id stringlengths 14 15 | text stringlengths 35 2.51k | source stringlengths 61 154 |
|---|---|---|
9f6cfcdc65e7-0 | Source code for langchain.llms.replicate
"""Wrapper around Replicate 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 im... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/replicate.html |
9f6cfcdc65e7-1 | def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]:
"""Build extra kwargs from additional params that were passed in."""
all_required_field_names = {field.alias for field in cls.__fields__.values()}
extra = values.get("model_kwargs", {})
for field_name in list(values):
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/replicate.html |
9f6cfcdc65e7-2 | **kwargs: Any,
) -> str:
"""Call to replicate endpoint."""
try:
import replicate as replicate_python
except ImportError:
raise ImportError(
"Could not import replicate python package. "
"Please install it with `pip install replicate`."
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/replicate.html |
2f2251f98c5f-0 | 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... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/google_palm.html |
2f2251f98c5f-1 | ),
before_sleep=before_sleep_log(logger, logging.WARNING),
)
[docs]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:
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/google_palm.html |
2f2251f98c5f-2 | """Decode using top-k sampling: consider the set of top_k most probable tokens.
Must be positive."""
max_output_tokens: Optional[int] = None
"""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 compl... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/google_palm.html |
2f2251f98c5f-3 | raise ValueError("max_output_tokens must be greater than zero")
return values
def _generate(
self,
prompts: List[str],
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> LLMResult:
generations = []... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/google_palm.html |
f0d04eaecca2-0 | Source code for langchain.llms.beam
"""Wrapper around Beam API."""
import base64
import json
import logging
import subprocess
import textwrap
import time
from typing import Any, Dict, List, Mapping, Optional
import requests
from pydantic import Extra, Field, root_validator
from langchain.callbacks.manager import Callba... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/beam.html |
f0d04eaecca2-1 | max_length=50)
llm._deploy()
call_result = llm._call(input)
"""
model_name: str = ""
name: str = ""
cpu: str = ""
memory: str = ""
gpu: str = ""
python_version: str = ""
python_packages: List[str] = []
max_length: str = ""
url: str = ""
"""model endpoi... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/beam.html |
f0d04eaecca2-2 | def validate_environment(cls, values: Dict) -> Dict:
"""Validate that api key and python package exists in environment."""
beam_client_id = get_from_dict_or_env(
values, "beam_client_id", "BEAM_CLIENT_ID"
)
beam_client_secret = get_from_dict_or_env(
values, "beam_... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/beam.html |
f0d04eaecca2-3 | inputs={{"prompt": beam.Types.String(), "max_length": beam.Types.String()}},
outputs={{"text": beam.Types.String()}},
handler="run.py:beam_langchain",
)
"""
)
script_name = "app.py"
with open(script_name, "w") as file:
file.write(
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/beam.html |
f0d04eaecca2-4 | """Call to Beam."""
try:
import beam # type: ignore
if beam.__path__ == "":
raise ImportError
except ImportError:
raise ImportError(
"Could not import beam python package. "
"Please install it with `curl "
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/beam.html |
f0d04eaecca2-5 | **kwargs: Any,
) -> str:
"""Call to Beam."""
url = "https://apps.beam.cloud/" + self.app_id if self.app_id else self.url
payload = {"prompt": prompt, "max_length": self.max_length}
payload.update(kwargs)
headers = {
"Accept": "*/*",
"Accept-Encoding": ... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/beam.html |
4ec5083658a3-0 | 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... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/aleph_alpha.html |
4ec5083658a3-1 | """Total probability mass of tokens to consider at each step."""
