id stringlengths 14 16 | text stringlengths 45 2.73k | source stringlengths 49 114 |
|---|---|---|
7187430eb733-1 | 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_examples(config: dict) -> dict:
... | https://python.langchain.com/en/latest/_modules/langchain/prompts/loading.html |
7187430eb733-2 | 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 ValueError(
"Only one of example_prompt and example_prompt_path should "
... | https://python.langchain.com/en/latest/_modules/langchain/prompts/loading.html |
7187430eb733-3 | # 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)
elif file_path.suffix == ".py":
spec = importlib.util... | https://python.langchain.com/en/latest/_modules/langchain/prompts/loading.html |
96353d918080-0 | 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 Extra, root_validator
from langchain.prompts.base import (
DEFAULT_FORMATTER_MAPPING,
S... | https://python.langchain.com/en/latest/_modules/langchain/prompts/prompt.html |
96353d918080-1 | """
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://python.langchain.com/en/latest/_modules/langchain/prompts/prompt.html |
96353d918080-2 | [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://python.langchain.com/en/latest/_modules/langchain/prompts/prompt.html |
c8da2a4ce895-0 | 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://python.langchain.com/en/latest/_modules/langchain/prompts/few_shot_with_templates.html |
c8da2a4ce895-1 | 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://python.langchain.com/en/latest/_modules/langchain/prompts/few_shot_with_templates.html |
c8da2a4ce895-2 | 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 examples to use.
... | https://python.langchain.com/en/latest/_modules/langchain/prompts/few_shot_with_templates.html |
c8da2a4ce895-3 | if self.example_selector:
raise ValueError("Saving an example selector is not currently supported")
return super().dict(**kwargs)
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 21, 2023. | https://python.langchain.com/en/latest/_modules/langchain/prompts/few_shot_with_templates.html |
5b7f77c3533f-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 BaseModel, Extra, Field, roo... | https://python.langchain.com/en/latest/_modules/langchain/prompts/base.html |
5b7f77c3533f-1 | "jinja2 not installed, which is needed to use the jinja2_formatter. "
"Please install it with `pip install jinja2`."
)
env = Environment()
ast = env.parse(template)
variables = meta.find_undeclared_variables(ast)
return variables
DEFAULT_FORMATTER_MAPPING: Dict[str, Callable] = {
... | https://python.langchain.com/en/latest/_modules/langchain/prompts/base.html |
5b7f77c3533f-2 | """Base class for all prompt templates, returning a prompt."""
input_variables: List[str]
"""A list of the names of the variables the prompt template expects."""
output_parser: Optional[BaseOutputParser] = None
"""How to parse the output of calling an LLM on this formatted prompt."""
partial_variabl... | https://python.langchain.com/en/latest/_modules/langchain/prompts/base.html |
5b7f77c3533f-3 | prompt_dict["input_variables"] = list(
set(self.input_variables).difference(kwargs)
)
prompt_dict["partial_variables"] = {**self.partial_variables, **kwargs}
return type(self)(**prompt_dict)
def _merge_partial_and_user_variables(self, **kwargs: Any) -> Dict[str, Any]:
# G... | https://python.langchain.com/en/latest/_modules/langchain/prompts/base.html |
5b7f77c3533f-4 | # Convert file to Path object.
