mirror of https://github.com/hwchase17/langchain
anthropic[minor]: package move (#17974)
Erick Friis
4 months ago
committed by
GitHub
parent
a2d5fa7649
commit
3b5bdbfee8
@ -1,3 +1,4 @@
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from langchain_anthropic.chat_models import ChatAnthropicMessages
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from langchain_anthropic.chat_models import ChatAnthropic, ChatAnthropicMessages
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from langchain_anthropic.llms import Anthropic, AnthropicLLM
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__all__ = ["ChatAnthropicMessages"]
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__all__ = ["ChatAnthropicMessages", "ChatAnthropic", "Anthropic", "AnthropicLLM"]
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@ -0,0 +1,352 @@
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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=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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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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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,
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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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yield chunk
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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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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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@ -0,0 +1,74 @@
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"""Test Anthropic API wrapper."""
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from typing import Generator
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import pytest
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from langchain_core.callbacks import CallbackManager
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from langchain_core.outputs import LLMResult
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from langchain_anthropic import Anthropic
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from tests.unit_tests._utils import FakeCallbackHandler
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@pytest.mark.requires("anthropic")
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def test_anthropic_model_name_param() -> None:
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llm = Anthropic(model_name="foo")
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assert llm.model == "foo"
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@pytest.mark.requires("anthropic")
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def test_anthropic_model_param() -> None:
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llm = Anthropic(model="foo")
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assert llm.model == "foo"
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def test_anthropic_call() -> None:
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"""Test valid call to anthropic."""
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llm = Anthropic(model="claude-instant-1")
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output = llm("Say foo:")
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assert isinstance(output, str)
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def test_anthropic_streaming() -> None:
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"""Test streaming tokens from anthropic."""
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llm = Anthropic(model="claude-instant-1")
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generator = llm.stream("I'm Pickle Rick")
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assert isinstance(generator, Generator)
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for token in generator:
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assert isinstance(token, str)
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def test_anthropic_streaming_callback() -> None:
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"""Test that streaming correctly invokes on_llm_new_token callback."""
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callback_handler = FakeCallbackHandler()
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callback_manager = CallbackManager([callback_handler])
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llm = Anthropic(
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streaming=True,
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callback_manager=callback_manager,
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verbose=True,
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)
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llm("Write me a sentence with 100 words.")
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assert callback_handler.llm_streams > 1
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async def test_anthropic_async_generate() -> None:
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"""Test async generate."""
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llm = Anthropic()
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output = await llm.agenerate(["How many toes do dogs have?"])
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assert isinstance(output, LLMResult)
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async def test_anthropic_async_streaming_callback() -> None:
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"""Test that streaming correctly invokes on_llm_new_token callback."""
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callback_handler = FakeCallbackHandler()
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callback_manager = CallbackManager([callback_handler])
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llm = Anthropic(
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streaming=True,
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callback_manager=callback_manager,
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verbose=True,
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)
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result = await llm.agenerate(["How many toes do dogs have?"])
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assert callback_handler.llm_streams > 1
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assert isinstance(result, LLMResult)
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@ -0,0 +1,255 @@
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"""A fake callback handler for testing purposes."""
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from typing import Any, Union
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from langchain_core.callbacks import BaseCallbackHandler
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from langchain_core.pydantic_v1 import BaseModel
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class BaseFakeCallbackHandler(BaseModel):
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"""Base fake callback handler for testing."""
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starts: int = 0
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ends: int = 0
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errors: int = 0
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text: int = 0
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ignore_llm_: bool = False
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ignore_chain_: bool = False
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ignore_agent_: bool = False
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ignore_retriever_: bool = False
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ignore_chat_model_: bool = False
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# to allow for similar callback handlers that are not technicall equal
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fake_id: Union[str, None] = None
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# add finer-grained counters for easier debugging of failing tests
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chain_starts: int = 0
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chain_ends: int = 0
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llm_starts: int = 0
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llm_ends: int = 0
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llm_streams: int = 0
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tool_starts: int = 0
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tool_ends: int = 0
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agent_actions: int = 0
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agent_ends: int = 0
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chat_model_starts: int = 0
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retriever_starts: int = 0
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retriever_ends: int = 0
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retriever_errors: int = 0
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retries: int = 0
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class BaseFakeCallbackHandlerMixin(BaseFakeCallbackHandler):
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"""Base fake callback handler mixin for testing."""
