id stringlengths 14 16 | text stringlengths 31 2.41k | source stringlengths 53 121 |
|---|---|---|
105f7996bd4b-4 | )
ZapierNLARunAction.__doc__ = (
ZapierNLAWrapper.run.__doc__ + ZapierNLARunAction.__doc__ # type: ignore
)
# other useful actions
[docs]class ZapierNLAListActions(BaseTool):
"""
Args:
None
"""
name = "ZapierNLA_list_actions"
description = BASE_ZAPIER_TOOL_PROMPT + (
"This tool ... | https://api.python.langchain.com/en/latest/_modules/langchain/tools/zapier/tool.html |
a6545542e0b8-0 | Source code for langchain.tools.json.tool
# flake8: noqa
"""Tools for working with JSON specs."""
from __future__ import annotations
import json
import re
from pathlib import Path
from typing import Dict, List, Optional, Union
from pydantic import BaseModel
from langchain.callbacks.manager import (
AsyncCallbackMan... | https://api.python.langchain.com/en/latest/_modules/langchain/tools/json/tool.html |
a6545542e0b8-1 | val = self.dict_
for i in items:
if i:
val = val[i]
if not isinstance(val, dict):
raise ValueError(
f"Value at path `{text}` is not a dict, get the value directly."
)
return str(list(val.keys()))
... | https://api.python.langchain.com/en/latest/_modules/langchain/tools/json/tool.html |
a6545542e0b8-2 | def _run(
self,
tool_input: str,
run_manager: Optional[CallbackManagerForToolRun] = None,
) -> str:
return self.spec.keys(tool_input)
async def _arun(
self,
tool_input: str,
run_manager: Optional[AsyncCallbackManagerForToolRun] = None,
) -> str:
... | https://api.python.langchain.com/en/latest/_modules/langchain/tools/json/tool.html |
cffefdf8e9ac-0 | Source code for langchain.tools.google_serper.tool
"""Tool for the Serper.dev Google Search API."""
from typing import Optional
from pydantic.fields import Field
from langchain.callbacks.manager import (
AsyncCallbackManagerForToolRun,
CallbackManagerForToolRun,
)
from langchain.tools.base import BaseTool
from ... | https://api.python.langchain.com/en/latest/_modules/langchain/tools/google_serper/tool.html |
cffefdf8e9ac-1 | )
api_wrapper: GoogleSerperAPIWrapper = Field(default_factory=GoogleSerperAPIWrapper)
def _run(
self,
query: str,
run_manager: Optional[CallbackManagerForToolRun] = None,
) -> str:
"""Use the tool."""
return str(self.api_wrapper.results(query))
async def _arun(
... | https://api.python.langchain.com/en/latest/_modules/langchain/tools/google_serper/tool.html |
a940812f8722-0 | Source code for langchain.prompts.few_shot
"""Prompt template that contains few shot examples."""
from typing import Any, Dict, List, Optional
from pydantic import Extra, root_validator
from langchain.prompts.base import (
DEFAULT_FORMATTER_MAPPING,
StringPromptTemplate,
check_valid_template,
)
from langcha... | https://api.python.langchain.com/en/latest/_modules/langchain/prompts/few_shot.html |
a940812f8722-1 | def check_examples_and_selector(cls, values: Dict) -> Dict:
"""Check that one and only one of examples/example_selector are provided."""
examples = values.get("examples", None)
example_selector = values.get("example_selector", None)
if examples and example_selector:
raise Val... | https://api.python.langchain.com/en/latest/_modules/langchain/prompts/few_shot.html |
a940812f8722-2 | .. code-block:: python
prompt.format(variable1="foo")
"""
kwargs = self._merge_partial_and_user_variables(**kwargs)
# Get the examples to use.
examples = self._get_examples(**kwargs)
examples = [
{k: e[k] for k in self.example_prompt.input_variables} for e... | https://api.python.langchain.com/en/latest/_modules/langchain/prompts/few_shot.html |
6672a463e78f-0 | Source code for langchain.prompts.loading
"""Load prompts from disk."""
