langchain/llms/forefrontai.html | Source code for langchain.llms.forefrontai
"""Wrapper around ForefrontAI APIs."""
from typing import Any, Dict, List, Mapping, Optional
import requests
from pydantic import Extra, root_validator
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
from langchain.llms.util... |
f93c35a5f1e3-1 | https://python.langchain.com/en/latest/_modules/langchain/llms/forefrontai.html | """Validate that api key exists in environment."""
forefrontai_api_key = get_from_dict_or_env(
values, "forefrontai_api_key", "FOREFRONTAI_API_KEY"
)
values["forefrontai_api_key"] = forefrontai_api_key
return values
@property
def _default_params(self) -> Mapping[str, ... |
f93c35a5f1e3-2 | https://python.langchain.com/en/latest/_modules/langchain/llms/forefrontai.html | "Content-Type": "application/json",
},
json={"text": prompt, **self._default_params},
)
response_json = response.json()
text = response_json["result"][0]["completion"]
if stop is not None:
# I believe this is required since the stop tokens
... |
ec91f497f3e0-0 | https://python.langchain.com/en/latest/_modules/langchain/llms/sagemaker_endpoint.html | Source code for langchain.llms.sagemaker_endpoint
"""Wrapper around Sagemaker InvokeEndpoint API."""
from abc import abstractmethod
from typing import Any, Dict, Generic, List, Mapping, Optional, TypeVar, Union
from pydantic import Extra, root_validator
from langchain.callbacks.manager import CallbackManagerForLLMRun
f... |
ec91f497f3e0-1 | https://python.langchain.com/en/latest/_modules/langchain/llms/sagemaker_endpoint.html | def transform_input(self, prompt: INPUT_TYPE, model_kwargs: Dict) -> bytes:
"""Transforms the input to a format that model can accept
as the request Body. Should return bytes or seekable file
like object in the format specified in the content_type
request header.
"""
@abstrac... |
ec91f497f3e0-2 | https://python.langchain.com/en/latest/_modules/langchain/llms/sagemaker_endpoint.html | region_name=region_name,
credentials_profile_name=credentials_profile_name
)
"""
client: Any #: :meta private:
endpoint_name: str = ""
"""The name of the endpoint from the deployed Sagemaker model.
Must be unique within an AWS Region."""
region_name: str = ""
"""... |
ec91f497f3e0-3 | https://python.langchain.com/en/latest/_modules/langchain/llms/sagemaker_endpoint.html | """Key word arguments to pass to the model."""
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>
"""
class Config:
"""Conf... |
ec91f497f3e0-4 | https://python.langchain.com/en/latest/_modules/langchain/llms/sagemaker_endpoint.html | def _llm_type(self) -> str:
"""Return type of llm."""
return "sagemaker_endpoint"
def _call(
self,
prompt: str,
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
) -> str:
"""Call out to Sagemaker inference endpoint.... |
3c5853f73c86-0 | https://python.langchain.com/en/latest/_modules/langchain/llms/huggingface_pipeline.html | Source code for langchain.llms.huggingface_pipeline
"""Wrapper around HuggingFace Pipeline APIs."""
import importlib.util
import logging
from typing import Any, List, Mapping, Optional
from pydantic import Extra
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
from la... |
3c5853f73c86-1 | https://python.langchain.com/en/latest/_modules/langchain/llms/huggingface_pipeline.html | model_id: str = DEFAULT_MODEL_ID
"""Model name to use."""
model_kwargs: Optional[dict] = None
"""Key word arguments passed to the model."""
pipeline_kwargs: Optional[dict] = None
"""Key word arguments passed to the pipeline."""
class Config:
"""Configuration for this pydantic object."""
... |
3c5853f73c86-2 | https://python.langchain.com/en/latest/_modules/langchain/llms/huggingface_pipeline.html | except ImportError as e:
raise ValueError(
f"Could not load the {task} model due to missing dependencies."
