mirror of
https://github.com/hwchase17/langchain
synced 2024-11-08 07:10:35 +00:00
355 lines
12 KiB
Python
355 lines
12 KiB
Python
import re
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import warnings
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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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)
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import anthropic
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from langchain_core._api.deprecation import deprecated
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from langchain_core.callbacks import (
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AsyncCallbackManagerForLLMRun,
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CallbackManagerForLLMRun,
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)
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from langchain_core.language_models import BaseLanguageModel
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from langchain_core.language_models.llms import LLM
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from langchain_core.outputs import GenerationChunk
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from langchain_core.prompt_values import PromptValue
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from langchain_core.pydantic_v1 import Field, SecretStr, root_validator
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from langchain_core.utils import (
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get_from_dict_or_env,
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get_pydantic_field_names,
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)
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from langchain_core.utils.utils import build_extra_kwargs, convert_to_secret_str
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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=1024, 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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max_retries: int = 2
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"""Number of retries allowed for requests sent to the Anthropic Completion API."""
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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(values, "anthropic_api_key", "ANTHROPIC_API_KEY")
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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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values["client"] = anthropic.Anthropic(
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base_url=values["anthropic_api_url"],
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api_key=values["anthropic_api_key"].get_secret_value(),
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timeout=values["default_request_timeout"],
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max_retries=values["max_retries"],
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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["anthropic_api_key"].get_secret_value(),
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timeout=values["default_request_timeout"],
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max_retries=values["max_retries"],
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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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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(self, stop: Optional[List[str]] = None) -> List[str]:
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if not self.HUMAN_PROMPT or not self.AI_PROMPT:
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raise NameError("Please ensure the anthropic package is loaded")
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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 AnthropicLLM(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_community.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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"This Anthropic LLM is deprecated. "
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"Please use `from langchain_community.chat_models import ChatAnthropic` "
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"instead"
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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("Please ensure the anthropic package is loaded")
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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(r"^\n*Human:", self.HUMAN_PROMPT, prompt)
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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 f"{self.HUMAN_PROMPT} {prompt}{self.AI_PROMPT} Sure, here you go:\n"
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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, stop=stop, run_manager=run_manager, **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, stop=stop, run_manager=run_manager, **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 = 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,
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**kwargs: Any,
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) -> Iterator[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.
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Returns:
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A generator representing the stream of tokens from Anthropic.
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Example:
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.. 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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"""
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stop = self._get_anthropic_stop(stop)
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params = {**self._default_params, **kwargs}
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for token in self.client.completions.create(
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prompt=self._wrap_prompt(prompt), stop_sequences=stop, stream=True, **params
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):
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chunk = GenerationChunk(text=token.completion)
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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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yield 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,
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**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.
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Returns:
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A generator representing the stream of tokens from Anthropic.
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Example:
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.. 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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"""
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stop = self._get_anthropic_stop(stop)
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params = {**self._default_params, **kwargs}
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async for token in 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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stream=True,
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**params,
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):
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chunk = GenerationChunk(text=token.completion)
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if run_manager:
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await run_manager.on_llm_new_token(chunk.text, chunk=chunk)
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yield chunk
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def get_num_tokens(self, text: str) -> int:
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"""Calculate number of tokens."""
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if not self.count_tokens:
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raise NameError("Please ensure the anthropic package is loaded")
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return self.count_tokens(text)
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@deprecated(since="0.1.0", removal="0.2.0", alternative="AnthropicLLM")
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class Anthropic(AnthropicLLM):
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pass
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