if isinstance(file_path, str):
save_path = Path(file_path)
else:
save_path = file_path
directory_path = save_path.parent
directory_path.mkdir(parents=True, exist_ok=True)
# Fetch dictionary to save
prompt_dict = self.... | https://python.langchain.com/en/latest/_modules/langchain/prompts/base.html |
41724e9c393f-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://python.langchain.com/en/latest/_modules/langchain/prompts/example_selector/length_based.html |
41724e9c393f-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://python.langchain.com/en/latest/_modules/langchain/prompts/example_selector/length_based.html |
56caddaf8dc3-0 | 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://python.langchain.com/en/latest/_modules/langchain/prompts/example_selector/semantic_similarity.html |
56caddaf8dc3-1 | 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://python.langchain.com/en/latest/_modules/langchain/prompts/example_selector/semantic_similarity.html |
56caddaf8dc3-2 | 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://python.langchain.com/en/latest/_modules/langchain/prompts/example_selector/semantic_similarity.html |
56caddaf8dc3-3 | 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://python.langchain.com/en/latest/_modules/langchain/prompts/example_selector/semantic_similarity.html |
56caddaf8dc3-4 | string_examples, embeddings, metadatas=examples, **vectorstore_cls_kwargs
)
return cls(vectorstore=vectorstore, k=k, fetch_k=fetch_k, input_keys=input_keys)
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 21, 2023. | https://python.langchain.com/en/latest/_modules/langchain/prompts/example_selector/semantic_similarity.html |
73c68057b193-0 | Source code for langchain.llms.llamacpp
"""Wrapper around llama.cpp."""
import logging
from typing import Any, Dict, List, Optional
from pydantic import Field, root_validator
from langchain.llms.base import LLM
logger = logging.getLogger(__name__)
[docs]class LlamaCpp(LLM):
"""Wrapper around the llama.cpp model.
... | https://python.langchain.com/en/latest/_modules/langchain/llms/llamacpp.html |
73c68057b193-1 | """Only load the vocabulary, no weights."""
use_mlock: bool = Field(False, alias="use_mlock")
"""Force system to keep model in RAM."""
n_threads: Optional[int] = Field(None, alias="n_threads")
"""Number of threads to use.
If None, the number of threads is automatically determined."""
n_batch: O... | https://python.langchain.com/en/latest/_modules/langchain/llms/llamacpp.html |
73c68057b193-2 | @root_validator()
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that llama-cpp-python library is installed."""
model_path = values["model_path"]
n_ctx = values["n_ctx"]
n_parts = values["n_parts"]
seed = values["seed"]
f16_kv = values["f16_kv"]
... | https://python.langchain.com/en/latest/_modules/langchain/llms/llamacpp.html |
73c68057b193-3 | "suffix": self.suffix,
"max_tokens": self.max_tokens,
"temperature": self.temperature,
"top_p": self.top_p,
"logprobs": self.logprobs,
"echo": self.echo,
"stop_sequences": self.stop,
"repeat_penalty": self.repeat_penalty,
"t... | https://python.langchain.com/en/latest/_modules/langchain/llms/llamacpp.html |
73c68057b193-4 | """Call the Llama model and return the output."""
text = self.client(
prompt=prompt,
max_tokens=params["max_tokens"],
temperature=params["temperature"],
top_p=params["top_p"],
logprobs=params["logprobs"],
echo=params["echo"],
st... | https://python.langchain.com/en/latest/_modules/langchain/llms/llamacpp.html |
6cbc504e75d2-0 | 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.llms.base import LLM
from langchain.llms.utils import enforce_stop_tokens
from langchain.utils import get_from_dict_or_env... | https://python.langchain.com/en/latest/_modules/langchain/llms/huggingface_hub.html |
6cbc504e75d2-1 | @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, "huggingfacehub_api_token", "HUGGINGFACEHUB_API_TOKEN"
)
try:
... | https://python.langchain.com/en/latest/_modules/langchain/llms/huggingface_hub.html |
6cbc504e75d2-2 | 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 = hf("Tell me a joke.")