) from e
if importlib.util.find_spec("torch") is not None:
import torch
cuda_device_count = torch.cuda.device_count()
if device <... |
3c5853f73c86-3 | https://python.langchain.com/en/latest/_modules/langchain/llms/huggingface_pipeline.html | @property
def _identifying_params(self) -> Mapping[str, Any]:
"""Get the identifying parameters."""
return {
"model_id": self.model_id,
"model_kwargs": self.model_kwargs,
"pipeline_kwargs": self.pipeline_kwargs,
}
@property
def _llm_type(self) -> s... |
63b4321e5e67-0 | https://python.langchain.com/en/latest/_modules/langchain/llms/anyscale.html | Source code for langchain.llms.anyscale
"""Wrapper around Anyscale"""
from typing import Any, Dict, List, Mapping, Optional
import requests
from pydantic import Extra, root_validator
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
from langchain.llms.utils import enf... |
63b4321e5e67-1 | https://python.langchain.com/en/latest/_modules/langchain/llms/anyscale.html | """Validate that api key and python package exists in environment."""
anyscale_service_url = get_from_dict_or_env(
values, "anyscale_service_url", "ANYSCALE_SERVICE_URL"
)
anyscale_service_route = get_from_dict_or_env(
values, "anyscale_service_route", "ANYSCALE_SERVICE_R... |
63b4321e5e67-2 | https://python.langchain.com/en/latest/_modules/langchain/llms/anyscale.html | stop: Optional list of stop words to use when generating.
Returns:
The string generated by the model.
Example:
.. code-block:: python
response = anyscale("Tell me a joke.")
"""
anyscale_service_endpoint = (
f"{self.anyscale_service_url}... |
9941fefb6b0a-0 | https://python.langchain.com/en/latest/_modules/langchain/llms/stochasticai.html | Source code for langchain.llms.stochasticai
"""Wrapper around StochasticAI APIs."""
import logging
import time
from typing import Any, Dict, List, Mapping, Optional
import requests
from pydantic import Extra, Field, root_validator
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base... |
9941fefb6b0a-1 | https://python.langchain.com/en/latest/_modules/langchain/llms/stochasticai.html | logger.warning(
f"""{field_name} was transfered to model_kwargs.
Please confirm that {field_name} is what you intended."""
)
extra[field_name] = values.pop(field_name)
values["model_kwargs"] = extra
return values
@root_validator... |
9941fefb6b0a-2 | https://python.langchain.com/en/latest/_modules/langchain/llms/stochasticai.html | json={"prompt": prompt, "params": params},
headers={
"apiKey": f"{self.stochasticai_api_key}",
"Accept": "application/json",
"Content-Type": "application/json",
},
)
response_post.raise_for_status()
response_post_json = resp... |
573cab5d56f3-0 | https://python.langchain.com/en/latest/_modules/langchain/llms/self_hosted_hugging_face.html | Source code for langchain.llms.self_hosted_hugging_face
"""Wrapper around HuggingFace Pipeline API to run on self-hosted remote hardware."""
import importlib.util
import logging
from typing import Any, Callable, List, Mapping, Optional
from pydantic import Extra
from langchain.callbacks.manager import CallbackManagerFo... |
573cab5d56f3-1 | https://python.langchain.com/en/latest/_modules/langchain/llms/self_hosted_hugging_face.html | def _load_transformer(
model_id: str = DEFAULT_MODEL_ID,
task: str = DEFAULT_TASK,
device: int = 0,
model_kwargs: Optional[dict] = None,
) -> Any:
"""Inference function to send to the remote hardware.
Accepts a huggingface model_id and returns a pipeline for the task.
"""
from transforme... |
573cab5d56f3-2 | https://python.langchain.com/en/latest/_modules/langchain/llms/self_hosted_hugging_face.html | "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_count,
)
pipeline = ... |
573cab5d56f3-3 | https://python.langchain.com/en/latest/_modules/langchain/llms/self_hosted_hugging_face.html | Example passing fn that generates a pipeline (bc the pipeline is not serializable):
.. code-block:: python
from langchain.llms import SelfHostedHuggingFaceLLM
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
import runhouse as rh
def get_pipe... |
573cab5d56f3-4 | https://python.langchain.com/en/latest/_modules/langchain/llms/self_hosted_hugging_face.html | """Configuration for this pydantic object."""
extra = Extra.forbid
def __init__(self, **kwargs: Any):
"""Construct the pipeline remotely using an auxiliary function.
The load function needs to be importable to be imported
and run on the server, i.e. in a module and not a REPL or clos... |
8375355f167f-0 | https://python.langchain.com/en/latest/_modules/langchain/llms/cohere.html | Source code for langchain.llms.cohere
"""Wrapper around Cohere APIs."""
import logging
from typing import Any, Dict, List, Optional
from pydantic import Extra, root_validator
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
from langchain.llms.utils import enforce_sto... |
8375355f167f-1 | https://python.langchain.com/en/latest/_modules/langchain/llms/cohere.html | truncate: Optional[str] = None
"""Specify how the client handles inputs longer than the maximum token
length: Truncate from START, END or NONE"""
cohere_api_key: Optional[str] = None
stop: Optional[List[str]] = None
class Config:
"""Configuration for this pydantic object."""
extra = ... |
8375355f167f-2 | https://python.langchain.com/en/latest/_modules/langchain/llms/cohere.html | self,
prompt: str,
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
) -> str:
"""Call out to Cohere's generate endpoint.