import importlib
import json
import logging
from pathlib import Path
from typing import Union
import yaml
from langchain.output_parsers.regex import RegexParser
from langchain.prompts.base import BasePromptTemplate
from langchain.prompts.few_shot i... | https://api.python.langchain.com/en/latest/_modules/langchain/prompts/loading.html |
6672a463e78f-1 | # Load the template.
if template_path.suffix == ".txt":
with open(template_path) as f:
template = f.read()
else:
raise ValueError
# Set the template variable to the extracted variable.
config[var_name] = template
return config
def _load_example... | https://api.python.langchain.com/en/latest/_modules/langchain/prompts/loading.html |
6672a463e78f-2 | """Load the few shot prompt from the config."""
# Load the suffix and prefix templates.
config = _load_template("suffix", config)
config = _load_template("prefix", config)
# Load the example prompt.
if "example_prompt_path" in config:
if "example_prompt" in config:
raise ValueErr... | https://api.python.langchain.com/en/latest/_modules/langchain/prompts/loading.html |
6672a463e78f-3 | file_path = Path(file)
else:
file_path = file
# Load from either json or yaml.
if file_path.suffix == ".json":
with open(file_path) as f:
config = json.load(f)
elif file_path.suffix == ".yaml":
with open(file_path, "r") as f:
config = yaml.safe_load(f)
... | https://api.python.langchain.com/en/latest/_modules/langchain/prompts/loading.html |
2f4fd244bd71-0 | Source code for langchain.prompts.few_shot_with_templates
"""Prompt template that contains few shot examples."""
from typing import Any, Dict, List, Optional
from pydantic import Extra, root_validator
from langchain.prompts.base import DEFAULT_FORMATTER_MAPPING, StringPromptTemplate
from langchain.prompts.example_selec... | https://api.python.langchain.com/en/latest/_modules/langchain/prompts/few_shot_with_templates.html |
2f4fd244bd71-1 | examples = values.get("examples", None)
example_selector = values.get("example_selector", None)
if examples and example_selector:
raise ValueError(
"Only one of 'examples' and 'example_selector' should be provided"
)
if examples is None and example_selecto... | https://api.python.langchain.com/en/latest/_modules/langchain/prompts/few_shot_with_templates.html |
2f4fd244bd71-2 | Args:
kwargs: Any arguments to be passed to the prompt template.
Returns:
A formatted string.
Example:
.. code-block:: python
prompt.format(variable1="foo")
"""
kwargs = self._merge_partial_and_user_variables(**kwargs)
# Get the example... | https://api.python.langchain.com/en/latest/_modules/langchain/prompts/few_shot_with_templates.html |
2f4fd244bd71-3 | """Return a dictionary of the prompt."""
if self.example_selector:
raise ValueError("Saving an example selector is not currently supported")
return super().dict(**kwargs) | https://api.python.langchain.com/en/latest/_modules/langchain/prompts/few_shot_with_templates.html |
3065d25e11fa-0 | Source code for langchain.prompts.prompt
"""Prompt schema definition."""
from __future__ import annotations
from pathlib import Path
from string import Formatter
from typing import Any, Dict, List, Union
from pydantic import root_validator
from langchain.prompts.base import (
DEFAULT_FORMATTER_MAPPING,
StringPr... | https://api.python.langchain.com/en/latest/_modules/langchain/prompts/prompt.html |
3065d25e11fa-1 | """
kwargs = self._merge_partial_and_user_variables(**kwargs)
return DEFAULT_FORMATTER_MAPPING[self.template_format](self.template, **kwargs)
@root_validator()
def template_is_valid(cls, values: Dict) -> Dict:
"""Check that template and input variables are consistent."""
if value... | https://api.python.langchain.com/en/latest/_modules/langchain/prompts/prompt.html |
3065d25e11fa-2 | [docs] @classmethod
def from_file(
cls, template_file: Union[str, Path], input_variables: List[str], **kwargs: Any
) -> PromptTemplate:
"""Load a prompt from a file.
