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576 lines
18 KiB
576 lines
18 KiB
import contextlib
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import datetime
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import functools
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import importlib
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import re
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import warnings
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from importlib.metadata import version
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from typing import (
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Any,
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AsyncIterator,
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Callable,
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Dict,
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Iterator,
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List,
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Mapping,
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Optional,
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Set,
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Tuple,
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Union,
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)
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from langchain.callbacks.manager import (
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AsyncCallbackManagerForLLMRun,
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CallbackManagerForLLMRun,
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)
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from langchain.llms.base import LLM
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from langchain.schema.language_model import BaseLanguageModel
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from langchain.schema.output import GenerationChunk
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from langchain.schema.prompt import PromptValue
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from langchain.utils import get_from_dict_or_env
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from packaging.version import parse
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from pydantic import Field, SecretStr, root_validator
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from requests import HTTPError, Response
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def xor_args(*arg_groups: Tuple[str, ...]) -> Callable:
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"""Validate specified keyword args are mutually exclusive."""
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def decorator(func: Callable) -> Callable:
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@functools.wraps(func)
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def wrapper(*args: Any, **kwargs: Any) -> Any:
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"""Validate exactly one arg in each group is not None."""
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counts = [
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sum(
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1
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for arg in arg_group
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if kwargs.get(arg) is not None
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)
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for arg_group in arg_groups
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]
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invalid_groups = [
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i for i, count in enumerate(counts) if count != 1
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]
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if invalid_groups:
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invalid_group_names = [
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", ".join(arg_groups[i]) for i in invalid_groups
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]
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raise ValueError(
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"Exactly one argument in each of the following"
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" groups must be defined:"
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f" {', '.join(invalid_group_names)}"
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)
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return func(*args, **kwargs)
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return wrapper
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return decorator
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def raise_for_status_with_text(response: Response) -> None:
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"""Raise an error with the response text."""
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try:
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response.raise_for_status()
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except HTTPError as e:
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raise ValueError(response.text) from e
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@contextlib.contextmanager
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def mock_now(dt_value): # type: ignore
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"""Context manager for mocking out datetime.now() in unit tests.
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Example:
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with mock_now(datetime.datetime(2011, 2, 3, 10, 11)):
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assert datetime.datetime.now() == datetime.datetime(2011, 2, 3, 10, 11)
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"""
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class MockDateTime(datetime.datetime):
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"""Mock datetime.datetime.now() with a fixed datetime."""
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@classmethod
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def now(cls): # type: ignore
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# Create a copy of dt_value.
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return datetime.datetime(
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dt_value.year,
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dt_value.month,
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dt_value.day,
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dt_value.hour,
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dt_value.minute,
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dt_value.second,
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dt_value.microsecond,
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dt_value.tzinfo,
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)
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real_datetime = datetime.datetime
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datetime.datetime = MockDateTime
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try:
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yield datetime.datetime
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finally:
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datetime.datetime = real_datetime
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def guard_import(
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module_name: str,
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*,
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pip_name: Optional[str] = None,
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package: Optional[str] = None,
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) -> Any:
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"""Dynamically imports a module and raises a helpful exception if the module is not
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installed."""
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try:
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module = importlib.import_module(module_name, package)
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except ImportError:
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raise ImportError(
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f"Could not import {module_name} python package. Please"
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" install it with `pip install"
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f" {pip_name or module_name}`."
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)
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return module
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def check_package_version(
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package: str,
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lt_version: Optional[str] = None,
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lte_version: Optional[str] = None,
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gt_version: Optional[str] = None,
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gte_version: Optional[str] = None,
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) -> None:
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"""Check the version of a package."""
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imported_version = parse(version(package))
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if lt_version is not None and imported_version >= parse(
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lt_version
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):
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raise ValueError(
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f"Expected {package} version to be < {lt_version}."
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f" Received {imported_version}."
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)
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if lte_version is not None and imported_version > parse(
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lte_version
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):
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raise ValueError(
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f"Expected {package} version to be <= {lte_version}."
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f" Received {imported_version}."
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)
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if gt_version is not None and imported_version <= parse(
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gt_version
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):
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raise ValueError(
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f"Expected {package} version to be > {gt_version}."
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f" Received {imported_version}."