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def on_llm_start_common(self) -> None:
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self.llm_starts += 1
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self.starts += 1
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def on_llm_end_common(self) -> None:
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self.llm_ends += 1
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self.ends += 1
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def on_llm_error_common(self) -> None:
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self.errors += 1
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def on_llm_new_token_common(self) -> None:
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self.llm_streams += 1
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|
||||
def on_retry_common(self) -> None:
|
||||
self.retries += 1
|
||||
|
||||
def on_chain_start_common(self) -> None:
|
||||
self.chain_starts += 1
|
||||
self.starts += 1
|
||||
|
||||
def on_chain_end_common(self) -> None:
|
||||
self.chain_ends += 1
|
||||
self.ends += 1
|
||||
|
||||
def on_chain_error_common(self) -> None:
|
||||
self.errors += 1
|
||||
|
||||
def on_tool_start_common(self) -> None:
|
||||
self.tool_starts += 1
|
||||
self.starts += 1
|
||||
|
||||
def on_tool_end_common(self) -> None:
|
||||
self.tool_ends += 1
|
||||
self.ends += 1
|
||||
|
||||
def on_tool_error_common(self) -> None:
|
||||
self.errors += 1
|
||||
|
||||
def on_agent_action_common(self) -> None:
|
||||
self.agent_actions += 1
|
||||
self.starts += 1
|
||||
|
||||
def on_agent_finish_common(self) -> None:
|
||||
self.agent_ends += 1
|
||||
self.ends += 1
|
||||
|
||||
def on_chat_model_start_common(self) -> None:
|
||||
self.chat_model_starts += 1
|
||||
self.starts += 1
|
||||
|
||||
def on_text_common(self) -> None:
|
||||
self.text += 1
|
||||
|
||||
def on_retriever_start_common(self) -> None:
|
||||
self.starts += 1
|
||||
self.retriever_starts += 1
|
||||
|
||||
def on_retriever_end_common(self) -> None:
|
||||
self.ends += 1
|
||||
self.retriever_ends += 1
|
||||
|
||||
def on_retriever_error_common(self) -> None:
|
||||
self.errors += 1
|
||||
self.retriever_errors += 1
|
||||
|
||||
|
||||
class FakeCallbackHandler(BaseCallbackHandler, BaseFakeCallbackHandlerMixin):
|
||||
"""Fake callback handler for testing."""
|
||||
|
||||
@property
|
||||
def ignore_llm(self) -> bool:
|
||||
"""Whether to ignore LLM callbacks."""
|
||||
return self.ignore_llm_
|
||||
|
||||
@property
|
||||
def ignore_chain(self) -> bool:
|
||||
"""Whether to ignore chain callbacks."""
|
||||
return self.ignore_chain_
|
||||
|
||||
@property
|
||||
def ignore_agent(self) -> bool:
|
||||
"""Whether to ignore agent callbacks."""
|
||||
return self.ignore_agent_
|
||||
|
||||
@property
|
||||
def ignore_retriever(self) -> bool:
|
||||
"""Whether to ignore retriever callbacks."""
|
||||
return self.ignore_retriever_
|
||||
|
||||
def on_llm_start(
|
||||
self,
|
||||
*args: Any,
|
||||
**kwargs: Any,
|
||||
) -> Any:
|
||||
self.on_llm_start_common()
|
||||
|
||||
def on_llm_new_token(
|
||||
self,
|
||||
*args: Any,
|
||||
**kwargs: Any,
|
||||
) -> Any:
|
||||
self.on_llm_new_token_common()
|
||||
|
||||
def on_llm_end(
|
||||
self,
|
||||
*args: Any,
|
||||
**kwargs: Any,
|
||||
) -> Any:
|
||||
self.on_llm_end_common()
|
||||
|
||||
def on_llm_error(
|
||||
self,
|
||||
*args: Any,
|
||||
**kwargs: Any,
|
||||
) -> Any:
|
||||
self.on_llm_error_common()
|
||||
|
||||
def on_retry(
|
||||
self,
|
||||
*args: Any,
|
||||
**kwargs: Any,
|
||||
) -> Any:
|
||||
self.on_retry_common()
|
||||
|
||||
def on_chain_start(
|
||||
self,
|
||||
*args: Any,
|
||||
**kwargs: Any,
|
||||
) -> Any:
|