Args:
template_file: The path to the file containing the prompt template.
input_variables: A li... | https://api.python.langchain.com/en/latest/_modules/langchain/prompts/prompt.html |
33fc832210c9-0 | Source code for langchain.prompts.pipeline
from typing import Any, Dict, List, Tuple
from pydantic import root_validator
from langchain.prompts.base import BasePromptTemplate
from langchain.prompts.chat import BaseChatPromptTemplate
from langchain.schema import PromptValue
def _get_inputs(inputs: dict, input_variables:... | https://api.python.langchain.com/en/latest/_modules/langchain/prompts/pipeline.html |
33fc832210c9-1 | if isinstance(prompt, BaseChatPromptTemplate):
kwargs[k] = prompt.format_messages(**_inputs)
else:
kwargs[k] = prompt.format(**_inputs)
_inputs = _get_inputs(kwargs, self.final_prompt.input_variables)
return self.final_prompt.format_prompt(**_inputs)
[docs] ... | https://api.python.langchain.com/en/latest/_modules/langchain/prompts/pipeline.html |
d7c82cd7cccf-0 | Source code for langchain.prompts.chat
"""Chat prompt template."""
from __future__ import annotations
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Any, Callable, List, Sequence, Tuple, Type, TypeVar, Union
from pydantic import Field, root_validator
from langchain.load.serializable imp... | https://api.python.langchain.com/en/latest/_modules/langchain/prompts/chat.html |
d7c82cd7cccf-1 | f" got {value}"
)
return value
@property
def input_variables(self) -> List[str]:
"""Input variables for this prompt template."""
return [self.variable_name]
MessagePromptTemplateT = TypeVar(
"MessagePromptTemplateT", bound="BaseStringMessagePromptTemplate"
)
class Bas... | https://api.python.langchain.com/en/latest/_modules/langchain/prompts/chat.html |
d7c82cd7cccf-2 | text = self.prompt.format(**kwargs)
return ChatMessage(
content=text, role=self.role, additional_kwargs=self.additional_kwargs
)
[docs]class HumanMessagePromptTemplate(BaseStringMessagePromptTemplate):
[docs] def format(self, **kwargs: Any) -> BaseMessage:
text = self.prompt.forma... | https://api.python.langchain.com/en/latest/_modules/langchain/prompts/chat.html |
d7c82cd7cccf-3 | """Format kwargs into a list of messages."""
[docs]class ChatPromptTemplate(BaseChatPromptTemplate, ABC):
input_variables: List[str]
messages: List[Union[BaseMessagePromptTemplate, BaseMessage]]
@root_validator(pre=True)
def validate_input_variables(cls, values: dict) -> dict:
messages = values[... | https://api.python.langchain.com/en/latest/_modules/langchain/prompts/chat.html |
d7c82cd7cccf-4 | [docs] @classmethod
def from_strings(
cls, string_messages: List[Tuple[Type[BaseMessagePromptTemplate], str]]
) -> ChatPromptTemplate:
messages = [
role(prompt=PromptTemplate.from_template(template))
for role, template in string_messages
]
return cls.fr... | https://api.python.langchain.com/en/latest/_modules/langchain/prompts/chat.html |
d7c82cd7cccf-5 | def _prompt_type(self) -> str:
return "chat"
[docs] def save(self, file_path: Union[Path, str]) -> None:
raise NotImplementedError | https://api.python.langchain.com/en/latest/_modules/langchain/prompts/chat.html |
50e608f52343-0 | Source code for langchain.prompts.base
"""BasePrompt schema definition."""
