id stringlengths 14 15 | text stringlengths 44 2.47k | source stringlengths 61 181 |
|---|---|---|
d653fb48aaf8-18 | try:
import openai
openai.api_key = openai_api_key
if openai_api_base:
openai.api_base = openai_api_base
if openai_organization:
openai.organization = openai_organization
if openai_proxy:
openai.proxy = {"http": ... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/openai.html |
d653fb48aaf8-19 | params: Dict[str, Any] = {**{"model": self.model_name}, **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
if params.get("max_tokens") == -1:
# ... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/openai.html |
d653fb48aaf8-20 | async for stream_resp in await acompletion_with_retry(
self, messages=messages, run_manager=run_manager, **params
):
token = stream_resp["choices"][0]["delta"].get("content", "")
chunk = GenerationChunk(text=token)
yield chunk
if run_manager:
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/openai.html |
d653fb48aaf8-21 | prompts: List[str],
stop: Optional[List[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> LLMResult:
if self.streaming:
generation: Optional[GenerationChunk] = None
async for chunk in self._astream(prompts[0], stop,... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/openai.html |
d653fb48aaf8-22 | # tiktoken NOT supported for Python < 3.8
if sys.version_info[1] < 8:
return super().get_token_ids(text)
try:
import tiktoken
except ImportError:
raise ImportError(
"Could not import tiktoken python package. "
"This is needed in... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/openai.html |
feab62409b84-0 | Source code for langchain.llms.xinference
from typing import TYPE_CHECKING, Any, Dict, Generator, List, Mapping, Optional, Union
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
if TYPE_CHECKING:
from xinference.client import RESTfulChatModelHandle, RESTfulGenerat... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/xinference.html |
feab62409b84-1 | server_url="http://0.0.0.0:9997",
model_uid = {model_uid} # replace model_uid with the model UID return from launching the model
)
llm(
prompt="Q: where can we visit in the capital of France? A:",
generate_config={"max_tokens": 1024, "stream": True},
)
To ... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/xinference.html |
feab62409b84-2 | self.client = RESTfulClient(server_url)
@property
def _llm_type(self) -> str:
"""Return type of llm."""
return "xinference"
@property
def _identifying_params(self) -> Mapping[str, Any]:
"""Get the identifying parameters."""
return {
**{"server_url": self.serve... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/xinference.html |
feab62409b84-3 | else:
completion = model.generate(prompt=prompt, generate_config=generate_config)
return completion["choices"][0]["text"]
def _stream_generate(
self,
model: Union["RESTfulGenerateModelHandle", "RESTfulChatModelHandle"],
prompt: str,
run_manager: Optional[Callb... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/xinference.html |
9958d31ad9e3-0 | Source code for langchain.llms.aleph_alpha
from typing import Any, Dict, List, Optional, Sequence
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
from langchain.llms.utils import enforce_stop_tokens
from langchain.pydantic_v1 import Extra, root_validator
from langcha... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/aleph_alpha.html |
9958d31ad9e3-1 | """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 |
9958d31ad9e3-2 | 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 |
9958d31ad9e3-3 | hosting: Optional[str] = None
"""Determines in which datacenters the request may be processed.
You can either set the parameter to "aleph-alpha" or omit it (defaulting to None).
