import re
import warnings
from typing import (
Any,
AsyncIterator,
Callable,
Dict,
Iterator,
List,
Mapping,
Optional,
)
import anthropic
from langchain_core._api.deprecation import deprecated
from langchain_core.callbacks import (
AsyncCallbackManagerForLLMRun,
CallbackManagerForLLMRun,
)
from langchain_core.language_models import BaseLanguageModel
from langchain_core.language_models.llms import LLM
from langchain_core.outputs import GenerationChunk
from langchain_core.prompt_values import PromptValue
from langchain_core.pydantic_v1 import Field, SecretStr, root_validator
from langchain_core.utils import (
get_from_dict_or_env,
get_pydantic_field_names,
)
from langchain_core.utils.utils import build_extra_kwargs, convert_to_secret_str
class _AnthropicCommon(BaseLanguageModel):
client: Any = None #: :meta private:
async_client: Any = None #: :meta private:
model: str = Field(default="claude-2", alias="model_name")
"""Model name to use."""
max_tokens_to_sample: int = Field(default=1024, alias="max_tokens")
"""Denotes the number of tokens to predict per generation."""
temperature: Optional[float] = None
"""A non-negative float that tunes the degree of randomness in generation."""
top_k: Optional[int] = None
"""Number of most likely tokens to consider at each step."""
top_p: Optional[float] = None
"""Total probability mass of tokens to consider at each step."""
streaming: bool = False
"""Whether to stream the results."""
default_request_timeout: Optional[float] = None
"""Timeout for requests to Anthropic Completion API. Default is 600 seconds."""
max_retries: int = 2
"""Number of retries allowed for requests sent to the Anthropic Completion API."""
anthropic_api_url: Optional[str] = None
anthropic_api_key: Optional[SecretStr] = None
HUMAN_PROMPT: Optional[str] = None
AI_PROMPT: Optional[str] = None
count_tokens: Optional[Callable[[str], int]] = None
model_kwargs: Dict[str, Any] = Field(default_factory=dict)
@root_validator(pre=True)
def build_extra(cls, values: Dict) -> Dict:
extra = values.get("model_kwargs", {})
all_required_field_names = get_pydantic_field_names(cls)
values["model_kwargs"] = build_extra_kwargs(
extra, values, all_required_field_names
)
return values
@root_validator()
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that api key and python package exists in environment."""
values["anthropic_api_key"] = convert_to_secret_str(
get_from_dict_or_env(values, "anthropic_api_key", "ANTHROPIC_API_KEY")
)
# Get custom api url from environment.
values["anthropic_api_url"] = get_from_dict_or_env(
values,
"anthropic_api_url",
"ANTHROPIC_API_URL",
default="https://api.anthropic.com",
)
values["client"] = anthropic.Anthropic(
base_url=values["anthropic_api_url"],
api_key=values["anthropic_api_key"].get_secret_value(),
timeout=values["default_request_timeout"],
max_retries=values["max_retries"],
)
values["async_client"] = anthropic.AsyncAnthropic(
base_url=values["anthropic_api_url"],
api_key=values["anthropic_api_key"].get_secret_value(),
timeout=values["default_request_timeout"],
max_retries=values["max_retries"],
)
values["HUMAN_PROMPT"] = anthropic.HUMAN_PROMPT
values["AI_PROMPT"] = anthropic.AI_PROMPT
values["count_tokens"] = values["client"].count_tokens
return values
@property
def _default_params(self) -> Mapping[str, Any]:
"""Get the default parameters for calling Anthropic API."""
d = {
"max_tokens_to_sample": self.max_tokens_to_sample,
"model": self.model,
}
if self.temperature is not None:
d["temperature"] = self.temperature
if self.top_k is not None:
d["top_k"] = self.top_k
if self.top_p is not None:
d["top_p"] = self.top_p
return {**d, **self.model_kwargs}
@property
def _identifying_params(self) -> Mapping[str, Any]:
"""Get the identifying parameters."""
return {**{}, **self._default_params}
def _get_anthropic_stop(self, stop: Optional[List[str]] = None) -> List[str]:
if not self.HUMAN_PROMPT or not self.AI_PROMPT:
raise NameError("Please ensure the anthropic package is loaded")
if stop is None:
stop = []
# Never want model to invent new turns of Human / Assistant dialog.
stop.extend([self.HUMAN_PROMPT])
return stop
[docs]
class AnthropicLLM(LLM, _AnthropicCommon):
"""Anthropic large language model.
To use, you should have the environment variable ``ANTHROPIC_API_KEY``
set with your API key, or pass it as a named parameter to the constructor.