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 whet... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/aleph_alpha.html |
4ec5083658a3-2 | echo: bool = False
"""Echo the prompt in the completion."""
use_multiplicative_frequency_penalty: bool = False
sequence_penalty: float = 0.0
sequence_penalty_min_length: int = 2
use_multiplicative_sequence_penalty: bool = False
completion_bias_inclusion: Optional[Sequence[str]] = None
comple... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/aleph_alpha.html |
4ec5083658a3-3 | def validate_environment(cls, values: Dict) -> Dict:
"""Validate that api key and python package exists in environment."""
aleph_alpha_api_key = get_from_dict_or_env(
values, "aleph_alpha_api_key", "ALEPH_ALPHA_API_KEY"
)
try:
import aleph_alpha_client
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/aleph_alpha.html |
4ec5083658a3-4 | "disable_optimizations": self.disable_optimizations,
"minimum_tokens": self.minimum_tokens,
"echo": self.echo,
"use_multiplicative_frequency_penalty": self.use_multiplicative_frequency_penalty, # noqa: E501
"sequence_penalty": self.sequence_penalty,
"sequence... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/aleph_alpha.html |
4ec5083658a3-5 | """Call out to Aleph Alpha's completion endpoint.
Args:
prompt: The prompt to pass into the model.
stop: Optional list of stop words to use when generating.
Returns:
The string generated by the model.
Example:
.. code-block:: python
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/aleph_alpha.html |
4aa96d57ed11-0 | Source code for langchain.llms.textgen
"""Wrapper around text-generation-webui."""
import logging
from typing import Any, Dict, List, Optional
import requests
from pydantic import Field
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
logger = logging.getLogger(__name... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/textgen.html |
4aa96d57ed11-1 | number. Higher value = higher range of possible random results."""
typical_p: Optional[float] = 1
"""If not set to 1, select only tokens that are at least this much more likely to
appear than random tokens, given the prior text."""
epsilon_cutoff: Optional[float] = 0 # In units of 1e-4
"""Epsilon c... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/textgen.html |
4aa96d57ed11-2 | """Seed (-1 for random)"""
add_bos_token: bool = Field(True, alias="add_bos_token")
"""Add the bos_token to the beginning of prompts.
Disabling this can make the replies more creative."""
truncation_length: Optional[int] = 2048
"""Truncate the prompt up to this length. The leftmost tokens are remove... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/textgen.html |
4aa96d57ed11-3 | "num_beams": self.num_beams,
"penalty_alpha": self.penalty_alpha,
"length_penalty": self.length_penalty,
"early_stopping": self.early_stopping,
"seed": self.seed,
"add_bos_token": self.add_bos_token,
"truncation_length": self.truncation_length,
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/textgen.html |
4aa96d57ed11-4 | return params
def _call(
self,
prompt: str,
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> str:
"""Call the textgen web API and return the output.
Args:
prompt: The prompt to use fo... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/textgen.html |
e1cf133d3fe7-0 | Source code for langchain.llms.octoai_endpoint
"""Wrapper around OctoAI 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.llms.utils import enforce_... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/octoai_endpoint.html |
e1cf133d3fe7-1 | """OCTOAI API Token"""
[docs] class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
[docs] @root_validator(allow_reuse=True)
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that api key and python package exists in environment."""
oct... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/octoai_endpoint.html |
e1cf133d3fe7-2 | _model_kwargs = self.model_kwargs or {}
# Prepare the payload JSON
parameter_payload = {"inputs": prompt, "parameters": _model_kwargs}
try:
# Initialize the OctoAI client
from octoai import client
octoai_client = client.Client(token=self.octoai_api_token)
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/octoai_endpoint.html |
b28e95e7b76d-0 | 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... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/huggingface_pipeline.html |
b28e95e7b76d-1 | """
pipeline: Any #: :meta private:
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."""
[docs] class Config:
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/huggingface_pipeline.html |
b28e95e7b76d-2 | else:
raise ValueError(
f"Got invalid task {task}, "
f"currently only {VALID_TASKS} are supported"
)
except ImportError as e:
raise ValueError(
f"Could not load the {task} model due to missing dependencies."
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/huggingface_pipeline.html |
b28e95e7b76d-3 | )
return cls(
pipeline=pipeline,
model_id=model_id,
model_kwargs=_model_kwargs,
pipeline_kwargs=_pipeline_kwargs,
**kwargs,
)
@property
def _identifying_params(self) -> Mapping[str, Any]:
"""Get the identifying parameters."""