Args:
prompt: The prompt to pass into the model.
stop: Optional list of stop words to use ... |
d194b5d081f7-0 | https://python.langchain.com/en/latest/_modules/langchain/llms/aleph_alpha.html | Source code for langchain.llms.aleph_alpha
"""Wrapper around Aleph Alpha APIs."""
from typing import Any, Dict, List, Optional, Sequence
from pydantic import Extra, root_validator
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
from langchain.llms.utils import enforc... |
d194b5d081f7-1 | https://python.langchain.com/en/latest/_modules/langchain/llms/aleph_alpha.html | presence_penalty: float = 0.0
"""Penalizes repeated tokens."""
frequency_penalty: float = 0.0
"""Penalizes repeated tokens according to frequency."""
repetition_penalties_include_prompt: Optional[bool] = False
"""Flag deciding whether presence penalty or frequency penalty are
updated from the pr... |
d194b5d081f7-2 | https://python.langchain.com/en/latest/_modules/langchain/llms/aleph_alpha.html | sequence_penalty_min_length: int = 2
use_multiplicative_sequence_penalty: bool = False
completion_bias_inclusion: Optional[Sequence[str]] = None
completion_bias_inclusion_first_token_only: bool = False
completion_bias_exclusion: Optional[Sequence[str]] = None
completion_bias_exclusion_first_token_on... |
d194b5d081f7-3 | https://python.langchain.com/en/latest/_modules/langchain/llms/aleph_alpha.html | import aleph_alpha_client
values["client"] = aleph_alpha_client.Client(token=aleph_alpha_api_key)
except ImportError:
raise ImportError(
"Could not import aleph_alpha_client python package. "
"Please install it with `pip install aleph_alpha_client`."
... |
d194b5d081f7-4 | https://python.langchain.com/en/latest/_modules/langchain/llms/aleph_alpha.html | "use_multiplicative_sequence_penalty": self.use_multiplicative_sequence_penalty, # noqa: E501
"completion_bias_inclusion": self.completion_bias_inclusion,
"completion_bias_inclusion_first_token_only": self.completion_bias_inclusion_first_token_only, # noqa: E501
"completion_bias_ex... |
d194b5d081f7-5 | https://python.langchain.com/en/latest/_modules/langchain/llms/aleph_alpha.html | params = self._default_params
if self.stop_sequences is not None and stop is not None:
raise ValueError(
"stop sequences found in both the input and default params."
)
elif self.stop_sequences is not None:
params["stop_sequences"] = self.stop_sequences... |
2e471f84d25f-0 | https://python.langchain.com/en/latest/_modules/langchain/llms/deepinfra.html | Source code for langchain.llms.deepinfra
"""Wrapper around DeepInfra APIs."""
from typing import Any, Dict, List, Mapping, Optional
import requests
from pydantic import Extra, root_validator
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
from langchain.llms.utils im... |
2e471f84d25f-1 | https://python.langchain.com/en/latest/_modules/langchain/llms/deepinfra.html | def _identifying_params(self) -> Mapping[str, Any]:
"""Get the identifying parameters."""
return {
**{"model_id": self.model_id},
**{"model_kwargs": self.model_kwargs},
}
@property
def _llm_type(self) -> str:
"""Return type of llm."""
return "deepi... |
2e471f84d25f-2 | https://python.langchain.com/en/latest/_modules/langchain/llms/deepinfra.html | © Copyright 2023, Harrison Chase.
Last updated on Jun 04, 2023. |
db54ad32ef3c-0 | https://python.langchain.com/en/latest/_modules/langchain/llms/ai21.html | Source code for langchain.llms.ai21
"""Wrapper around AI21 APIs."""
from typing import Any, Dict, List, Optional
import requests
from pydantic import BaseModel, Extra, root_validator
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
from langchain.utils import get_from... |
db54ad32ef3c-1 | https://python.langchain.com/en/latest/_modules/langchain/llms/ai21.html | """Penalizes repeated tokens according to count."""
frequencyPenalty: AI21PenaltyData = AI21PenaltyData()
"""Penalizes repeated tokens according to frequency."""
numResults: int = 1
"""How many completions to generate for each prompt."""
logitBias: Optional[Dict[str, float]] = None
"""Adjust the... |
db54ad32ef3c-2 | https://python.langchain.com/en/latest/_modules/langchain/llms/ai21.html | """Get the identifying parameters."""