||||
self.on_chain_start_common()
|
||||
|
||||
def on_chain_end(
|
||||
self,
|
||||
*args: Any,
|
||||
**kwargs: Any,
|
||||
) -> Any:
|
||||
self.on_chain_end_common()
|
||||
|
||||
def on_chain_error(
|
||||
self,
|
||||
*args: Any,
|
||||
**kwargs: Any,
|
||||
) -> Any:
|
||||
self.on_chain_error_common()
|
||||
|
||||
def on_tool_start(
|
||||
self,
|
||||
*args: Any,
|
||||
**kwargs: Any,
|
||||
) -> Any:
|
||||
self.on_tool_start_common()
|
||||
|
||||
def on_tool_end(
|
||||
self,
|
||||
*args: Any,
|
||||
**kwargs: Any,
|
||||
) -> Any:
|
||||
self.on_tool_end_common()
|
||||
|
||||
def on_tool_error(
|
||||
self,
|
||||
*args: Any,
|
||||
**kwargs: Any,
|
||||
) -> Any:
|
||||
self.on_tool_error_common()
|
||||
|
||||
def on_agent_action(
|
||||
self,
|
||||
*args: Any,
|
||||
**kwargs: Any,
|
||||
) -> Any:
|
||||
self.on_agent_action_common()
|
||||
|
||||
def on_agent_finish(
|
||||
self,
|
||||
*args: Any,
|
||||
**kwargs: Any,
|
||||
) -> Any:
|
||||
self.on_agent_finish_common()
|
||||
|
||||
def on_text(
|
||||
self,
|
||||
*args: Any,
|
||||
**kwargs: Any,
|
||||
) -> Any:
|
||||
self.on_text_common()
|
||||
|
||||
def on_retriever_start(
|
||||
self,
|
||||
*args: Any,
|
||||
**kwargs: Any,
|
||||
) -> Any:
|
||||
self.on_retriever_start_common()
|
||||
|
||||
def on_retriever_end(
|
||||
self,
|
||||
*args: Any,
|
||||
**kwargs: Any,
|
||||
) -> Any:
|
||||
self.on_retriever_end_common()
|
||||
|
||||
def on_retriever_error(
|
||||
self,
|
||||
*args: Any,
|
||||
**kwargs: Any,
|
||||
) -> Any:
|
||||
self.on_retriever_error_common()
|
||||
|
||||
def __deepcopy__(self, memo: dict) -> "FakeCallbackHandler":
|
||||
return self
|
||||
@ -1,10 +1,54 @@
|
||||
"""Test chat model integration."""
|
||||
|
||||
import os
|
||||
|
||||
from langchain_anthropic.chat_models import ChatAnthropicMessages
|
||||
import pytest
|
||||
|
||||
from langchain_anthropic import ChatAnthropic, ChatAnthropicMessages
|
||||
|
||||
os.environ["ANTHROPIC_API_KEY"] = "foo"
|
||||
|
||||
|
||||
def test_initialization() -> None:
|
||||
"""Test chat model initialization."""
|
||||
ChatAnthropicMessages(model_name="claude-instant-1.2", anthropic_api_key="xyz")
|
||||
ChatAnthropicMessages(model="claude-instant-1.2", anthropic_api_key="xyz")
|
||||
|
||||
|
||||
@pytest.mark.requires("anthropic")
|
||||
def test_anthropic_model_name_param() -> None:
|
||||
llm = ChatAnthropic(model_name="foo")
|
||||
assert llm.model == "foo"
|
||||
|
||||
|
||||
@pytest.mark.requires("anthropic")
|
||||
def test_anthropic_model_param() -> None:
|
||||
llm = ChatAnthropic(model="foo")
|
||||
assert llm.model == "foo"
|
||||
|
||||
|
||||
@pytest.mark.requires("anthropic")
|
||||
def test_anthropic_model_kwargs() -> None:
|
||||
llm = ChatAnthropic(model_name="foo", model_kwargs={"foo": "bar"})
|
||||
assert llm.model_kwargs == {"foo": "bar"}
|
||||
|
||||
|
||||
@pytest.mark.requires("anthropic")
|
||||
def test_anthropic_invalid_model_kwargs() -> None:
|
||||
with pytest.raises(ValueError):
|
||||
ChatAnthropic(model="foo", model_kwargs={"max_tokens_to_sample": 5})
|
||||
|
||||
|
||||
@pytest.mark.requires("anthropic")
|
||||
def test_anthropic_incorrect_field() -> None:
|
||||
with pytest.warns(match="not default parameter"):
|
||||
llm = ChatAnthropic(model="foo", foo="bar")
|
||||
assert llm.model_kwargs == {"foo": "bar"}
|
||||
|
||||
|
||||
@pytest.mark.requires("anthropic")
|
||||
def test_anthropic_initialization() -> None:
|
||||
"""Test anthropic initialization."""
|
||||
# Verify that chat anthropic can be initialized using a secret key provided
|
||||
# as a parameter rather than an environment variable.
|
||||
ChatAnthropic(model="test", anthropic_api_key="test")
|
||||
|
||||
Loading…
Reference in New Issue