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)
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if gte_version is not None and imported_version < parse(
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gte_version
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):
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raise ValueError(
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f"Expected {package} version to be >= {gte_version}."
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f" Received {imported_version}."
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)
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def get_pydantic_field_names(pydantic_cls: Any) -> Set[str]:
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"""Get field names, including aliases, for a pydantic class.
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Args:
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pydantic_cls: Pydantic class."""
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all_required_field_names = set()
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for field in pydantic_cls.__fields__.values():
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all_required_field_names.add(field.name)
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if field.has_alias:
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all_required_field_names.add(field.alias)
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return all_required_field_names
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def build_extra_kwargs(
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extra_kwargs: Dict[str, Any],
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values: Dict[str, Any],
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all_required_field_names: Set[str],
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) -> Dict[str, Any]:
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"""Build extra kwargs from values and extra_kwargs.
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Args:
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extra_kwargs: Extra kwargs passed in by user.
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values: Values passed in by user.
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all_required_field_names: All required field names for the pydantic class.
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"""
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for field_name in list(values):
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if field_name in extra_kwargs:
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raise ValueError(f"Found {field_name} supplied twice.")
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if field_name not in all_required_field_names:
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warnings.warn(
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f"""WARNING! {field_name} is not default parameter.
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{field_name} was transferred to model_kwargs.
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Please confirm that {field_name} is what you intended."""
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)
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extra_kwargs[field_name] = values.pop(field_name)
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invalid_model_kwargs = all_required_field_names.intersection(
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extra_kwargs.keys()
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)
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if invalid_model_kwargs:
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raise ValueError(
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f"Parameters {invalid_model_kwargs} should be specified"
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" explicitly. Instead they were passed in as part of"
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" `model_kwargs` parameter."
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)
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return extra_kwargs
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def convert_to_secret_str(value: Union[SecretStr, str]) -> SecretStr:
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"""Convert a string to a SecretStr if needed."""
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if isinstance(value, SecretStr):
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return value
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return SecretStr(value)
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class _AnthropicCommon(BaseLanguageModel):
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client: Any = None #: :meta private:
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async_client: Any = None #: :meta private:
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model: str = Field(default="claude-2", alias="model_name")
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"""Model name to use."""
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max_tokens_to_sample: int = Field(default=256, alias="max_tokens")
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"""Denotes the number of tokens to predict per generation."""
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temperature: Optional[float] = None
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"""A non-negative float that tunes the degree of randomness in generation."""
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top_k: Optional[int] = None
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"""Number of most likely tokens to consider at each step."""
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top_p: Optional[float] = None
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"""Total probability mass of tokens to consider at each step."""
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streaming: bool = False
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"""Whether to stream the results."""
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default_request_timeout: Optional[float] = None
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"""Timeout for requests to Anthropic Completion API. Default is 600 seconds."""
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anthropic_api_url: Optional[str] = None
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anthropic_api_key: Optional[SecretStr] = None
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HUMAN_PROMPT: Optional[str] = None
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AI_PROMPT: Optional[str] = None
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count_tokens: Optional[Callable[[str], int]] = None
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model_kwargs: Dict[str, Any] = Field(default_factory=dict)
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@root_validator(pre=True)
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def build_extra(cls, values: Dict) -> Dict:
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extra = values.get("model_kwargs", {})
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all_required_field_names = get_pydantic_field_names(cls)
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values["model_kwargs"] = build_extra_kwargs(
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extra, values, all_required_field_names
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)
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return values
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@root_validator()
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def validate_environment(cls, values: Dict) -> Dict:
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"""Validate that api key and python package exists in environment."""
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values["anthropic_api_key"] = convert_to_secret_str(
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get_from_dict_or_env(
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values, "anthropic_api_key", "ANTHROPIC_API_KEY"
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)
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)
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# Get custom api url from environment.