return {**{"model": self.model}, **self._default_params}
@property
def _llm_type(self) -> str:
"""Return type of llm."""
return "ai21"
def _call(
self,
prompt: str,
stop: Optional[List[str]] = None,
run_manager: Op... |
db54ad32ef3c-3 | https://python.langchain.com/en/latest/_modules/langchain/llms/ai21.html | optional_detail = response.json().get("error")
raise ValueError(
f"AI21 /complete call failed with status code {response.status_code}."
f" Details: {optional_detail}"
)
response_json = response.json()
return response_json["completions"][0]["data"][... |
76651b012e37-0 | https://python.langchain.com/en/latest/_modules/langchain/llms/nlpcloud.html | Source code for langchain.llms.nlpcloud
"""Wrapper around NLPCloud APIs."""
from typing import Any, Dict, List, Mapping, Optional
from pydantic import Extra, root_validator
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
from langchain.utils import get_from_dict_or_e... |
76651b012e37-1 | https://python.langchain.com/en/latest/_modules/langchain/llms/nlpcloud.html | top_k: int = 50
"""The number of highest probability tokens to keep for top-k filtering."""
repetition_penalty: float = 1.0
"""Penalizes repeated tokens. 1.0 means no penalty."""
length_penalty: float = 1.0
"""Exponential penalty to the length."""
do_sample: bool = True
"""Whether to use sam... |
76651b012e37-2 | https://python.langchain.com/en/latest/_modules/langchain/llms/nlpcloud.html | "temperature": self.temperature,
"min_length": self.min_length,
"max_length": self.max_length,
"length_no_input": self.length_no_input,
"remove_input": self.remove_input,
"remove_end_sequence": self.remove_end_sequence,
"bad_words": self.bad_words,... |
76651b012e37-3 | https://python.langchain.com/en/latest/_modules/langchain/llms/nlpcloud.html | "NLPCloud only supports a single stop sequence per generation."
"Pass in a list of length 1."
)
elif stop and len(stop) == 1:
end_sequence = stop[0]
else:
end_sequence = None
response = self.client.generation(
prompt, end_sequence=e... |
982debb03433-0 | https://python.langchain.com/en/latest/_modules/langchain/llms/bananadev.html | Source code for langchain.llms.bananadev
"""Wrapper around Banana API."""
import logging
from typing import Any, Dict, List, Mapping, Optional
from pydantic import Extra, Field, root_validator
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
from langchain.llms.utils ... |
982debb03433-1 | https://python.langchain.com/en/latest/_modules/langchain/llms/bananadev.html | if field_name in extra:
raise ValueError(f"Found {field_name} supplied twice.")
logger.warning(
f"""{field_name} was transfered to model_kwargs.
Please confirm that {field_name} is what you intended."""
)
extra[f... |
982debb03433-2 | https://python.langchain.com/en/latest/_modules/langchain/llms/bananadev.html | # a json specific to your model.
"prompt": prompt,
**params,
}
response = banana.run(api_key, model_key, model_inputs)
try:
text = response["modelOutputs"][0]["output"]
except (KeyError, TypeError):
returned = response["modelOutputs"][0]
... |
7e7f232a3a80-0 | https://python.langchain.com/en/latest/_modules/langchain/llms/human.html | Source code for langchain.llms.human
from typing import Any, Callable, List, Mapping, Optional
from pydantic import Field
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
from langchain.llms.utils import enforce_stop_tokens
def _display_prompt(prompt: str) -> None:
... |
7e7f232a3a80-1 | https://python.langchain.com/en/latest/_modules/langchain/llms/human.html | def _call(
self,
prompt: str,
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
) -> str:
"""
Displays the prompt to the user and returns their input as a response.
Args:
prompt (str): The prompt to be displa... |
25a88548a67f-0 | https://python.langchain.com/en/latest/_modules/langchain/llms/cerebriumai.html | Source code for langchain.llms.cerebriumai
"""Wrapper around CerebriumAI API."""
import logging
from typing import Any, Dict, List, Mapping, Optional
from pydantic import Extra, Field, root_validator
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
from langchain.llms... |
25a88548a67f-1 | https://python.langchain.com/en/latest/_modules/langchain/llms/cerebriumai.html | extra = values.get("model_kwargs", {})
for field_name in list(values):
if field_name not in all_required_field_names:
if field_name in extra:
raise ValueError(f"Found {field_name} supplied twice.")
logger.warning(
f"""{field_nam... |
25a88548a67f-2 | https://python.langchain.com/en/latest/_modules/langchain/llms/cerebriumai.html | "Could not import cerebrium python package. "
"Please install it with `pip install cerebrium`."