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/huggingface_pipeline.html |
4f60d15c2512-0 | 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... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/forefrontai.html |
4f60d15c2512-1 | def validate_environment(cls, values: Dict) -> Dict:
"""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... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/forefrontai.html |
4f60d15c2512-2 | """
response = requests.post(
url=self.endpoint_url,
headers={
"Authorization": f"Bearer {self.forefrontai_api_key}",
"Content-Type": "application/json",
},
json={"text": prompt, **self._default_params, **kwargs},
)
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/forefrontai.html |
44e08aca2d59-0 | Source code for langchain.llms.self_hosted
"""Run model inference on self-hosted remote hardware."""
import importlib.util
import logging
import pickle
from typing import Any, Callable, List, Mapping, Optional
from pydantic import Extra
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llm... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/self_hosted.html |
44e08aca2d59-1 | )
if device < 0 and cuda_device_count > 0:
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 ass... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/self_hosted.html |
44e08aca2d59-2 | llm = SelfHostedPipeline(
model_load_fn=load_pipeline,
hardware=gpu,
model_reqs=model_reqs, inference_fn=inference_fn
)
Example for <2GB model (can be serialized and sent directly to the server):
.. code-block:: python
from langchain.ll... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/self_hosted.html |
44e08aca2d59-3 | load_fn_kwargs: Optional[dict] = None
"""Key word arguments to pass to the model load function."""
model_reqs: List[str] = ["./", "torch"]
"""Requirements to install on hardware to inference the model."""
[docs] class Config:
"""Configuration for this pydantic object."""
extra = Extra.for... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/self_hosted.html |
44e08aca2d59-4 | if not isinstance(pipeline, str):
logger.warning(
"Serializing pipeline to send to remote hardware. "
"Note, it can be quite slow"
"to serialize and send large models with each execution. "
"Consider sending the pipeline"
"to th... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/self_hosted.html |
1e1bd291aa97-0 | Source code for langchain.llms.manifest
"""Wrapper around HazyResearch's Manifest library."""
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
[docs]class ManifestWrapper(... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/manifest.html |
1e1bd291aa97-1 | if stop is not None and len(stop) != 1:
raise NotImplementedError(
f"Manifest currently only supports a single stop token, got {stop}"
)
params = self.llm_kwargs or {}
params = {**params, **kwargs}
if stop is not None:
params["stop_token"] = st... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/manifest.html |
89c54fb9a69c-0 | 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, root_validator
from langchain.callbacks.manager import (
AsyncCallbackManagerForLLMRun,
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/anthropic.html |
89c54fb9a69c-1 | """Validate that api key and python package exists in environment."""
anthropic_api_key = get_from_dict_or_env(
values, "anthropic_api_key", "ANTHROPIC_API_KEY"
)
"""Get custom api url from environment."""
anthropic_api_url = get_from_dict_or_env(
values,
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/anthropic.html |
89c54fb9a69c-2 | @property
def _identifying_params(self) -> Mapping[str, Any]:
"""Get the identifying parameters."""
return {**{}, **self._default_params}
def _get_anthropic_stop(self, stop: Optional[List[str]] = None) -> List[str]:
if not self.HUMAN_PROMPT or not self.AI_PROMPT:
raise NameEr... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/anthropic.html |
89c54fb9a69c-3 | response = model(prompt)
"""
[docs] @root_validator()
def raise_warning(cls, values: Dict) -> Dict:
"""Raise warning that this class is deprecated."""
warnings.warn(
"This Anthropic LLM is deprecated. "
"Please use `from langchain.chat_models import ChatAnthropic` inst... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/anthropic.html |
89c54fb9a69c-4 | Returns:
The string generated by the model.
Example:
.. code-block:: python
prompt = "What are the biggest risks facing humanity?"
prompt = f"\n\nHuman: {prompt}\n\nAssistant:"
response = model(prompt)
"""
stop = self._get_a... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/anthropic.html |
89c54fb9a69c-5 | )
current_completion = ""
async for data in stream_resp:
delta = data["completion"][len(current_completion) :]
current_completion = data["completion"]
if run_manager:
await run_manager.on_llm_new_token(delta, **data)
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/anthropic.html |
0bd0815bca5c-0 | 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... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/llamacpp.html |
0bd0815bca5c-1 | f16_kv: bool = Field(True, alias="f16_kv")
"""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."""