Not setting this value, or setting it to None, gives us maximal
flexibility in processing your request in our
own datacen... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/aleph_alpha.html |
9958d31ad9e3-4 | """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:
from aleph_alpha_client import Client
values["client"] = Client(
token... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/aleph_alpha.html |
9958d31ad9e3-5 | "logit_bias": self.logit_bias,
"log_probs": self.log_probs,
"tokens": self.tokens,
"disable_optimizations": self.disable_optimizations,
"minimum_tokens": self.minimum_tokens,
"echo": self.echo,
"use_multiplicative_frequency_penalty": self.use_multi... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/aleph_alpha.html |
9958d31ad9e3-6 | run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> str:
"""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:
T... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/aleph_alpha.html |
7c5037094f75-0 | Source code for langchain.llms.symblai_nebula
import json
import logging
from typing import Any, Callable, Dict, List, Mapping, Optional
import requests
from requests import ConnectTimeout, ReadTimeout, RequestException
from tenacity import (
before_sleep_log,
retry,
retry_if_exception_type,
stop_after_... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/symblai_nebula.html |
7c5037094f75-1 | nebula_service_path: Optional[str] = None
nebula_api_key: Optional[str] = None
model: Optional[str] = None
max_new_tokens: Optional[int] = 128
temperature: Optional[float] = 0.6
top_p: Optional[float] = 0.95
repetition_penalty: Optional[float] = 1.0
top_k: Optional[int] = 0
penalty_alpha... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/symblai_nebula.html |
7c5037094f75-2 | values["nebula_service_path"] = nebula_service_path
values["nebula_api_key"] = nebula_api_key
return values
@property
def _default_params(self) -> Dict[str, Any]:
"""Get the default parameters for calling Cohere API."""
return {
"max_new_tokens": self.max_new_tokens,
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/symblai_nebula.html |
7c5037094f75-3 | def _process_response(response: Any, stop: Optional[List[str]]) -> str:
text = response["output"]["text"]
if stop:
text = enforce_stop_tokens(text, stop)
return text
def _call(
self,
prompt: str,
stop: Optional[List[str]] = None,
run_manager: Optio... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/symblai_nebula.html |
7c5037094f75-4 | params: Optional[Dict] = None,
) -> Any:
"""Generate text from the model."""
params = params or {}
headers = {
"Content-Type": "application/json",
"ApiKey": f"{self.nebula_api_key}",
}
body = {
"prompt": {
"instruction": instruction,
"conversation": {"... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/symblai_nebula.html |
7c5037094f75-5 | """Use tenacity to retry the completion call."""
retry_decorator = _create_retry_decorator(llm)
@retry_decorator
def _completion_with_retry(**_kwargs: Any) -> Any:
return make_request(llm, **_kwargs)
return _completion_with_retry(**kwargs) | https://api.python.langchain.com/en/latest/_modules/langchain/llms/symblai_nebula.html |
a0c486a45a2a-0 | Source code for langchain.llms.deepsparse
# flake8: noqa
from typing import Any, AsyncIterator, Dict, Iterator, List, Optional, Union
from langchain.pydantic_v1 import root_validator
from langchain.callbacks.manager import (
AsyncCallbackManagerForLLMRun,
CallbackManagerForLLMRun,
)
from langchain.llms.base imp... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/deepsparse.html |
a0c486a45a2a-1 | sequences generated for each prompt. Common parameters are:
max_length, max_new_tokens, num_return_sequences, output_scores,
top_p, top_k, repetition_penalty."""
streaming: bool = False
"""Whether to stream the results, token by token."""
@property
def _identifying_params(self) -> Dict[str, Any]... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/deepsparse.html |
a0c486a45a2a-2 | stop: A list of strings to stop generation when encountered.
Returns:
The generated text.
Example:
.. code-block:: python
from langchain.llms import DeepSparse
llm = DeepSparse(model="zoo:nlg/text_generation/codegen_mono-350m/pytorch/huggingface/bi... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/deepsparse.html |
a0c486a45a2a-3 | llm("Tell me a joke.")
"""
if self.streaming:
combined_output = ""
async for chunk in self._astream(
prompt=prompt, stop=stop, run_manager=run_manager, **kwargs
):
combined_output += chunk.text
text = combined_output
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/deepsparse.html |
a0c486a45a2a-4 | stop=["'","\n"]):
print(chunk, end='', flush=True)
"""
inference = self.pipeline(
sequences=prompt, generation_config=self.generation_config, streaming=True
)
for token in inference:
chunk = GenerationChunk(text=token.generations[0].text)
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/deepsparse.html |
a0c486a45a2a-5 | sequences=prompt, generation_config=self.generation_config, streaming=True
)
for token in inference:
chunk = GenerationChunk(text=token.generations[0].text)
yield chunk
if run_manager:
await run_manager.on_llm_new_token(token=chunk.text) | https://api.python.langchain.com/en/latest/_modules/langchain/llms/deepsparse.html |
904c79c135f3-0 | Source code for langchain.llms.huggingface_text_gen_inference
import logging
from typing import Any, AsyncIterator, Dict, Iterator, List, Optional
from langchain.callbacks.manager import (
AsyncCallbackManagerForLLMRun,
CallbackManagerForLLMRun,
)
from langchain.llms.base import LLM
from langchain.pydantic_v1 i... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/huggingface_text_gen_inference.html |
904c79c135f3-1 | callbacks=callbacks,
streaming=True
)
print(llm("What is Deep Learning?"))