"""
_mod... | https://python.langchain.com/en/latest/_modules/langchain/llms/huggingface_hub.html |
610dd0b335ae-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.llms.base import LLM
from langchain.llms.utils import enforce_stop_tokens
from langchain.utils import get_from_d... | https://python.langchain.com/en/latest/_modules/langchain/llms/bananadev.html |
610dd0b335ae-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://python.langchain.com/en/latest/_modules/langchain/llms/bananadev.html |
610dd0b335ae-2 | "prompt": prompt,
**params,
}
response = banana.run(api_key, model_key, model_inputs)
try:
text = response["modelOutputs"][0]["output"]
except (KeyError, TypeError):
returned = response["modelOutputs"][0]
raise ValueError(
"... | https://python.langchain.com/en/latest/_modules/langchain/llms/bananadev.html |
1bb39b90686e-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.llms.base import LLM
from langchain.llms.utils import enforce_stop_tokens
DEFAULT_MODEL_ID = ... | https://python.langchain.com/en/latest/_modules/langchain/llms/huggingface_pipeline.html |
1bb39b90686e-1 | """Key word arguments to pass to the model."""
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
[docs] @classmethod
def from_model_id(
cls,
model_id: str,
task: str,
device: int = -1,
model_kwargs: Optional[dict] = None,
... | https://python.langchain.com/en/latest/_modules/langchain/llms/huggingface_pipeline.html |
1bb39b90686e-2 | import torch
cuda_device_count = torch.cuda.device_count()
if device < -1 or (device >= cuda_device_count):
raise ValueError(
f"Got device=={device}, "
f"device is required to be within [-1, {cuda_device_count})"
)
... | https://python.langchain.com/en/latest/_modules/langchain/llms/huggingface_pipeline.html |
1bb39b90686e-3 | response = self.pipeline(prompt)
if self.pipeline.task == "text-generation":
# Text generation return includes the starter text.
text = response[0]["generated_text"][len(prompt) :]
elif self.pipeline.task == "text2text-generation":
text = response[0]["generated_text"]... | https://python.langchain.com/en/latest/_modules/langchain/llms/huggingface_pipeline.html |
39c09b0ac60e-0 | Source code for langchain.llms.huggingface_endpoint
"""Wrapper around HuggingFace APIs."""
from typing import Any, Dict, List, Mapping, Optional
import requests
from pydantic import Extra, root_validator
from langchain.llms.base import LLM
from langchain.llms.utils import enforce_stop_tokens
from langchain.utils import... | https://python.langchain.com/en/latest/_modules/langchain/llms/huggingface_endpoint.html |
39c09b0ac60e-1 | extra = Extra.forbid
@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, "huggingfacehub_api_token", "HUGGINGFACEHUB_API_TOKEN"
)
... | https://python.langchain.com/en/latest/_modules/langchain/llms/huggingface_endpoint.html |
39c09b0ac60e-2 | 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 = hf("Tell me a joke.")
"""
_mod... | https://python.langchain.com/en/latest/_modules/langchain/llms/huggingface_endpoint.html |
39c09b0ac60e-3 | # stop tokens when making calls to huggingface_hub.
text = enforce_stop_tokens(text, stop)
return text
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 21, 2023. | https://python.langchain.com/en/latest/_modules/langchain/llms/huggingface_endpoint.html |
e0b92abebd2b-0 | Source code for langchain.llms.gpt4all
"""Wrapper for the GPT4All model."""
from functools import partial
from typing import Any, Dict, List, Mapping, Optional, Set
from pydantic import Extra, Field, root_validator
from langchain.llms.base import LLM
from langchain.llms.utils import enforce_stop_tokens
[docs]class GPT4... | https://python.langchain.com/en/latest/_modules/langchain/llms/gpt4all.html |
e0b92abebd2b-1 | vocab_only: bool = Field(False, alias="vocab_only")
"""Only load the vocabulary, no weights."""
use_mlock: bool = Field(False, alias="use_mlock")
"""Force system to keep model in RAM."""
embedding: bool = Field(False, alias="embedding")
"""Use embedding mode only."""
n_threads: Optional[int] = F... | https://python.langchain.com/en/latest/_modules/langchain/llms/gpt4all.html |
e0b92abebd2b-2 | """Get the identifying parameters."""