)
params = self.model_kwargs or {}
response = model_api_request(
self.endpoint_url, {"prompt": prompt, **params}, self.cerebriumai_api_key
)
text = resp... |
26919889f763-0 | https://python.langchain.com/en/latest/_modules/langchain/llms/petals.html | Source code for langchain.llms.petals
"""Wrapper around Petals API."""
import logging
from typing import Any, Dict, List, Mapping, Optional
from pydantic import Extra, Field, root_validator
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
from langchain.llms.utils imp... |
26919889f763-1 | https://python.langchain.com/en/latest/_modules/langchain/llms/petals.html | max_length: Optional[int] = None
"""The maximum length of the sequence to be generated."""
model_kwargs: Dict[str, Any] = Field(default_factory=dict)
"""Holds any model parameters valid for `create` call
not explicitly specified."""
huggingface_api_key: Optional[str] = None
class Config:
... |
26919889f763-2 | https://python.langchain.com/en/latest/_modules/langchain/llms/petals.html | values["tokenizer"] = BloomTokenizerFast.from_pretrained(model_name)
values["client"] = DistributedBloomForCausalLM.from_pretrained(model_name)
values["huggingface_api_key"] = huggingface_api_key
except ImportError:
raise ValueError(
"Could not import transfor... |
26919889f763-3 | https://python.langchain.com/en/latest/_modules/langchain/llms/petals.html | if stop is not None:
# I believe this is required since the stop tokens
# are not enforced by the model parameters
text = enforce_stop_tokens(text, stop)
return text
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 04, 2023. |
acbe60dc84cd-0 | https://python.langchain.com/en/latest/_modules/langchain/llms/fake.html | Source code for langchain.llms.fake
"""Fake LLM wrapper for testing purposes."""
from typing import Any, List, Mapping, Optional
from langchain.callbacks.manager import (
AsyncCallbackManagerForLLMRun,
CallbackManagerForLLMRun,
)
from langchain.llms.base import LLM
[docs]class FakeListLLM(LLM):
"""Fake LLM ... |
b89d61c50d5c-0 | https://python.langchain.com/en/latest/_modules/langchain/llms/pipelineai.html | Source code for langchain.llms.pipelineai
"""Wrapper around Pipeline Cloud API."""
import logging
from typing import Any, Dict, List, Mapping, Optional
from pydantic import BaseModel, Extra, Field, root_validator
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
from l... |
b89d61c50d5c-1 | https://python.langchain.com/en/latest/_modules/langchain/llms/pipelineai.html | if field_name not in all_required_field_names:
if field_name in extra:
raise ValueError(f"Found {field_name} supplied twice.")
logger.warning(
f"""{field_name} was transfered to pipeline_kwargs.
Please confirm that {field_name} ... |
b89d61c50d5c-2 | https://python.langchain.com/en/latest/_modules/langchain/llms/pipelineai.html | run = client.run_pipeline(self.pipeline_key, [prompt, params])
try:
text = run.result_preview[0][0]
except AttributeError:
raise AttributeError(
f"A pipeline run should have a `result_preview` attribute."
f"Run was: {run}"
)
if ... |
48f3f05c617f-0 | https://python.langchain.com/en/latest/_modules/langchain/llms/llamacpp.html | Source code for langchain.llms.llamacpp
"""Wrapper around llama.cpp."""
import logging
from typing import Any, Dict, Generator, List, Optional
from pydantic import Field, root_validator
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
logger = logging.getLogger(__name... |
48f3f05c617f-1 | https://python.langchain.com/en/latest/_modules/langchain/llms/llamacpp.html | """Use half-precision for key/value cache."""
logits_all: bool = Field(False, alias="logits_all")
"""Return logits for all tokens, not just the last token."""
vocab_only: bool = Field(False, alias="vocab_only")
"""Only load the vocabulary, no weights."""
use_mlock: bool = Field(False, alias="use_mlo... |
48f3f05c617f-2 | https://python.langchain.com/en/latest/_modules/langchain/llms/llamacpp.html | repeat_penalty: Optional[float] = 1.1
"""The penalty to apply to repeated tokens."""
top_k: Optional[int] = 40
"""The top-k value to use for sampling."""
last_n_tokens_size: Optional[int] = 64
"""The number of tokens to look back when applying the repeat_penalty."""