"""
max_new_tokens: int = 512
"""Maximum number of generated tokens"""
top_k: Optional[int] = None
"""The number of highest probability vocabulary tokens to keep for
top-k-filtering... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/huggingface_text_gen_inference.html |
904c79c135f3-2 | streaming: bool = False
"""Whether to generate a stream of tokens asynchronously"""
do_sample: bool = False
"""Activate logits sampling"""
watermark: bool = False
"""Watermarking with [A Watermark for Large Language Models]
(https://arxiv.org/abs/2301.10226)"""
server_kwargs: Dict[str, Any] ... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/huggingface_text_gen_inference.html |
904c79c135f3-3 | f"Parameters {invalid_model_kwargs} should be specified explicitly. "
f"Instead they were passed in as part of `model_kwargs` parameter."
)
values["model_kwargs"] = extra
return values
@root_validator()
def validate_environment(cls, values: Dict) -> Dict:
"""V... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/huggingface_text_gen_inference.html |
904c79c135f3-4 | "seed": self.seed,
"do_sample": self.do_sample,
"watermark": self.watermark,
**self.model_kwargs,
}
def _invocation_params(
self, runtime_stop: Optional[List[str]], **kwargs: Any
) -> Dict[str, Any]:
params = {**self._default_params, **kwargs}
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/huggingface_text_gen_inference.html |
904c79c135f3-5 | completion += chunk.text
return completion
invocation_params = self._invocation_params(stop, **kwargs)
res = await self.async_client.generate(prompt, **invocation_params)
# remove stop sequences from the end of the generated text
for stop_seq in invocation_params["stop_sequen... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/huggingface_text_gen_inference.html |
904c79c135f3-6 | async def _astream(
self,
prompt: str,
stop: Optional[List[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> AsyncIterator[GenerationChunk]:
invocation_params = self._invocation_params(stop, **kwargs)
async for res ... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/huggingface_text_gen_inference.html |
9717f7ceb565-0 | Source code for langchain.llms.gooseai
import logging
from typing import Any, Dict, List, Mapping, Optional
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
from langchain.pydantic_v1 import Extra, Field, root_validator
from langchain.utils import get_from_dict_or_env... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/gooseai.html |
9717f7ceb565-1 | """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[str, float]] = Field(default_fac... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/gooseai.html |
9717f7ceb565-2 | )
try:
import openai
openai.api_key = gooseai_api_key
openai.api_base = "https://api.goose.ai/v1"
values["client"] = openai.Completion
except ImportError:
raise ImportError(
"Could not import openai python package. "
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/gooseai.html |
9717f7ceb565-3 | 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, **kwargs}
response = self.client.create(engine=self.model_name, prompt=prompt, **params)
text = respo... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/gooseai.html |
a4c17e2be863-0 | Source code for langchain.llms.base
"""Base interface for large language models to expose."""
from __future__ import annotations
import asyncio
import functools
import inspect
import json
import logging
import warnings
from abc import ABC, abstractmethod
from functools import partial
from pathlib import Path
from typin... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/base.html |
a4c17e2be863-1 | return langchain.verbose
@functools.lru_cache
def _log_error_once(msg: str) -> None:
"""Log an error once."""
logger.error(msg)
[docs]def create_base_retry_decorator(
error_types: List[Type[BaseException]],
max_retries: int = 1,
run_manager: Optional[
Union[AsyncCallbackManagerForLLMRun, Cal... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/base.html |
a4c17e2be863-2 | return retry(
reraise=True,
stop=stop_after_attempt(max_retries),
wait=wait_exponential(multiplier=1, min=min_seconds, max=max_seconds),
retry=retry_instance,
before_sleep=_before_sleep,
)
[docs]def get_prompts(
params: Dict[str, Any], prompts: List[str]
) -> Tuple[Dict[i... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/base.html |
a4c17e2be863-3 | langchain.llm_cache.update(prompt, llm_string, result)
llm_output = new_results.llm_output
return llm_output
[docs]class BaseLLM(BaseLanguageModel[str], ABC):
"""Base LLM abstract interface.