from __future__ import annotations
import json
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Any, Callable, Dict, List, Mapping, Optional, Set, Union
import yaml
from pydantic import Field, root_validator
from l... | https://api.python.langchain.com/en/latest/_modules/langchain/prompts/base.html |
50e608f52343-1 | if error_message:
raise KeyError(error_message.strip())
def _get_jinja2_variables_from_template(template: str) -> Set[str]:
try:
from jinja2 import Environment, meta
except ImportError:
raise ImportError(
"jinja2 not installed, which is needed to use the jinja2_formatter. "
... | https://api.python.langchain.com/en/latest/_modules/langchain/prompts/base.html |
50e608f52343-2 | """Return prompt as string."""
return self.text
def to_messages(self) -> List[BaseMessage]:
"""Return prompt as messages."""
return [HumanMessage(content=self.text)]
[docs]class BasePromptTemplate(Serializable, ABC):
"""Base class for all prompt templates, returning a prompt."""
inpu... | https://api.python.langchain.com/en/latest/_modules/langchain/prompts/base.html |
50e608f52343-3 | f"Found overlapping input and partial variables: {overall}"
)
return values
[docs] def partial(self, **kwargs: Union[str, Callable[[], str]]) -> BasePromptTemplate:
"""Return a partial of the prompt template."""
prompt_dict = self.__dict__.copy()
prompt_dict["input_variabl... | https://api.python.langchain.com/en/latest/_modules/langchain/prompts/base.html |
50e608f52343-4 | """Save the prompt.
Args:
file_path: Path to directory to save prompt to.
Example:
.. code-block:: python
prompt.save(file_path="path/prompt.yaml")
"""
if self.partial_variables:
raise ValueError("Cannot save prompt with partial variables.")
... | https://api.python.langchain.com/en/latest/_modules/langchain/prompts/base.html |
8bdd7a161c64-0 | Source code for langchain.prompts.example_selector.semantic_similarity
"""Example selector that selects examples based on SemanticSimilarity."""
from __future__ import annotations
from typing import Any, Dict, List, Optional, Type
from pydantic import BaseModel, Extra
from langchain.embeddings.base import Embeddings
fr... | https://api.python.langchain.com/en/latest/_modules/langchain/prompts/example_selector/semantic_similarity.html |
8bdd7a161c64-1 | return ids[0]
[docs] def select_examples(self, input_variables: Dict[str, str]) -> List[dict]:
"""Select which examples to use based on semantic similarity."""
# Get the docs with the highest similarity.
if self.input_keys:
input_variables = {key: input_variables[key] for key in s... | https://api.python.langchain.com/en/latest/_modules/langchain/prompts/example_selector/semantic_similarity.html |
8bdd7a161c64-2 | instead of all variables.
vectorstore_cls_kwargs: optional kwargs containing url for vector store
Returns:
The ExampleSelector instantiated, backed by a vector store.
"""
if input_keys:
string_examples = [
" ".join(sorted_values({k: eg[k] for k... | https://api.python.langchain.com/en/latest/_modules/langchain/prompts/example_selector/semantic_similarity.html |
8bdd7a161c64-3 | examples = [dict(e.metadata) for e in example_docs]
# If example keys are provided, filter examples to those keys.
if self.example_keys:
examples = [{k: eg[k] for k in self.example_keys} for eg in examples]
return examples
[docs] @classmethod
def from_examples(
cls,
... | https://api.python.langchain.com/en/latest/_modules/langchain/prompts/example_selector/semantic_similarity.html |
8bdd7a161c64-4 | )
return cls(vectorstore=vectorstore, k=k, fetch_k=fetch_k, input_keys=input_keys) | https://api.python.langchain.com/en/latest/_modules/langchain/prompts/example_selector/semantic_similarity.html |
62125bbde01f-0 | Source code for langchain.prompts.example_selector.ngram_overlap
"""Select and order examples based on ngram overlap score (sentence_bleu score).
https://www.nltk.org/_modules/nltk/translate/bleu_score.html
https://aclanthology.org/P02-1040.pdf
"""
from typing import Dict, List
import numpy as np
from pydantic import B... | https://api.python.langchain.com/en/latest/_modules/langchain/prompts/example_selector/ngram_overlap.html |
62125bbde01f-1 | """
examples: List[dict]
"""A list of the examples that the prompt template expects."""
example_prompt: PromptTemplate
"""Prompt template used to format the examples."""
threshold: float = -1.0
"""Threshold at which algorithm stops. Set to -1.0 by default.