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values["anthropic_api_url"] = get_from_dict_or_env(
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values,
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"anthropic_api_url",
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"ANTHROPIC_API_URL",
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default="https://api.anthropic.com",
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)
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try:
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import anthropic
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check_package_version("anthropic", gte_version="0.3")
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values["client"] = anthropic.Anthropic(
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base_url=values["anthropic_api_url"],
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api_key=values[
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"anthropic_api_key"
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].get_secret_value(),
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timeout=values["default_request_timeout"],
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)
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values["async_client"] = anthropic.AsyncAnthropic(
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base_url=values["anthropic_api_url"],
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api_key=values[
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"anthropic_api_key"
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].get_secret_value(),
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timeout=values["default_request_timeout"],
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)
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values["HUMAN_PROMPT"] = anthropic.HUMAN_PROMPT
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values["AI_PROMPT"] = anthropic.AI_PROMPT
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values["count_tokens"] = values["client"].count_tokens
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except ImportError:
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raise ImportError(
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"Could not import anthropic python package. "
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"Please it install it with `pip install anthropic`."
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)
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return values
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@property
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def _default_params(self) -> Mapping[str, Any]:
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"""Get the default parameters for calling Anthropic API."""
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d = {
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"max_tokens_to_sample": self.max_tokens_to_sample,
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"model": self.model,
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}
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if self.temperature is not None:
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d["temperature"] = self.temperature
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if self.top_k is not None:
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d["top_k"] = self.top_k
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if self.top_p is not None:
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d["top_p"] = self.top_p
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return {**d, **self.model_kwargs}
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@property
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def _identifying_params(self) -> Mapping[str, Any]:
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"""Get the identifying parameters."""
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return {**{}, **self._default_params}
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def _get_anthropic_stop(
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self, stop: Optional[List[str]] = None
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) -> List[str]:
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if not self.HUMAN_PROMPT or not self.AI_PROMPT:
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raise NameError(
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"Please ensure the anthropic package is loaded"
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)
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if stop is None:
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stop = []
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# Never want model to invent new turns of Human / Assistant dialog.
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stop.extend([self.HUMAN_PROMPT])
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return stop
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class Anthropic(LLM, _AnthropicCommon):
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"""Anthropic large language models.
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To use, you should have the ``anthropic`` python package installed, and the
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environment variable ``ANTHROPIC_API_KEY`` set with your API key, or pass
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it as a named parameter to the constructor.
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Example:
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.. code-block:: python
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import anthropic
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from langchain.llms import Anthropic
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model = Anthropic(model="<model_name>", anthropic_api_key="my-api-key")
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# Simplest invocation, automatically wrapped with HUMAN_PROMPT
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# and AI_PROMPT.
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response = model("What are the biggest risks facing humanity?")
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# Or if you want to use the chat mode, build a few-shot-prompt, or
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# put words in the Assistant's mouth, use HUMAN_PROMPT and AI_PROMPT:
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raw_prompt = "What are the biggest risks facing humanity?"
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prompt = f"{anthropic.HUMAN_PROMPT} {prompt}{anthropic.AI_PROMPT}"
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response = model(prompt)
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"""
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class Config:
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"""Configuration for this pydantic object."""
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allow_population_by_field_name = True
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arbitrary_types_allowed = True
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@root_validator()
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def raise_warning(cls, values: Dict) -> Dict:
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"""Raise warning that this class is deprecated."""
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warnings.warn(
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"There may be an updated version of"
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f" {cls.__name__} available."
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)
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return values
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@property
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def _llm_type(self) -> str:
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"""Return type of llm."""
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return "anthropic-llm"
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def _wrap_prompt(self, prompt: str) -> str:
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if not self.HUMAN_PROMPT or not self.AI_PROMPT:
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raise NameError(
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"Please ensure the anthropic package is loaded"
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)
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if prompt.startswith(self.HUMAN_PROMPT):
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return prompt # Already wrapped.
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# Guard against common errors in specifying wrong number of newlines.
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corrected_prompt, n_subs = re.subn(
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r"^\n*Human:", self.HUMAN_PROMPT, prompt
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)
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if n_subs == 1:
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return corrected_prompt
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# As a last resort, wrap the prompt ourselves to emulate instruct-style.
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return (
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f"{self.HUMAN_PROMPT} {prompt}{self.AI_PROMPT} Sure, here"
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" you go:\n"
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)
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def _call(
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self,
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prompt: str,
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stop: Optional[List[str]] = None,
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run_manager: Optional[CallbackManagerForLLMRun] = None,
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**kwargs: Any,
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) -> str:
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r"""Call out to Anthropic's completion endpoint.
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Args:
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prompt: The prompt to pass into the model.
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stop: Optional list of stop words to use when generating.
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Returns:
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The string generated by the model.