It should take in a prompt and return a string."""
cache: Optional[bool] = None
verbose: bool = Field(def... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/base.html |
a4c17e2be863-4 | return _get_verbosity()
else:
return verbose
# --- Runnable methods ---
@property
def OutputType(self) -> Type[str]:
"""Get the input type for this runnable."""
return str
def _convert_input(self, input: LanguageModelInput) -> PromptValue:
if isinstance(input,... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/base.html |
a4c17e2be863-5 | # model doesn't implement async invoke, so use default implementation
return await asyncio.get_running_loop().run_in_executor(
None, partial(self.invoke, input, config, stop=stop, **kwargs)
)
config = config or {}
llm_result = await self.agenerate_prompt(
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/base.html |
a4c17e2be863-6 | return cast(List[str], [e for _ in inputs])
else:
raise e
else:
batches = [
inputs[i : i + max_concurrency]
for i in range(0, len(inputs), max_concurrency)
]
return [
output
fo... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/base.html |
a4c17e2be863-7 | return [g[0].text for g in llm_result.generations]
except Exception as e:
if return_exceptions:
return cast(List[str], [e for _ in inputs])
else:
raise e
else:
batches = [
inputs[i : i + max_concurren... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/base.html |
a4c17e2be863-8 | name=config.get("run_name"),
)
try:
generation: Optional[GenerationChunk] = None
for chunk in self._stream(
prompt, stop=stop, run_manager=run_manager, **kwargs
):
yield chunk.text
if gene... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/base.html |
a4c17e2be863-9 | [prompt],
invocation_params=params,
options=options,
name=config.get("run_name"),
)
try:
generation: Optional[GenerationChunk] = None
async for chunk in self._astream(
prompt, stop=stop, run_manag... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/base.html |
a4c17e2be863-10 | def _astream(
self,
prompt: str,
stop: Optional[List[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> AsyncIterator[GenerationChunk]:
raise NotImplementedError()
[docs] def generate_prompt(
self,
prompts... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/base.html |
a4c17e2be863-11 | **kwargs,
)
if new_arg_supported
else self._generate(prompts, stop=stop)
)
except BaseException as e:
for run_manager in run_managers:
run_manager.on_llm_error(e)
raise e
flattened_outputs = output.flatte... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/base.html |
a4c17e2be863-12 | ):
# We've received a list of callbacks args to apply to each input
assert len(callbacks) == len(prompts)
assert tags is None or (
isinstance(tags, list) and len(tags) == len(prompts)
)
assert metadata is None or (
isinstance(me... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/base.html |
a4c17e2be863-13 | params = self.dict()
params["stop"] = stop
options = {"stop": stop}
(
existing_prompts,
llm_string,
missing_prompt_idxs,
missing_prompts,
) = get_prompts(params, prompts)
disregard_cache = self.cache is not None and not self.cache
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/base.html |
a4c17e2be863-14 | )
llm_output = update_cache(
existing_prompts, llm_string, missing_prompt_idxs, new_results, prompts
)
run_info = (
[RunInfo(run_id=run_manager.run_id) for run_manager in run_managers]
if run_managers
else None
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/base.html |
a4c17e2be863-15 | ]
)
if run_managers:
output.run = [
RunInfo(run_id=run_manager.run_id) for run_manager in run_managers
]
return output
[docs] async def agenerate(
self,
prompts: List[str],
stop: Optional[List[str]] = None,
callbacks: Opt... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/base.html |
a4c17e2be863-16 | metadata_list = cast(
List[Optional[Dict[str, Any]]], metadata or ([{}] * len(prompts))
)
run_name_list = run_name or cast(
List[Optional[str]], ([None] * len(prompts))
)
callback_managers = [
AsyncCallbackManager.configure(... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/base.html |
a4c17e2be863-17 | )
run_managers = await asyncio.gather(
*[
callback_manager.on_llm_start(
dumpd(self),
[prompt],
invocation_params=params,
options=options,
name=run_name... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/base.html |
a4c17e2be863-18 | generations = [existing_prompts[i] for i in range(len(prompts))]
return LLMResult(generations=generations, llm_output=llm_output, run=run_info)
[docs] def __call__(
self,
prompt: str,
stop: Optional[List[str]] = None,
callbacks: Callbacks = None,
*,
tags: Optio... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/base.html |