return {
"seed": self.seed,
"n_predict": self.n_predict,
"n_threads": self.n_threads,
"n_batch": self.n_batch,
"repeat_last_n": self.repeat_last_n,
"repeat_penalty": self.repeat_penalty,
"to... | https://python.langchain.com/en/latest/_modules/langchain/llms/gpt4all.html |
e0b92abebd2b-3 | return {
"model": self.model,
**self._default_params,
**{
k: v
for k, v in self.__dict__.items()
if k in GPT4All._llama_param_names()
},
}
@property
def _llm_type(self) -> str:
"""Return the type of l... | https://python.langchain.com/en/latest/_modules/langchain/llms/gpt4all.html |
d5ec64d7e934-0 | 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.llms.base import LLM
from langchain.llms.utils import enforce_stop_tokens
from langchain.utils import get_from_dict_or_env
[d... | https://python.langchain.com/en/latest/_modules/langchain/llms/aleph_alpha.html |
d5ec64d7e934-1 | presence_penalty: float = 0.0
"""Penalizes repeated tokens."""
frequency_penalty: float = 0.0
"""Penalizes repeated tokens according to frequency."""
repetition_penalties_include_prompt: Optional[bool] = False
"""Flag deciding whether presence penalty or frequency penalty are
updated from the pr... | https://python.langchain.com/en/latest/_modules/langchain/llms/aleph_alpha.html |
d5ec64d7e934-2 | sequence_penalty: float = 0.0
sequence_penalty_min_length: int = 2
use_multiplicative_sequence_penalty: bool = False
completion_bias_inclusion: Optional[Sequence[str]] = None
completion_bias_inclusion_first_token_only: bool = False
completion_bias_exclusion: Optional[Sequence[str]] = None
comple... | https://python.langchain.com/en/latest/_modules/langchain/llms/aleph_alpha.html |
d5ec64d7e934-3 | values, "aleph_alpha_api_key", "ALEPH_ALPHA_API_KEY"
)
try:
import aleph_alpha_client
values["client"] = aleph_alpha_client.Client(token=aleph_alpha_api_key)
except ImportError:
raise ValueError(
"Could not import aleph_alpha_client python pack... | https://python.langchain.com/en/latest/_modules/langchain/llms/aleph_alpha.html |
d5ec64d7e934-4 | "sequence_penalty": self.sequence_penalty,
"sequence_penalty_min_length": self.sequence_penalty_min_length,
"use_multiplicative_sequence_penalty": self.use_multiplicative_sequence_penalty, # noqa: E501
"completion_bias_inclusion": self.completion_bias_inclusion,
"complet... | https://python.langchain.com/en/latest/_modules/langchain/llms/aleph_alpha.html |
d5ec64d7e934-5 | from aleph_alpha_client import CompletionRequest, Prompt
params = self._default_params
if self.stop_sequences is not None and stop is not None:
raise ValueError(
"stop sequences found in both the input and default params."
)
elif self.stop_sequences is not... | https://python.langchain.com/en/latest/_modules/langchain/llms/aleph_alpha.html |
df8fc22a796c-0 | Source code for langchain.llms.openai
"""Wrapper around OpenAI APIs."""
from __future__ import annotations
import logging
import sys
import warnings
from typing import (
AbstractSet,
Any,
Callable,
Collection,
Dict,
Generator,
List,
Literal,
Mapping,
Optional,
Set,
Tuple,... | https://python.langchain.com/en/latest/_modules/langchain/llms/openai.html |
df8fc22a796c-1 | def _streaming_response_template() -> Dict[str, Any]:
return {
"choices": [
{
"text": "",
"finish_reason": None,
"logprobs": None,
}
]
}
def _create_retry_decorator(llm: Union[BaseOpenAI, OpenAIChat]) -> Callable[[Any], Any]... | https://python.langchain.com/en/latest/_modules/langchain/llms/openai.html |
df8fc22a796c-2 | ) -> Any:
"""Use tenacity to retry the async completion call."""