For negative threshold:
select_... | https://api.python.langchain.com/en/latest/_modules/langchain/prompts/example_selector/ngram_overlap.html |
62125bbde01f-2 | k = len(self.examples)
score = [0.0] * k
first_prompt_template_key = self.example_prompt.input_variables[0]
for i in range(k):
score[i] = ngram_overlap_score(
inputs, [self.examples[i][first_prompt_template_key]]
)
while True:
arg_max =... | https://api.python.langchain.com/en/latest/_modules/langchain/prompts/example_selector/ngram_overlap.html |
c69aedf17c80-0 | Source code for langchain.prompts.example_selector.length_based
"""Select examples based on length."""
import re
from typing import Callable, Dict, List
from pydantic import BaseModel, validator
from langchain.prompts.example_selector.base import BaseExampleSelector
from langchain.prompts.prompt import PromptTemplate
d... | https://api.python.langchain.com/en/latest/_modules/langchain/prompts/example_selector/length_based.html |
c69aedf17c80-1 | get_text_length = values["get_text_length"]
string_examples = [example_prompt.format(**eg) for eg in values["examples"]]
return [get_text_length(eg) for eg in string_examples]
[docs] def select_examples(self, input_variables: Dict[str, str]) -> List[dict]:
"""Select which examples to use base... | https://api.python.langchain.com/en/latest/_modules/langchain/prompts/example_selector/length_based.html |
1ac7ca91b95f-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 |
1ac7ca91b95f-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 |
1ac7ca91b95f-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 |
1ac7ca91b95f-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 |
1ac7ca91b95f-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 |
1ac7ca91b95f-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 |
1ac7ca91b95f-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 |
fd45908b0cc6-0 | 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 |
fd45908b0cc6-1 | """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 |
fd45908b0cc6-2 | 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 |
fd45908b0cc6-3 | """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
values["client"] = aleph_alpha_client.Client(token=aleph_alp... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/aleph_alpha.html |
fd45908b0cc6-4 | "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_penalty_min_length": self.sequence_penalty_min_length,
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/aleph_alpha.html |
fd45908b0cc6-5 | 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
response = aleph_alpha("Tell me a joke.")
"""
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/aleph_alpha.html |
0fde7549855c-0 | Source code for langchain.llms.baseten
"""Wrapper around Baseten deployed model API."""
import logging
from typing import Any, Dict, List, Mapping, Optional
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/baseten.html |
0fde7549855c-1 | """Return type of model."""
return "baseten"
def _call(
self,
prompt: str,
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> str:
"""Call to Baseten deployed model endpoint."""
try:
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/baseten.html |
c62e9d8027c6-0 | 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 |
c62e9d8027c6-1 | ),
before_sleep=before_sleep_log(logger, logging.WARNING),
)
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:
r... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/google_palm.html |
c62e9d8027c6-2 | 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 completions to generate for each prompt. Note that the API may
not return the full n ... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/google_palm.html |
c62e9d8027c6-3 | return values
def _generate(
self,
prompts: List[str],
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> LLMResult:
generations = []
for prompt in prompts:
completion = generate_with_r... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/google_palm.html |
b4492914f85a-0 | 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... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/stochasticai.html |
b4492914f85a-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/stochasticai.html |
b4492914f85a-2 | response = StochasticAI("Tell me a joke.")