a4c17e2be863-19 | callbacks=callbacks,
tags=tags,
metadata=metadata,
**kwargs,
)
return result.generations[0][0].text
[docs] def predict(
self, text: str, *, stop: Optional[Sequence[str]] = None, **kwargs: Any
) -> str:
if stop is None:
_stop = None
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/base.html |
a4c17e2be863-20 | _stop = None
else:
_stop = list(stop)
content = await self._call_async(text, stop=_stop, **kwargs)
return AIMessage(content=content)
@property
def _identifying_params(self) -> Mapping[str, Any]:
"""Get the identifying parameters."""
return {}
def __str__(s... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/base.html |
a4c17e2be863-21 | prompt_dict = self.dict()
if save_path.suffix == ".json":
with open(file_path, "w") as f:
json.dump(prompt_dict, f, indent=4)
elif save_path.suffix == ".yaml":
with open(file_path, "w") as f:
yaml.dump(prompt_dict, f, default_flow_style=False)
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/base.html |
a4c17e2be863-22 | # TODO: add caching here.
generations = []
new_arg_supported = inspect.signature(self._call).parameters.get("run_manager")
for prompt in prompts:
text = (
self._call(prompt, stop=stop, run_manager=run_manager, **kwargs)
if new_arg_supported
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/base.html |
2841f55680e1-0 | Source code for langchain.llms.cerebriumai
import logging
from typing import Any, Dict, List, Mapping, Optional
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
from langchain.llms.utils import enforce_stop_tokens
from langchain.pydantic_v1 import Extra, Field, root_v... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/cerebriumai.html |
2841f55680e1-1 | 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... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/cerebriumai.html |
2841f55680e1-2 | from cerebrium import model_api_request
except ImportError:
raise ValueError(
"Could not import cerebrium python package. "
"Please install it with `pip install cerebrium`."
)
params = self.model_kwargs or {}
response = model_api_request(
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/cerebriumai.html |
19b8acf4ca07-0 | Source code for langchain.llms.ollama
import json
from typing import Any, Dict, Iterator, List, Mapping, Optional
import requests
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import BaseLLM
from langchain.pydantic_v1 import Extra
from langchain.schema import LLMResult
from l... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/ollama.html |
19b8acf4ca07-1 | of the output. A lower value will result in more focused and
coherent text. (Default: 5.0)"""
num_ctx: Optional[int]
"""Sets the size of the context window used to generate the
next token. (Default: 2048) """
num_gpu: Optional[int]
"""The number of GPUs to use. On macOS it defaults to 1 to
e... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/ollama.html |
19b8acf4ca07-2 | impact more, while a value of 1.0 disables this setting. (default: 1)"""
top_k: Optional[int]
"""Reduces the probability of generating nonsense. A higher value (e.g. 100)
will give more diverse answers, while a lower value (e.g. 10)
will be more conservative. (Default: 40)"""
top_p: Optional[int]
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/ollama.html |
19b8acf4ca07-3 | return {**{"model": self.model}, **self._default_params}
def _create_stream(
self,
prompt: str,
stop: Optional[List[str]] = None,
**kwargs: Any,
) -> Iterator[str]:
if self.stop is not None and stop is not None:
raise ValueError("`stop` found in both the input... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/ollama.html |
19b8acf4ca07-4 | if final_chunk is None:
final_chunk = chunk
else:
final_chunk += chunk
if run_manager:
run_manager.on_llm_new_token(
chunk.text,
verbose=verbose,
)
if f... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/ollama.html |
19b8acf4ca07-5 | for prompt in prompts:
final_chunk = super()._stream_with_aggregation(
prompt,
stop=stop,
run_manager=run_manager,
verbose=self.verbose,
**kwargs,
)
generations.append([final_chunk])
return LLMRes... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/ollama.html |
8c8f0fe97fb6-0 | Source code for langchain.llms.titan_takeoff
from typing import Any, Iterator, List, Mapping, Optional
import requests
from requests.exceptions import ConnectionError
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
from langchain.llms.utils import enforce_stop_tokens... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/titan_takeoff.html |
8c8f0fe97fb6-1 | """Get the default parameters for calling Titan Takeoff Server."""