retry_decorator = _create_retry_decorator(llm)
@retry_decorator
async def _completion_with_retry(**kwargs: Any) -> Any:
# Use OpenAI's async api https://github.com/openai/openai-python#async-api
return await llm.client.acre... | https://python.langchain.com/en/latest/_modules/langchain/llms/openai.html |
df8fc22a796c-3 | openai_organization: Optional[str] = None
batch_size: int = 20
"""Batch size to use when passing multiple documents to generate."""
request_timeout: Optional[Union[float, Tuple[float, float]]] = None
"""Timeout for requests to OpenAI completion API. Default is 600 seconds."""
logit_bias: Optional[Di... | https://python.langchain.com/en/latest/_modules/langchain/llms/openai.html |
df8fc22a796c-4 | extra = Extra.ignore
@root_validator(pre=True)
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_kwar... | https://python.langchain.com/en/latest/_modules/langchain/llms/openai.html |
df8fc22a796c-5 | if openai_organization:
openai.organization = openai_organization
values["client"] = openai.Completion
except ImportError:
raise ValueError(
"Could not import openai python package. "
"Please install it with `pip install openai`."
... | https://python.langchain.com/en/latest/_modules/langchain/llms/openai.html |
df8fc22a796c-6 | response = openai.generate(["Tell me a joke."])
"""
# TODO: write a unit test for this
params = self._invocation_params
sub_prompts = self.get_sub_prompts(params, prompts, stop)
choices = []
token_usage: Dict[str, int] = {}
# Get the token usage from the response.... | https://python.langchain.com/en/latest/_modules/langchain/llms/openai.html |
df8fc22a796c-7 | params = self._invocation_params
sub_prompts = self.get_sub_prompts(params, prompts, stop)
choices = []
token_usage: Dict[str, int] = {}
# Get the token usage from the response.
# Includes prompt, completion, and total tokens used.
_keys = {"completion_tokens", "prompt_to... | https://python.langchain.com/en/latest/_modules/langchain/llms/openai.html |
df8fc22a796c-8 | def get_sub_prompts(
self,
params: Dict[str, Any],
prompts: List[str],
stop: Optional[List[str]] = None,
) -> List[List[str]]:
"""Get the sub prompts for llm call."""
if stop is not None:
if "stop" in params:
raise ValueError("`stop` found ... | https://python.langchain.com/en/latest/_modules/langchain/llms/openai.html |
df8fc22a796c-9 | llm_output = {"token_usage": token_usage, "model_name": self.model_name}
return LLMResult(generations=generations, llm_output=llm_output)
def stream(self, prompt: str, stop: Optional[List[str]] = None) -> Generator:
"""Call OpenAI with streaming flag and return the resulting generator.
BETA:... | https://python.langchain.com/en/latest/_modules/langchain/llms/openai.html |
df8fc22a796c-10 | return self._default_params
@property
def _identifying_params(self) -> Mapping[str, Any]:
"""Get the identifying parameters."""
return {**{"model_name": self.model_name}, **self._default_params}
@property
def _llm_type(self) -> str:
"""Return type of llm."""
return "opena... | https://python.langchain.com/en/latest/_modules/langchain/llms/openai.html |
df8fc22a796c-11 | model_token_mapping = {
"gpt-4": 8192,
"gpt-4-0314": 8192,
"gpt-4-32k": 32768,
"gpt-4-32k-0314": 32768,
"gpt-3.5-turbo": 4096,
"gpt-3.5-turbo-0301": 4096,
"text-ada-001": 2049,
"ada": 2049,
"text-babbage-001": 20... | https://python.langchain.com/en/latest/_modules/langchain/llms/openai.html |
df8fc22a796c-12 | Args:
prompt: The prompt to pass into the model.
Returns:
The maximum number of tokens to generate for a prompt.