use_mmap: Optional[bool] = Tr... |
48f3f05c617f-3 | https://python.langchain.com/en/latest/_modules/langchain/llms/llamacpp.html | )
except Exception as e:
raise ValueError(
f"Could not load Llama model from path: {model_path}. "
f"Received error {e}"
)
return values
@property
def _default_params(self) -> Dict[str, Any]:
"""Get the default parameters for callin... |
48f3f05c617f-4 | https://python.langchain.com/en/latest/_modules/langchain/llms/llamacpp.html | # llama_cpp expects the "stop" key not this, so we remove it:
params.pop("stop_sequences")
# then sets it as configured, or default to an empty list:
params["stop"] = self.stop or stop or []
return params
def _call(
self,
prompt: str,
stop: Optional[List[str]]... |
48f3f05c617f-5 | https://python.langchain.com/en/latest/_modules/langchain/llms/llamacpp.html | ) -> Generator[Dict, None, None]:
"""Yields results objects as they are generated in real time.
BETA: this is a beta feature while we figure out the right abstraction.
Once that happens, this interface could change.
It also calls the callback manager's on_llm_new_token event with
... |
48f3f05c617f-6 | https://python.langchain.com/en/latest/_modules/langchain/llms/llamacpp.html | tokenized_text = self.client.tokenize(text.encode("utf-8"))
return len(tokenized_text)
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 04, 2023. |
9548bb8296e9-0 | https://python.langchain.com/en/latest/_modules/langchain/llms/gooseai.html | Source code for langchain.llms.gooseai
"""Wrapper around GooseAI API."""
import logging
from typing import Any, Dict, List, Mapping, Optional
from pydantic import Extra, Field, root_validator
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
from langchain.utils import... |
9548bb8296e9-1 | https://python.langchain.com/en/latest/_modules/langchain/llms/gooseai.html | n: int = 1
"""How many completions to generate for each prompt."""
model_kwargs: Dict[str, Any] = Field(default_factory=dict)
"""Holds any model parameters valid for `create` call not explicitly specified."""
logit_bias: Optional[Dict[str, float]] = Field(default_factory=dict)
"""Adjust the probabil... |
9548bb8296e9-2 | https://python.langchain.com/en/latest/_modules/langchain/llms/gooseai.html | openai.api_base = "https://api.goose.ai/v1"
values["client"] = openai.Completion
except ImportError:
raise ImportError(
"Could not import openai python package. "
"Please install it with `pip install openai`."
)
return values
@prope... |
9548bb8296e9-3 | https://python.langchain.com/en/latest/_modules/langchain/llms/gooseai.html | response = self.client.create(engine=self.model_name, prompt=prompt, **params)
text = response.choices[0].text
return text
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 04, 2023. |
ec170480f731-0 | https://python.langchain.com/en/latest/_modules/langchain/llms/modal.html | Source code for langchain.llms.modal
"""Wrapper around Modal API."""
import logging
from typing import Any, Dict, List, Mapping, Optional
import requests
from pydantic import Extra, Field, root_validator
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
from langchain.... |
ec170480f731-1 | https://python.langchain.com/en/latest/_modules/langchain/llms/modal.html | Please confirm that {field_name} is what you intended."""
)
extra[field_name] = values.pop(field_name)
values["model_kwargs"] = extra
return values
@property
def _identifying_params(self) -> Mapping[str, Any]:
"""Get the identifying parameters."""
... |
2064188564fd-0 | https://python.langchain.com/en/latest/_modules/langchain/llms/google_palm.html | Source code for langchain.llms.google_palm
"""Wrapper arround Google's PaLM Text APIs."""
from __future__ import annotations
import logging
from typing import Any, Callable, Dict, List, Optional
from pydantic import BaseModel, root_validator
from tenacity import (
before_sleep_log,
retry,
retry_if_exception... |
2064188564fd-1 | https://python.langchain.com/en/latest/_modules/langchain/llms/google_palm.html | def generate_with_retry(llm: GooglePalm, **kwargs: Any) -> Any:
"""Use tenacity to retry the completion call."""
retry_decorator = _create_retry_decorator()
@retry_decorator
def _generate_with_retry(**kwargs: Any) -> Any:
return llm.client.generate_text(**kwargs)
return _generate_with_retry(... |
2064188564fd-2 | https://python.langchain.com/en/latest/_modules/langchain/llms/google_palm.html | """Maximum number of tokens to include in a candidate. Must be greater than zero.
If unset, will default to 64."""
n: int = 1
"""Number of chat completions to generate for each prompt. Note that the API may
not return the full n completions if duplicates are generated."""