Example:
.. code-block:: python
from langchain_anthropic import AnthropicLLM
model = AnthropicLLM()
"""
class Config:
"""Configuration for this pydantic object."""
allow_population_by_field_name = True
arbitrary_types_allowed = True
@root_validator()
def raise_warning(cls, values: Dict) -> Dict:
"""Raise warning that this class is deprecated."""
warnings.warn(
"This Anthropic LLM is deprecated. "
"Please use `from langchain_anthropic import ChatAnthropic` "
"instead"
)
return values
@property
def _llm_type(self) -> str:
"""Return type of llm."""
return "anthropic-llm"
@property
def lc_secrets(self) -> Dict[str, str]:
return {"anthropic_api_key": "ANTHROPIC_API_KEY"}
@classmethod
def is_lc_serializable(cls) -> bool:
return True
@property
def _identifying_params(self) -> Dict[str, Any]:
"""Get the identifying parameters."""
return {
"model": self.model,
"max_tokens": self.max_tokens_to_sample,
"temperature": self.temperature,
"top_k": self.top_k,
"top_p": self.top_p,
"model_kwargs": self.model_kwargs,
"streaming": self.streaming,
"default_request_timeout": self.default_request_timeout,
"max_retries": self.max_retries,
}
def _wrap_prompt(self, prompt: str) -> str:
if not self.HUMAN_PROMPT or not self.AI_PROMPT:
raise NameError("Please ensure the anthropic package is loaded")
if prompt.startswith(self.HUMAN_PROMPT):
return prompt # Already wrapped.
# Guard against common errors in specifying wrong number of newlines.
corrected_prompt, n_subs = re.subn(r"^\n*Human:", self.HUMAN_PROMPT, prompt)
if n_subs == 1:
return corrected_prompt
# As a last resort, wrap the prompt ourselves to emulate instruct-style.
return f"{self.HUMAN_PROMPT} {prompt}{self.AI_PROMPT} Sure, here you go:\n"
def _call(
self,
prompt: str,
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> str:
r"""Call out to Anthropic's completion endpoint.
Args:
prompt: The prompt to pass into the model.
stop: Optional list of stop words to use when generating.
Returns:
The string generated by the model.
Example:
.. code-block:: python
prompt = "What are the biggest risks facing humanity?"
prompt = f"\n\nHuman: {prompt}\n\nAssistant:"
response = model.invoke(prompt)
"""
if self.streaming:
completion = ""
for chunk in self._stream(
prompt=prompt, stop=stop, run_manager=run_manager, **kwargs
):
completion += chunk.text
return completion
stop = self._get_anthropic_stop(stop)
params = {**self._default_params, **kwargs}
response = self.client.completions.create(
prompt=self._wrap_prompt(prompt),
stop_sequences=stop,
**params,
)
return response.completion
[docs]
def convert_prompt(self, prompt: PromptValue) -> str:
return self._wrap_prompt(prompt.to_string())
async def _acall(
self,
prompt: str,
stop: Optional[List[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> str:
"""Call out to Anthropic's completion endpoint asynchronously."""
if self.streaming:
completion = ""
async for chunk in self._astream(
prompt=prompt, stop=stop, run_manager=run_manager, **kwargs
):
completion += chunk.text
return completion
stop = self._get_anthropic_stop(stop)
params = {**self._default_params, **kwargs}
response = await self.async_client.completions.create(
prompt=self._wrap_prompt(prompt),
stop_sequences=stop,
**params,
)
return response.completion
def _stream(
self,
prompt: str,
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> Iterator[GenerationChunk]:
r"""Call Anthropic completion_stream and return the resulting generator.
Args:
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
prompt = "Write a poem about a stream."
prompt = f"\n\nHuman: {prompt}\n\nAssistant:"
generator = anthropic.stream(prompt)
for token in generator:
yield token
"""
stop = self._get_anthropic_stop(stop)
params = {**self._default_params, **kwargs}
for token in self.client.completions.create(
prompt=self._wrap_prompt(prompt), stop_sequences=stop, stream=True, **params
):
chunk = GenerationChunk(text=token.completion)
if run_manager:
run_manager.on_llm_new_token(chunk.text, chunk=chunk)
yield chunk
async def _astream(
self,
prompt: str,
stop: Optional[List[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> AsyncIterator[GenerationChunk]:
r"""Call Anthropic completion_stream and return the resulting generator.
Args:
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
prompt = "Write a poem about a stream."
prompt = f"\n\nHuman: {prompt}\n\nAssistant:"
generator = anthropic.stream(prompt)
for token in generator:
yield token
"""
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)
if run_manager:
await run_manager.on_llm_new_token(chunk.text, chunk=chunk)
yield chunk
[docs]
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)
[docs]
@deprecated(since="0.1.0", removal="0.3.0", alternative="AnthropicLLM")
class Anthropic(AnthropicLLM):
"""Anthropic large language model."""
pass