Example:
.. code-block:: python
max_tokens = openai.max_token_for_prompt("Tell me a joke.")
"""
num_tokens = self.get_num_t... | https://python.langchain.com/en/latest/_modules/langchain/llms/openai.html |
df8fc22a796c-13 | .. code-block:: python
from langchain.llms import AzureOpenAI
openai = AzureOpenAI(model_name="text-davinci-003")
"""
deployment_name: str = ""
"""Deployment name to use."""
@property
def _identifying_params(self) -> Mapping[str, Any]:
return {
**{"deploym... | https://python.langchain.com/en/latest/_modules/langchain/llms/openai.html |
df8fc22a796c-14 | """Maximum number of retries to make when generating."""
prefix_messages: List = Field(default_factory=list)
"""Series of messages for Chat input."""
streaming: bool = False
"""Whether to stream the results or not."""
allowed_special: Union[Literal["all"], AbstractSet[str]] = set()
"""Set of spe... | https://python.langchain.com/en/latest/_modules/langchain/llms/openai.html |
df8fc22a796c-15 | default="",
)
openai_organization = get_from_dict_or_env(
values, "openai_organization", "OPENAI_ORGANIZATION", default=""
)
try:
import openai
openai.api_key = openai_api_key
if openai_api_base:
openai.api_base = openai_api... | https://python.langchain.com/en/latest/_modules/langchain/llms/openai.html |
df8fc22a796c-16 | 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://python.langchain.com/en/latest/_modules/langchain/llms/openai.html |
df8fc22a796c-17 | ) -> LLMResult:
messages, params = self._get_chat_params(prompts, stop)
if self.streaming:
response = ""
params["stream"] = True
async for stream_resp in await acompletion_with_retry(
self, messages=messages, **params
):
tok... | https://python.langchain.com/en/latest/_modules/langchain/llms/openai.html |
df8fc22a796c-18 | # tiktoken NOT supported for Python < 3.8
if sys.version_info[1] < 8:
return super().get_num_tokens(text)
try:
import tiktoken
except ImportError:
raise ValueError(
"Could not import tiktoken python package. "
"This is needed in... | https://python.langchain.com/en/latest/_modules/langchain/llms/openai.html |
09469663bc06-0 | Source code for langchain.llms.cerebriumai
"""Wrapper around CerebriumAI API."""
import logging
from typing import Any, Dict, List, Mapping, Optional
from pydantic import Extra, Field, root_validator
from langchain.llms.base import LLM
from langchain.llms.utils import enforce_stop_tokens
from langchain.utils import get... | https://python.langchain.com/en/latest/_modules/langchain/llms/cerebriumai.html |
09469663bc06-1 | 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://python.langchain.com/en/latest/_modules/langchain/llms/cerebriumai.html |
09469663bc06-2 | "Please install it with `pip install cerebrium`."
)
params = self.model_kwargs or {}
response = model_api_request(
self.endpoint_url, {"prompt": prompt, **params}, self.cerebriumai_api_key
)
text = response["data"]["result"]
if stop is not None:
... | https://python.langchain.com/en/latest/_modules/langchain/llms/cerebriumai.html |
e72b8aafe7bd-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.llms.base import LLM
from langchain.utils import get_from_dict_or_env
logger = logging.getLogger(__name__)
[docs]... | https://python.langchain.com/en/latest/_modules/langchain/llms/gooseai.html |
e72b8aafe7bd-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://python.langchain.com/en/latest/_modules/langchain/llms/gooseai.html |
e72b8aafe7bd-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 ValueError(
"Could not import openai python package. "
... | https://python.langchain.com/en/latest/_modules/langchain/llms/gooseai.html |
e72b8aafe7bd-3 | params["stop"] = stop
response = self.client.create(engine=self.model_name, prompt=prompt, **params)
text = response.choices[0].text
return text
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 21, 2023. | https://python.langchain.com/en/latest/_modules/langchain/llms/gooseai.html |
50ef8657802c-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.llms.base import LLM
from langchain.llms.utils import enforce_stop_tokens
... | https://python.langchain.com/en/latest/_modules/langchain/llms/stochasticai.html |
50ef8657802c-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://python.langchain.com/en/latest/_modules/langchain/llms/stochasticai.html |
50ef8657802c-2 | json={"prompt": prompt, "params": params},
headers={
"apiKey": f"{self.stochasticai_api_key}",
"Accept": "application/json",
"Content-Type": "application/json",
},
)
response_post.raise_for_status()
response_post_json = resp... | https://python.langchain.com/en/latest/_modules/langchain/llms/stochasticai.html |
ead5239983e6-0 | 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.llms.base import LLM
from langchain.llms.utils import enforce_stop_tokens
from langchain.utils import get_from... | https://python.langchain.com/en/latest/_modules/langchain/llms/forefrontai.html |
ead5239983e6-1 | """Validate that api key exists in environment."""