@root_validator()
... |
2064188564fd-3 | https://python.langchain.com/en/latest/_modules/langchain/llms/google_palm.html | stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
) -> LLMResult:
generations = []
for prompt in prompts:
completion = generate_with_retry(
self,
model=self.model_name,
prompt=prompt,
... |
35a92242ca9c-0 | https://python.langchain.com/en/latest/_modules/langchain/llms/promptlayer_openai.html | Source code for langchain.llms.promptlayer_openai
"""PromptLayer wrapper."""
import datetime
from typing import List, Optional
from langchain.callbacks.manager import (
AsyncCallbackManagerForLLMRun,
CallbackManagerForLLMRun,
)
from langchain.llms import OpenAI, OpenAIChat
from langchain.schema import LLMResult... |
35a92242ca9c-1 | https://python.langchain.com/en/latest/_modules/langchain/llms/promptlayer_openai.html | from promptlayer.utils import get_api_key, promptlayer_api_request
request_start_time = datetime.datetime.now().timestamp()
generated_responses = super()._generate(prompts, stop, run_manager)
request_end_time = datetime.datetime.now().timestamp()
for i in range(len(prompts)):
... |
35a92242ca9c-2 | https://python.langchain.com/en/latest/_modules/langchain/llms/promptlayer_openai.html | resp = {
"text": generation.text,
"llm_output": generated_responses.llm_output,
}
pl_request_id = await promptlayer_api_request_async(
"langchain.PromptLayerOpenAI.async",
"langchain",
[prompt],
self.... |
35a92242ca9c-3 | https://python.langchain.com/en/latest/_modules/langchain/llms/promptlayer_openai.html | """
pl_tags: Optional[List[str]]
return_pl_id: Optional[bool] = False
def _generate(
self,
prompts: List[str],
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
) -> LLMResult:
"""Call OpenAI generate and then call PromptLay... |
35a92242ca9c-4 | https://python.langchain.com/en/latest/_modules/langchain/llms/promptlayer_openai.html | ) -> LLMResult:
from promptlayer.utils import get_api_key, promptlayer_api_request_async
request_start_time = datetime.datetime.now().timestamp()
generated_responses = await super()._agenerate(prompts, stop, run_manager)
request_end_time = datetime.datetime.now().timestamp()
for ... |
e26dd70f1a34-0 | https://python.langchain.com/en/latest/_modules/langchain/llms/vertexai.html | Source code for langchain.llms.vertexai
"""Wrapper around Google VertexAI models."""
from typing import TYPE_CHECKING, Any, Dict, List, Optional
from pydantic import BaseModel, root_validator
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
from langchain.llms.utils i... |
e26dd70f1a34-1 | https://python.langchain.com/en/latest/_modules/langchain/llms/vertexai.html | def _default_params(self) -> Dict[str, Any]:
base_params = {
"temperature": self.temperature,
"max_output_tokens": self.max_output_tokens,
"top_k": self.top_p,
"top_p": self.top_k,
}
return {**base_params}
def _predict(self, prompt: str, stop: ... |
e26dd70f1a34-2 | https://python.langchain.com/en/latest/_modules/langchain/llms/vertexai.html | tuned_model_name = values.get("tuned_model_name")
if tuned_model_name:
values["client"] = TextGenerationModel.get_tuned_model(tuned_model_name)
else:
values["client"] = TextGenerationModel.from_pretrained(values["model_name"])
return values
def _call(
self,
... |
e298183c4283-0 | https://python.langchain.com/en/latest/_modules/langchain/llms/ctransformers.html | Source code for langchain.llms.ctransformers
"""Wrapper around the C Transformers library."""
from typing import Any, Dict, Optional, Sequence
from pydantic import root_validator
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
[docs]class CTransformers(LLM):
"""W... |
e298183c4283-1 | https://python.langchain.com/en/latest/_modules/langchain/llms/ctransformers.html | def _llm_type(self) -> str:
"""Return type of llm."""
return "ctransformers"
@root_validator()
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that ``ctransformers`` package is installed."""
try:
from ctransformers import AutoModelForCausalLM
... |
e298183c4283-2 | https://python.langchain.com/en/latest/_modules/langchain/llms/ctransformers.html | © Copyright 2023, Harrison Chase.
