import re import warnings from typing import ( Any, AsyncIterator, Callable, Dict, Iterator, List, Mapping, Optional, ) 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 ( check_package_version, 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=256, 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.""" 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", ) try: import anthropic check_package_version("anthropic", gte_version="0.3") values["client"] = anthropic.Anthropic( base_url=values["anthropic_api_url"], api_key=values["anthropic_api_key"].get_secret_value(), timeout=values["default_request_timeout"], ) 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"], ) values["HUMAN_PROMPT"] = anthropic.HUMAN_PROMPT values["AI_PROMPT"] = anthropic.AI_PROMPT values["count_tokens"] = values["client"].count_tokens except ImportError: raise ImportError( "Could not import anthropic python package. " "Please it install it with `pip install anthropic`." ) 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 class Anthropic(LLM, _AnthropicCommon): """Anthropic large language models. To use, you should have the ``anthropic`` python package installed, and 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 import anthropic from langchain_community.llms import Anthropic model = Anthropic(model="", anthropic_api_key="my-api-key") # Simplest invocation, automatically wrapped with HUMAN_PROMPT # and AI_PROMPT. response = model("What are the biggest risks facing humanity?") # Or if you want to use the chat mode, build a few-shot-prompt, or # put words in the Assistant's mouth, use HUMAN_PROMPT and AI_PROMPT: raw_prompt = "What are the biggest risks facing humanity?" prompt = f"{anthropic.HUMAN_PROMPT} {prompt}{anthropic.AI_PROMPT}" response = model(prompt) """ 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_community.chat_models import ChatAnthropic` " "instead" ) return values @property def _llm_type(self) -> str: """Return type of llm.""" return "anthropic-llm" 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(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 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) yield chunk if run_manager: run_manager.on_llm_new_token(chunk.text, chunk=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) 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)