forefrontai_api_key = get_from_dict_or_env(
values, "forefrontai_api_key", "FOREFRONTAI_API_KEY"
)
values["forefrontai_api_key"] = forefrontai_api_key
return values
@property
def _default_params(self) -> Mapping[str, ... | https://python.langchain.com/en/latest/_modules/langchain/llms/forefrontai.html |
ead5239983e6-2 | },
json={"text": prompt, **self._default_params},
)
response_json = response.json()
text = response_json["result"][0]["completion"]
if stop is not None:
# I believe this is required since the stop tokens
# are not enforced by the model parameters
... | https://python.langchain.com/en/latest/_modules/langchain/llms/forefrontai.html |
5827b5f03dff-0 | Source code for langchain.llms.self_hosted_hugging_face
"""Wrapper around HuggingFace Pipeline API to run on self-hosted remote hardware."""
import importlib.util
import logging
from typing import Any, Callable, List, Mapping, Optional
from pydantic import Extra
from langchain.llms.self_hosted import SelfHostedPipeline... | https://python.langchain.com/en/latest/_modules/langchain/llms/self_hosted_hugging_face.html |
5827b5f03dff-1 | task: str = DEFAULT_TASK,
device: int = 0,
model_kwargs: Optional[dict] = None,
) -> Any:
"""Inference function to send to the remote hardware.
Accepts a huggingface model_id and returns a pipeline for the task.
"""
from transformers import AutoModelForCausalLM, AutoModelForSeq2SeqLM, AutoTokeni... | https://python.langchain.com/en/latest/_modules/langchain/llms/self_hosted_hugging_face.html |
5827b5f03dff-2 | "Provide device={deviceId} to `from_model_id` to use available"
"GPUs for execution. deviceId is -1 for CPU and "
"can be a positive integer associated with CUDA device id.",
cuda_device_count,
)
pipeline = hf_pipeline(
task=task,
model=mod... | https://python.langchain.com/en/latest/_modules/langchain/llms/self_hosted_hugging_face.html |
5827b5f03dff-3 | .. code-block:: python
from langchain.llms import SelfHostedHuggingFaceLLM
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
import runhouse as rh
def get_pipeline():
model_id = "gpt2"
tokenizer = AutoTokenizer.from_pre... | https://python.langchain.com/en/latest/_modules/langchain/llms/self_hosted_hugging_face.html |
5827b5f03dff-4 | extra = Extra.forbid
def __init__(self, **kwargs: Any):
"""Construct the pipeline remotely using an auxiliary function.
The load function needs to be importable to be imported
and run on the server, i.e. in a module and not a REPL or closure.
Then, initialize the remote inference fun... | https://python.langchain.com/en/latest/_modules/langchain/llms/self_hosted_hugging_face.html |
f475f412ff45-0 | Source code for langchain.llms.promptlayer_openai
"""PromptLayer wrapper."""
import datetime
from typing import List, Optional
from langchain.llms import OpenAI, OpenAIChat
from langchain.schema import LLMResult
[docs]class PromptLayerOpenAI(OpenAI):
"""Wrapper around OpenAI large language models.