"""
params = self.model_kwargs or {}
params = {**params, **kwargs}
response_post = requests.post(
url=self.api_url,
json={"prompt": prompt, "params": params},
headers={
"apiKey": f"{self.stocha... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/stochasticai.html |
56c5238ee178-0 | 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 |
56c5238ee178-1 | """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 |
56c5238ee178-2 | extra = Extra.forbid
@root_validator()
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that api key and python package exists in environment."""
cohere_api_key = get_from_dict_or_env(
values, "cohere_api_key", "COHERE_API_KEY"
)
try:
impor... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/cohere.html |
56c5238ee178-3 | """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-block:: python
respon... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/cohere.html |
253cf09f5076-0 | Source code for langchain.llms.openlm
from typing import Any, Dict
from pydantic import root_validator
from langchain.llms.openai import BaseOpenAI
[docs]class OpenLM(BaseOpenAI):
@property
def _invocation_params(self) -> Dict[str, Any]:
return {**{"model": self.model_name}, **super()._invocation_params... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/openlm.html |
a679f808e73d-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 |
a679f808e73d-1 | """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):
if field_name not in all_required_field_names:
if ... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/replicate.html |
a679f808e73d-2 | try:
import replicate as replicate_python
except ImportError:
raise ImportError(
"Could not import replicate python package. "
"Please install it with `pip install replicate`."
)
# get the model and version
model_str, version_st... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/replicate.html |
9624ca20bd3b-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 |
9624ca20bd3b-1 | """Your Prediction Guard access token."""
stop: Optional[List[str]] = None
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 ... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/predictionguard.html |
9624ca20bd3b-2 | 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
if self.stop is not None and stop is not None:
raise ... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/predictionguard.html |
349ef8efbd58-0 | 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 |
349ef8efbd58-1 | 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 |
59ebb5f794d9-0 | 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 |
59ebb5f794d9-1 | """The MIME type of the response data returned from endpoint"""
@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 t... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/sagemaker_endpoint.html |
59ebb5f794d9-2 | )
credentials_profile_name = (
"default"
)
se = SagemakerEndpoint(
endpoint_name=endpoint_name,
region_name=region_name,
credentials_profile_name=credentials_profile_name
)
"""
client: Any #: :meta p... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/sagemaker_endpoint.html |
59ebb5f794d9-3 | def transform_output(self, output: bytes) -> str:
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[D... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/sagemaker_endpoint.html |
59ebb5f794d9-4 | @property
def _identifying_params(self) -> Mapping[str, Any]:
"""Get the identifying parameters."""
_model_kwargs = self.model_kwargs or {}
return {
**{"endpoint_name": self.endpoint_name},
**{"model_kwargs": _model_kwargs},
}
@property
def _llm_type(s... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/sagemaker_endpoint.html |
59ebb5f794d9-5 | text = self.content_handler.transform_output(response["Body"])
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 |
463436fc7ab0-0 | 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 |
463436fc7ab0-1 | 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 |
463436fc7ab0-2 | )
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 |
463436fc7ab0-3 | 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 |
5547c4f4eda3-0 | 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 |
5547c4f4eda3-1 | 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 |
5547c4f4eda3-2 | """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 |
5547c4f4eda3-3 | "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 |
5547c4f4eda3-4 | 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 |
576ad606a94a-0 | 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... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/nlpcloud.html |
576ad606a94a-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."""
length_penalty: float = 1.0
"""Exponential penalty t... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/nlpcloud.html |
576ad606a94a-2 | @property
def _default_params(self) -> Mapping[str, Any]:
"""Get the default parameters for calling NLPCloud API."""
return {
"temperature": self.temperature,
"min_length": self.min_length,
"max_length": self.max_length,
"length_no_input": self.length_... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/nlpcloud.html |
576ad606a94a-3 | Returns:
The string generated by the model.
Example:
.. code-block:: python
response = nlpcloud("Tell me a joke.")
"""
if stop and len(stop) > 1:
raise ValueError(
"NLPCloud only supports a single stop sequence per generation."
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/nlpcloud.html |
85f469c96910-0 | 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 |
85f469c96910-1 | 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 |
85f469c96910-2 | )
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 |
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