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/llamacpp.html |
0bd0815bca5c-2 | """Whether to echo the prompt."""
stop: Optional[List[str]] = []
"""A list of strings to stop generation when encountered."""
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_token... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/llamacpp.html |
0bd0815bca5c-3 | 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... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/llamacpp.html |
0bd0815bca5c-4 | Returns:
Dictionary containing the combined parameters.
"""
# Raise error if stop sequences are in both input and default params
if self.stop and stop is not None:
raise ValueError("`stop` found in both the input and default params.")
params = self._default_params... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/llamacpp.html |
0bd0815bca5c-5 | return combined_text_output
else:
params = self._get_parameters(stop)
params = {**params, **kwargs}
result = self.client(prompt=prompt, **params)
return result["choices"][0]["text"]
[docs] def stream(
self,
prompt: str,
stop: Optional[Li... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/llamacpp.html |
0bd0815bca5c-6 | """
params = self._get_parameters(stop)
result = self.client(prompt=prompt, stream=True, **params)
for chunk in result:
token = chunk["choices"][0]["text"]
log_probs = chunk["choices"][0].get("logprobs", None)
if run_manager:
run_manager.on_llm... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/llamacpp.html |
a0f77b83ed5d-0 | 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... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/petals.html |
a0f77b83ed5d-1 | """Whether or not to use sampling; use greedy decoding otherwise."""
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.""... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/petals.html |
a0f77b83ed5d-2 | )
try:
from petals import DistributedBloomForCausalLM
from transformers import BloomTokenizerFast
model_name = values["model_name"]
values["tokenizer"] = BloomTokenizerFast.from_pretrained(model_name)
values["client"] = DistributedBloomForCausalLM.from... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/petals.html |
a0f77b83ed5d-3 | """Call the Petals API."""
params = self._default_params
params = {**params, **kwargs}
inputs = self.tokenizer(prompt, return_tensors="pt")["input_ids"]
outputs = self.client.generate(inputs, **params)
text = self.tokenizer.decode(outputs[0])
if stop is not None:
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/petals.html |
077e5cda6d64-0 | 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... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/predictionguard.html |
077e5cda6d64-1 | """Your Prediction Guard access token."""
stop: Optional[List[str]] = None
[docs] class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
[docs] @root_validator()
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that the access token and pyt... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/predictionguard.html |
077e5cda6d64-2 | Args:
prompt: The prompt to pass into the model.
Returns:
The string generated by the model.
Example:
.. code-block:: python
response = pgllm("Tell me a joke.")
"""
import predictionguard as pg
params = self._default_params
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/predictionguard.html |
5ecfd85c6eeb-0 | 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... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/sagemaker_endpoint.html |
5ecfd85c6eeb-1 | [docs] @abstractmethod
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 he... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/sagemaker_endpoint.html |
5ecfd85c6eeb-2 | "default"
)
se = SagemakerEndpoint(
endpoint_name=endpoint_name,
region_name=region_name,
credentials_profile_name=credentials_profile_name
)
"""
client: Any #: :meta private:
endpoint_name: str = ""
"""The name of the ... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/sagemaker_endpoint.html |
5ecfd85c6eeb-3 | response_json = json.loads(output.read().decode("utf-8"))
return response_json[0]["generated_text"]
"""
model_kwargs: Optional[Dict] = None
"""Key word arguments to pass to the model."""
endpoint_kwargs: Optional[Dict] = None
"""Optional attributes passed to the invoke_endpoint
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/sagemaker_endpoint.html |
5ecfd85c6eeb-4 | """Get the identifying parameters."""