Last updated on Jun 04, 2023. |
d3225a388a6f-0 | https://python.langchain.com/en/latest/_modules/langchain/llms/predictionguard.html | Source code for langchain.llms.predictionguard
"""Wrapper around Prediction Guard APIs."""
import logging
from typing import Any, Dict, List, Optional
from pydantic import Extra, root_validator
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
from langchain.llms.utils... |
d3225a388a6f-1 | https://python.langchain.com/en/latest/_modules/langchain/llms/predictionguard.html | class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
@root_validator()
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that the access token and python package exists in environment."""
token = get_from_dict_or_env(values, "token", "PR... |
d3225a388a6f-2 | https://python.langchain.com/en/latest/_modules/langchain/llms/predictionguard.html | import predictionguard as pg
params = self._default_params
if self.stop is not None and stop is not None:
raise ValueError("`stop` found in both the input and default params.")
elif self.stop is not None:
params["stop_sequences"] = self.stop
else:
para... |
ca578177f6ef-0 | https://python.langchain.com/en/latest/_modules/langchain/llms/anthropic.html | Source code for langchain.llms.anthropic
"""Wrapper around Anthropic APIs."""
import re
import warnings
from typing import Any, Callable, Dict, Generator, List, Mapping, Optional, Tuple, Union
from pydantic import BaseModel, Extra, root_validator
from langchain.callbacks.manager import (
AsyncCallbackManagerForLLMR... |
ca578177f6ef-1 | https://python.langchain.com/en/latest/_modules/langchain/llms/anthropic.html | values, "anthropic_api_key", "ANTHROPIC_API_KEY"
)
try:
import anthropic
values["client"] = anthropic.Client(
api_key=anthropic_api_key,
default_request_timeout=values["default_request_timeout"],
)
values["HUMAN_PROMPT"] = a... |
ca578177f6ef-2 | https://python.langchain.com/en/latest/_modules/langchain/llms/anthropic.html | stop.extend([self.HUMAN_PROMPT])
return stop
[docs]class Anthropic(LLM, _AnthropicCommon):
r"""Wrapper around Anthropic's large language models.
To use, you should have the ``anthropic`` python package installed, and the
environment variable ``ANTHROPIC_API_KEY`` set with your API key, or pass
i... |
ca578177f6ef-3 | https://python.langchain.com/en/latest/_modules/langchain/llms/anthropic.html | def _wrap_prompt(self, prompt: str) -> str:
if not self.HUMAN_PROMPT or not self.AI_PROMPT:
raise NameError("Please ensure the anthropic package is loaded")
if prompt.startswith(self.HUMAN_PROMPT):
return prompt # Already wrapped.
# Guard against common errors in specify... |
ca578177f6ef-4 | https://python.langchain.com/en/latest/_modules/langchain/llms/anthropic.html | delta = data["completion"][len(current_completion) :]
current_completion = data["completion"]
if run_manager:
run_manager.on_llm_new_token(delta, **data)
return current_completion
response = self.client.completion(
prompt=self._wrap_pro... |
ca578177f6ef-5 | https://python.langchain.com/en/latest/_modules/langchain/llms/anthropic.html | prompt: The prompt to pass into the model.
stop: Optional list of stop words to use when generating.
Returns:
A generator representing the stream of tokens from Anthropic.
Example:
.. code-block:: python
prompt = "Write a poem about a stream."
... |
6070efcd4f4f-0 | https://python.langchain.com/en/latest/_modules/langchain/llms/openai.html | Source code for langchain.llms.openai
"""Wrapper around OpenAI APIs."""
from __future__ import annotations
import logging
import sys
import warnings
from typing import (
AbstractSet,
Any,
Callable,
Collection,
Dict,
Generator,
List,
Literal,
Mapping,
Optional,
Set,
Tuple,... |
6070efcd4f4f-1 | https://python.langchain.com/en/latest/_modules/langchain/llms/openai.html | response["choices"][0]["logprobs"] = stream_response["choices"][0]["logprobs"]
def _streaming_response_template() -> Dict[str, Any]:
return {
"choices": [
{
"text": "",
"finish_reason": None,
"logprobs": None,
}
]
}
def _cre... |
6070efcd4f4f-2 | https://python.langchain.com/en/latest/_modules/langchain/llms/openai.html | llm: Union[BaseOpenAI, OpenAIChat], **kwargs: Any
) -> Any:
"""Use tenacity to retry the async completion call."""
retry_decorator = _create_retry_decorator(llm)
@retry_decorator
async def _completion_with_retry(**kwargs: Any) -> Any:
# Use OpenAI's async api https://github.com/openai/openai-pyt... |
6070efcd4f4f-3 | https://python.langchain.com/en/latest/_modules/langchain/llms/openai.html | openai_api_base: Optional[str] = None
openai_organization: Optional[str] = None
# to support explicit proxy for OpenAI
openai_proxy: Optional[str] = None
batch_size: int = 20
"""Batch size to use when passing multiple documents to generate."""
request_timeout: Optional[Union[float, Tuple[float, ... |
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