params = {
"generate_max_length": self.generate_max_length,
"sampling_topk": self.sampling_topk,
"sampling_topp": self.sampling_topp,
"sampling_temperature": self.sampling_temperature,
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/titan_takeoff.html |
8c8f0fe97fb6-2 | response.raise_for_status()
response.encoding = "utf-8"
text = ""
if "message" in response.json():
text = response.json()["message"]
else:
raise ValueError("Something went wrong.")
if stop is not None:
text = enf... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/titan_takeoff.html |
8c8f0fe97fb6-3 | run_manager.on_llm_new_token(token=chunk.text)
@property
def _identifying_params(self) -> Mapping[str, Any]:
"""Get the identifying parameters."""
return {"base_url": self.base_url, **{}, **self._default_params} | https://api.python.langchain.com/en/latest/_modules/langchain/llms/titan_takeoff.html |
d7c1f371e20b-0 | Source code for langchain.llms.nlpcloud
from typing import Any, Dict, List, Mapping, Optional
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
from langchain.pydantic_v1 import Extra, root_validator
from langchain.utils import get_from_dict_or_env
[docs]class NLPCloud... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/nlpcloud.html |
d7c1f371e20b-1 | top_p: int = 1
"""Total probability mass of tokens to consider at each step."""
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."""
num_beams: int = 1
"""Number of b... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/nlpcloud.html |
d7c1f371e20b-2 | "length_no_input": self.length_no_input,
"remove_input": self.remove_input,
"remove_end_sequence": self.remove_end_sequence,
"bad_words": self.bad_words,
"top_p": self.top_p,
"top_k": self.top_k,
"repetition_penalty": self.repetition_penalty,
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/nlpcloud.html |
d7c1f371e20b-3 | "Pass in a list of length 1."
)
elif stop and len(stop) == 1:
end_sequence = stop[0]
else:
end_sequence = None
params = {**self._default_params, **kwargs}
response = self.client.generation(prompt, end_sequence=end_sequence, **params)
return res... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/nlpcloud.html |
75a402a47dd5-0 | Source code for langchain.llms.bananadev
import logging
from typing import Any, Dict, List, Mapping, Optional
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
from langchain.llms.utils import enforce_stop_tokens
from langchain.pydantic_v1 import Extra, Field, root_val... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/bananadev.html |
75a402a47dd5-1 | all_required_field_names = {field.alias for field in cls.__fields__.values()}
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... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/bananadev.html |
75a402a47dd5-2 | try:
from banana_dev import Client
except ImportError:
raise ImportError(
"Could not import banana-dev python package. "
"Please install it with `pip install banana-dev`."
)
params = self.model_kwargs or {}
params = {**params, *... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/bananadev.html |
8ea3f5e4262e-0 | Source code for langchain.llms.anyscale
from typing import Any, Dict, List, Mapping, Optional
import requests
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
from langchain.llms.utils import enforce_stop_tokens
from langchain.pydantic_v1 import Extra, root_validator
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/anyscale.html |
8ea3f5e4262e-1 | @root_validator()
def validate_environment(cls, values: Dict) -> Dict:
"""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_... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/anyscale.html |
8ea3f5e4262e-2 | def _call(
self,
prompt: str,
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> str:
"""Call out to Anyscale Service endpoint.
Args:
prompt: The prompt to pass into the model.