_model_kwargs = self.model_kwargs or {}
return {
**{"endpoint_name": self.endpoint_name},
**{"model_kwargs": _model_kwargs},
}
@property
def _llm_type(self) -> str:
"""Return type of llm."""
return "sagemaker_e... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/sagemaker_endpoint.html |
5ecfd85c6eeb-5 | if stop is not None:
# This is a bit hacky, but I can't figure out a better way to enforce
# stop tokens when making calls to the sagemaker endpoint.
text = enforce_stop_tokens(text, stop)
return text | https://api.python.langchain.com/en/latest/_modules/langchain/llms/sagemaker_endpoint.html |
01df9c1c8b1a-0 | Source code for langchain.llms.openllm
"""Wrapper around OpenLLM APIs."""
from __future__ import annotations
import copy
import json
import logging
from typing import (
TYPE_CHECKING,
Any,
Dict,
List,
Literal,
Optional,
TypedDict,
Union,
overload,
)
from pydantic import PrivateAttr
f... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/openllm.html |
01df9c1c8b1a-1 | )
llm("What is the difference between a duck and a goose?")
For all available supported models, you can run 'openllm models'.
If you have a OpenLLM server running, you can also use it remotely:
.. code-block:: python
from langchain.llms import OpenLLM
llm = OpenLLM(se... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/openllm.html |
01df9c1c8b1a-2 | @overload
def __init__(
self,
model_name: Optional[str] = ...,
*,
model_id: Optional[str] = ...,
embedded: Literal[True, False] = ...,
**llm_kwargs: Any,
) -> None:
...
@overload
def __init__(
self,
*,
server_url: str = ...,... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/openllm.html |
01df9c1c8b1a-3 | super().__init__(
**{
"server_url": server_url,
"server_type": server_type,
"llm_kwargs": llm_kwargs,
}
)
self._runner = None # type: ignore
self._client = client
else:
as... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/openllm.html |
01df9c1c8b1a-4 | model_id='google/flan-t5-large',
embedded=False,
)
tools = load_tools(["serpapi", "llm-math"], llm=llm)
agent = initialize_agent(
tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION
)
svc = bentoml.Service("langchain-openllm... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/openllm.html |
01df9c1c8b1a-5 | return "openllm_client" if self._client else "openllm"
def _call(
self,
prompt: str,
stop: Optional[List[str]] = None,
run_manager: CallbackManagerForLLMRun | None = None,
**kwargs: Any,
) -> str:
try:
import openllm
except ImportError as e:
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/openllm.html |
01df9c1c8b1a-6 | )
if self._client:
return await self._client.acall(
"generate", prompt, **config.model_dump(flatten=True)
)
else:
assert self._runner is not None
(
prompt,
generate_kwargs,
postprocess_kwargs,... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/openllm.html |
37b11832c544-0 | 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... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/gooseai.html |
37b11832c544-1 | presence_penalty: float = 0
"""Penalizes repeated tokens."""
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[... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/gooseai.html |
37b11832c544-2 | gooseai_api_key = get_from_dict_or_env(
values, "gooseai_api_key", "GOOSEAI_API_KEY"
)
try:
import openai
openai.api_key = gooseai_api_key
openai.api_base = "https://api.goose.ai/v1"
values["client"] = openai.Completion
except ImportErr... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/gooseai.html |
37b11832c544-3 | **kwargs: Any,
) -> str:
"""Call the GooseAI API."""
params = self._default_params
if stop is not None:
if "stop" in params:
raise ValueError("`stop` found in both the input and default params.")
params["stop"] = stop
params = {**params, **kwar... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/gooseai.html |
80b0141a0d4b-0 | 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.... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/modal.html |
80b0141a0d4b-1 | 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[field_name] = values.pop(field_name)
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/modal.html |
d27d2a58fef1-0 | Source code for langchain.llms.rwkv
"""Wrapper for the RWKV model.
