Source code for langchain_anthropic.llms

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"}
[docs] @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): pass