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/anyscale.html |
b1fe033da626-0 | Source code for langchain.llms.amazon_api_gateway
from typing import Any, Dict, List, Mapping, Optional
import requests
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
from langchain.llms.utils import enforce_stop_tokens
from langchain.pydantic_v1 import Extra
[docs]... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/amazon_api_gateway.html |
b1fe033da626-1 | """Get the identifying parameters."""
_model_kwargs = self.model_kwargs or {}
return {
**{"api_url": self.api_url, "headers": self.headers},
**{"model_kwargs": _model_kwargs},
}
@property
def _llm_type(self) -> str:
"""Return type of llm."""
return... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/amazon_api_gateway.html |
1dc84f6ad31d-0 | Source code for langchain.llms.sagemaker_endpoint
"""Sagemaker InvokeEndpoint API."""
from abc import abstractmethod
from typing import Any, Dict, Generic, List, Mapping, Optional, TypeVar, Union
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
from langchain.llms.uti... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/sagemaker_endpoint.html |
1dc84f6ad31d-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 |
1dc84f6ad31d-2 | or ~/.aws/config files, which has either access keys or role information
specified. If not specified, the default credential profile or, if on an
EC2 instance, credentials from IMDS will be used.
client: boto3 client for Sagemaker Endpoint
content_handler: Implementation for mode... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/sagemaker_endpoint.html |
1dc84f6ad31d-3 | 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
"""
content_handler: LLMContentHandler
"""The content handler class that provides an input and
outpu... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/sagemaker_endpoint.html |
1dc84f6ad31d-4 | if values.get("client") is not None:
return values
"""Validate that AWS credentials to and python package exists in environment."""
try:
import boto3
try:
if values["credentials_profile_name"] is not None:
session = boto3.Session(
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/sagemaker_endpoint.html |
1dc84f6ad31d-5 | """Call out to Sagemaker inference 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
resp... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/sagemaker_endpoint.html |
b6b38b925c07-0 | Source code for langchain.llms.fake
import asyncio
import time
from typing import Any, AsyncIterator, Iterator, List, Mapping, Optional
from langchain.callbacks.manager import (
AsyncCallbackManagerForLLMRun,
CallbackManagerForLLMRun,
)
from langchain.llms.base import LLM
from langchain.schema.language_model im... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/fake.html |
b6b38b925c07-1 | else:
self.i = 0
return response
@property
def _identifying_params(self) -> Mapping[str, Any]:
return {"responses": self.responses}
[docs]class FakeStreamingListLLM(FakeListLLM):
"""Fake streaming list LLM for testing purposes."""
[docs] def stream(
self,
input... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/fake.html |
76d12824666c-0 | Source code for langchain.llms.ctransformers
from functools import partial
from typing import Any, Dict, List, Optional, Sequence
from langchain.callbacks.manager import (
AsyncCallbackManagerForLLMRun,
CallbackManagerForLLMRun,
)
from langchain.llms.base import LLM
from langchain.pydantic_v1 import root_valida... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/ctransformers.html |
76d12824666c-1 | "model_type": self.model_type,
"model_file": self.model_file,
"config": self.config,
}
@property
def _llm_type(self) -> str:
"""Return type of llm."""
return "ctransformers"
@root_validator()
def validate_environment(cls, values: Dict) -> Dict:
"""... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/ctransformers.html |
76d12824666c-2 | _run_manager = run_manager or CallbackManagerForLLMRun.get_noop_manager()
for chunk in self.client(prompt, stop=stop, stream=True):
text.append(chunk)
_run_manager.on_llm_new_token(chunk, verbose=self.verbose)
return "".join(text)
async def _acall(
self,
promp... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/ctransformers.html |
7e73622bfa7f-0 | Source code for langchain.llms.octoai_endpoint
from typing import Any, Dict, List, Mapping, Optional
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
from langchain.llms.utils import enforce_stop_tokens
from langchain.pydantic_v1 import Extra, root_validator
from lang... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/octoai_endpoint.html |
7e73622bfa7f-1 | """OCTOAI API Token"""
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."""
octoai_api_toke... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/octoai_endpoint.html |
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