To use, you s... | https://python.langchain.com/en/latest/_modules/langchain/llms/promptlayer_openai.html |
f475f412ff45-1 | for i in range(len(prompts)):
prompt = prompts[i]
generation = generated_responses.generations[i][0]
resp = {
"text": generation.text,
"llm_output": generated_responses.llm_output,
}
pl_request_id = promptlayer_api_request(
... | https://python.langchain.com/en/latest/_modules/langchain/llms/promptlayer_openai.html |
f475f412ff45-2 | self._identifying_params,
self.pl_tags,
resp,
request_start_time,
request_end_time,
get_api_key(),
return_pl_id=self.return_pl_id,
)
if self.return_pl_id:
if generation.generation_info... | https://python.langchain.com/en/latest/_modules/langchain/llms/promptlayer_openai.html |
f475f412ff45-3 | ) -> LLMResult:
"""Call OpenAI generate and then call PromptLayer API to log the request."""
from promptlayer.utils import get_api_key, promptlayer_api_request
request_start_time = datetime.datetime.now().timestamp()
generated_responses = super()._generate(prompts, stop)
request_... | https://python.langchain.com/en/latest/_modules/langchain/llms/promptlayer_openai.html |
f475f412ff45-4 | generation = generated_responses.generations[i][0]
resp = {
"text": generation.text,
"llm_output": generated_responses.llm_output,
}
pl_request_id = await promptlayer_api_request_async(
"langchain.PromptLayerOpenAIChat.async",
... | https://python.langchain.com/en/latest/_modules/langchain/llms/promptlayer_openai.html |
007c6f758101-0 | 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.llms.base import LLM
from langchain.llms.utils import enforce_stop_tokens
logger = logging.getLogger(... | https://python.langchain.com/en/latest/_modules/langchain/llms/modal.html |
007c6f758101-1 | Please confirm that {field_name} is what you intended."""
)
extra[field_name] = values.pop(field_name)
values["model_kwargs"] = extra
return values
@property
def _identifying_params(self) -> Mapping[str, Any]:
"""Get the identifying parameters."""
... | https://python.langchain.com/en/latest/_modules/langchain/llms/modal.html |
312f26067b36-0 | 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.llms.base import LLM
from langchain.utils import get_from_dict_or_env
logger = logging.getLogger(__name__)
[d... | https://python.langchain.com/en/latest/_modules/langchain/llms/replicate.html |
312f26067b36-1 | """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://python.langchain.com/en/latest/_modules/langchain/llms/replicate.html |
312f26067b36-2 | )
# get the model and version
model_str, version_str = self.model.split(":")
model = replicate_python.models.get(model_str)
version = model.versions.get(version_str)
# sort through the openapi schema to get the name of the first input
input_properties = sorted(
... | https://python.langchain.com/en/latest/_modules/langchain/llms/replicate.html |
cbbcda269424-0 | Source code for langchain.llms.anthropic
"""Wrapper around Anthropic APIs."""
import re
from typing import Any, Callable, Dict, Generator, List, Mapping, Optional
from pydantic import BaseModel, Extra, root_validator
from langchain.llms.base import LLM
from langchain.utils import get_from_dict_or_env
class _AnthropicCo... | https://python.langchain.com/en/latest/_modules/langchain/llms/anthropic.html |
cbbcda269424-1 | values["AI_PROMPT"] = anthropic.AI_PROMPT
values["count_tokens"] = anthropic.count_tokens
except ImportError:
raise ValueError(
"Could not import anthropic python package. "
"Please it install it with `pip install anthropic`."
)
return ... | https://python.langchain.com/en/latest/_modules/langchain/llms/anthropic.html |
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