Based on https://github.com/saharNooby/rwkv.cpp/blob/master/rwkv/chat_with_bot.py
https://github.com/BlinkDL/ChatRWKV/blob/main/v2/chat.py
"""
from typing import Any, Dict, List, Mapping, Optional, Set
from pydantic import BaseModel, Extra, roo... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/rwkv.html |
d27d2a58fef1-1 | """Positive values penalize new tokens based on their existing frequency
in the text so far, decreasing the model's likelihood to repeat the same
line verbatim.."""
penalty_alpha_presence: float = 0.4
"""Positive values penalize new tokens based on whether they appear
in the text so far, increasing ... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/rwkv.html |
d27d2a58fef1-2 | def validate_environment(cls, values: Dict) -> Dict:
"""Validate that the python package exists in the environment."""
try:
import tokenizers
except ImportError:
raise ImportError(
"Could not import tokenizers python package. "
"Please inst... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/rwkv.html |
d27d2a58fef1-3 | """Return the type of llm."""
return "rwkv"
[docs] def run_rnn(self, _tokens: List[str], newline_adj: int = 0) -> Any:
AVOID_REPEAT_TOKENS = []
AVOID_REPEAT = "，：？！"
for i in AVOID_REPEAT:
dd = self.pipeline.encode(i)
assert len(dd) == 1
AVOID_REPEA... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/rwkv.html |
d27d2a58fef1-4 | )
END_OF_TEXT = 0
if token == END_OF_TEXT:
break
if token not in occurrence:
occurrence[token] = 1
else:
occurrence[token] += 1
logits = self.run_rnn([token])
xxx = self.tokenizer.decode(self.model_to... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/rwkv.html |
3ac2ee076bb4-0 | Source code for langchain.llms.huggingface_hub
"""Wrapper around HuggingFace 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.llms.utils import enf... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/huggingface_hub.html |
3ac2ee076bb4-1 | """Configuration for this pydantic object."""
extra = Extra.forbid
[docs] @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, "hugg... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/huggingface_hub.html |
3ac2ee076bb4-2 | prompt: str,
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> str:
"""Call out to HuggingFace Hub's inference endpoint.
Args:
prompt: The prompt to pass into the model.
stop: Optional list of... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/huggingface_hub.html |
2e70c5ca2066-0 | Source code for langchain.llms.cohere
"""Wrapper around Cohere APIs."""
from __future__ import annotations
import logging
from typing import Any, Callable, Dict, List, Optional
from pydantic import Extra, root_validator
from tenacity import (
before_sleep_log,
retry,
retry_if_exception_type,
stop_after_... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/cohere.html |
2e70c5ca2066-1 | """Wrapper around Cohere large language models.
To use, you should have the ``cohere`` python package installed, and the
environment variable ``COHERE_API_KEY`` set with your API key, or pass
it as a named parameter to the constructor.
Example:
.. code-block:: python
from langchain.l... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/cohere.html |
2e70c5ca2066-2 | """Configuration for this pydantic object."""
extra = Extra.forbid
[docs] @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... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/cohere.html |
2e70c5ca2066-3 | **kwargs: Any,
) -> 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 when generating.
Returns:
The string generated by the model.
Example:
.. code-... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/cohere.html |
46eee3f07fb0-0 | 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 ... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/bananadev.html |
46eee3f07fb0-1 | 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 model_kwargs.
Please confirm that {field_name} is ... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/bananadev.html |
46eee3f07fb0-2 | )
params = self.model_kwargs or {}
params = {**params, **kwargs}
api_key = self.banana_api_key
model_key = self.model_key
model_inputs = {
# a json specific to your model.
"prompt": prompt,
**params,
}
response = banana.run(api_... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/bananadev.html |
0484d5e74466-0 | 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... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/pipelineai.html |
0484d5e74466-1 | extra = values.get("pipeline_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_... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/pipelineai.html |
0484d5e74466-2 | raise ValueError(
"Could not import pipeline-ai python package. "
"Please install it with `pip install pipeline-ai`."
)
client = PipelineCloud(token=self.pipeline_api_key)
params = self.pipeline_kwargs or {}
params = {**params, **kwargs}
run = ... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/pipelineai.html |
319d42b5bcda-0 | 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:
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/human.html |
319d42b5bcda-1 | """Returns the type of LLM."""
return "human-input"
def _call(
self,
prompt: str,
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> str:
"""
Displays the prompt to the user and returns the... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/human.html |
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