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Example:
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.. code-block:: python
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prompt = "What are the biggest risks facing humanity?"
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prompt = f"\n\nHuman: {prompt}\n\nAssistant:"
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response = model(prompt)
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"""
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if self.streaming:
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completion = ""
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for chunk in self._stream(
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prompt=prompt,
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stop=stop,
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run_manager=run_manager,
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**kwargs,
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):
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completion += chunk.text
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return completion
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stop = self._get_anthropic_stop(stop)
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params = {**self._default_params, **kwargs}
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response = self.client.completions.create(
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prompt=self._wrap_prompt(prompt),
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stop_sequences=stop,
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**params,
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)
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return response.completion
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def convert_prompt(self, prompt: PromptValue) -> str:
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return self._wrap_prompt(prompt.to_string())
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async def _acall(
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self,
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prompt: str,
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stop: Optional[List[str]] = None,
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run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
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**kwargs: Any,
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) -> str:
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"""Call out to Anthropic's completion endpoint asynchronously."""
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if self.streaming:
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completion = ""
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async for chunk in self._astream(
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prompt=prompt,
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stop=stop,
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run_manager=run_manager,
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**kwargs,
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):
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completion += chunk.text
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return completion
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stop = self._get_anthropic_stop(stop)
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params = {**self._default_params, **kwargs}
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|
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response = await self.async_client.completions.create(
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prompt=self._wrap_prompt(prompt),
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stop_sequences=stop,
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**params,
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)
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return response.completion
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|
|
def _stream(
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self,
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prompt: str,
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stop: Optional[List[str]] = None,
|
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run_manager: Optional[CallbackManagerForLLMRun] = None,
|
|
**kwargs: Any,
|
|
) -> Iterator[GenerationChunk]:
|
|
r"""Call Anthropic completion_stream and return the resulting generator.
|
|
|
|
Args:
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prompt: The prompt to pass into the model.
|
|
stop: Optional list of stop words to use when generating.
|
|
Returns:
|
|
A generator representing the stream of tokens from Anthropic.
|
|
Example:
|
|
.. code-block:: python
|
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|
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prompt = "Write a poem about a stream."
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prompt = f"\n\nHuman: {prompt}\n\nAssistant:"
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generator = anthropic.stream(prompt)
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for token in generator:
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yield token
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"""
|
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stop = self._get_anthropic_stop(stop)
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params = {**self._default_params, **kwargs}
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|
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for token in self.client.completions.create(
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prompt=self._wrap_prompt(prompt),
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stop_sequences=stop,
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stream=True,
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**params,
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):
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chunk = GenerationChunk(text=token.completion)
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yield chunk
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if run_manager:
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run_manager.on_llm_new_token(chunk.text, chunk=chunk)
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|
|
async def _astream(
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self,
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prompt: str,
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stop: Optional[List[str]] = None,
|
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run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
|
|
**kwargs: Any,
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) -> AsyncIterator[GenerationChunk]:
|
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r"""Call Anthropic completion_stream and return the resulting generator.
|
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|
|
Args:
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prompt: The prompt to pass into the model.
|
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stop: Optional list of stop words to use when generating.
|
|
Returns:
|
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A generator representing the stream of tokens from Anthropic.
|
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Example:
|
|
.. code-block:: python
|
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prompt = "Write a poem about a stream."
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prompt = f"\n\nHuman: {prompt}\n\nAssistant:"
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generator = anthropic.stream(prompt)
|
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for token in generator:
|
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yield token
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|
"""
|
|
stop = self._get_anthropic_stop(stop)
|
|
params = {**self._default_params, **kwargs}
|
|
|
|
async for token in await self.async_client.completions.create(
|
|
prompt=self._wrap_prompt(prompt),
|
|
stop_sequences=stop,
|
|
stream=True,
|
|
**params,
|
|
):
|
|
chunk = GenerationChunk(text=token.completion)
|
|
yield chunk
|
|
if run_manager:
|
|
await run_manager.on_llm_new_token(
|
|
chunk.text, chunk=chunk
|
|
)
|
|
|
|
def get_num_tokens(self, text: str) -> int:
|
|
"""Calculate number of tokens."""
|
|
if not self.count_tokens:
|
|
raise NameError(
|
|
"Please ensure the anthropic package is loaded"
|
|
)
|
|
return self.count_tokens(text)
|